agent-app/rag-server/node_modules/@vladmandic/face-api/dist/tfjs.esm.js

/*
  Face-API
  homepage: <https://github.com/vladmandic/face-api>
  author: <https://github.com/vladmandic>'
*/

var zq=Object.create;var f0=Object.defineProperty;var Bq=Object.getOwnPropertyDescriptor;var Vq=Object.getOwnPropertyNames;var Gq=Object.getPrototypeOf,Wq=Object.prototype.hasOwnProperty;var br=(r,t)=>()=>(t||r((t={exports:{}}).exports,t),t.exports),Kt=(r,t)=>{for(var e in t)f0(r,e,{get:t[e],enumerable:!0})},Uq=(r,t,e,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let o of Vq(t))!Wq.call(r,o)&&o!==e&&f0(r,o,{get:()=>t[o],enumerable:!(n=Bq(t,o))||n.enumerable});return r};var Gl=(r,t,e)=>(e=r!=null?zq(Gq(r)):{},Uq(t||!r||!r.__esModule?f0(e,"default",{value:r,enumerable:!0}):e,r));var W_=br((gft,G_)=>{G_.exports=He;var yo=null;try{yo=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 He(r,t,e){this.low=r|0,this.high=t|0,this.unsigned=!!e}He.prototype.__isLong__;Object.defineProperty(He.prototype,"__isLong__",{value:!0});function zn(r){return(r&&r.__isLong__)===!0}He.isLong=zn;var R_={},F_={};function oc(r,t){var e,n,o;return t?(r>>>=0,(o=0<=r&&r<256)&&(n=F_[r],n)?n:(e=qe(r,(r|0)<0?-1:0,!0),o&&(F_[r]=e),e)):(r|=0,(o=-128<=r&&r<128)&&(n=R_[r],n)?n:(e=qe(r,r<0?-1:0,!1),o&&(R_[r]=e),e))}He.fromInt=oc;function bo(r,t){if(isNaN(r))return t?nc:wo;if(t){if(r<0)return nc;if(r>=L_)return V_}else{if(r<=-M_)return Ln;if(r+1>=M_)return B_}return r<0?bo(-r,t).neg():qe(r%Hp|0,r/Hp|0,t)}He.fromNumber=bo;function qe(r,t,e){return new He(r,t,e)}He.fromBits=qe;var Jg=Math.pow;function k0(r,t,e){if(r.length===0)throw Error("empty string");if(r==="NaN"||r==="Infinity"||r==="+Infinity"||r==="-Infinity")return wo;if(typeof t=="number"?(e=t,t=!1):t=!!t,e=e||10,e<2||36<e)throw RangeError("radix");var n;if((n=r.indexOf("-"))>0)throw Error("interior hyphen");if(n===0)return k0(r.substring(1),t,e).neg();for(var o=bo(Jg(e,8)),s=wo,i=0;i<r.length;i+=8){var a=Math.min(8,r.length-i),u=parseInt(r.substring(i,i+a),e);if(a<8){var l=bo(Jg(e,a));s=s.mul(l).add(bo(u))}else s=s.mul(o),s=s.add(bo(u))}return s.unsigned=t,s}He.fromString=k0;function oi(r,t){return typeof r=="number"?bo(r,t):typeof r=="string"?k0(r,t):qe(r.low,r.high,typeof t=="boolean"?t:r.unsigned)}He.fromValue=oi;var O_=65536,fK=1<<24,Hp=O_*O_,L_=Hp*Hp,M_=L_/2,P_=oc(fK),wo=oc(0);He.ZERO=wo;var nc=oc(0,!0);He.UZERO=nc;var Up=oc(1);He.ONE=Up;var z_=oc(1,!0);He.UONE=z_;var N0=oc(-1);He.NEG_ONE=N0;var B_=qe(-1,2147483647,!1);He.MAX_VALUE=B_;var V_=qe(-1,-1,!0);He.MAX_UNSIGNED_VALUE=V_;var Ln=qe(0,-2147483648,!1);He.MIN_VALUE=Ln;var xt=He.prototype;xt.toInt=function(){return this.unsigned?this.low>>>0:this.low};xt.toNumber=function(){return this.unsigned?(this.high>>>0)*Hp+(this.low>>>0):this.high*Hp+(this.low>>>0)};xt.toString=function(t){if(t=t||10,t<2||36<t)throw RangeError("radix");if(this.isZero())return"0";if(this.isNegative())if(this.eq(Ln)){var e=bo(t),n=this.div(e),o=n.mul(e).sub(this);return n.toString(t)+o.toInt().toString(t)}else return"-"+this.neg().toString(t);for(var s=bo(Jg(t,6),this.unsigned),i=this,a="";;){var u=i.div(s),l=i.sub(u.mul(s)).toInt()>>>0,c=l.toString(t);if(i=u,i.isZero())return c+a;for(;c.length<6;)c="0"+c;a=""+c+a}};xt.getHighBits=function(){return this.high};xt.getHighBitsUnsigned=function(){return this.high>>>0};xt.getLowBits=function(){return this.low};xt.getLowBitsUnsigned=function(){return this.low>>>0};xt.getNumBitsAbs=function(){if(this.isNegative())return this.eq(Ln)?64:this.neg().getNumBitsAbs();for(var t=this.high!=0?this.high:this.low,e=31;e>0&&!(t&1<<e);e--);return this.high!=0?e+33:e+1};xt.isZero=function(){return this.high===0&&this.low===0};xt.eqz=xt.isZero;xt.isNegative=function(){return!this.unsigned&&this.high<0};xt.isPositive=function(){return this.unsigned||this.high>=0};xt.isOdd=function(){return(this.low&1)===1};xt.isEven=function(){return(this.low&1)===0};xt.equals=function(t){return zn(t)||(t=oi(t)),this.unsigned!==t.unsigned&&this.high>>>31===1&&t.high>>>31===1?!1:this.high===t.high&&this.low===t.low};xt.eq=xt.equals;xt.notEquals=function(t){return!this.eq(t)};xt.neq=xt.notEquals;xt.ne=xt.notEquals;xt.lessThan=function(t){return this.comp(t)<0};xt.lt=xt.lessThan;xt.lessThanOrEqual=function(t){return this.comp(t)<=0};xt.lte=xt.lessThanOrEqual;xt.le=xt.lessThanOrEqual;xt.greaterThan=function(t){return this.comp(t)>0};xt.gt=xt.greaterThan;xt.greaterThanOrEqual=function(t){return this.comp(t)>=0};xt.gte=xt.greaterThanOrEqual;xt.ge=xt.greaterThanOrEqual;xt.compare=function(t){if(zn(t)||(t=oi(t)),this.eq(t))return 0;var e=this.isNegative(),n=t.isNegative();return e&&!n?-1:!e&&n?1:this.unsigned?t.high>>>0>this.high>>>0||t.high===this.high&&t.low>>>0>this.low>>>0?-1:1:this.sub(t).isNegative()?-1:1};xt.comp=xt.compare;xt.negate=function(){return!this.unsigned&&this.eq(Ln)?Ln:this.not().add(Up)};xt.neg=xt.negate;xt.add=function(t){zn(t)||(t=oi(t));var e=this.high>>>16,n=this.high&65535,o=this.low>>>16,s=this.low&65535,i=t.high>>>16,a=t.high&65535,u=t.low>>>16,l=t.low&65535,c=0,p=0,m=0,f=0;return f+=s+l,m+=f>>>16,f&=65535,m+=o+u,p+=m>>>16,m&=65535,p+=n+a,c+=p>>>16,p&=65535,c+=e+i,c&=65535,qe(m<<16|f,c<<16|p,this.unsigned)};xt.subtract=function(t){return zn(t)||(t=oi(t)),this.add(t.neg())};xt.sub=xt.subtract;xt.multiply=function(t){if(this.isZero())return wo;if(zn(t)||(t=oi(t)),yo){var e=yo.mul(this.low,this.high,t.low,t.high);return qe(e,yo.get_high(),this.unsigned)}if(t.isZero())return wo;if(this.eq(Ln))return t.isOdd()?Ln:wo;if(t.eq(Ln))return this.isOdd()?Ln:wo;if(this.isNegative())return t.isNegative()?this.neg().mul(t.neg()):this.neg().mul(t).neg();if(t.isNegative())return this.mul(t.neg()).neg();if(this.lt(P_)&&t.lt(P_))return bo(this.toNumber()*t.toNumber(),this.unsigned);var n=this.high>>>16,o=this.high&65535,s=this.low>>>16,i=this.low&65535,a=t.high>>>16,u=t.high&65535,l=t.low>>>16,c=t.low&65535,p=0,m=0,f=0,d=0;return d+=i*c,f+=d>>>16,d&=65535,f+=s*c,m+=f>>>16,f&=65535,f+=i*l,m+=f>>>16,f&=65535,m+=o*c,p+=m>>>16,m&=65535,m+=s*l,p+=m>>>16,m&=65535,m+=i*u,p+=m>>>16,m&=65535,p+=n*c+o*l+s*u+i*a,p&=65535,qe(f<<16|d,p<<16|m,this.unsigned)};xt.mul=xt.multiply;xt.divide=function(t){if(zn(t)||(t=oi(t)),t.isZero())throw Error("division by zero");if(yo){if(!this.unsigned&&this.high===-2147483648&&t.low===-1&&t.high===-1)return this;var e=(this.unsigned?yo.div_u:yo.div_s)(this.low,this.high,t.low,t.high);return qe(e,yo.get_high(),this.unsigned)}if(this.isZero())return this.unsigned?nc:wo;var n,o,s;if(this.unsigned){if(t.unsigned||(t=t.toUnsigned()),t.gt(this))return nc;if(t.gt(this.shru(1)))return z_;s=nc}else{if(this.eq(Ln)){if(t.eq(Up)||t.eq(N0))return Ln;if(t.eq(Ln))return Up;var i=this.shr(1);return n=i.div(t).shl(1),n.eq(wo)?t.isNegative()?Up:N0:(o=this.sub(t.mul(n)),s=n.add(o.div(t)),s)}else if(t.eq(Ln))return this.unsigned?nc:wo;if(this.isNegative())return t.isNegative()?this.neg().div(t.neg()):this.neg().div(t).neg();if(t.isNegative())return this.div(t.neg()).neg();s=wo}for(o=this;o.gte(t);){n=Math.max(1,Math.floor(o.toNumber()/t.toNumber()));for(var a=Math.ceil(Math.log(n)/Math.LN2),u=a<=48?1:Jg(2,a-48),l=bo(n),c=l.mul(t);c.isNegative()||c.gt(o);)n-=u,l=bo(n,this.unsigned),c=l.mul(t);l.isZero()&&(l=Up),s=s.add(l),o=o.sub(c)}return s};xt.div=xt.divide;xt.modulo=function(t){if(zn(t)||(t=oi(t)),yo){var e=(this.unsigned?yo.rem_u:yo.rem_s)(this.low,this.high,t.low,t.high);return qe(e,yo.get_high(),this.unsigned)}return this.sub(this.div(t).mul(t))};xt.mod=xt.modulo;xt.rem=xt.modulo;xt.not=function(){return qe(~this.low,~this.high,this.unsigned)};xt.and=function(t){return zn(t)||(t=oi(t)),qe(this.low&t.low,this.high&t.high,this.unsigned)};xt.or=function(t){return zn(t)||(t=oi(t)),qe(this.low|t.low,this.high|t.high,this.unsigned)};xt.xor=function(t){return zn(t)||(t=oi(t)),qe(this.low^t.low,this.high^t.high,this.unsigned)};xt.shiftLeft=function(t){return zn(t)&&(t=t.toInt()),(t&=63)===0?this:t<32?qe(this.low<<t,this.high<<t|this.low>>>32-t,this.unsigned):qe(0,this.low<<t-32,this.unsigned)};xt.shl=xt.shiftLeft;xt.shiftRight=function(t){return zn(t)&&(t=t.toInt()),(t&=63)===0?this:t<32?qe(this.low>>>t|this.high<<32-t,this.high>>t,this.unsigned):qe(this.high>>t-32,this.high>=0?0:-1,this.unsigned)};xt.shr=xt.shiftRight;xt.shiftRightUnsigned=function(t){if(zn(t)&&(t=t.toInt()),t&=63,t===0)return this;var e=this.high;if(t<32){var n=this.low;return qe(n>>>t|e<<32-t,e>>>t,this.unsigned)}else return t===32?qe(e,0,this.unsigned):qe(e>>>t-32,0,this.unsigned)};xt.shru=xt.shiftRightUnsigned;xt.shr_u=xt.shiftRightUnsigned;xt.toSigned=function(){return this.unsigned?qe(this.low,this.high,!1):this};xt.toUnsigned=function(){return this.unsigned?this:qe(this.low,this.high,!0)};xt.toBytes=function(t){return t?this.toBytesLE():this.toBytesBE()};xt.toBytesLE=function(){var t=this.high,e=this.low;return[e&255,e>>>8&255,e>>>16&255,e>>>24,t&255,t>>>8&255,t>>>16&255,t>>>24]};xt.toBytesBE=function(){var t=this.high,e=this.low;return[t>>>24,t>>>16&255,t>>>8&255,t&255,e>>>24,e>>>16&255,e>>>8&255,e&255]};He.fromBytes=function(t,e,n){return n?He.fromBytesLE(t,e):He.fromBytesBE(t,e)};He.fromBytesLE=function(t,e){return new He(t[0]|t[1]<<8|t[2]<<16|t[3]<<24,t[4]|t[5]<<8|t[6]<<16|t[7]<<24,e)};He.fromBytesBE=function(t,e){return new He(t[4]<<24|t[5]<<16|t[6]<<8|t[7],t[0]<<24|t[1]<<16|t[2]<<8|t[3],e)}});var TE=br(()=>{});var _E=br(()=>{});var nA=br((rA,sN)=>{(function(r,t,e){function n(a){var u=this,l=i();u.next=function(){var c=2091639*u.s0+u.c*23283064365386963e-26;return u.s0=u.s1,u.s1=u.s2,u.s2=c-(u.c=c|0)},u.c=1,u.s0=l(" "),u.s1=l(" "),u.s2=l(" "),u.s0-=l(a),u.s0<0&&(u.s0+=1),u.s1-=l(a),u.s1<0&&(u.s1+=1),u.s2-=l(a),u.s2<0&&(u.s2+=1),l=null}function o(a,u){return u.c=a.c,u.s0=a.s0,u.s1=a.s1,u.s2=a.s2,u}function s(a,u){var l=new n(a),c=u&&u.state,p=l.next;return p.int32=function(){return l.next()*4294967296|0},p.double=function(){return p()+(p()*2097152|0)*11102230246251565e-32},p.quick=p,c&&(typeof c=="object"&&o(c,l),p.state=function(){return o(l,{})}),p}function i(){var a=4022871197,u=function(l){l=String(l);for(var c=0;c<l.length;c++){a+=l.charCodeAt(c);var p=.02519603282416938*a;a=p>>>0,p-=a,p*=a,a=p>>>0,p-=a,a+=p*4294967296}return(a>>>0)*23283064365386963e-26};return u}t&&t.exports?t.exports=s:e&&e.amd?e(function(){return s}):this.alea=s})(rA,typeof sN=="object"&&sN,typeof define=="function"&&define)});var sA=br((oA,iN)=>{(function(r,t,e){function n(i){var a=this,u="";a.x=0,a.y=0,a.z=0,a.w=0,a.next=function(){var c=a.x^a.x<<11;return a.x=a.y,a.y=a.z,a.z=a.w,a.w^=a.w>>>19^c^c>>>8},i===(i|0)?a.x=i:u+=i;for(var l=0;l<u.length+64;l++)a.x^=u.charCodeAt(l)|0,a.next()}function o(i,a){return a.x=i.x,a.y=i.y,a.z=i.z,a.w=i.w,a}function s(i,a){var u=new n(i),l=a&&a.state,c=function(){return(u.next()>>>0)/4294967296};return c.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=u.next,c.quick=c,l&&(typeof l=="object"&&o(l,u),c.state=function(){return o(u,{})}),c}t&&t.exports?t.exports=s:e&&e.amd?e(function(){return s}):this.xor128=s})(oA,typeof iN=="object"&&iN,typeof define=="function"&&define)});var aA=br((iA,aN)=>{(function(r,t,e){function n(i){var a=this,u="";a.next=function(){var c=a.x^a.x>>>2;return a.x=a.y,a.y=a.z,a.z=a.w,a.w=a.v,(a.d=a.d+362437|0)+(a.v=a.v^a.v<<4^(c^c<<1))|0},a.x=0,a.y=0,a.z=0,a.w=0,a.v=0,i===(i|0)?a.x=i:u+=i;for(var l=0;l<u.length+64;l++)a.x^=u.charCodeAt(l)|0,l==u.length&&(a.d=a.x<<10^a.x>>>4),a.next()}function o(i,a){return a.x=i.x,a.y=i.y,a.z=i.z,a.w=i.w,a.v=i.v,a.d=i.d,a}function s(i,a){var u=new n(i),l=a&&a.state,c=function(){return(u.next()>>>0)/4294967296};return c.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=u.next,c.quick=c,l&&(typeof l=="object"&&o(l,u),c.state=function(){return o(u,{})}),c}t&&t.exports?t.exports=s:e&&e.amd?e(function(){return s}):this.xorwow=s})(iA,typeof aN=="object"&&aN,typeof define=="function"&&define)});var uA=br((lA,lN)=>{(function(r,t,e){function n(i){var a=this;a.next=function(){var l=a.x,c=a.i,p,m,f;return p=l[c],p^=p>>>7,m=p^p<<24,p=l[c+1&7],m^=p^p>>>10,p=l[c+3&7],m^=p^p>>>3,p=l[c+4&7],m^=p^p<<7,p=l[c+7&7],p=p^p<<13,m^=p^p<<9,l[c]=m,a.i=c+1&7,m};function u(l,c){var p,m,f=[];if(c===(c|0))m=f[0]=c;else for(c=""+c,p=0;p<c.length;++p)f[p&7]=f[p&7]<<15^c.charCodeAt(p)+f[p+1&7]<<13;for(;f.length<8;)f.push(0);for(p=0;p<8&&f[p]===0;++p);for(p==8?m=f[7]=-1:m=f[p],l.x=f,l.i=0,p=256;p>0;--p)l.next()}u(a,i)}function o(i,a){return a.x=i.x.slice(),a.i=i.i,a}function s(i,a){i==null&&(i=+new Date);var u=new n(i),l=a&&a.state,c=function(){return(u.next()>>>0)/4294967296};return c.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=u.next,c.quick=c,l&&(l.x&&o(l,u),c.state=function(){return o(u,{})}),c}t&&t.exports?t.exports=s:e&&e.amd?e(function(){return s}):this.xorshift7=s})(lA,typeof lN=="object"&&lN,typeof define=="function"&&define)});var pA=br((cA,uN)=>{(function(r,t,e){function n(i){var a=this;a.next=function(){var l=a.w,c=a.X,p=a.i,m,f;return a.w=l=l+1640531527|0,f=c[p+34&127],m=c[p=p+1&127],f^=f<<13,m^=m<<17,f^=f>>>15,m^=m>>>12,f=c[p]=f^m,a.i=p,f+(l^l>>>16)|0};function u(l,c){var p,m,f,d,h,g=[],x=128;for(c===(c|0)?(m=c,c=null):(c=c+"\0",m=0,x=Math.max(x,c.length)),f=0,d=-32;d<x;++d)c&&(m^=c.charCodeAt((d+32)%c.length)),d===0&&(h=m),m^=m<<10,m^=m>>>15,m^=m<<4,m^=m>>>13,d>=0&&(h=h+1640531527|0,p=g[d&127]^=m+h,f=p==0?f+1:0);for(f>=128&&(g[(c&&c.length||0)&127]=-1),f=127,d=4*128;d>0;--d)m=g[f+34&127],p=g[f=f+1&127],m^=m<<13,p^=p<<17,m^=m>>>15,p^=p>>>12,g[f]=m^p;l.w=h,l.X=g,l.i=f}u(a,i)}function o(i,a){return a.i=i.i,a.w=i.w,a.X=i.X.slice(),a}function s(i,a){i==null&&(i=+new Date);var u=new n(i),l=a&&a.state,c=function(){return(u.next()>>>0)/4294967296};return c.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=u.next,c.quick=c,l&&(l.X&&o(l,u),c.state=function(){return o(u,{})}),c}t&&t.exports?t.exports=s:e&&e.amd?e(function(){return s}):this.xor4096=s})(cA,typeof uN=="object"&&uN,typeof define=="function"&&define)});var fA=br((mA,cN)=>{(function(r,t,e){function n(i){var a=this,u="";a.next=function(){var c=a.b,p=a.c,m=a.d,f=a.a;return c=c<<25^c>>>7^p,p=p-m|0,m=m<<24^m>>>8^f,f=f-c|0,a.b=c=c<<20^c>>>12^p,a.c=p=p-m|0,a.d=m<<16^p>>>16^f,a.a=f-c|0},a.a=0,a.b=0,a.c=-1640531527,a.d=1367130551,i===Math.floor(i)?(a.a=i/4294967296|0,a.b=i|0):u+=i;for(var l=0;l<u.length+20;l++)a.b^=u.charCodeAt(l)|0,a.next()}function o(i,a){return a.a=i.a,a.b=i.b,a.c=i.c,a.d=i.d,a}function s(i,a){var u=new n(i),l=a&&a.state,c=function(){return(u.next()>>>0)/4294967296};return c.double=function(){do var p=u.next()>>>11,m=(u.next()>>>0)/4294967296,f=(p+m)/(1<<21);while(f===0);return f},c.int32=u.next,c.quick=c,l&&(typeof l=="object"&&o(l,u),c.state=function(){return o(u,{})}),c}t&&t.exports?t.exports=s:e&&e.amd?e(function(){return s}):this.tychei=s})(mA,typeof cN=="object"&&cN,typeof define=="function"&&define)});var dA=br(()=>{});var gA=br((hA,ty)=>{(function(r,t,e){var n=256,o=6,s=52,i="random",a=e.pow(n,o),u=e.pow(2,s),l=u*2,c=n-1,p;function m(w,I,N){var E=[];I=I==!0?{entropy:!0}:I||{};var A=g(h(I.entropy?[w,b(t)]:w==null?x():w,3),E),D=new f(E),F=function(){for(var M=D.g(o),V=a,G=0;M<u;)M=(M+G)*n,V*=n,G=D.g(1);for(;M>=l;)M/=2,V/=2,G>>>=1;return(M+G)/V};return F.int32=function(){return D.g(4)|0},F.quick=function(){return D.g(4)/4294967296},F.double=F,g(b(D.S),t),(I.pass||N||function(M,V,G,W){return W&&(W.S&&d(W,D),M.state=function(){return d(D,{})}),G?(e[i]=M,V):M})(F,A,"global"in I?I.global:this==e,I.state)}function f(w){var I,N=w.length,E=this,A=0,D=E.i=E.j=0,F=E.S=[];for(N||(w=[N++]);A<n;)F[A]=A++;for(A=0;A<n;A++)F[A]=F[D=c&D+w[A%N]+(I=F[A])],F[D]=I;(E.g=function(M){for(var V,G=0,W=E.i,q=E.j,H=E.S;M--;)V=H[W=c&W+1],G=G*n+H[c&(H[W]=H[q=c&q+V])+(H[q]=V)];return E.i=W,E.j=q,G})(n)}function d(w,I){return I.i=w.i,I.j=w.j,I.S=w.S.slice(),I}function h(w,I){var N=[],E=typeof w,A;if(I&&E=="object")for(A in w)try{N.push(h(w[A],I-1))}catch(D){}return N.length?N:E=="string"?w:w+"\0"}function g(w,I){for(var N=w+"",E,A=0;A<N.length;)I[c&A]=c&(E^=I[c&A]*19)+N.charCodeAt(A++);return b(I)}function x(){try{var w;return p&&(w=p.randomBytes)?w=w(n):(w=new Uint8Array(n),(r.crypto||r.msCrypto).getRandomValues(w)),b(w)}catch(E){var I=r.navigator,N=I&&I.plugins;return[+new Date,r,N,r.screen,b(t)]}}function b(w){return String.fromCharCode.apply(0,w)}if(g(e.random(),t),typeof ty=="object"&&ty.exports){ty.exports=m;try{p=dA()}catch(w){}}else typeof define=="function"&&define.amd?define(function(){return m}):e["seed"+i]=m})(typeof self!="undefined"?self:hA,[],Math)});var hh=br((c_t,xA)=>{var BX=nA(),VX=sA(),GX=aA(),WX=uA(),UX=pA(),HX=fA(),hc=gA();hc.alea=BX;hc.xor128=VX;hc.xorwow=GX;hc.xorshift7=WX;hc.xor4096=UX;hc.tychei=HX;xA.exports=hc});var $k=br(()=>{});var hw=br(()=>{});var e_=br(()=>{});var KH=br(()=>{});var jH=br(()=>{});var XH=br(()=>{});var YH=br((RC,n_)=>{var r_=(()=>{var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(t){t=t||{};function e(){return it.buffer!=re&&Xe(it.buffer),be}function n(){return it.buffer!=re&&Xe(it.buffer),de}function o(){return it.buffer!=re&&Xe(it.buffer),Ee}function s(){return it.buffer!=re&&Xe(it.buffer),On}function i(){return it.buffer!=re&&Xe(it.buffer),lr}function a(){return it.buffer!=re&&Xe(it.buffer),Zn}function u(){return it.buffer!=re&&Xe(it.buffer),Br}var l=typeof t!="undefined"?t:{},c,p;l.ready=new Promise(function(P,U){c=P,p=U});var m;typeof process!="undefined"&&process.listeners&&(m={uncaughtException:process.listeners("uncaughtException"),unhandledRejection:process.listeners("unhandledRejection")});var f=Object.assign({},l),d=[],h="./this.program",g=(P,U)=>{throw U},x=typeof window=="object",b=typeof importScripts=="function",w=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",I=l.ENVIRONMENT_IS_PTHREAD||!1,N="";function E(P){return l.locateFile?l.locateFile(P,N):N+P}var A,D,F,M;function V(P){if(P instanceof Yu)return;Y("exiting due to exception: "+P)}if(w){var G=hw(),W=e_();b?N=W.dirname(N)+"/":N=__dirname+"/",A=(U,dt)=>(U=wp(U)?new URL(U):W.normalize(U),G.readFileSync(U,dt?void 0:"utf8")),F=U=>{var dt=A(U,!0);return dt.buffer||(dt=new Uint8Array(dt)),dt},D=(U,dt,Pt)=>{U=wp(U)?new URL(U):W.normalize(U),G.readFile(U,function(Jt,Zt){Jt?Pt(Jt):dt(Zt.buffer)})},process.argv.length>1&&(h=process.argv[1].replace(/\\/g,"/")),d=process.argv.slice(2),process.on("uncaughtException",function(U){if(!(U instanceof Yu))throw U}),process.on("unhandledRejection",function(U){throw U}),g=(U,dt)=>{if(zo())throw process.exitCode=U,dt;V(dt),process.exit(U)},l.inspect=function(){return"[Emscripten Module object]"};let P;try{P=KH()}catch(U){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),U}global.Worker=P.Worker}else(x||b)&&(b?N=self.location.href:typeof document!="undefined"&&document.currentScript&&(N=document.currentScript.src),typeof r!="undefined"&&r&&(N=r),N.indexOf("blob:")!==0?N=N.substr(0,N.replace(/[?#].*/,"").lastIndexOf("/")+1):N="",w||(A=P=>{var U=new XMLHttpRequest;return U.open("GET",P,!1),U.send(null),U.responseText},b&&(F=P=>{var U=new XMLHttpRequest;return U.open("GET",P,!1),U.responseType="arraybuffer",U.send(null),new Uint8Array(U.response)}),D=(P,U,dt)=>{var Pt=new XMLHttpRequest;Pt.open("GET",P,!0),Pt.responseType="arraybuffer",Pt.onload=()=>{if(Pt.status==200||Pt.status==0&&Pt.response){U(Pt.response);return}dt()},Pt.onerror=dt,Pt.send(null)}),M=P=>document.title=P);w&&typeof performance=="undefined"&&(global.performance=jH().performance);var q=console.log.bind(console),H=console.warn.bind(console);w&&(q=P=>G.writeSync(1,P+`
`),H=P=>G.writeSync(2,P+`
`));var j=l.print||q,Y=l.printErr||H;Object.assign(l,f),f=null,l.arguments&&(d=l.arguments),l.thisProgram&&(h=l.thisProgram),l.quit&&(g=l.quit);var Z=4,et=Atomics.load,nt=Atomics.store,st=Atomics.compareExchange,lt;l.wasmBinary&&(lt=l.wasmBinary);var ot=l.noExitRuntime||!0;typeof WebAssembly!="object"&&Xu("no native wasm support detected");var it,ft,gt=!1,Ct;function Rt(P,U){P||Xu(U)}var Dt=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function Ht(P,U,dt){U>>>=0;for(var Pt=U+dt,Jt=U;P[Jt]&&!(Jt>=Pt);)++Jt;if(Jt-U>16&&P.buffer&&Dt)return Dt.decode(P.buffer instanceof SharedArrayBuffer?P.slice(U,Jt):P.subarray(U,Jt));for(var Zt="";U<Jt;){var bt=P[U++];if(!(bt&128)){Zt+=String.fromCharCode(bt);continue}var $t=P[U++]&63;if((bt&224)==192){Zt+=String.fromCharCode((bt&31)<<6|$t);continue}var or=P[U++]&63;if((bt&240)==224?bt=(bt&15)<<12|$t<<6|or:bt=(bt&7)<<18|$t<<12|or<<6|P[U++]&63,bt<65536)Zt+=String.fromCharCode(bt);else{var eo=bt-65536;Zt+=String.fromCharCode(55296|eo>>10,56320|eo&1023)}}return Zt}function qt(P,U){return P>>>=0,P?Ht(n(),P,U):""}function pe(P,U,dt,Pt){if(dt>>>=0,!(Pt>0))return 0;for(var Jt=dt,Zt=dt+Pt-1,bt=0;bt<P.length;++bt){var $t=P.charCodeAt(bt);if($t>=55296&&$t<=57343){var or=P.charCodeAt(++bt);$t=65536+(($t&1023)<<10)|or&1023}if($t<=127){if(dt>=Zt)break;U[dt++>>>0]=$t}else if($t<=2047){if(dt+1>=Zt)break;U[dt++>>>0]=192|$t>>6,U[dt++>>>0]=128|$t&63}else if($t<=65535){if(dt+2>=Zt)break;U[dt++>>>0]=224|$t>>12,U[dt++>>>0]=128|$t>>6&63,U[dt++>>>0]=128|$t&63}else{if(dt+3>=Zt)break;U[dt++>>>0]=240|$t>>18,U[dt++>>>0]=128|$t>>12&63,U[dt++>>>0]=128|$t>>6&63,U[dt++>>>0]=128|$t&63}}return U[dt>>>0]=0,dt-Jt}function ye(P,U,dt){return pe(P,n(),U,dt)}var re,be,de,Ee,Ae,On,lr,Zn,Br;I&&(re=l.buffer);function Xe(P){re=P,l.HEAP8=be=new Int8Array(P),l.HEAP16=Ee=new Int16Array(P),l.HEAP32=On=new Int32Array(P),l.HEAPU8=de=new Uint8Array(P),l.HEAPU16=Ae=new Uint16Array(P),l.HEAPU32=lr=new Uint32Array(P),l.HEAPF32=Zn=new Float32Array(P),l.HEAPF64=Br=new Float64Array(P)}var Vr=l.INITIAL_MEMORY||16777216;if(I)it=l.wasmMemory,re=l.buffer;else if(l.wasmMemory)it=l.wasmMemory;else if(it=new WebAssembly.Memory({initial:Vr/65536,maximum:65536,shared:!0}),!(it.buffer instanceof SharedArrayBuffer))throw Y("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"),w&&Y("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and/or recent version)"),Error("bad memory");it&&(re=it.buffer),Vr=re.byteLength,Xe(re);var Gr,Jn=[],Qn=[],Qr=[],Ca=!1;function zo(){return ot}function Ti(){if(l.preRun)for(typeof l.preRun=="function"&&(l.preRun=[l.preRun]);l.preRun.length;)Vd(l.preRun.shift());Ud(Jn)}function Er(){Ca=!0,!I&&Ud(Qn)}function va(){if(!I){if(l.postRun)for(typeof l.postRun=="function"&&(l.postRun=[l.postRun]);l.postRun.length;)c_(l.postRun.shift());Ud(Qr)}}function Vd(P){Jn.unshift(P)}function Gd(P){Qn.unshift(P)}function c_(P){Qr.unshift(P)}var zl=0,bp=null,Sa=null;function MC(P){zl++,l.monitorRunDependencies&&l.monitorRunDependencies(zl)}function Ig(P){if(zl--,l.monitorRunDependencies&&l.monitorRunDependencies(zl),zl==0&&(bp!==null&&(clearInterval(bp),bp=null),Sa)){var U=Sa;Sa=null,U()}}function Xu(P){l.onAbort&&l.onAbort(P),P="Aborted("+P+")",Y(P),gt=!0,Ct=1,P+=". Build with -sASSERTIONS for more info.";var U=new WebAssembly.RuntimeError(P);throw p(U),U}var PC="data:application/octet-stream;base64,";function Cg(P){return P.startsWith(PC)}function wp(P){return P.startsWith("file://")}var tn;tn="tfjs-backend-wasm-threaded-simd.wasm",Cg(tn)||(tn=E(tn));function vg(P){try{if(P==tn&&lt)return new Uint8Array(lt);if(F)return F(P);throw"both async and sync fetching of the wasm failed"}catch(U){Xu(U)}}function LC(){if(!lt&&(x||b)){if(typeof fetch=="function"&&!wp(tn))return fetch(tn,{credentials:"same-origin"}).then(function(P){if(!P.ok)throw"failed to load wasm binary file at '"+tn+"'";return P.arrayBuffer()}).catch(function(){return vg(tn)});if(D)return new Promise(function(P,U){D(tn,function(dt){P(new Uint8Array(dt))},U)})}return Promise.resolve().then(function(){return vg(tn)})}function zC(){var P={env:Mg,wasi_snapshot_preview1:Mg};function U(bt,$t){var or=bt.exports;if(l.asm=or,jC(l.asm._emscripten_tls_init),Gr=l.asm.__indirect_function_table,Gd(l.asm.__wasm_call_ctors),ft=$t,!I){var eo=Yt.unusedWorkers.length;Yt.unusedWorkers.forEach(function(ka){Yt.loadWasmModuleToWorker(ka,function(){--eo||Ig("wasm-instantiate")})})}}I||MC("wasm-instantiate");function dt(bt){U(bt.instance,bt.module)}function Pt(bt){return LC().then(function($t){return WebAssembly.instantiate($t,P)}).then(function($t){return $t}).then(bt,function($t){Y("failed to asynchronously prepare wasm: "+$t),Xu($t)})}function Jt(){return!lt&&typeof WebAssembly.instantiateStreaming=="function"&&!Cg(tn)&&!wp(tn)&&!w&&typeof fetch=="function"?fetch(tn,{credentials:"same-origin"}).then(function(bt){var $t=WebAssembly.instantiateStreaming(bt,P);return $t.then(dt,function(or){return Y("wasm streaming compile failed: "+or),Y("falling back to ArrayBuffer instantiation"),Pt(dt)})}):Pt(dt)}if(l.instantiateWasm)try{var Zt=l.instantiateWasm(P,U);return Zt}catch(bt){Y("Module.instantiateWasm callback failed with error: "+bt),p(bt)}return Jt().catch(p),{}}var p_,m_,Sg={};function Yu(P){this.name="ExitStatus",this.message="Program terminated with exit("+P+")",this.status=P}function BC(P){var U=Yt.pthreads[P];delete Yt.pthreads[P],U.terminate(),c0(P),Yt.runningWorkers.splice(Yt.runningWorkers.indexOf(U),1),U.pthread_ptr=0}function VC(P){var U=Yt.pthreads[P];U.postMessage({cmd:"cancel"})}function Wd(P){var U=Yt.pthreads[P];Rt(U),Yt.returnWorkerToPool(U)}function GC(P){var U=Yt.getNewWorker();if(!U)return 6;Yt.runningWorkers.push(U),Yt.pthreads[P.pthread_ptr]=U,U.pthread_ptr=P.pthread_ptr;var dt={cmd:"run",start_routine:P.startRoutine,arg:P.arg,pthread_ptr:P.pthread_ptr};return U.runPthread=()=>{w&&U.ref(),U.postMessage(dt,P.transferList),delete U.runPthread},U.loaded&&U.runPthread(),0}var Ng={varargs:void 0,get:function(){Ng.varargs+=4;var P=s()[Ng.varargs-4>>>2];return P},getStr:function(P){var U=qt(P);return U}};function kg(P){if(I)return Bl(1,1,P);Ct=P,zo()||(Yt.terminateAllThreads(),l.onExit&&l.onExit(P),gt=!0),g(P,new Yu(P))}function WC(P,U){if(Ct=P,!U&&I)throw _g(P),"unwind";kg(P)}var Tg=WC;function UC(P){if(P instanceof Yu||P=="unwind")return Ct;g(1,P)}var Yt={unusedWorkers:[],runningWorkers:[],tlsInitFunctions:[],pthreads:{},init:function(){I?Yt.initWorker():Yt.initMainThread()},initMainThread:function(){for(var P=8;P--;)Yt.allocateUnusedWorker()},initWorker:function(){ot=!1},setExitStatus:function(P){Ct=P},terminateAllThreads:function(){for(var P of Object.values(Yt.pthreads))Yt.returnWorkerToPool(P);for(var P of Yt.unusedWorkers)P.terminate();Yt.unusedWorkers=[]},returnWorkerToPool:function(P){var U=P.pthread_ptr;delete Yt.pthreads[U],Yt.unusedWorkers.push(P),Yt.runningWorkers.splice(Yt.runningWorkers.indexOf(P),1),P.pthread_ptr=0,w&&P.unref(),c0(U)},receiveObjectTransfer:function(P){},threadInitTLS:function(){Yt.tlsInitFunctions.forEach(P=>P())},loadWasmModuleToWorker:function(P,U){P.onmessage=Zt=>{var bt=Zt.data,$t=bt.cmd;if(P.pthread_ptr&&(Yt.currentProxiedOperationCallerThread=P.pthread_ptr),bt.targetThread&&bt.targetThread!=Gg()){var or=Yt.pthreads[bt.targetThread];or?or.postMessage(bt,bt.transferList):Y('Internal error! Worker sent a message "'+$t+'" to target pthread '+bt.targetThread+", but that thread no longer exists!"),Yt.currentProxiedOperationCallerThread=void 0;return}$t==="processProxyingQueue"?Hd(bt.queue):$t==="spawnThread"?GC(bt):$t==="cleanupThread"?Wd(bt.thread):$t==="killThread"?BC(bt.thread):$t==="cancelThread"?VC(bt.thread):$t==="loaded"?(P.loaded=!0,w&&P.unref(),U&&U(P),P.runPthread&&P.runPthread()):$t==="print"?j("Thread "+bt.threadId+": "+bt.text):$t==="printErr"?Y("Thread "+bt.threadId+": "+bt.text):$t==="alert"?alert("Thread "+bt.threadId+": "+bt.text):bt.target==="setimmediate"?P.postMessage(bt):$t==="callHandler"?l[bt.handler](...bt.args):$t&&Y("worker sent an unknown command "+$t),Yt.currentProxiedOperationCallerThread=void 0},P.onerror=Zt=>{var bt="worker sent an error!";throw Y(bt+" "+Zt.filename+":"+Zt.lineno+": "+Zt.message),Zt},w&&(P.on("message",function(Zt){P.onmessage({data:Zt})}),P.on("error",function(Zt){P.onerror(Zt)}),P.on("detachedExit",function(){}));var dt=[],Pt=["onExit","onAbort","print","printErr"];for(var Jt of Pt)l.hasOwnProperty(Jt)&&dt.push(Jt);P.postMessage({cmd:"load",handlers:dt,urlOrBlob:l.mainScriptUrlOrBlob||r,wasmMemory:it,wasmModule:ft})},allocateUnusedWorker:function(){var P,U=E("tfjs-backend-wasm-threaded-simd.worker.js");P=new Worker(U),Yt.unusedWorkers.push(P)},getNewWorker:function(){return Yt.unusedWorkers.length==0&&(Yt.allocateUnusedWorker(),Yt.loadWasmModuleToWorker(Yt.unusedWorkers[0])),Yt.unusedWorkers.pop()}};l.PThread=Yt;function Ud(P){for(;P.length>0;)P.shift()(l)}function HC(){var P=Gg(),U=s()[P+52>>>2],dt=s()[P+56>>>2],Pt=U-dt;y_(U,Pt),Wg(U)}l.establishStackSpace=HC;function _g(P){if(I)return Bl(2,0,P);try{Tg(P)}catch(U){UC(U)}}var Ip=[];function qC(P){var U=Ip[P];return U||(P>=Ip.length&&(Ip.length=P+1),Ip[P]=U=Gr.get(P)),U}function KC(P,U){var dt=qC(P)(U);zo()?Yt.setExitStatus(dt):x_(dt)}l.invokeEntryPoint=KC;function jC(P){Yt.tlsInitFunctions.push(P)}function XC(P){d_(P,!b,1,!x),Yt.threadInitTLS()}function YC(P){I?postMessage({cmd:"cleanupThread",thread:P}):Wd(P)}function Eg(P,U,dt,Pt){return I?Bl(3,1,P,U,dt,Pt):Ag(P,U,dt,Pt)}function Ag(P,U,dt,Pt){if(typeof SharedArrayBuffer=="undefined")return Y("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;var Jt=[],Zt=0;if(I&&(Jt.length===0||Zt))return Eg(P,U,dt,Pt);if(Zt)return Zt;var bt={startRoutine:dt,pthread_ptr:P,arg:Pt,transferList:Jt};return I?(bt.cmd="spawnThread",postMessage(bt,Jt),0):GC(bt)}function ZC(){return 65536}var JC=!0;function QC(){return JC}function Hd(P){Atomics.store(s(),P>>2,1),Gg()&&g_(P),Atomics.compareExchange(s(),P>>2,1,0)}l.executeNotifiedProxyingQueue=Hd;function tv(P,U,dt,Pt){if(P==U)setTimeout(()=>Hd(Pt));else if(I)postMessage({targetThread:P,cmd:"processProxyingQueue",queue:Pt});else{var Jt=Yt.pthreads[P];if(!Jt)return;Jt.postMessage({cmd:"processProxyingQueue",queue:Pt})}return 1}function ev(P,U,dt){return-1}function rv(){Xu("")}function Zu(P){Zu.shown||(Zu.shown={}),Zu.shown[P]||(Zu.shown[P]=1,w&&(P="warning: "+P),Y(P))}function nv(){w||b||Zu("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread")}function ov(){return Date.now()}function Dg(){return 4294901760}function sv(){return Dg()}var qd;w?qd=()=>{var P=process.hrtime();return P[0]*1e3+P[1]/1e6}:qd=()=>performance.timeOrigin+performance.now();function iv(P,U,dt){n().copyWithin(P>>>0,U>>>0,U+dt>>>0)}function av(){return w?XH().cpus().length:navigator.hardwareConcurrency}function lv(P){var U=p0(),dt=P();return Wg(U),dt}function Bl(P,U){var dt=arguments.length-2,Pt=arguments;return lv(()=>{for(var Jt=dt,Zt=Ug(Jt*8),bt=Zt>>3,$t=0;$t<dt;$t++){var or=Pt[2+$t];u()[bt+$t>>>0]=or}return h_(P,Jt,Zt,U)})}var Kd=[];function uv(P,U,dt){Kd.length=U;for(var Pt=dt>>3,Jt=0;Jt<U;Jt++)Kd[Jt]=u()[Pt+Jt>>>0];var Zt=P<0,bt=Zt?Sg[-P-1]:yv[P];return bt.apply(null,Kd)}function cv(P){try{return it.grow(P-re.byteLength+65535>>>16),Xe(it.buffer),1}catch(U){}}function pv(P){var U=n().length;if(P=P>>>0,P<=U)return!1;var dt=Dg();if(P>dt)return!1;let Pt=(or,eo)=>or+(eo-or%eo)%eo;for(var Jt=1;Jt<=4;Jt*=2){var Zt=U*(1+.2/Jt);Zt=Math.min(Zt,P+100663296);var bt=Math.min(dt,Pt(Math.max(P,Zt),65536)),$t=cv(bt);if($t)return!0}return!1}function mv(){throw"unwind"}function $g(P){return I?Bl(4,1,P):52}function Rg(P,U,dt,Pt,Jt){return I?Bl(5,1,P,U,dt,Pt,Jt):70}var fv=[null,[],[]];function dv(P,U){var dt=fv[P];U===0||U===10?((P===1?j:Y)(Ht(dt,0)),dt.length=0):dt.push(U)}function Fg(P,U,dt,Pt){if(I)return Bl(6,1,P,U,dt,Pt);for(var Jt=0,Zt=0;Zt<dt;Zt++){var bt=i()[U>>>2],$t=i()[U+4>>>2];U+=8;for(var or=0;or<$t;or++)dv(P,n()[bt+or>>>0]);Jt+=$t}return i()[Pt>>>2]=Jt,0}function Og(P){var U=l["_"+P];return U}function hv(P,U){e().set(P,U>>>0)}function gv(P,U,dt,Pt,Jt){var Zt={string:Mn=>{var Np=0;if(Mn!=null&&Mn!==0){var I_=(Mn.length<<2)+1;Np=Ug(I_),ye(Mn,Np,I_)}return Np},array:Mn=>{var Np=Ug(Mn.length);return hv(Mn,Np),Np}};function bt(Mn){return U==="string"?qt(Mn):U==="boolean"?!!Mn:Mn}var $t=Og(P),or=[],eo=0;if(Pt)for(var ka=0;ka<Pt.length;ka++){var w_=Zt[dt[ka]];w_?(eo===0&&(eo=p0()),or[ka]=w_(Pt[ka])):or[ka]=Pt[ka]}var m0=$t.apply(null,or);function Lq(Mn){return eo!==0&&Wg(eo),bt(Mn)}return m0=Lq(m0),m0}function xv(P,U,dt,Pt){dt=dt||[];var Jt=dt.every(bt=>bt==="number"||bt==="boolean"),Zt=U!=="string";return Zt&&Jt&&!Pt?Og(P):function(){return gv(P,U,dt,arguments,Pt)}}Yt.init();var yv=[null,kg,_g,Eg,$g,Rg,Fg],Mg={__emscripten_init_main_thread_js:XC,__emscripten_thread_cleanup:YC,__pthread_create_js:Ag,_emscripten_default_pthread_stack_size:ZC,_emscripten_get_now_is_monotonic:QC,_emscripten_notify_task_queue:tv,_emscripten_set_offscreencanvas_size:ev,abort:rv,emscripten_check_blocking_allowed:nv,emscripten_date_now:ov,emscripten_get_heap_max:sv,emscripten_get_now:qd,emscripten_memcpy_big:iv,emscripten_num_logical_cores:av,emscripten_receive_on_main_thread_js:uv,emscripten_resize_heap:pv,emscripten_unwind_to_js_event_loop:mv,exit:Tg,fd_close:$g,fd_seek:Rg,fd_write:Fg,memory:it||l.wasmMemory},f_=zC(),bv=l.___wasm_call_ctors=function(){return(bv=l.___wasm_call_ctors=l.asm.__wasm_call_ctors).apply(null,arguments)},wv=l._init=function(){return(wv=l._init=l.asm.init).apply(null,arguments)},Iv=l._init_with_threads_count=function(){return(Iv=l._init_with_threads_count=l.asm.init_with_threads_count).apply(null,arguments)},Cv=l._get_threads_count=function(){return(Cv=l._get_threads_count=l.asm.get_threads_count).apply(null,arguments)},vv=l._register_tensor=function(){return(vv=l._register_tensor=l.asm.register_tensor).apply(null,arguments)},Sv=l._dispose_data=function(){return(Sv=l._dispose_data=l.asm.dispose_data).apply(null,arguments)},Nv=l._dispose=function(){return(Nv=l._dispose=l.asm.dispose).apply(null,arguments)},kv=l._Abs=function(){return(kv=l._Abs=l.asm.Abs).apply(null,arguments)},Tv=l._Acos=function(){return(Tv=l._Acos=l.asm.Acos).apply(null,arguments)},_v=l._Acosh=function(){return(_v=l._Acosh=l.asm.Acosh).apply(null,arguments)},Ev=l._Add=function(){return(Ev=l._Add=l.asm.Add).apply(null,arguments)},Av=l._AddN=function(){return(Av=l._AddN=l.asm.AddN).apply(null,arguments)},Dv=l._All=function(){return(Dv=l._All=l.asm.All).apply(null,arguments)},$v=l._Any=function(){return($v=l._Any=l.asm.Any).apply(null,arguments)},Rv=l._ArgMax=function(){return(Rv=l._ArgMax=l.asm.ArgMax).apply(null,arguments)},Fv=l._ArgMin=function(){return(Fv=l._ArgMin=l.asm.ArgMin).apply(null,arguments)},Ov=l._Asin=function(){return(Ov=l._Asin=l.asm.Asin).apply(null,arguments)},Mv=l._Asinh=function(){return(Mv=l._Asinh=l.asm.Asinh).apply(null,arguments)},Pv=l._Atan=function(){return(Pv=l._Atan=l.asm.Atan).apply(null,arguments)},Lv=l._Atan2=function(){return(Lv=l._Atan2=l.asm.Atan2).apply(null,arguments)},zv=l._Atanh=function(){return(zv=l._Atanh=l.asm.Atanh).apply(null,arguments)},Bv=l._AvgPool=function(){return(Bv=l._AvgPool=l.asm.AvgPool).apply(null,arguments)},Vv=l._AvgPool3D=function(){return(Vv=l._AvgPool3D=l.asm.AvgPool3D).apply(null,arguments)},Gv=l._AvgPool3DGrad=function(){return(Gv=l._AvgPool3DGrad=l.asm.AvgPool3DGrad).apply(null,arguments)},Wv=l._AvgPoolGrad=function(){return(Wv=l._AvgPoolGrad=l.asm.AvgPoolGrad).apply(null,arguments)},Uv=l._BatchMatMul=function(){return(Uv=l._BatchMatMul=l.asm.BatchMatMul).apply(null,arguments)},Hv=l._Bincount=function(){return(Hv=l._Bincount=l.asm.Bincount).apply(null,arguments)},qv=l._BitwiseAnd=function(){return(qv=l._BitwiseAnd=l.asm.BitwiseAnd).apply(null,arguments)},Kv=l._Ceil=function(){return(Kv=l._Ceil=l.asm.Ceil).apply(null,arguments)},jv=l._ClipByValue=function(){return(jv=l._ClipByValue=l.asm.ClipByValue).apply(null,arguments)},Xv=l._Conv2D=function(){return(Xv=l._Conv2D=l.asm.Conv2D).apply(null,arguments)},Yv=l._Conv2DBackpropInput=function(){return(Yv=l._Conv2DBackpropInput=l.asm.Conv2DBackpropInput).apply(null,arguments)},Zv=l._Conv3D=function(){return(Zv=l._Conv3D=l.asm.Conv3D).apply(null,arguments)},Jv=l._Conv3DBackpropFilterV2=function(){return(Jv=l._Conv3DBackpropFilterV2=l.asm.Conv3DBackpropFilterV2).apply(null,arguments)},Qv=l._Conv3DBackpropInputV2=function(){return(Qv=l._Conv3DBackpropInputV2=l.asm.Conv3DBackpropInputV2).apply(null,arguments)},tS=l._Cos=function(){return(tS=l._Cos=l.asm.Cos).apply(null,arguments)},eS=l._Cosh=function(){return(eS=l._Cosh=l.asm.Cosh).apply(null,arguments)},rS=l._CropAndResize=function(){return(rS=l._CropAndResize=l.asm.CropAndResize).apply(null,arguments)},nS=l._Cumprod=function(){return(nS=l._Cumprod=l.asm.Cumprod).apply(null,arguments)},oS=l._Cumsum=function(){return(oS=l._Cumsum=l.asm.Cumsum).apply(null,arguments)},sS=l._DenseBincount=function(){return(sS=l._DenseBincount=l.asm.DenseBincount).apply(null,arguments)},iS=l._DepthToSpace=function(){return(iS=l._DepthToSpace=l.asm.DepthToSpace).apply(null,arguments)},aS=l._DepthwiseConv2dNative=function(){return(aS=l._DepthwiseConv2dNative=l.asm.DepthwiseConv2dNative).apply(null,arguments)},lS=l._Diag=function(){return(lS=l._Diag=l.asm.Diag).apply(null,arguments)},uS=l._Dilation2D=function(){return(uS=l._Dilation2D=l.asm.Dilation2D).apply(null,arguments)},cS=l._Dilation2DBackpropFilter=function(){return(cS=l._Dilation2DBackpropFilter=l.asm.Dilation2DBackpropFilter).apply(null,arguments)},pS=l._Dilation2DBackpropInput=function(){return(pS=l._Dilation2DBackpropInput=l.asm.Dilation2DBackpropInput).apply(null,arguments)},mS=l._Elu=function(){return(mS=l._Elu=l.asm.Elu).apply(null,arguments)},fS=l._EluGrad=function(){return(fS=l._EluGrad=l.asm.EluGrad).apply(null,arguments)},dS=l._Equal=function(){return(dS=l._Equal=l.asm.Equal).apply(null,arguments)},hS=l._Erf=function(){return(hS=l._Erf=l.asm.Erf).apply(null,arguments)},gS=l._Exp=function(){return(gS=l._Exp=l.asm.Exp).apply(null,arguments)},xS=l._Expm1=function(){return(xS=l._Expm1=l.asm.Expm1).apply(null,arguments)},yS=l._FlipLeftRight=function(){return(yS=l._FlipLeftRight=l.asm.FlipLeftRight).apply(null,arguments)},bS=l._Floor=function(){return(bS=l._Floor=l.asm.Floor).apply(null,arguments)},wS=l._FloorDiv=function(){return(wS=l._FloorDiv=l.asm.FloorDiv).apply(null,arguments)},IS=l._FusedBatchNorm=function(){return(IS=l._FusedBatchNorm=l.asm.FusedBatchNorm).apply(null,arguments)},CS=l._FusedConv2D=function(){return(CS=l._FusedConv2D=l.asm.FusedConv2D).apply(null,arguments)},vS=l._FusedDepthwiseConv2D=function(){return(vS=l._FusedDepthwiseConv2D=l.asm.FusedDepthwiseConv2D).apply(null,arguments)},SS=l._Gather=function(){return(SS=l._Gather=l.asm.Gather).apply(null,arguments)},NS=l._GatherNd=function(){return(NS=l._GatherNd=l.asm.GatherNd).apply(null,arguments)},kS=l._Greater=function(){return(kS=l._Greater=l.asm.Greater).apply(null,arguments)},TS=l._GreaterEqual=function(){return(TS=l._GreaterEqual=l.asm.GreaterEqual).apply(null,arguments)},_S=l._IsFinite=function(){return(_S=l._IsFinite=l.asm.IsFinite).apply(null,arguments)},ES=l._IsInf=function(){return(ES=l._IsInf=l.asm.IsInf).apply(null,arguments)},AS=l._IsNan=function(){return(AS=l._IsNan=l.asm.IsNan).apply(null,arguments)},DS=l._LRN=function(){return(DS=l._LRN=l.asm.LRN).apply(null,arguments)},$S=l._LRNGrad=function(){return($S=l._LRNGrad=l.asm.LRNGrad).apply(null,arguments)},RS=l._LeakyRelu=function(){return(RS=l._LeakyRelu=l.asm.LeakyRelu).apply(null,arguments)},FS=l._Less=function(){return(FS=l._Less=l.asm.Less).apply(null,arguments)},OS=l._LessEqual=function(){return(OS=l._LessEqual=l.asm.LessEqual).apply(null,arguments)},MS=l._LinSpace=function(){return(MS=l._LinSpace=l.asm.LinSpace).apply(null,arguments)},PS=l._Log=function(){return(PS=l._Log=l.asm.Log).apply(null,arguments)},LS=l._Log1p=function(){return(LS=l._Log1p=l.asm.Log1p).apply(null,arguments)},zS=l._LogicalAnd=function(){return(zS=l._LogicalAnd=l.asm.LogicalAnd).apply(null,arguments)},BS=l._LogicalNot=function(){return(BS=l._LogicalNot=l.asm.LogicalNot).apply(null,arguments)},VS=l._LogicalOr=function(){return(VS=l._LogicalOr=l.asm.LogicalOr).apply(null,arguments)},GS=l._LogicalXor=function(){return(GS=l._LogicalXor=l.asm.LogicalXor).apply(null,arguments)},WS=l._Max=function(){return(WS=l._Max=l.asm.Max).apply(null,arguments)},US=l._MaxPool=function(){return(US=l._MaxPool=l.asm.MaxPool).apply(null,arguments)},HS=l._MaxPool3D=function(){return(HS=l._MaxPool3D=l.asm.MaxPool3D).apply(null,arguments)},qS=l._MaxPool3DGrad=function(){return(qS=l._MaxPool3DGrad=l.asm.MaxPool3DGrad).apply(null,arguments)},KS=l._MaxPoolGrad=function(){return(KS=l._MaxPoolGrad=l.asm.MaxPoolGrad).apply(null,arguments)},jS=l._MaxPoolWithArgmax=function(){return(jS=l._MaxPoolWithArgmax=l.asm.MaxPoolWithArgmax).apply(null,arguments)},XS=l._Maximum=function(){return(XS=l._Maximum=l.asm.Maximum).apply(null,arguments)},YS=l._Mean=function(){return(YS=l._Mean=l.asm.Mean).apply(null,arguments)},ZS=l._Min=function(){return(ZS=l._Min=l.asm.Min).apply(null,arguments)},JS=l._Minimum=function(){return(JS=l._Minimum=l.asm.Minimum).apply(null,arguments)},QS=l._MirrorPad=function(){return(QS=l._MirrorPad=l.asm.MirrorPad).apply(null,arguments)},t0=l._Mod=function(){return(t0=l._Mod=l.asm.Mod).apply(null,arguments)},e0=l._Multinomial=function(){return(e0=l._Multinomial=l.asm.Multinomial).apply(null,arguments)},r0=l._Multiply=function(){return(r0=l._Multiply=l.asm.Multiply).apply(null,arguments)},n0=l._Neg=function(){return(n0=l._Neg=l.asm.Neg).apply(null,arguments)},o0=l._NonMaxSuppressionV3=function(){return(o0=l._NonMaxSuppressionV3=l.asm.NonMaxSuppressionV3).apply(null,arguments)},s0=l._NonMaxSuppressionV4=function(){return(s0=l._NonMaxSuppressionV4=l.asm.NonMaxSuppressionV4).apply(null,arguments)},Pg=l._NonMaxSuppressionV5=function(){return(Pg=l._NonMaxSuppressionV5=l.asm.NonMaxSuppressionV5).apply(null,arguments)},Lg=l._NotEqual=function(){return(Lg=l._NotEqual=l.asm.NotEqual).apply(null,arguments)},jd=l._OneHot=function(){return(jd=l._OneHot=l.asm.OneHot).apply(null,arguments)},i0=l._PadV2=function(){return(i0=l._PadV2=l.asm.PadV2).apply(null,arguments)},a0=l._Pow=function(){return(a0=l._Pow=l.asm.Pow).apply(null,arguments)},Cp=l._Prelu=function(){return(Cp=l._Prelu=l.asm.Prelu).apply(null,arguments)},zg=l._Prod=function(){return(zg=l._Prod=l.asm.Prod).apply(null,arguments)},vp=l._RealDiv=function(){return(vp=l._RealDiv=l.asm.RealDiv).apply(null,arguments)},Sp=l._Reciprocal=function(){return(Sp=l._Reciprocal=l.asm.Reciprocal).apply(null,arguments)},l0=l._Relu=function(){return(l0=l._Relu=l.asm.Relu).apply(null,arguments)},X=l._Relu6=function(){return(X=l._Relu6=l.asm.Relu6).apply(null,arguments)},ct=l._ResizeBilinear=function(){return(ct=l._ResizeBilinear=l.asm.ResizeBilinear).apply(null,arguments)},Ft=l._ResizeBilinearGrad=function(){return(Ft=l._ResizeBilinearGrad=l.asm.ResizeBilinearGrad).apply(null,arguments)},me=l._ResizeNearestNeighbor=function(){return(me=l._ResizeNearestNeighbor=l.asm.ResizeNearestNeighbor).apply(null,arguments)},Ye=l._ResizeNearestNeighborGrad=function(){return(Ye=l._ResizeNearestNeighborGrad=l.asm.ResizeNearestNeighborGrad).apply(null,arguments)},Ze=l._Reverse=function(){return(Ze=l._Reverse=l.asm.Reverse).apply(null,arguments)},se=l._RotateWithOffset=function(){return(se=l._RotateWithOffset=l.asm.RotateWithOffset).apply(null,arguments)},ne=l._Round=function(){return(ne=l._Round=l.asm.Round).apply(null,arguments)},yr=l._Rsqrt=function(){return(yr=l._Rsqrt=l.asm.Rsqrt).apply(null,arguments)},to=l._ScatterNd=function(){return(to=l._ScatterNd=l.asm.ScatterNd).apply(null,arguments)},Na=l._SearchSorted=function(){return(Na=l._SearchSorted=l.asm.SearchSorted).apply(null,arguments)},Bg=l._SelectV2=function(){return(Bg=l._SelectV2=l.asm.SelectV2).apply(null,arguments)},Xd=l._Selu=function(){return(Xd=l._Selu=l.asm.Selu).apply(null,arguments)},u0=l._Sigmoid=function(){return(u0=l._Sigmoid=l.asm.Sigmoid).apply(null,arguments)},on=l._Sign=function(){return(on=l._Sign=l.asm.Sign).apply(null,arguments)},Vl=l._Sin=function(){return(Vl=l._Sin=l.asm.Sin).apply(null,arguments)},Vg=l._Sinh=function(){return(Vg=l._Sinh=l.asm.Sinh).apply(null,arguments)},iq=l._Softmax=function(){return(iq=l._Softmax=l.asm.Softmax).apply(null,arguments)},aq=l._Softplus=function(){return(aq=l._Softplus=l.asm.Softplus).apply(null,arguments)},lq=l._SparseFillEmptyRows=function(){return(lq=l._SparseFillEmptyRows=l.asm.SparseFillEmptyRows).apply(null,arguments)},uq=l._SparseReshape=function(){return(uq=l._SparseReshape=l.asm.SparseReshape).apply(null,arguments)},cq=l._SparseSegmentReduction=function(){return(cq=l._SparseSegmentReduction=l.asm.SparseSegmentReduction).apply(null,arguments)},pq=l._SparseToDense=function(){return(pq=l._SparseToDense=l.asm.SparseToDense).apply(null,arguments)},mq=l._Sqrt=function(){return(mq=l._Sqrt=l.asm.Sqrt).apply(null,arguments)},fq=l._Square=function(){return(fq=l._Square=l.asm.Square).apply(null,arguments)},dq=l._SquaredDifference=function(){return(dq=l._SquaredDifference=l.asm.SquaredDifference).apply(null,arguments)},hq=l._Step=function(){return(hq=l._Step=l.asm.Step).apply(null,arguments)},gq=l._StridedSlice=function(){return(gq=l._StridedSlice=l.asm.StridedSlice).apply(null,arguments)},xq=l._Sub=function(){return(xq=l._Sub=l.asm.Sub).apply(null,arguments)},yq=l._Sum=function(){return(yq=l._Sum=l.asm.Sum).apply(null,arguments)},bq=l._Tan=function(){return(bq=l._Tan=l.asm.Tan).apply(null,arguments)},wq=l._Tanh=function(){return(wq=l._Tanh=l.asm.Tanh).apply(null,arguments)},Iq=l._TensorScatterUpdate=function(){return(Iq=l._TensorScatterUpdate=l.asm.TensorScatterUpdate).apply(null,arguments)},Cq=l._Tile=function(){return(Cq=l._Tile=l.asm.Tile).apply(null,arguments)},vq=l._TopK=function(){return(vq=l._TopK=l.asm.TopK).apply(null,arguments)},Sq=l._Transform=function(){return(Sq=l._Transform=l.asm.Transform).apply(null,arguments)},Nq=l._Transpose=function(){return(Nq=l._Transpose=l.asm.Transpose).apply(null,arguments)},kq=l.__FusedMatMul=function(){return(kq=l.__FusedMatMul=l.asm._FusedMatMul).apply(null,arguments)},Tq=l._malloc=function(){return(Tq=l._malloc=l.asm.malloc).apply(null,arguments)},_q=l._free=function(){return(_q=l._free=l.asm.free).apply(null,arguments)},Eq=l.__emscripten_tls_init=function(){return(Eq=l.__emscripten_tls_init=l.asm._emscripten_tls_init).apply(null,arguments)},Gg=l._pthread_self=function(){return(Gg=l._pthread_self=l.asm.pthread_self).apply(null,arguments)},Aq=l.___errno_location=function(){return(Aq=l.___errno_location=l.asm.__errno_location).apply(null,arguments)},d_=l.__emscripten_thread_init=function(){return(d_=l.__emscripten_thread_init=l.asm._emscripten_thread_init).apply(null,arguments)},Dq=l.__emscripten_thread_crashed=function(){return(Dq=l.__emscripten_thread_crashed=l.asm._emscripten_thread_crashed).apply(null,arguments)},$q=l._emscripten_main_thread_process_queued_calls=function(){return($q=l._emscripten_main_thread_process_queued_calls=l.asm.emscripten_main_thread_process_queued_calls).apply(null,arguments)},Rq=l._emscripten_main_browser_thread_id=function(){return(Rq=l._emscripten_main_browser_thread_id=l.asm.emscripten_main_browser_thread_id).apply(null,arguments)},h_=l._emscripten_run_in_main_runtime_thread_js=function(){return(h_=l._emscripten_run_in_main_runtime_thread_js=l.asm.emscripten_run_in_main_runtime_thread_js).apply(null,arguments)},Fq=l._emscripten_dispatch_to_thread_=function(){return(Fq=l._emscripten_dispatch_to_thread_=l.asm.emscripten_dispatch_to_thread_).apply(null,arguments)},g_=l.__emscripten_proxy_execute_task_queue=function(){return(g_=l.__emscripten_proxy_execute_task_queue=l.asm._emscripten_proxy_execute_task_queue).apply(null,arguments)},c0=l.__emscripten_thread_free_data=function(){return(c0=l.__emscripten_thread_free_data=l.asm._emscripten_thread_free_data).apply(null,arguments)},x_=l.__emscripten_thread_exit=function(){return(x_=l.__emscripten_thread_exit=l.asm._emscripten_thread_exit).apply(null,arguments)},y_=l._emscripten_stack_set_limits=function(){return(y_=l._emscripten_stack_set_limits=l.asm.emscripten_stack_set_limits).apply(null,arguments)},p0=l.stackSave=function(){return(p0=l.stackSave=l.asm.stackSave).apply(null,arguments)},Wg=l.stackRestore=function(){return(Wg=l.stackRestore=l.asm.stackRestore).apply(null,arguments)},Ug=l.stackAlloc=function(){return(Ug=l.stackAlloc=l.asm.stackAlloc).apply(null,arguments)},Oq=l.dynCall_iijjiiii=function(){return(Oq=l.dynCall_iijjiiii=l.asm.dynCall_iijjiiii).apply(null,arguments)},Mq=l.dynCall_jiji=function(){return(Mq=l.dynCall_jiji=l.asm.dynCall_jiji).apply(null,arguments)};l.keepRuntimeAlive=zo,l.wasmMemory=it,l.cwrap=xv,l.ExitStatus=Yu,l.PThread=Yt;var Hg;Sa=function P(){Hg||b_(),Hg||(Sa=P)};function b_(P){if(P=P||d,zl>0)return;if(I){c(l),Er(),startWorker(l);return}if(Ti(),zl>0)return;function U(){Hg||(Hg=!0,l.calledRun=!0,!gt&&(Er(),c(l),l.onRuntimeInitialized&&l.onRuntimeInitialized(),va()))}l.setStatus?(l.setStatus("Running..."),setTimeout(function(){setTimeout(function(){l.setStatus("")},1),U()},1)):U()}if(l.preInit)for(typeof l.preInit=="function"&&(l.preInit=[l.preInit]);l.preInit.length>0;)l.preInit.pop()();b_();var qg;m&&(qg={uncaughtException:process.listeners("uncaughtException").filter(function(P){return!m.uncaughtException.indexOf(P)>-1}),unhandledRejection:process.listeners("unhandledRejection").filter(function(P){return!m.unhandledRejection.indexOf(P)>-1})});var Kg;if(typeof WasmBackendModule!="undefined")Kg=WasmBackendModule;else if(typeof t!="undefined")Kg=t;else throw new Error("Could not find wasm module in post.js");if(qg){var Pq=Kg._dispose;Kg._dispose=function(){Pq(),qg.uncaughtException.forEach(function(P){process.removeListener("uncaughtException",P)}),qg.unhandledRejection.forEach(function(P){process.removeListener("unhandledRejection",P)})}}return t.ready}})();typeof RC=="object"&&typeof n_=="object"?n_.exports=r_:typeof define=="function"&&define.amd?define([],function(){return r_}):typeof RC=="object"&&(RC.WasmBackendModuleThreadedSimd=r_)});var JH=br((fnr,ZH)=>{ZH.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")+"//# sourceURL="+f)},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.startWorker=instance=>{Module=instance;postMessage({"cmd":"loaded"})};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}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)}else if(e.data.cmd==="run"){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 QH=br((FC,s_)=>{var o_=(()=>{var r=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(r=r||__filename),function(t){t=t||{};var e=typeof t!="undefined"?t:{},n,o;e.ready=new Promise(function(X,ct){n=X,o=ct});var s;typeof process!="undefined"&&process.listeners&&(s={uncaughtException:process.listeners("uncaughtException"),unhandledRejection:process.listeners("unhandledRejection")});var i=Object.assign({},e),a=[],u="./this.program",l=(X,ct)=>{throw ct},c=typeof window=="object",p=typeof importScripts=="function",m=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",f="";function d(X){return e.locateFile?e.locateFile(X,f):f+X}var h,g,x,b;function w(X){if(X instanceof bp)return;A("exiting due to exception: "+X)}if(m){var I=hw(),N=e_();p?f=N.dirname(f)+"/":f=__dirname+"/",h=(X,ct)=>(X=Ti(X)?new URL(X):N.normalize(X),I.readFileSync(X,ct?void 0:"utf8")),x=X=>{var ct=h(X,!0);return ct.buffer||(ct=new Uint8Array(ct)),ct},g=(X,ct,Ft)=>{X=Ti(X)?new URL(X):N.normalize(X),I.readFile(X,function(me,Ye){me?Ft(me):ct(Ye.buffer)})},process.argv.length>1&&(u=process.argv[1].replace(/\\/g,"/")),a=process.argv.slice(2),process.on("uncaughtException",function(X){if(!(X instanceof bp))throw X}),process.on("unhandledRejection",function(X){throw X}),l=(X,ct)=>{if(de())throw process.exitCode=X,ct;w(ct),process.exit(X)},e.inspect=function(){return"[Emscripten Module object]"}}else(c||p)&&(p?f=self.location.href:typeof document!="undefined"&&document.currentScript&&(f=document.currentScript.src),r&&(f=r),f.indexOf("blob:")!==0?f=f.substr(0,f.replace(/[?#].*/,"").lastIndexOf("/")+1):f="",h=X=>{var ct=new XMLHttpRequest;return ct.open("GET",X,!1),ct.send(null),ct.responseText},p&&(x=X=>{var ct=new XMLHttpRequest;return ct.open("GET",X,!1),ct.responseType="arraybuffer",ct.send(null),new Uint8Array(ct.response)}),g=(X,ct,Ft)=>{var me=new XMLHttpRequest;me.open("GET",X,!0),me.responseType="arraybuffer",me.onload=()=>{if(me.status==200||me.status==0&&me.response){ct(me.response);return}Ft()},me.onerror=Ft,me.send(null)},b=X=>document.title=X);var E=e.print||console.log.bind(console),A=e.printErr||console.warn.bind(console);Object.assign(e,i),i=null,e.arguments&&(a=e.arguments),e.thisProgram&&(u=e.thisProgram),e.quit&&(l=e.quit);var D=4,F;e.wasmBinary&&(F=e.wasmBinary);var M=e.noExitRuntime||!0;typeof WebAssembly!="object"&&Qr("no native wasm support detected");var V,G=!1,W;function q(X,ct){X||Qr(ct)}var H=typeof TextDecoder!="undefined"?new TextDecoder("utf8"):void 0;function j(X,ct,Ft){ct>>>=0;for(var me=ct+Ft,Ye=ct;X[Ye]&&!(Ye>=me);)++Ye;if(Ye-ct>16&&X.buffer&&H)return H.decode(X.subarray(ct,Ye));for(var Ze="";ct<Ye;){var se=X[ct++];if(!(se&128)){Ze+=String.fromCharCode(se);continue}var ne=X[ct++]&63;if((se&224)==192){Ze+=String.fromCharCode((se&31)<<6|ne);continue}var yr=X[ct++]&63;if((se&240)==224?se=(se&15)<<12|ne<<6|yr:se=(se&7)<<18|ne<<12|yr<<6|X[ct++]&63,se<65536)Ze+=String.fromCharCode(se);else{var to=se-65536;Ze+=String.fromCharCode(55296|to>>10,56320|to&1023)}}return Ze}function Y(X,ct){return X>>>=0,X?j(lt,X,ct):""}function Z(X,ct,Ft,me){if(Ft>>>=0,!(me>0))return 0;for(var Ye=Ft,Ze=Ft+me-1,se=0;se<X.length;++se){var ne=X.charCodeAt(se);if(ne>=55296&&ne<=57343){var yr=X.charCodeAt(++se);ne=65536+((ne&1023)<<10)|yr&1023}if(ne<=127){if(Ft>=Ze)break;ct[Ft++>>>0]=ne}else if(ne<=2047){if(Ft+1>=Ze)break;ct[Ft++>>>0]=192|ne>>6,ct[Ft++>>>0]=128|ne&63}else if(ne<=65535){if(Ft+2>=Ze)break;ct[Ft++>>>0]=224|ne>>12,ct[Ft++>>>0]=128|ne>>6&63,ct[Ft++>>>0]=128|ne&63}else{if(Ft+3>=Ze)break;ct[Ft++>>>0]=240|ne>>18,ct[Ft++>>>0]=128|ne>>12&63,ct[Ft++>>>0]=128|ne>>6&63,ct[Ft++>>>0]=128|ne&63}}return ct[Ft>>>0]=0,Ft-Ye}function et(X,ct,Ft){return Z(X,lt,ct,Ft)}var nt,st,lt,ot,it,ft,gt,Ct,Rt;function Dt(X){nt=X,e.HEAP8=st=new Int8Array(X),e.HEAP16=ot=new Int16Array(X),e.HEAP32=ft=new Int32Array(X),e.HEAPU8=lt=new Uint8Array(X),e.HEAPU16=it=new Uint16Array(X),e.HEAPU32=gt=new Uint32Array(X),e.HEAPF32=Ct=new Float32Array(X),e.HEAPF64=Rt=new Float64Array(X)}var Ht=e.INITIAL_MEMORY||16777216,qt,pe=[],ye=[],re=[],be=!1;function de(){return M}function Ee(){if(e.preRun)for(typeof e.preRun=="function"&&(e.preRun=[e.preRun]);e.preRun.length;)lr(e.preRun.shift());Sa(pe)}function Ae(){be=!0,Sa(ye)}function On(){if(e.postRun)for(typeof e.postRun=="function"&&(e.postRun=[e.postRun]);e.postRun.length;)Br(e.postRun.shift());Sa(re)}function lr(X){pe.unshift(X)}function Zn(X){ye.unshift(X)}function Br(X){re.unshift(X)}var Xe=0,Vr=null,Gr=null;function Jn(X){Xe++,e.monitorRunDependencies&&e.monitorRunDependencies(Xe)}function Qn(X){if(Xe--,e.monitorRunDependencies&&e.monitorRunDependencies(Xe),Xe==0&&(Vr!==null&&(clearInterval(Vr),Vr=null),Gr)){var ct=Gr;Gr=null,ct()}}function Qr(X){e.onAbort&&e.onAbort(X),X="Aborted("+X+")",A(X),G=!0,W=1,X+=". Build with -sASSERTIONS for more info.";var ct=new WebAssembly.RuntimeError(X);throw o(ct),ct}var Ca="data:application/octet-stream;base64,";function zo(X){return X.startsWith(Ca)}function Ti(X){return X.startsWith("file://")}var Er;Er="tfjs-backend-wasm.wasm",zo(Er)||(Er=d(Er));function va(X){try{if(X==Er&&F)return new Uint8Array(F);if(x)return x(X);throw"both async and sync fetching of the wasm failed"}catch(ct){Qr(ct)}}function Vd(){if(!F&&(c||p)){if(typeof fetch=="function"&&!Ti(Er))return fetch(Er,{credentials:"same-origin"}).then(function(X){if(!X.ok)throw"failed to load wasm binary file at '"+Er+"'";return X.arrayBuffer()}).catch(function(){return va(Er)});if(g)return new Promise(function(X,ct){g(Er,function(Ft){X(new Uint8Array(Ft))},ct)})}return Promise.resolve().then(function(){return va(Er)})}function Gd(){var X={env:Wd,wasi_snapshot_preview1:Wd};function ct(se,ne){var yr=se.exports;e.asm=yr,V=e.asm.memory,Dt(V.buffer),qt=e.asm.__indirect_function_table,Zn(e.asm.__wasm_call_ctors),Qn("wasm-instantiate")}Jn("wasm-instantiate");function Ft(se){ct(se.instance)}function me(se){return Vd().then(function(ne){return WebAssembly.instantiate(ne,X)}).then(function(ne){return ne}).then(se,function(ne){A("failed to asynchronously prepare wasm: "+ne),Qr(ne)})}function Ye(){return!F&&typeof WebAssembly.instantiateStreaming=="function"&&!zo(Er)&&!Ti(Er)&&!m&&typeof fetch=="function"?fetch(Er,{credentials:"same-origin"}).then(function(se){var ne=WebAssembly.instantiateStreaming(se,X);return ne.then(Ft,function(yr){return A("wasm streaming compile failed: "+yr),A("falling back to ArrayBuffer instantiation"),me(Ft)})}):me(Ft)}if(e.instantiateWasm)try{var Ze=e.instantiateWasm(X,ct);return Ze}catch(se){A("Module.instantiateWasm callback failed with error: "+se),o(se)}return Ye().catch(o),{}}var c_,zl;function bp(X){this.name="ExitStatus",this.message="Program terminated with exit("+X+")",this.status=X}function Sa(X){for(;X.length>0;)X.shift()(e)}function MC(){Qr("")}function Ig(){return 4294901760}function Xu(){return Ig()}function PC(X,ct,Ft){lt.copyWithin(X>>>0,ct>>>0,ct+Ft>>>0)}function Cg(X){try{return V.grow(X-nt.byteLength+65535>>>16),Dt(V.buffer),1}catch(ct){}}function wp(X){var ct=lt.length;X=X>>>0;var Ft=Ig();if(X>Ft)return!1;let me=(yr,to)=>yr+(to-yr%to)%to;for(var Ye=1;Ye<=4;Ye*=2){var Ze=ct*(1+.2/Ye);Ze=Math.min(Ze,X+100663296);var se=Math.min(Ft,me(Math.max(X,Ze),65536)),ne=Cg(se);if(ne)return!0}return!1}var tn={varargs:void 0,get:function(){tn.varargs+=4;var X=ft[tn.varargs-4>>>2];return X},getStr:function(X){var ct=Y(X);return ct}};function vg(X){return 52}function LC(X,ct,Ft,me,Ye){return 70}var zC=[null,[],[]];function p_(X,ct){var Ft=zC[X];ct===0||ct===10?((X===1?E:A)(j(Ft,0)),Ft.length=0):Ft.push(ct)}function m_(X,ct,Ft,me){for(var Ye=0,Ze=0;Ze<Ft;Ze++){var se=gt[ct>>>2],ne=gt[ct+4>>>2];ct+=8;for(var yr=0;yr<ne;yr++)p_(X,lt[se+yr>>>0]);Ye+=ne}return gt[me>>>2]=Ye,0}function Sg(X){var ct=e["_"+X];return ct}function Yu(X,ct){st.set(X,ct>>>0)}function BC(X,ct,Ft,me,Ye){var Ze={string:on=>{var Vl=0;if(on!=null&&on!==0){var Vg=(on.length<<2)+1;Vl=jd(Vg),et(on,Vl,Vg)}return Vl},array:on=>{var Vl=jd(on.length);return Yu(on,Vl),Vl}};function se(on){return ct==="string"?Y(on):ct==="boolean"?!!on:on}var ne=Sg(X),yr=[],to=0;if(me)for(var Na=0;Na<me.length;Na++){var Bg=Ze[Ft[Na]];Bg?(to===0&&(to=Pg()),yr[Na]=Bg(me[Na])):yr[Na]=me[Na]}var Xd=ne.apply(null,yr);function u0(on){return to!==0&&Lg(to),se(on)}return Xd=u0(Xd),Xd}function VC(X,ct,Ft,me){Ft=Ft||[];var Ye=Ft.every(se=>se==="number"||se==="boolean"),Ze=ct!=="string";return Ze&&Ye&&!me?Sg(X):function(){return BC(X,ct,Ft,arguments,me)}}var Wd={abort:MC,emscripten_get_heap_max:Xu,emscripten_memcpy_big:PC,emscripten_resize_heap:wp,fd_close:vg,fd_seek:LC,fd_write:m_},GC=Gd(),Ng=e.___wasm_call_ctors=function(){return(Ng=e.___wasm_call_ctors=e.asm.__wasm_call_ctors).apply(null,arguments)},kg=e._init=function(){return(kg=e._init=e.asm.init).apply(null,arguments)},WC=e._init_with_threads_count=function(){return(WC=e._init_with_threads_count=e.asm.init_with_threads_count).apply(null,arguments)},Tg=e._get_threads_count=function(){return(Tg=e._get_threads_count=e.asm.get_threads_count).apply(null,arguments)},UC=e._register_tensor=function(){return(UC=e._register_tensor=e.asm.register_tensor).apply(null,arguments)},Yt=e._dispose_data=function(){return(Yt=e._dispose_data=e.asm.dispose_data).apply(null,arguments)},Ud=e._dispose=function(){return(Ud=e._dispose=e.asm.dispose).apply(null,arguments)},HC=e._Abs=function(){return(HC=e._Abs=e.asm.Abs).apply(null,arguments)},_g=e._Acos=function(){return(_g=e._Acos=e.asm.Acos).apply(null,arguments)},Ip=e._Acosh=function(){return(Ip=e._Acosh=e.asm.Acosh).apply(null,arguments)},qC=e._Add=function(){return(qC=e._Add=e.asm.Add).apply(null,arguments)},KC=e._AddN=function(){return(KC=e._AddN=e.asm.AddN).apply(null,arguments)},jC=e._All=function(){return(jC=e._All=e.asm.All).apply(null,arguments)},XC=e._Any=function(){return(XC=e._Any=e.asm.Any).apply(null,arguments)},YC=e._ArgMax=function(){return(YC=e._ArgMax=e.asm.ArgMax).apply(null,arguments)},Eg=e._ArgMin=function(){return(Eg=e._ArgMin=e.asm.ArgMin).apply(null,arguments)},Ag=e._Asin=function(){return(Ag=e._Asin=e.asm.Asin).apply(null,arguments)},ZC=e._Asinh=function(){return(ZC=e._Asinh=e.asm.Asinh).apply(null,arguments)},JC=e._Atan=function(){return(JC=e._Atan=e.asm.Atan).apply(null,arguments)},QC=e._Atan2=function(){return(QC=e._Atan2=e.asm.Atan2).apply(null,arguments)},Hd=e._Atanh=function(){return(Hd=e._Atanh=e.asm.Atanh).apply(null,arguments)},tv=e._AvgPool=function(){return(tv=e._AvgPool=e.asm.AvgPool).apply(null,arguments)},ev=e._AvgPool3D=function(){return(ev=e._AvgPool3D=e.asm.AvgPool3D).apply(null,arguments)},rv=e._AvgPool3DGrad=function(){return(rv=e._AvgPool3DGrad=e.asm.AvgPool3DGrad).apply(null,arguments)},Zu=e._AvgPoolGrad=function(){return(Zu=e._AvgPoolGrad=e.asm.AvgPoolGrad).apply(null,arguments)},nv=e._BatchMatMul=function(){return(nv=e._BatchMatMul=e.asm.BatchMatMul).apply(null,arguments)},ov=e._Bincount=function(){return(ov=e._Bincount=e.asm.Bincount).apply(null,arguments)},Dg=e._BitwiseAnd=function(){return(Dg=e._BitwiseAnd=e.asm.BitwiseAnd).apply(null,arguments)},sv=e._Ceil=function(){return(sv=e._Ceil=e.asm.Ceil).apply(null,arguments)},qd=e._ClipByValue=function(){return(qd=e._ClipByValue=e.asm.ClipByValue).apply(null,arguments)},iv=e._Conv2D=function(){return(iv=e._Conv2D=e.asm.Conv2D).apply(null,arguments)},av=e._Conv2DBackpropInput=function(){return(av=e._Conv2DBackpropInput=e.asm.Conv2DBackpropInput).apply(null,arguments)},lv=e._Conv3D=function(){return(lv=e._Conv3D=e.asm.Conv3D).apply(null,arguments)},Bl=e._Conv3DBackpropFilterV2=function(){return(Bl=e._Conv3DBackpropFilterV2=e.asm.Conv3DBackpropFilterV2).apply(null,arguments)},Kd=e._Conv3DBackpropInputV2=function(){return(Kd=e._Conv3DBackpropInputV2=e.asm.Conv3DBackpropInputV2).apply(null,arguments)},uv=e._Cos=function(){return(uv=e._Cos=e.asm.Cos).apply(null,arguments)},cv=e._Cosh=function(){return(cv=e._Cosh=e.asm.Cosh).apply(null,arguments)},pv=e._CropAndResize=function(){return(pv=e._CropAndResize=e.asm.CropAndResize).apply(null,arguments)},mv=e._Cumprod=function(){return(mv=e._Cumprod=e.asm.Cumprod).apply(null,arguments)},$g=e._Cumsum=function(){return($g=e._Cumsum=e.asm.Cumsum).apply(null,arguments)},Rg=e._DenseBincount=function(){return(Rg=e._DenseBincount=e.asm.DenseBincount).apply(null,arguments)},fv=e._DepthToSpace=function(){return(fv=e._DepthToSpace=e.asm.DepthToSpace).apply(null,arguments)},dv=e._DepthwiseConv2dNative=function(){return(dv=e._DepthwiseConv2dNative=e.asm.DepthwiseConv2dNative).apply(null,arguments)},Fg=e._Diag=function(){return(Fg=e._Diag=e.asm.Diag).apply(null,arguments)},Og=e._Dilation2D=function(){return(Og=e._Dilation2D=e.asm.Dilation2D).apply(null,arguments)},hv=e._Dilation2DBackpropFilter=function(){return(hv=e._Dilation2DBackpropFilter=e.asm.Dilation2DBackpropFilter).apply(null,arguments)},gv=e._Dilation2DBackpropInput=function(){return(gv=e._Dilation2DBackpropInput=e.asm.Dilation2DBackpropInput).apply(null,arguments)},xv=e._Elu=function(){return(xv=e._Elu=e.asm.Elu).apply(null,arguments)},yv=e._EluGrad=function(){return(yv=e._EluGrad=e.asm.EluGrad).apply(null,arguments)},Mg=e._Equal=function(){return(Mg=e._Equal=e.asm.Equal).apply(null,arguments)},f_=e._Erf=function(){return(f_=e._Erf=e.asm.Erf).apply(null,arguments)},bv=e._Exp=function(){return(bv=e._Exp=e.asm.Exp).apply(null,arguments)},wv=e._Expm1=function(){return(wv=e._Expm1=e.asm.Expm1).apply(null,arguments)},Iv=e._FlipLeftRight=function(){return(Iv=e._FlipLeftRight=e.asm.FlipLeftRight).apply(null,arguments)},Cv=e._Floor=function(){return(Cv=e._Floor=e.asm.Floor).apply(null,arguments)},vv=e._FloorDiv=function(){return(vv=e._FloorDiv=e.asm.FloorDiv).apply(null,arguments)},Sv=e._FusedBatchNorm=function(){return(Sv=e._FusedBatchNorm=e.asm.FusedBatchNorm).apply(null,arguments)},Nv=e._FusedConv2D=function(){return(Nv=e._FusedConv2D=e.asm.FusedConv2D).apply(null,arguments)},kv=e._FusedDepthwiseConv2D=function(){return(kv=e._FusedDepthwiseConv2D=e.asm.FusedDepthwiseConv2D).apply(null,arguments)},Tv=e._Gather=function(){return(Tv=e._Gather=e.asm.Gather).apply(null,arguments)},_v=e._GatherNd=function(){return(_v=e._GatherNd=e.asm.GatherNd).apply(null,arguments)},Ev=e._Greater=function(){return(Ev=e._Greater=e.asm.Greater).apply(null,arguments)},Av=e._GreaterEqual=function(){return(Av=e._GreaterEqual=e.asm.GreaterEqual).apply(null,arguments)},Dv=e._IsFinite=function(){return(Dv=e._IsFinite=e.asm.IsFinite).apply(null,arguments)},$v=e._IsInf=function(){return($v=e._IsInf=e.asm.IsInf).apply(null,arguments)},Rv=e._IsNan=function(){return(Rv=e._IsNan=e.asm.IsNan).apply(null,arguments)},Fv=e._LRN=function(){return(Fv=e._LRN=e.asm.LRN).apply(null,arguments)},Ov=e._LRNGrad=function(){return(Ov=e._LRNGrad=e.asm.LRNGrad).apply(null,arguments)},Mv=e._LeakyRelu=function(){return(Mv=e._LeakyRelu=e.asm.LeakyRelu).apply(null,arguments)},Pv=e._Less=function(){return(Pv=e._Less=e.asm.Less).apply(null,arguments)},Lv=e._LessEqual=function(){return(Lv=e._LessEqual=e.asm.LessEqual).apply(null,arguments)},zv=e._LinSpace=function(){return(zv=e._LinSpace=e.asm.LinSpace).apply(null,arguments)},Bv=e._Log=function(){return(Bv=e._Log=e.asm.Log).apply(null,arguments)},Vv=e._Log1p=function(){return(Vv=e._Log1p=e.asm.Log1p).apply(null,arguments)},Gv=e._LogicalAnd=function(){return(Gv=e._LogicalAnd=e.asm.LogicalAnd).apply(null,arguments)},Wv=e._LogicalNot=function(){return(Wv=e._LogicalNot=e.asm.LogicalNot).apply(null,arguments)},Uv=e._LogicalOr=function(){return(Uv=e._LogicalOr=e.asm.LogicalOr).apply(null,arguments)},Hv=e._LogicalXor=function(){return(Hv=e._LogicalXor=e.asm.LogicalXor).apply(null,arguments)},qv=e._Max=function(){return(qv=e._Max=e.asm.Max).apply(null,arguments)},Kv=e._MaxPool=function(){return(Kv=e._MaxPool=e.asm.MaxPool).apply(null,arguments)},jv=e._MaxPool3D=function(){return(jv=e._MaxPool3D=e.asm.MaxPool3D).apply(null,arguments)},Xv=e._MaxPool3DGrad=function(){return(Xv=e._MaxPool3DGrad=e.asm.MaxPool3DGrad).apply(null,arguments)},Yv=e._MaxPoolGrad=function(){return(Yv=e._MaxPoolGrad=e.asm.MaxPoolGrad).apply(null,arguments)},Zv=e._MaxPoolWithArgmax=function(){return(Zv=e._MaxPoolWithArgmax=e.asm.MaxPoolWithArgmax).apply(null,arguments)},Jv=e._Maximum=function(){return(Jv=e._Maximum=e.asm.Maximum).apply(null,arguments)},Qv=e._Mean=function(){return(Qv=e._Mean=e.asm.Mean).apply(null,arguments)},tS=e._Min=function(){return(tS=e._Min=e.asm.Min).apply(null,arguments)},eS=e._Minimum=function(){return(eS=e._Minimum=e.asm.Minimum).apply(null,arguments)},rS=e._MirrorPad=function(){return(rS=e._MirrorPad=e.asm.MirrorPad).apply(null,arguments)},nS=e._Mod=function(){return(nS=e._Mod=e.asm.Mod).apply(null,arguments)},oS=e._Multinomial=function(){return(oS=e._Multinomial=e.asm.Multinomial).apply(null,arguments)},sS=e._Multiply=function(){return(sS=e._Multiply=e.asm.Multiply).apply(null,arguments)},iS=e._Neg=function(){return(iS=e._Neg=e.asm.Neg).apply(null,arguments)},aS=e._NonMaxSuppressionV3=function(){return(aS=e._NonMaxSuppressionV3=e.asm.NonMaxSuppressionV3).apply(null,arguments)},lS=e._NonMaxSuppressionV4=function(){return(lS=e._NonMaxSuppressionV4=e.asm.NonMaxSuppressionV4).apply(null,arguments)},uS=e._NonMaxSuppressionV5=function(){return(uS=e._NonMaxSuppressionV5=e.asm.NonMaxSuppressionV5).apply(null,arguments)},cS=e._NotEqual=function(){return(cS=e._NotEqual=e.asm.NotEqual).apply(null,arguments)},pS=e._OneHot=function(){return(pS=e._OneHot=e.asm.OneHot).apply(null,arguments)},mS=e._PadV2=function(){return(mS=e._PadV2=e.asm.PadV2).apply(null,arguments)},fS=e._Pow=function(){return(fS=e._Pow=e.asm.Pow).apply(null,arguments)},dS=e._Prelu=function(){return(dS=e._Prelu=e.asm.Prelu).apply(null,arguments)},hS=e._Prod=function(){return(hS=e._Prod=e.asm.Prod).apply(null,arguments)},gS=e._RealDiv=function(){return(gS=e._RealDiv=e.asm.RealDiv).apply(null,arguments)},xS=e._Reciprocal=function(){return(xS=e._Reciprocal=e.asm.Reciprocal).apply(null,arguments)},yS=e._Relu=function(){return(yS=e._Relu=e.asm.Relu).apply(null,arguments)},bS=e._Relu6=function(){return(bS=e._Relu6=e.asm.Relu6).apply(null,arguments)},wS=e._ResizeBilinear=function(){return(wS=e._ResizeBilinear=e.asm.ResizeBilinear).apply(null,arguments)},IS=e._ResizeBilinearGrad=function(){return(IS=e._ResizeBilinearGrad=e.asm.ResizeBilinearGrad).apply(null,arguments)},CS=e._ResizeNearestNeighbor=function(){return(CS=e._ResizeNearestNeighbor=e.asm.ResizeNearestNeighbor).apply(null,arguments)},vS=e._ResizeNearestNeighborGrad=function(){return(vS=e._ResizeNearestNeighborGrad=e.asm.ResizeNearestNeighborGrad).apply(null,arguments)},SS=e._Reverse=function(){return(SS=e._Reverse=e.asm.Reverse).apply(null,arguments)},NS=e._RotateWithOffset=function(){return(NS=e._RotateWithOffset=e.asm.RotateWithOffset).apply(null,arguments)},kS=e._Round=function(){return(kS=e._Round=e.asm.Round).apply(null,arguments)},TS=e._Rsqrt=function(){return(TS=e._Rsqrt=e.asm.Rsqrt).apply(null,arguments)},_S=e._ScatterNd=function(){return(_S=e._ScatterNd=e.asm.ScatterNd).apply(null,arguments)},ES=e._SearchSorted=function(){return(ES=e._SearchSorted=e.asm.SearchSorted).apply(null,arguments)},AS=e._SelectV2=function(){return(AS=e._SelectV2=e.asm.SelectV2).apply(null,arguments)},DS=e._Selu=function(){return(DS=e._Selu=e.asm.Selu).apply(null,arguments)},$S=e._Sigmoid=function(){return($S=e._Sigmoid=e.asm.Sigmoid).apply(null,arguments)},RS=e._Sign=function(){return(RS=e._Sign=e.asm.Sign).apply(null,arguments)},FS=e._Sin=function(){return(FS=e._Sin=e.asm.Sin).apply(null,arguments)},OS=e._Sinh=function(){return(OS=e._Sinh=e.asm.Sinh).apply(null,arguments)},MS=e._Softmax=function(){return(MS=e._Softmax=e.asm.Softmax).apply(null,arguments)},PS=e._Softplus=function(){return(PS=e._Softplus=e.asm.Softplus).apply(null,arguments)},LS=e._SparseFillEmptyRows=function(){return(LS=e._SparseFillEmptyRows=e.asm.SparseFillEmptyRows).apply(null,arguments)},zS=e._SparseReshape=function(){return(zS=e._SparseReshape=e.asm.SparseReshape).apply(null,arguments)},BS=e._SparseSegmentReduction=function(){return(BS=e._SparseSegmentReduction=e.asm.SparseSegmentReduction).apply(null,arguments)},VS=e._SparseToDense=function(){return(VS=e._SparseToDense=e.asm.SparseToDense).apply(null,arguments)},GS=e._Sqrt=function(){return(GS=e._Sqrt=e.asm.Sqrt).apply(null,arguments)},WS=e._Square=function(){return(WS=e._Square=e.asm.Square).apply(null,arguments)},US=e._SquaredDifference=function(){return(US=e._SquaredDifference=e.asm.SquaredDifference).apply(null,arguments)},HS=e._Step=function(){return(HS=e._Step=e.asm.Step).apply(null,arguments)},qS=e._StridedSlice=function(){return(qS=e._StridedSlice=e.asm.StridedSlice).apply(null,arguments)},KS=e._Sub=function(){return(KS=e._Sub=e.asm.Sub).apply(null,arguments)},jS=e._Sum=function(){return(jS=e._Sum=e.asm.Sum).apply(null,arguments)},XS=e._Tan=function(){return(XS=e._Tan=e.asm.Tan).apply(null,arguments)},YS=e._Tanh=function(){return(YS=e._Tanh=e.asm.Tanh).apply(null,arguments)},ZS=e._TensorScatterUpdate=function(){return(ZS=e._TensorScatterUpdate=e.asm.TensorScatterUpdate).apply(null,arguments)},JS=e._Tile=function(){return(JS=e._Tile=e.asm.Tile).apply(null,arguments)},QS=e._TopK=function(){return(QS=e._TopK=e.asm.TopK).apply(null,arguments)},t0=e._Transform=function(){return(t0=e._Transform=e.asm.Transform).apply(null,arguments)},e0=e._Transpose=function(){return(e0=e._Transpose=e.asm.Transpose).apply(null,arguments)},r0=e.__FusedMatMul=function(){return(r0=e.__FusedMatMul=e.asm._FusedMatMul).apply(null,arguments)},n0=e._malloc=function(){return(n0=e._malloc=e.asm.malloc).apply(null,arguments)},o0=e._free=function(){return(o0=e._free=e.asm.free).apply(null,arguments)},s0=e.___errno_location=function(){return(s0=e.___errno_location=e.asm.__errno_location).apply(null,arguments)},Pg=e.stackSave=function(){return(Pg=e.stackSave=e.asm.stackSave).apply(null,arguments)},Lg=e.stackRestore=function(){return(Lg=e.stackRestore=e.asm.stackRestore).apply(null,arguments)},jd=e.stackAlloc=function(){return(jd=e.stackAlloc=e.asm.stackAlloc).apply(null,arguments)},i0=e.dynCall_iijjiiii=function(){return(i0=e.dynCall_iijjiiii=e.asm.dynCall_iijjiiii).apply(null,arguments)},a0=e.dynCall_jiji=function(){return(a0=e.dynCall_jiji=e.asm.dynCall_jiji).apply(null,arguments)};e.cwrap=VC;var Cp;Gr=function X(){Cp||zg(),Cp||(Gr=X)};function zg(X){if(X=X||a,Xe>0||(Ee(),Xe>0))return;function ct(){Cp||(Cp=!0,e.calledRun=!0,!G&&(Ae(),n(e),e.onRuntimeInitialized&&e.onRuntimeInitialized(),On()))}e.setStatus?(e.setStatus("Running..."),setTimeout(function(){setTimeout(function(){e.setStatus("")},1),ct()},1)):ct()}if(e.preInit)for(typeof e.preInit=="function"&&(e.preInit=[e.preInit]);e.preInit.length>0;)e.preInit.pop()();zg();var vp;s&&(vp={uncaughtException:process.listeners("uncaughtException").filter(function(X){return!s.uncaughtException.indexOf(X)>-1}),unhandledRejection:process.listeners("unhandledRejection").filter(function(X){return!s.unhandledRejection.indexOf(X)>-1})});var Sp;if(typeof t!="undefined")Sp=t;else if(typeof WasmBackendModuleThreadedSimd!="undefined")Sp=WasmBackendModuleThreadedSimd;else throw new Error("Could not find wasm module in post.js");if(vp){var l0=Sp._dispose;Sp._dispose=function(){l0(),vp.uncaughtException.forEach(function(X){process.removeListener("uncaughtException",X)}),vp.unhandledRejection.forEach(function(X){process.removeListener("unhandledRejection",X)})}}return t.ready}})();typeof FC=="object"&&typeof s_=="object"?s_.exports=o_:typeof define=="function"&&define.amd?define([],function(){return o_}):typeof FC=="object"&&(FC.WasmBackendModule=o_)});var Ta=class{constructor(t,e){this.backend=t,this.dataMover=e,this.data=new WeakMap,this.dataIdsCount=0}get(t){return this.data.has(t)||this.dataMover.moveData(this.backend,t),this.data.get(t)}set(t,e){this.dataIdsCount++,this.data.set(t,e)}has(t){return this.data.has(t)}delete(t){return this.dataIdsCount--,this.data.delete(t)}numDataIds(){return this.dataIdsCount}},Bo=class{refCount(t){return Pn("refCount")}incRef(t){return Pn("incRef")}timerAvailable(){return!0}time(t){return Pn("time")}read(t){return Pn("read")}readSync(t){return Pn("readSync")}readToGPU(t,e){return Pn("readToGPU")}numDataIds(){return Pn("numDataIds")}disposeData(t,e){return Pn("disposeData")}write(t,e,n){return Pn("write")}move(t,e,n,o,s){return Pn("move")}createTensorFromGPUData(t,e,n){return Pn("createTensorFromGPUData")}memory(){return Pn("memory")}floatPrecision(){return Pn("floatPrecision")}epsilon(){return this.floatPrecision()===32?1e-7:1e-4}dispose(){return Pn("dispose")}};function Pn(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 C_(r){let t=r.length,e=0;for(;t>0;)e=Math.random()*t|0,t--,jg(r,t,e)}function Hq(r,t){if(r.length!==t.length)throw new Error(`Array sizes must match to be shuffled together First array length was ${r.length}Second array length was ${t.length}`);let e=r.length,n=0;for(;e>0;)n=Math.random()*e|0,e--,jg(r,e,n),jg(t,e,n)}function kp(r,t,e){return Math.max(r,Math.min(t,e))}function qq(r){return r%2===0?r:r+1}function jg(r,t,e){let n=r[t];r[t]=r[e],r[e]=n}function Kq(r){let t=0;for(let e=0;e<r.length;e++)t+=r[e];return t}function jq(r,t){let e=Math.random();return t*e+(1-e)*r}function Xq(r,t){let e=0;for(let n=0;n<r.length;n++){let o=Number(r[n])-Number(t[n]);e+=o*o}return e}function _(r,t){if(!r)throw new Error(typeof t=="string"?t:t())}function $e(r,t,e=""){_(sn(r,t),()=>e+` Shapes ${r} and ${t} must match`)}function ro(r){_(r!=null,()=>"The input to the tensor constructor must be a non-null value.")}function jt(r){if(r.length===0)return 1;let t=r[0];for(let e=1;e<r.length;e++)t*=r[e];return t}function Yq(r){return r.length===0}function d0(r,t){if(r===t)return!0;if(r==null||t==null||r.length!==t.length)return!1;for(let e=0;e<r.length;e++)if(r[e]!==null&&t[e]!==null&&r[e]!==t[e])return!1;return!0}function sn(r,t){if(r===t)return!0;if(r==null||t==null||r.length!==t.length)return!1;for(let e=0;e<r.length;e++)if(r[e]!==t[e])return!1;return!0}function _a(r){return r%1===0}function Zq(r){if(Math.tanh!=null)return Math.tanh(r);if(r===1/0)return 1;if(r===-1/0)return-1;{let t=Math.exp(2*r);return(t-1)/(t+1)}}function Jq(r){let t=Math.ceil(Math.sqrt(r));return[t,Math.ceil(r/t)]}function Qq(r){let t=new Uint32Array(r);for(let e=0;e<r;++e)t[e]=e;return C_(t),t}function Qu(r,t){return t<=r.length?r:r+" ".repeat(t-r.length)}function tK(r,t=o=>0,e,n){return new Promise((o,s)=>{let i=0,a=()=>{if(r()){o();return}i++;let u=t(i);if(e!=null&&i>=e){s();return}n!=null?n(a,u):setTimeout(a,u)};a()})}function eK(r,t){let e=1,n=-1;for(let s=0;s<r.length;++s)if(r[s]>=0)e*=r[s];else if(r[s]===-1){if(n!==-1)throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${n} and dim ${s}`);n=s}else if(r[s]<0)throw Error(`Shapes can not be < 0. Found ${r[s]} at dim ${s}`);if(n===-1){if(t>0&&t!==e)throw Error(`Size(${t}) must match the product of shape ${r}`);return r}if(e===0)throw Error(`Cannot infer the missing size in [${r}] when there are 0 elements`);if(t%e!==0)throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${e}`);let o=r.slice();return o[n]=t/e,o}function fr(r,t){let e=t.length;return r=r==null?t.map((n,o)=>o):[].concat(r),_(r.every(n=>n>=-e&&n<e),()=>`All values in axis param must be in range [-${e}, ${e}) but got axis ${r}`),_(r.every(n=>_a(n)),()=>`All values in axis param must be integers but got axis ${r}`),r.map(n=>n<0?e+n:n)}function h0(r,t){let e=[],n=[],o=t!=null&&Array.isArray(t)&&t.length===0,s=t==null||o?null:fr(t,r).sort(),i=0;for(let a=0;a<r.length;++a){if(s!=null){if(s[i]===a&&r[a]!==1)throw new Error(`Can't squeeze axis ${a} since its dim '${r[a]}' is not 1`);(s[i]==null||s[i]>a)&&r[a]===1&&(e.push(r[a]),n.push(a)),s[i]<=a&&i++}r[a]!==1&&(e.push(r[a]),n.push(a))}return{newShape:e,keptDims:n}}function g0(r,t){return Xg(r,t)}function Xg(r,t){let e=null;if(r==null||r==="float32")e=new Float32Array(t);else if(r==="int32")e=new Int32Array(t);else if(r==="bool")e=new Uint8Array(t);else if(r==="string")e=new Array(t);else throw new Error(`Unknown data type ${r}`);return e}function x0(r,t){for(let e=0;e<r.length;e++){let n=r[e];if(isNaN(n)||!isFinite(n))throw Error(`A tensor of type ${t} being uploaded contains ${n}.`)}}function y0(r){return r==="bool"||r==="complex64"||r==="float32"||r==="int32"||r==="string"}function rK(r,t){return!(t==="complex64"||t==="float32"&&r!=="complex64"||t==="int32"&&r!=="float32"&&r!=="complex64"||t==="bool"&&r==="bool")}function Tp(r){if(r==="float32"||r==="int32")return 4;if(r==="complex64")return 8;if(r==="bool")return 1;throw new Error(`Unknown dtype ${r}`)}function b0(r){if(r==null)return 0;let t=0;return r.forEach(e=>t+=e.length),t}function Vo(r){return typeof r=="string"||r instanceof String}function v_(r){return typeof r=="boolean"}function S_(r){return typeof r=="number"}function Wl(r){return Array.isArray(r)?Wl(r[0]):r instanceof Float32Array?"float32":r instanceof Int32Array||r instanceof Uint8Array||r instanceof Uint8ClampedArray?"int32":S_(r)?"float32":Vo(r)?"string":v_(r)?"bool":"float32"}function _i(r){return!!(r&&r.constructor&&r.call&&r.apply)}function _p(r,t){for(let e=t;e<r;++e)if(r%e===0)return e;return r}function Ei(r){let t=r.length;if(t<2)return[];let e=new Array(t-1);e[t-2]=r[t-1];for(let n=t-3;n>=0;--n)e[n]=e[n+1]*r[n+1];return e}function N_(r,t,e,n=!1){let o=new Array;if(t.length===1){let s=t[0]*(n?2:1);for(let i=0;i<s;i++)o[i]=e[r+i]}else{let s=t[0],i=t.slice(1),a=i.reduce((u,l)=>u*l)*(n?2:1);for(let u=0;u<s;u++)o[u]=N_(r+u*a,i,e,n)}return o}function Ju(r,t,e=!1){if(r.length===0)return t[0];let n=r.reduce((o,s)=>o*s)*(e?2:1);if(n===0)return[];if(n!==t.length)throw new Error(`[${r}] does not match the input size ${t.length}${e?" for a complex tensor":""}.`);return N_(0,r,t,e)}function nK(r,t){if(Array.isArray(r))return r;if(t==="float32")return r instanceof Float32Array?r:new Float32Array(r);if(t==="int32")return r instanceof Int32Array?r:new Int32Array(r);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(r));throw new Error(`Unknown dtype ${t}`)}function Yd(r,t){let e=Ep(r,t);for(let n=0;n<e.length;n++)e[n]=1;return e}function Ep(r,t){if(t==null||t==="float32"||t==="complex64")return new Float32Array(r);if(t==="int32")return new Int32Array(r);if(t==="bool")return new Uint8Array(r);throw new Error(`Unknown data type ${t}`)}function oK(r,t){let e=r.reduce((n,o)=>n*o,1);if(t==null||t==="float32")return Ju(r,new Float32Array(e));if(t==="int32")return Ju(r,new Int32Array(e));if(t==="bool")return Ju(r,new Uint8Array(e));throw new Error(`Unknown data type ${t}`)}function Le(r){r.forEach(t=>{_(Number.isInteger(t)&&t>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${r}].`)})}function sK(r,t,e){if(t===0)return 0;if(t===1)return r[0];let n=r[r.length-1];for(let o=0;o<r.length-1;++o)n+=e[o]*r[o];return n}function iK(r,t,e){if(t===0)return[];if(t===1)return[r];let n=new Array(t);for(let o=0;o<n.length-1;++o)n[o]=Math.floor(r/e[o]),r-=n[o]*e[o];return n[n.length-1]=r,n}function tc(r){return r&&r.then&&typeof r.then=="function"}var k_="tfjsflags",Zd=class{constructor(t){this.global=t,this.flags={},this.flagRegistry={},this.urlFlags={},this.getQueryParams=lK,this.populateURLFlags()}setPlatform(t,e){this.platform!=null&&(L().getBool("IS_TEST")||L().getBool("PROD")||console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${t}.`)),this.platformName=t,this.platform=e}registerFlag(t,e,n){if(this.flagRegistry[t]={evaluationFn:e,setHook:n},this.urlFlags[t]!=null){let o=this.urlFlags[t];L().getBool("IS_TEST")||L().getBool("PROD")||console.warn(`Setting feature override from URL ${t}: ${o}.`),this.set(t,o)}}async getAsync(t){return t in this.flags?this.flags[t]:(this.flags[t]=await this.evaluateFlag(t),this.flags[t])}get(t){if(t in this.flags)return this.flags[t];let e=this.evaluateFlag(t);if(tc(e))throw new Error(`Flag ${t} cannot be synchronously evaluated. Please use getAsync() instead.`);return this.flags[t]=e,this.flags[t]}getNumber(t){return this.get(t)}getBool(t){return this.get(t)}getString(t){return this.get(t)}getFlags(){return this.flags}get features(){return this.flags}set(t,e){if(this.flagRegistry[t]==null)throw new Error(`Cannot set flag ${t} as it has not been registered.`);this.flags[t]=e,this.flagRegistry[t].setHook!=null&&this.flagRegistry[t].setHook(e)}evaluateFlag(t){if(this.flagRegistry[t]==null)throw new Error(`Cannot evaluate flag '${t}': no evaluation function found.`);return this.flagRegistry[t].evaluationFn()}setFlags(t){this.flags=Object.assign({},t)}reset(){this.flags={},this.urlFlags={},this.populateURLFlags()}populateURLFlags(){if(typeof this.global=="undefined"||typeof this.global.location=="undefined"||typeof this.global.location.search=="undefined")return;let t=this.getQueryParams(this.global.location.search);k_ in t&&t[k_].split(",").forEach(n=>{let[o,s]=n.split(":");this.urlFlags[o]=cK(o,s)})}};function lK(r){let t={};return r.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(e,...n)=>(uK(t,n[0],n[1]),n.join("="))),t}function uK(r,t,e){r[decodeURIComponent(t)]=decodeURIComponent(e||"")}function cK(r,t){let e=t.toLowerCase();return e==="true"||e==="false"?e==="true":`${+e}`===e?+e:t}function L(){return w0}var w0=null;function T_(r){w0=r}var I0;function C0(){if(I0==null){let r;if(typeof window!="undefined")r=window;else if(typeof global!="undefined")r=global;else if(typeof process!="undefined")r=process;else if(typeof self!="undefined")r=self;else throw new Error("Could not find a global object");I0=r}return I0}function pK(){let r=C0();return r._tfGlobals==null&&(r._tfGlobals=new Map),r._tfGlobals}function Jd(r,t){let e=pK();if(e.has(r))return e.get(r);{let n=t();return e.set(r,n),e.get(r)}}var Ai="Abs",Go="Acos",Wo="Acosh",no="Add",Uo="AddN",Ea="All",Aa="Any",Di="ArgMax",$i="ArgMin",Ho="Asin",qo="Asinh",Ko="Atan",jo="Atanh",Xo="Atan2",Yo="AvgPool",Ul="AvgPoolGrad",Ri="AvgPool3D",Hl="AvgPool3DGrad",Zo="BatchMatMul",Fi="BatchToSpaceND",Da="Bincount",$a="BitwiseAnd",__="BroadcastTo",ql="BroadcastArgs",fo="Cast",Jo="Ceil",ho="ClipByValue",Ap="Complex",Kl="ComplexAbs",Oi="Concat",Qo="Conv2D",Dp="Conv2DBackpropFilter",ts="Conv2DBackpropInput",es="Conv3D",Ra="Conv3DBackpropFilterV2",Fa="Conv3DBackpropInputV2",rs="Cos",ns="Cosh",Oa="Cumprod",os="Cumsum",Ma="CropAndResize",jl="DenseBincount",Pa="DepthToSpace",ss="DepthwiseConv2dNative",$p="DepthwiseConv2dNativeBackpropFilter",Rp="DepthwiseConv2dNativeBackpropInput",Xl="Diag",is="Dilation2D",Yl="Dilation2DBackpropInput",Zl="Dilation2DBackpropFilter",Qd="Draw",as="RealDiv",Fp="Einsum",ls="Elu",La="EluGrad",us="Erf",za="Equal",cs="Exp",Mi="ExpandDims",ps="Expm1",Op="FFT",Jl="Fill",Ba="FlipLeftRight",ms="Floor",fs="FloorDiv",ds="FusedBatchNorm",Pi="GatherV2",Va="GatherNd",Ga="Greater",hs="GreaterEqual",go="Identity",Mp="IFFT",Pp="Imag",gs="IsFinite",xs="IsInf",ys="IsNan",bs="LeakyRelu",Wa="Less",Ua="LessEqual",Ha="LinSpace",ws="Log",Is="Log1p",qa="LogicalAnd",Ka="LogicalNot",ja="LogicalOr",E_="LogicalXor",A_="LogSoftmax",nft="LowerBound",Cs="LRN",Xa="LRNGrad",oft="MatrixBandPart",vs="Max",Ss="Maximum",Ns="MaxPool",Ql="MaxPoolGrad",Li="MaxPool3D",tu="MaxPool3DGrad",eu="MaxPoolWithArgmax",ks="Mean",Ts="Min",_s="Minimum",Es="MirrorPad",As="Mod",Ya="Multinomial",Ds="Multiply",zi="Neg",Za="NotEqual",Ja="NonMaxSuppressionV3",Qa="NonMaxSuppressionV4",tl="NonMaxSuppressionV5",Bi="OnesLike",$s="OneHot",Vi="Pack",Rs="PadV2",sft="Pool",Fs="Pow",Os="Prelu",Ms="Prod",Lp="RaggedGather",zp="RaggedRange",Bp="RaggedTensorToTensor",ru="Range",Vp="Real",Ps="Reciprocal",Ls="Relu",Gi="Reshape",zs="ResizeNearestNeighbor",el="ResizeNearestNeighborGrad",Bs="ResizeBilinear",rl="ResizeBilinearGrad",Vs="Relu6",Gs="Reverse",Ws="Round",Us="Rsqrt",nl="ScatterNd",ol="TensorScatterUpdate",sl="SearchSorted",Wi="Select",Hs="Selu",Ui="Slice",qs="Sin",Ks="Sinh",js="Sign",Xs="Sigmoid",Ys="Softplus",Zs="Sqrt",Js="Sum",Hi="SpaceToBatchND",qi="SplitV",Qs="Softmax",nu="SparseFillEmptyRows",il="SparseReshape",ou="SparseSegmentMean",su="SparseSegmentSum",al="SparseToDense",ti="SquaredDifference",iu="Square",ec="StaticRegexReplace",ll="StridedSlice",au="StringNGrams",lu="StringSplit",uu="StringToHashBucketFast",ei="Sub",ri="Tan",ni="Tanh",oo="Tile",ul="TopK",cl="Transform",so="Transpose",cu="Unique",Ki="Unpack",pu="UnsortedSegmentSum",ift="UpperBound",ji="ZerosLike",xo="Step",th="FromPixels",pl="RotateWithOffset",Xi="_FusedMatMul",Yi="FusedConv2D",Zi="FusedDepthwiseConv2D";function Ji(...r){L().getBool("IS_TEST")||L().getBool("PROD")||console.warn(...r)}function mK(...r){L().getBool("IS_TEST")||L().getBool("PROD")||console.log(...r)}var Gp=Jd("kernelRegistry",()=>new Map),eh=Jd("gradRegistry",()=>new Map);function Wp(r,t){let e=S0(r,t);return Gp.get(e)}function v0(r){return eh.get(r)}function Yg(r){let t=Gp.entries(),e=[];for(;;){let{done:n,value:o}=t.next();if(n)break;let[s,i]=o,[a]=s.split("_");a===r&&e.push(i)}return e}function rc(r){let{kernelName:t,backendName:e}=r,n=S0(t,e);Gp.has(n)&&Ji(`The kernel '${t}' for backend '${e}' is already registered`),Gp.set(n,r)}function $_(r){let{kernelName:t}=r;eh.has(t)&&L().getBool("DEBUG")&&Ji(`Overriding the gradient for '${t}'`),eh.set(t,r)}function pft(r,t){let e=S0(r,t);if(!Gp.has(e))throw new Error(`The kernel '${r}' for backend '${t}' is not registered`);Gp.delete(e)}function mft(r){if(!eh.has(r))throw new Error(`The gradient '${r}' for backend is not registered`);eh.delete(r)}function fft(r,t){Yg(r).forEach(n=>{let o=Object.assign({},n,{backendName:t});rc(o)})}function S0(r,t){return`${t}_${r}`}var y={};Kt(y,{arraysEqual:()=>sn,arraysEqualWithNull:()=>d0,assert:()=>_,assertNonNegativeIntegerDimensions:()=>Le,assertNonNull:()=>ro,assertShapesMatch:()=>$e,bytesFromStringArray:()=>b0,bytesPerElement:()=>Tp,checkConversionForErrors:()=>x0,clamp:()=>kp,computeStrides:()=>Ei,convertBackendValuesAndArrayBuffer:()=>nK,createScalarValue:()=>bK,createShuffledIndices:()=>Qq,decodeString:()=>Kp,distSquared:()=>Xq,encodeString:()=>fu,fetch:()=>IK,fingerPrint64:()=>yK,flatten:()=>si,getArrayFromDType:()=>Xg,getTypedArrayFromDType:()=>g0,hasEncodingLoss:()=>rK,hexToLong:()=>rh,indexToLoc:()=>iK,inferDtype:()=>Wl,inferFromImplicitShape:()=>eK,isBoolean:()=>v_,isFunction:()=>_i,isInt:()=>_a,isNumber:()=>S_,isPromise:()=>tc,isScalarShape:()=>Yq,isString:()=>Vo,isTypedArray:()=>sr,isValidDtype:()=>y0,locToIndex:()=>sK,makeOnesTypedArray:()=>Yd,makeZerosNestedTypedArray:()=>oK,makeZerosTypedArray:()=>Ep,nearestDivisor:()=>_p,nearestLargerEven:()=>qq,now:()=>ac,parseAxisParam:()=>fr,randUniform:()=>jq,repeatedTry:()=>tK,rightPad:()=>Qu,shuffle:()=>C_,shuffleCombo:()=>Hq,sizeFromShape:()=>jt,sizeToSquarishShape:()=>Jq,squeezeShape:()=>h0,sum:()=>Kq,swap:()=>jg,tanh:()=>Zq,toNestedArray:()=>Ju,toTypedArray:()=>qp});function Zg(r){return r instanceof Float32Array||r instanceof Int32Array||r instanceof Uint8Array||r instanceof Uint8ClampedArray}var _0=Gl(W_());var ic=_0.default||_0;function rh(r){return ic.fromString(r,!0,16)}var H_=rh("c3a5c85c97cb3127"),sc=rh("b492b66fbe98f273"),an=rh("9ae16a3b2f90404f");function T0(r){return r.xor(r.shru(47))}function q_(r,t,e){let n=r.slice(t,t+e);return ic.fromBytes(Array.from(n),!0,!0)}function ze(r,t){return q_(r,t,8)}function U_(r,t){return q_(r,t,4)}function Ar(r,t){return t===0?r:r.shru(t).or(r.shl(64-t))}function mu(r,t,e=rh("9ddfea08eb382d69")){let n=r.xor(t).mul(e);n=n.xor(n.shru(47));let o=t.xor(n).mul(e);return o=o.xor(o.shru(47)),o=o.mul(e),o}function dK(r,t,e,n,o,s){o=o.add(r),s=Ar(s.add(o).add(n),21);let i=o;return o=o.add(t),o=o.add(e),s=s.add(Ar(o,44)),[o.add(n),s.add(i)]}function Qg(r,t,e,n){return dK(ze(r,t),ze(r,t+8),ze(r,t+16),ze(r,t+24),e,n)}function hK(r,t=r.length){if(t>=8){let e=an.add(t*2),n=ze(r,0).add(an),o=ze(r,t-8),s=Ar(o,37).mul(e).add(n),i=Ar(n,25).add(o).mul(e);return mu(s,i,e)}if(t>=4){let e=an.add(t*2),n=U_(r,0);return mu(n.shl(3).add(t),U_(r,t-4),e)}if(t>0){let e=r[0],n=r[t>>1],o=r[t-1],s=e+(n<<8),i=t+(o<<2);return T0(an.mul(s).xor(H_.mul(i))).mul(an)}return an}function gK(r,t=r.length){let e=an.add(t*2),n=ze(r,0).mul(sc),o=ze(r,8),s=ze(r,t-8).mul(e),i=ze(r,t-16).mul(an);return mu(Ar(n.add(o),43).add(Ar(s,30)).add(i),n.add(Ar(o.add(an),18)).add(s),e)}function xK(r,t=r.length){let e=an.add(t*2),n=ze(r,0).mul(an),o=ze(r,8),s=ze(r,t-8).mul(e),i=ze(r,t-16).mul(an),a=Ar(n.add(o),43).add(Ar(s,30)).add(i),u=mu(a,n.add(Ar(o.add(an),18)).add(s),e),l=ze(r,16).mul(e),c=ze(r,24),p=a.add(ze(r,t-32)).mul(e),m=u.add(ze(r,t-24)).mul(e);return mu(Ar(l.add(c),43).add(Ar(p,30)).add(m),l.add(Ar(c.add(n),18)).add(p),e)}function yK(r,t=r.length){let e=ic.fromNumber(81,!0);if(t<=32)return t<=16?hK(r,t):gK(r,t);if(t<=64)return xK(r,t);let n=e,o=e.mul(sc).add(113),s=T0(o.mul(an).add(113)).mul(an),i=[ic.UZERO,ic.UZERO],a=[ic.UZERO,ic.UZERO];n=n.mul(an).add(ze(r,0));let u=0,l=(t-1>>6)*64,c=l+(t-1&63)-63;do n=Ar(n.add(o).add(i[0]).add(ze(r,u+8)),37).mul(sc),o=Ar(o.add(i[1]).add(ze(r,u+48)),42).mul(sc),n=n.xor(a[1]),o=o.add(i[0]).add(ze(r,u+40)),s=Ar(s.add(a[0]),33).mul(sc),i=Qg(r,u,i[1].mul(sc),n.add(a[0])),a=Qg(r,u+32,s.add(a[1]),o.add(ze(r,u+16))),[s,n]=[n,s],u+=64;while(u!==l);let p=sc.add(s.and(255).shl(1));return u=c,a[0]=a[0].add(t-1&63),i[0]=i[0].add(a[0]),a[0]=a[0].add(i[0]),n=Ar(n.add(o).add(i[0]).add(ze(r,u+8)),37).mul(p),o=Ar(o.add(i[1]).add(ze(r,u+48)),42).mul(p),n=n.xor(a[1].mul(9)),o=o.add(i[0].mul(9).add(ze(r,u+40))),s=Ar(s.add(a[0]),33).mul(p),i=Qg(r,u,i[1].mul(p),n.add(a[0])),a=Qg(r,u+32,s.add(a[1]),o.add(ze(r,u+16))),[s,n]=[n,s],mu(mu(i[0],a[0],p).add(T0(o).mul(H_)).add(s),mu(i[1],a[1],p).add(n),p)}function bK(r,t){return t==="string"?fu(r):qp([r],t)}function wK(r,t){return r instanceof Float32Array&&t==="float32"||r instanceof Int32Array&&t==="int32"||r instanceof Uint8Array&&t==="bool"}function qp(r,t){if(t==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(r)&&(r=si(r)),L().getBool("DEBUG")&&x0(r,t),wK(r,t))return r;if(t==null||t==="float32"||t==="complex64")return new Float32Array(r);if(t==="int32")return new Int32Array(r);if(t==="bool"){let e=new Uint8Array(r.length);for(let n=0;n<e.length;++n)Math.round(r[n])!==0&&(e[n]=1);return e}else throw new Error(`Unknown data type ${t}`)}function ac(){return L().platform.now()}function IK(r,t){return L().platform.fetch(r,t)}function fu(r,t="utf-8"){return t=t||"utf-8",L().platform.encode(r,t)}function Kp(r,t="utf-8"){return t=t||"utf-8",L().platform.decode(r,t)}function sr(r){return L().platform.isTypedArray!=null?L().platform.isTypedArray(r):Zg(r)}function si(r,t=[],e=!1){if(t==null&&(t=[]),typeof r=="boolean"||typeof r=="number"||typeof r=="string"||tc(r)||r==null||sr(r)&&e)t.push(r);else if(Array.isArray(r)||sr(r))for(let n=0;n<r.length;++n)si(r[n],t,e);else{let n=-1;for(let o of Object.keys(r))/^([1-9]+[0-9]*|0)$/.test(o)&&(n=Math.max(n,Number(o)));for(let o=0;o<=n;o++)si(r[o],t,e)}return t}var tx=class{constructor(t,e){this.backendTimer=t,this.logger=e,e==null&&(this.logger=new E0)}profileKernel(t,e,n){let o,s=()=>{o=n()},i,a=ac();if(this.backendTimer.timerAvailable())i=this.backendTimer.time(s);else{s();for(let l of o)l.dataSync();i=Promise.resolve({kernelMs:ac()-a})}if(L().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let l=0;l<o.length;l++){let c=o[l];c.data().then(p=>{CK(p,c.dtype,t)})}return{kernelName:t,outputs:o,inputs:e,timeMs:i.then(l=>l.kernelMs),extraInfo:i.then(l=>l.getExtraProfileInfo!=null?l.getExtraProfileInfo():"")}}logKernelProfile(t){let{kernelName:e,outputs:n,timeMs:o,inputs:s,extraInfo:i}=t;n.forEach(a=>{Promise.all([a.data(),o,i]).then(u=>{this.logger.logKernelProfile(e,a,u[0],u[1],s,u[2])})})}};function CK(r,t,e){if(t!=="float32")return!1;for(let n=0;n<r.length;n++){let o=r[n];if(isNaN(o)||!isFinite(o))return console.warn(`Found ${o} in the result of '${e}'`),!0}return!1}var E0=class{logKernelProfile(t,e,n,o,s,i){let a=typeof o=="number"?Qu(`${o}ms`,9):o.error,u=Qu(t,25),l=e.rank,c=e.size,p=Qu(e.shape.toString(),14),m="";for(let f in s){let d=s[f];if(d!=null){let h=d.shape||e.shape,g=h.length;m+=`${f}: ${g}D ${g>0?h:""} `}}console.log(`%c${u}	%c${a}	%c${l}D ${p}	%c${c}	%c${m}	%c${i}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function K_(r,t,e){let n={},o={};for(let u=0;u<t.length;u++)n[t[u].id]=!0;for(let u=0;u<r.length;u++){let l=r[u],c=l.inputs;for(let p in c){let m=c[p],f=!1;for(let d=0;d<t.length;d++)if(n[m.id]){l.outputs.forEach(h=>n[h.id]=!0),f=!0,o[l.id]=!0;break}if(f)break}}let s={};s[e.id]=!0;let i={};for(let u=r.length-1;u>=0;u--){let l=r[u],c=l.inputs;for(let p=0;p<l.outputs.length;p++)if(s[l.outputs[p].id]){for(let m in c)s[c[m].id]=!0,i[l.id]=!0;break}}let a=[];for(let u=0;u<r.length;u++){let l=r[u];if(o[l.id]&&i[l.id]){let c={};for(let m in l.inputs){let f=l.inputs[m];n[f.id]&&(c[m]=f)}let p=Object.assign({},l);p.inputs=c,p.outputs=l.outputs,a.push(p)}}return a}function j_(r,t,e,n){for(let o=t.length-1;o>=0;o--){let s=t[o],i=[];if(s.outputs.forEach(u=>{let l=r[u.id];l!=null?i.push(l):i.push(null)}),s.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);let a=s.gradient(i);for(let u in s.inputs){if(!(u in a))throw new Error(`Cannot backprop through input ${u}. Available gradients found: ${Object.keys(a)}.`);let l=e(()=>a[u]());if(l.dtype!=="float32")throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${u} must have 'float32' dtype, but has '${l.dtype}'`);let c=s.inputs[u];if(!sn(l.shape,c.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${u}' has shape '${l.shape}', which does not match the shape of the input '${c.shape}'`);if(r[c.id]==null)r[c.id]=l;else{let p=r[c.id];r[c.id]=n(p,l),p.dispose()}}}}var X_=20,nh=3,A0=7;function Y_(r,t,e,n){let o=Ei(t),s=vK(r,t,e,o),i=t.length,a=ex(r,t,e,o,s),u=["Tensor"];return n&&(u.push(`  dtype: ${e}`),u.push(`  rank: ${i}`),u.push(`  shape: [${t}]`),u.push("  values:")),u.push(a.map(l=>"    "+l).join(`
`)),u.join(`
`)}function vK(r,t,e,n){let o=jt(t),s=n[n.length-1],i=new Array(s).fill(0),a=t.length,u=e==="complex64"?sh(r):r;if(a>1)for(let l=0;l<o/s;l++){let c=l*s;for(let p=0;p<s;p++)i[p]=Math.max(i[p],oh(u[c+p],0,e).length)}return i}function oh(r,t,e){let n;return Array.isArray(r)?n=`${parseFloat(r[0].toFixed(A0))} + ${parseFloat(r[1].toFixed(A0))}j`:Vo(r)?n=`'${r}'`:e==="bool"?n=Z_(r):n=parseFloat(r.toFixed(A0)).toString(),Qu(n,t)}function Z_(r){return r===0?"false":"true"}function ex(r,t,e,n,o,s=!0){let i=e==="complex64"?2:1,a=t[0],u=t.length;if(u===0){if(e==="complex64"){let h=sh(r);return[oh(h[0],0,e)]}return e==="bool"?[Z_(r[0])]:[r[0].toString()]}if(u===1){if(a>X_){let g=nh*i,x=Array.from(r.slice(0,g)),b=Array.from(r.slice((a-nh)*i,a*i));return e==="complex64"&&(x=sh(x),b=sh(b)),["["+x.map((w,I)=>oh(w,o[I],e)).join(", ")+", ..., "+b.map((w,I)=>oh(w,o[a-nh+I],e)).join(", ")+"]"]}return["["+(e==="complex64"?sh(r):Array.from(r)).map((g,x)=>oh(g,o[x],e)).join(", ")+"]"]}let l=t.slice(1),c=n.slice(1),p=n[0]*i,m=[];if(a>X_){for(let h=0;h<nh;h++){let g=h*p,x=g+p;m.push(...ex(r.slice(g,x),l,e,c,o,!1))}m.push("...");for(let h=a-nh;h<a;h++){let g=h*p,x=g+p;m.push(...ex(r.slice(g,x),l,e,c,o,h===a-1))}}else for(let h=0;h<a;h++){let g=h*p,x=g+p;m.push(...ex(r.slice(g,x),l,e,c,o,h===a-1))}let f=u===2?",":"";m[0]="["+(a>0?m[0]+f:"");for(let h=1;h<m.length-1;h++)m[h]=" "+m[h]+f;let d=`,
`;for(let h=2;h<u;h++)d+=`
`;return m[m.length-1]=" "+m[m.length-1]+"]"+(s?"":d),m}function sh(r){let t=[];for(let e=0;e<r.length;e+=2)t.push([r[e],r[e+1]]);return t}var le=class{constructor(t,e,n){if(this.dtype=e,this.shape=t.slice(),this.size=jt(t),n!=null){let o=n.length;_(o===this.size,()=>`Length of values '${o}' does not match the size inferred by the shape '${this.size}'.`)}if(e==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Xg(e,this.size),this.strides=Ei(t)}set(t,...e){e.length===0&&(e=[0]),_(e.length===this.rank,()=>`The number of provided coordinates (${e.length}) must match the rank (${this.rank})`);let n=this.locToIndex(e);this.values[n]=t}get(...t){t.length===0&&(t=[0]);let e=0;for(let o of t){if(o<0||o>=this.shape[e]){let s=`Requested out of range element at ${t}.   Buffer shape=${this.shape}`;throw new Error(s)}e++}let n=t[t.length-1];for(let o=0;o<t.length-1;++o)n+=this.strides[o]*t[o];return this.values[n]}locToIndex(t){if(this.rank===0)return 0;if(this.rank===1)return t[0];let e=t[t.length-1];for(let n=0;n<t.length-1;++n)e+=this.strides[n]*t[n];return e}indexToLoc(t){if(this.rank===0)return[];if(this.rank===1)return[t];let e=new Array(this.shape.length);for(let n=0;n<e.length-1;++n)e[n]=Math.floor(t/this.strides[n]),t-=e[n]*this.strides[n];return e[e.length-1]=t,e}get rank(){return this.shape.length}toTensor(){return ii().makeTensor(this.values,this.shape,this.dtype)}},ii=null,jp=null,SK=null;function J_(r){ii=r}function Q_(r){jp=r}function tE(r){SK=r}var Lt=class{constructor(t,e,n,o){this.kept=!1,this.isDisposedInternal=!1,this.shape=t.slice(),this.dtype=e||"float32",this.size=jt(t),this.strides=Ei(t),this.dataId=n,this.id=o,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let t=await this.data();return jp.buffer(this.shape,this.dtype,t)}bufferSync(){return jp.buffer(this.shape,this.dtype,this.dataSync())}async array(){let t=await this.data();return Ju(this.shape,t,this.dtype==="complex64")}arraySync(){return Ju(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let t=ii().read(this.dataId);if(this.dtype==="string"){let e=await t;try{return e.map(n=>Kp(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return t}dataToGPU(t){return this.throwIfDisposed(),ii().readToGPU(this.dataId,t)}dataSync(){this.throwIfDisposed();let t=ii().readSync(this.dataId);if(this.dtype==="string")try{return t.map(e=>Kp(e))}catch(e){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return t}async bytes(){this.throwIfDisposed();let t=await ii().read(this.dataId);return this.dtype==="string"?t:new Uint8Array(t.buffer)}dispose(){this.isDisposed||(this.kerasMask&&this.kerasMask.dispose(),ii().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(t=!1){return jp.print(this,t)}clone(){return this.throwIfDisposed(),jp.clone(this)}toString(t=!1){let e=this.dataSync();return Y_(e,this.shape,this.dtype,t)}cast(t){return this.throwIfDisposed(),jp.cast(this,t)}variable(t=!0,e,n){return this.throwIfDisposed(),ii().makeVariable(this,t,e,n)}};Object.defineProperty(Lt,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});function O(){return Jd("Tensor",()=>Lt)}O();var ml=class extends Lt{constructor(t,e,n,o){super(t.shape,t.dtype,t.dataId,o),this.trainable=e,this.name=n}assign(t){if(t.dtype!==this.dtype)throw new Error(`dtype of the new value (${t.dtype}) and previous value (${this.dtype}) must match`);if(!sn(t.shape,this.shape))throw new Error(`shape of the new value (${t.shape}) and previous value (${this.shape}) must match`);ii().disposeTensor(this),this.dataId=t.dataId,ii().incRef(this,null)}dispose(){ii().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(ml,Symbol.hasInstance,{value:r=>r instanceof Lt&&r.assign!=null&&r.assign instanceof Function});var Io={};Kt(Io,{assertTypesMatch:()=>M0,getTensorsInContainer:()=>ih,isTensorInList:()=>kK,makeTypesMatch:()=>Xt});var D0;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(D0||(D0={}));var $0;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})($0||($0={}));var R0;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(R0||(R0={}));var F0;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(F0||(F0={}));var O0;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(O0||(O0={}));var NK={float32:F0,int32:$0,bool:R0,complex64:O0};function ur(r,t){if(r==="string"||t==="string"){if(r==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${r} with ${t}`)}return NK[r][t]}function lc(r){return ur(r,"int32")}function rx(r){return r!=null&&typeof r=="object"&&"texture"in r&&r.texture instanceof WebGLTexture}function nx(r){return typeof GPUBuffer!="undefined"&&r!=null&&typeof r=="object"&&"buffer"in r&&r.buffer instanceof GPUBuffer}function Xt(r,t){if(r.dtype===t.dtype)return[r,t];let e=ur(r.dtype,t.dtype);return[r.cast(e),t.cast(e)]}function M0(r,t){_(r.dtype===t.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${t.dtype}) input must match`)}function kK(r,t){return t.some(e=>e.id===r.id)}function ih(r){let t=[];return eE(r,t,new Set),t}function eE(r,t,e){if(r==null)return;if(r instanceof Lt){t.push(r);return}if(!TK(r))return;let n=r;for(let o in n){let s=n[o];e.has(s)||(e.add(s),eE(s,t,e))}}function TK(r){return Array.isArray(r)||typeof r=="object"}function P0(r){return r.kernelName!=null}var ox=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(t=>t.name)))}}}dispose(){for(let t in this.registeredVariables)this.registeredVariables[t].dispose()}},ah=class r{constructor(t){this.ENV=t,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ox}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let n=t[e];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:t,asyncInit:e}=this.initializeBackendsAndReturnBest();if(e)throw new Error(`The highest priority backend '${t}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(t)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(t){if(!(t in this.registry))if(t in this.registryFactory){let{asyncInit:e}=this.initializeBackend(t);if(e)return null}else return null;return this.registry[t]}findBackendFactory(t){return t in this.registryFactory?this.registryFactory[t].factory:null}registerBackend(t,e,n=1){return t in this.registryFactory?(Ji(`${t} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[t]={factory:e,priority:n},!0)}async setBackend(t){if(this.registryFactory[t]==null)throw new Error(`Backend name '${t}' not found in registry`);if(this.backendName=t,this.registry[t]==null){this.backendInstance=null;let{success:e,asyncInit:n}=this.initializeBackend(t);if(!(n?await e:e))return!1}return this.backendInstance=this.registry[t],this.setupRegisteredKernels(),this.profiler=new tx(this.backendInstance),!0}setupRegisteredKernels(){Yg(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(t){Yg(t).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[t])})}initializeBackend(t){let e=this.registryFactory[t];if(e==null)throw new Error(`Cannot initialize backend ${t}, no registration found.`);try{let n=e.factory();if(n&&!(n instanceof Bo)&&typeof n.then=="function"){let o=++this.pendingBackendInitId,s=n.then(i=>o<this.pendingBackendInitId?!1:(this.registry[t]=i,this.pendingBackendInit=null,!0)).catch(i=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,Ji(`Initialization of backend ${t} failed`),Ji(i.stack||i.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[t]=n,{success:!0,asyncInit:!1}}catch(n){return Ji(`Initialization of backend ${t} failed`),Ji(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(t){if(!(t in this.registryFactory))throw new Error(`${t} backend not found in registry`);this.backendName===t&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,t in this.registry&&(this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t]),delete this.registryFactory[t],this.backendName===t&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((t,e)=>this.registryFactory[e].priority-this.registryFactory[t].priority)}initializeBackendsAndReturnBest(){let t=this.getSortedBackends();for(let e=0;e<t.length;e++){let n=t[e],{success:o,asyncInit:s}=this.initializeBackend(n);if(s||o)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(t,e){let n=this.state.tensorInfo.get(e),o=n.backend,s=this.readSync(e),i=o.refCount(e);o.disposeData(e,!0),n.backend=t,t.move(e,s,n.shape,n.dtype,i),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(t,e){let n=null;if(e==null){if(typeof t!="function")throw new Error("Please provide a function to tidy()");e=t}else{if(typeof t!="string"&&!(t instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof e!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=t}let o;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(o),()=>(o=e(),o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o))}scopedRun(t,e,n){t();try{let o=n();return e(),o}catch(o){throw e(),o}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(t){let e=T.runKernel(go,{x:t}),n={x:t},o=i=>({x:()=>{let a="float32",u={x:i},l={dtype:a};return T.runKernel(fo,u,l)}}),s=[];return this.addTapeNode(this.state.activeScope.name,n,[e],o,s,{}),e}runKernel(t,e,n){if(this.backendName==null&&this.backend,!(Wp(t,this.backendName)!=null))throw new Error(`Kernel '${t}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:t,inputs:e,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(t,e,n){let o=this.backend.numDataIds(),s=0;n.forEach(u=>{s+=u.dtype==="complex64"?3:1});let i=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],a=o-e-s-i;if(a>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${a} data ids) after running '${t}'`)}runKernelFunc(t){let e,n=[],o=this.isTapeOn(),s=this.state.numBytes,i=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let a;this.backendName==null&&this.backend;let u,l=P0(t)?t.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(P0(t)){let{kernelName:d,inputs:h,attrs:g}=t;this.backendName==null&&this.backend;let x=Wp(d,this.backendName);_(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),a=()=>{let b=this.backend.numDataIds();u=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let I=w.map(N=>N.rank!=null?N:this.makeTensorFromTensorInfo(N));if(o){let N=this.getTensorsForGradient(d,h,I);n=this.saveTensorsForBackwardMode(N)}return I}}else{let{forwardFunc:d}=t,h=g=>{o&&(n=g.map(x=>this.keep(this.clone(x))))};a=()=>{let g=this.backend.numDataIds();u=this.tidy(()=>d(this.backend,h));let x=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,g,x),x}}let{inputs:c,attrs:p}=t,m=P0(t)?null:t.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?e=a():(f=this.profiler.profileKernel(l,c,()=>a()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),e=f.outputs)}),o&&this.addTapeNode(l,c,e,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-i,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:e.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(u)?e:e[0]}saveTensorsForBackwardMode(t){return t.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(t,e,n){let o=v0(t);if(o!=null){let s=o.inputsToSave||[],i=o.outputsToSave||[],a;o.saveAllInputs?(_(Array.isArray(e),()=>"saveAllInputs is true, expected inputs to be an array."),a=Object.keys(e).map(l=>e[l])):a=s.map(l=>e[l]);let u=n.filter((l,c)=>i[c]);return a.concat(u)}return[]}makeTensor(t,e,n,o){if(t==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",o=o||this.backend;let s=t;n==="string"&&Vo(t[0])&&(s=t.map(u=>fu(u)));let i=o.write(s,e,n),a=new Lt(e,n,i,this.nextTensorId());if(this.trackTensor(a,o),n==="string"){let u=this.state.tensorInfo.get(i),l=b0(s);this.state.numBytes+=l-u.bytes,u.bytes=l}return a}makeTensorFromDataId(t,e,n,o){n=n||"float32";let s={dataId:t,shape:e,dtype:n};return this.makeTensorFromTensorInfo(s,o)}makeTensorFromTensorInfo(t,e){let{dataId:n,shape:o,dtype:s}=t,i=new Lt(o,s,n,this.nextTensorId());return this.trackTensor(i,e),i}makeVariable(t,e=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==t.dtype&&(t=t.cast(o));let s=new ml(t,e,n,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(t,e){this.state.numTensors++,t.dtype==="string"&&this.state.numStringTensors++;let n=0;t.dtype!=="complex64"&&t.dtype!=="string"&&(n=t.size*Tp(t.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(t.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(t.dataId,{backend:e||this.backend,dtype:t.dtype,shape:t.shape,bytes:n})),t instanceof ml||this.track(t)}incRef(t,e){this.trackTensor(t,e),this.backend.incRef(t.dataId)}removeDataId(t,e){this.state.tensorInfo.has(t)&&this.state.tensorInfo.get(t).backend===e&&(this.state.tensorInfo.delete(t),this.state.numDataBuffers--)}disposeTensor(t){if(!this.state.tensorInfo.has(t.dataId))return;let e=this.state.tensorInfo.get(t.dataId);if(this.state.numTensors--,t.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=e.bytes),t.dtype!=="complex64"&&t.dtype!=="string"){let n=t.size*Tp(t.dtype);this.state.numBytes-=n}e.backend.disposeData(t.dataId)&&this.removeDataId(t.dataId,e.backend)}disposeVariables(){for(let t in this.state.registeredVariables){let e=this.state.registeredVariables[t];this.disposeVariable(e)}}disposeVariable(t){this.disposeTensor(t),this.state.registeredVariables[t.name]!=null&&delete this.state.registeredVariables[t.name]}memory(){let t=this.backend.memory();return t.numTensors=this.state.numTensors,t.numDataBuffers=this.state.numDataBuffers,t.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(t.unreliable=!0,t.reasons==null&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t}async profile(t){this.state.profiling=!0;let e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of this.state.activeProfile.kernels)o.kernelTimeMs=await o.kernelTimeMs,o.extraInfo=await o.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(t,e,n,o,s,i){let a={id:this.state.nextTapeNodeId++,kernelName:t,inputs:e,outputs:n,saved:s},u=v0(t);u!=null&&(o=u.gradFunc),o!=null&&(a.gradient=l=>(l=l.map((c,p)=>{if(c==null){let m=n[p],f=Ep(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),o(l.length>1?l:l[0],s,i))),this.state.activeTape.push(a)}keep(t){return t.kept=!0,t}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(t){let e={track:[],name:"unnamed scope",id:this.state.nextScopeId++};t&&(e.name=t),this.state.scopeStack.push(e),this.state.activeScope=e}endScope(t){let e=ih(t),n=new Set(e.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let i=this.state.activeScope.track[s];!i.kept&&!n.has(i.id)&&i.dispose()}let o=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],e.forEach(s=>{!s.kept&&s.scopeId===o.id&&this.track(s)})}gradients(t,e,n,o=!1){if(_(e.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",t));_(s instanceof Lt,()=>"The result y returned by f() must be a tensor.");let i=K_(this.state.activeTape,e,s);if(!o&&i.length===0&&e.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 a={};a[s.id]=n==null?_K(s.shape):n,j_(a,i,l=>this.tidy(l),EK);let u=e.map(l=>a[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let c of l.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:u}})}customGrad(t){return _(_i(t),()=>"The f passed in customGrad(f) must be a function."),(...e)=>{_(e.every(a=>a instanceof Lt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,o={};e.forEach((a,u)=>{o[u]=a});let s=(a,u)=>(n=t(...e,u),_(n.value instanceof Lt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),_(_i(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),i=(a,u)=>{let l=n.gradFunc(a,u),c=Array.isArray(l)?l:[l];_(c.length===e.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(...)."),_(c.every(m=>m instanceof Lt),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:i,inputs:o})}}readSync(t){return this.state.tensorInfo.get(t).backend.readSync(t)}read(t){return this.state.tensorInfo.get(t).backend.read(t)}readToGPU(t,e){return this.state.tensorInfo.get(t).backend.readToGPU(t,e)}async time(t){let e=ac(),n=await this.backend.time(t);return n.wallMs=ac()-e,n}track(t){return this.state.activeScope!=null&&(t.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(t)),t}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new ox;for(let t in this.registry)this.disposeRegisteredKernels(t),this.registry[t].dispose(),delete this.registry[t];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};ah.nextTensorId=0;ah.nextVariableId=0;function _K(r){let t=Yd(jt(r),"float32");return T.makeTensor(t,r,"float32")}function L0(){let r=C0();if(r._tfengine==null){let t=new Zd(r);r._tfengine=new ah(t)}return T_(r._tfengine.ENV),J_(()=>r._tfengine),r._tfengine}var T=L0();function EK(r,t){let e={a:r,b:t};return T.runKernel(no,e)}var du={};Kt(du,{isBrowser:()=>B0,isMobile:()=>$K,mockIsMobile:()=>DK});function AK(){return typeof navigator!="undefined"&&navigator!=null}var z0;function DK(r){z0=r}function $K(r){if(z0!==void 0)return z0;if(r||AK()){if(r||(r=navigator),r.product==="ReactNative")return!0;let t=r.userAgent||r.vendor||(typeof window!="undefined"?window.opera:"");if(!t){let e=r;return e.userAgentData&&e.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(t)||/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(t.substr(0,4))}return!1}function B0(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var vn=L();vn.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.")});vn.registerFlag("IS_BROWSER",()=>B0());vn.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");vn.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));vn.registerFlag("IS_SAFARI",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Safari/.test(navigator.userAgent)&&/Apple/.test(navigator.vendor));vn.registerFlag("PROD",()=>!1);vn.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>vn.getBool("DEBUG"));vn.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);vn.registerFlag("IS_TEST",()=>!1);vn.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>vn.getBool("DEBUG"));vn.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);vn.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU",()=>!1);vn.registerFlag("USE_SETTIMEOUTCUSTOM",()=>!1);function Wr(r,t){let e=r;if(sr(r))return t==="string"?[]:[r.length];if(rx(r)){let o=r.channels||"RGBA";return[r.height,r.width*o.length]}else if(nx(r))return[r.buffer.size/(t==null?4:Tp(t))];if(!Array.isArray(r))return[];let n=[];for(;Array.isArray(e)||sr(e)&&t!=="string";)n.push(e.length),e=e[0];return Array.isArray(r)&&L().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&nE(r,n,[]),n}function nE(r,t,e){if(e=e||[],!Array.isArray(r)&&!sr(r)){_(t.length===0,()=>`Element arr[${e.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}_(t.length>0,()=>`Element arr[${e.join("][")}] should be a primitive, but is an array of ${r.length} elements`),_(r.length===t[0],()=>`Element arr[${e.join("][")}] should have ${t[0]} elements, but has ${r.length} elements`);let n=t.slice(1);for(let o=0;o<r.length;++o)nE(r[o],n,e.concat(o))}function rE(r,t,e,n){if(r!=="string_or_numeric"){if(r==null)throw new Error("Expected dtype cannot be null.");if(r!=="numeric"&&r!==t||r==="numeric"&&t==="string")throw new Error(`Argument '${e}' passed to '${n}' must be ${r} tensor, but got ${t} tensor`)}}function C(r,t,e,n="numeric"){if(r instanceof O())return rE(n,r.dtype,t,e),r;let o=Wl(r);if(o!=="string"&&["bool","int32","float32"].indexOf(n)>=0&&(o=n),rE(n,o,t,e),r==null||!sr(r)&&!Array.isArray(r)&&typeof r!="number"&&typeof r!="boolean"&&typeof r!="string"){let u=r==null?"null":r.constructor.name;throw new Error(`Argument '${t}' passed to '${e}' must be a Tensor or TensorLike, but got '${u}'`)}let s=Wr(r,o);!sr(r)&&!Array.isArray(r)&&(r=[r]);let a=o!=="string"?qp(r,o):si(r,[],!0);return T.makeTensor(a,s,o)}function fl(r,t,e,n="numeric"){if(!Array.isArray(r))throw new Error(`Argument ${t} passed to ${e} must be a \`Tensor[]\` or \`TensorLike[]\``);return r.map((s,i)=>C(s,`${t}[${i}]`,e,n))}var V0="__op";function k(r){let t=Object.keys(r);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);let e=t[0],n=r[e];e.endsWith("_")&&(e=e.substring(0,e.length-1)),e=e+V0;let o=(...s)=>{T.startScope(e);try{let i=n(...s);return tc(i)&&console.error("Cannot return a Promise inside of tidy."),T.endScope(i),i}catch(i){throw T.endScope(null),i}};return Object.defineProperty(o,"name",{value:e,configurable:!0}),o}function RK(r,t){let e=C(r,"real","complex"),n=C(t,"imag","complex");$e(e.shape,n.shape,`real and imag shapes, ${e.shape} and ${n.shape}, must match in call to tf.complex().`);let o={real:e,imag:n};return T.runKernel(Ap,o)}var Sn=k({complex_:RK});function ln(r,t,e,n){if(n==null)n=Wl(r);else if(n==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(nx(r)||rx(r)){if(n!=="float32"&&n!=="int32")throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${n}.`);return T.backend.createTensorFromGPUData(r,t||e,n)}if(!sr(r)&&!Array.isArray(r)&&typeof r!="number"&&typeof r!="boolean"&&typeof r!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(t!=null){Le(t);let o=jt(t),s=jt(e);_(o===s,()=>`Based on the provided shape, [${t}], the tensor should have ${o} values but has ${s}`);for(let i=0;i<e.length;++i){let a=e[i],u=i===e.length-1?a!==jt(t.slice(i)):!0;_(e[i]===t[i]||!u,()=>`Error creating a new Tensor. Inferred shape (${e}) does not match the provided shape (${t}). `)}}return!sr(r)&&!Array.isArray(r)&&(r=[r]),t=t||e,r=n!=="string"?qp(r,n):si(r,[],!0),T.makeTensor(r,t,n)}function ir(r,t,e){let n=Wr(r,e);return ln(r,t,n,e)}var dl={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};var Ur=class r{static join(t){return new r(t).slice()}constructor(t){if(this.shards=[],this.previousShardIndex=0,t==null||(t instanceof Array||(t=[t]),t=t.map(n=>sr(n)?n.buffer:n),t.length===0))return;this.bufferUniformSize=t[0].byteLength;let e=0;for(let n=0;n<t.length;n++){let o=t[n];n!==t.length-1&&o.byteLength!==this.bufferUniformSize&&(this.bufferUniformSize=void 0);let s=e+o.byteLength;this.shards.push({buffer:o,start:e,end:s}),e=s}this.shards.length===0&&(this.byteLength=0),this.byteLength=this.shards[this.shards.length-1].end}slice(t=0,e=this.byteLength){if(this.shards.length===0)return new ArrayBuffer(0);if(t=isNaN(Number(t))?0:t,e=isNaN(Number(e))?0:e,t=Math.max(0,t),e=Math.min(this.byteLength,e),e<=t)return new ArrayBuffer(0);let n=this.findShardForByte(t);if(n===-1)throw new Error(`Could not find start shard for byte ${t}`);let o=e-t,s=new ArrayBuffer(o),i=new Uint8Array(s),a=0;for(let u=n;u<this.shards.length;u++){let l=this.shards[u],p=t+a-l.start,m=a,d=Math.min(e,l.end)-l.start,h=new Uint8Array(l.buffer,p,d-p);if(i.set(h,m),a+=h.length,e<l.end)break}return s}findShardForByte(t){if(this.shards.length===0||t<0||t>=this.byteLength)return-1;if(this.bufferUniformSize!=null)return this.previousShardIndex=Math.floor(t/this.bufferUniformSize),this.previousShardIndex;function e(o){return t<o.start?-1:t>=o.end?1:0}if(e(this.shards[this.previousShardIndex])===0)return this.previousShardIndex;let n=FK(this.shards,e);return n===-1?-1:(this.previousShardIndex=n,this.previousShardIndex)}};function FK(r,t){let e=0,n=r.length;for(;e<=n;){let o=Math.floor((n-e)/2)+e,s=t(r[o]);if(s===0)return o;s<0?n=o:e=o+1}return-1}function Cdt(){L().set("PROD",!0)}function vdt(){L().set("DEBUG",!0)}function Sdt(){L().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function G0(r){L().getBool("DEPRECATION_WARNINGS_ENABLED")&&console.warn(r+" You can disable deprecation warnings with tf.disableDeprecationWarnings().")}tE(G0);function Ndt(){T.disposeVariables()}function Bn(){return T}function lh(){return T.memory()}function kdt(r){return T.profile(r)}function B(r,t){return T.tidy(r,t)}function Tt(r){ih(r).forEach(e=>e.dispose())}function De(r){return T.keep(r)}function Tdt(r){return T.time(r)}function OK(r){return T.setBackend(r)}function _dt(){return T.ready()}function oE(){return T.backendName}function Edt(r){T.removeBackend(r)}function Adt(r){return T.findBackend(r)}function Ddt(r){return T.findBackendFactory(r)}function Xp(r,t,e=1){return T.registerBackend(r,t,e)}function sx(){return T.backend}function $dt(r,t){L().setPlatform(r,t)}var hu=4;async function aE(r,t){let e=[],n=[],o=Array.isArray(r)?r.map(i=>i.name):Object.keys(r);for(let i=0;i<o.length;++i){let a=o[i],u=Array.isArray(r)?r[i].tensor:r[a];if(u.dtype!=="float32"&&u.dtype!=="int32"&&u.dtype!=="bool"&&u.dtype!=="string"&&u.dtype!=="complex64")throw new Error(`Unsupported dtype in weight '${a}': ${u.dtype}`);let l={name:a,shape:u.shape,dtype:u.dtype};if(u.dtype==="string"){let c=new Promise(async p=>{let m=await u.bytes(),f=m.reduce((g,x)=>g+x.length,0)+hu*m.length,d=new Uint8Array(f),h=0;for(let g=0;g<m.length;g++){let x=m[g],b=new Uint8Array(new Uint32Array([x.length]).buffer);d.set(b,h),h+=hu,d.set(x,h),h+=x.length}p(d)});n.push(c)}else n.push(u.data());t!=null&&(l.group=t),e.push(l)}let s=await Promise.all(n);return{data:LK(s),specs:e}}function ix(r,t){let e=new Ur(r),n={},o=0;for(let s of t){let i=MK(s,(a,u)=>e.slice(o+a,o+u));n[s.name]=lE(s,e.slice(o,o+i)),o+=i}return n}function MK(r,t){let e=jt(r.shape),n;if("quantization"in r){let o=r.quantization;n=dl[o.dtype]}else if(r.dtype==="string"){let o=0;for(let s=0;s<e;s++)o+=hu+new Uint32Array(t(o,o+hu))[0];return o}else n=dl[r.dtype];return e*n}async function PK(r,t){let e=jt(r.shape),n;if("quantization"in r){let o=r.quantization;n=dl[o.dtype]}else if(r.dtype==="string"){let o=0;for(let s=0;s<e;s++)o+=hu+new Uint32Array(await t(o,o+hu))[0];return o}else n=dl[r.dtype];return e*n}function lE(r,t){let e=r.name,n=r.dtype,o=r.shape,s=jt(o),i,a=0;if("quantization"in r){let u=r.quantization;if(u.dtype==="uint8"||u.dtype==="uint16"){if(!("min"in u&&"scale"in u))throw new Error(`Weight ${r.name} with quantization ${u.dtype} doesn't have corresponding metadata min and scale.`)}else if(u.dtype==="float16"){if(n!=="float32")throw new Error(`Weight ${r.name} is quantized with ${u.dtype} which only supports weights of type float32 not ${n}.`)}else throw new Error(`Weight ${r.name} has unknown quantization dtype ${u.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);let l=dl[u.dtype],c=u.dtype==="uint8"?new Uint8Array(t):new Uint16Array(t);if(n==="float32")if(u.dtype==="uint8"||u.dtype==="uint16"){i=new Float32Array(c.length);for(let p=0;p<c.length;p++){let m=c[p];i[p]=m*u.scale+u.min}}else if(u.dtype==="float16")i=GK()(c);else throw new Error(`Unsupported quantization type ${u.dtype} for weight type float32.`);else if(n==="int32"){if(u.dtype!=="uint8"&&u.dtype!=="uint16")throw new Error(`Unsupported quantization type ${u.dtype} for weight type int32.`);i=new Int32Array(c.length);for(let p=0;p<c.length;p++){let m=c[p];i[p]=Math.round(m*u.scale+u.min)}}else throw new Error(`Unsupported dtype in weight '${e}': ${n}`);a+=s*l}else if(n==="string"){let u=jt(r.shape);i=[];for(let l=0;l<u;l++){let c=new Uint32Array(t.slice(a,a+hu))[0];a+=hu;let p=new Uint8Array(t.slice(a,a+c));i.push(p),a+=c}}else{let u=dl[n];if(n==="float32")i=new Float32Array(t);else if(n==="int32")i=new Int32Array(t);else if(n==="bool")i=new Uint8Array(t);else if(n==="complex64"){i=new Float32Array(t);let l=new Float32Array(i.length/2),c=new Float32Array(i.length/2);for(let d=0;d<l.length;d++)l[d]=i[d*2],c[d]=i[d*2+1];let p=ir(l,o,"float32"),m=ir(c,o,"float32"),f=Sn(p,m);return p.dispose(),m.dispose(),f}else throw new Error(`Unsupported dtype in weight '${e}': ${n}`);a+=s*u}return ir(i,o,n)}async function sE(r,t,e){let n=new Uint8Array(t);for(;n.byteLength<e;){let{done:o,value:s}=await r.read();if(o&&s==null){let a=e-n.byteLength;throw new Error(`Reader is done but ${a} bytes are still expected`)}let i=new Uint8Array(n.length+s.byteLength);i.set(n,0),i.set(new Uint8Array(s),n.length),n=i}return n.buffer}async function ax(r,t){let e={},n=r.getReader(),o=new ArrayBuffer(0);for(let s of t){let i=await PK(s,async(l,c)=>(o=await sE(n,o,c),o.slice(l,c)));o=await sE(n,o,i);let a=o.slice(0,i);o=o.slice(i);let u=lE(s,a);if(e[s.name]=u,oE()==="webgpu"){let l=sx();"uploadToGPU"in l&&jt(u.shape)>=L().get("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD")&&l.uploadToGPU(u.dataId)}}return e}function LK(r){if(r===null)throw new Error(`Invalid input value: ${JSON.stringify(r)}`);let t=0,e=[];r.forEach(s=>{if(t+=s.byteLength,e.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 n=new Uint8Array(t),o=0;return e.forEach(s=>{n.set(new Uint8Array(s.buffer),o),o+=s.byteLength}),n.buffer}var W0=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function iE(r){return W0?Buffer.byteLength(r,"utf8"):new Blob([r]).size}function uE(r){if(W0)return Buffer.from(r).toString("base64");let t=new Uint8Array(r),e="";for(let n=0,o=t.length;n<o;n++)e+=String.fromCharCode(t[n]);return btoa(e)}function cE(r){if(W0){let n=Buffer.from(r,"base64");return n.buffer.slice(n.byteOffset,n.byteOffset+n.byteLength)}let t=atob(r),e=new Uint8Array(t.length);for(let n=0;n<t.length;++n)e.set([t.charCodeAt(n)],n);return e.buffer}function pE(r){return Ur.join(r)}function U0(r){let t="/";for(r=r.trim();r.endsWith(t);)r=r.slice(0,r.length-1);let e=r.split(t);return e[e.length-1]}function lx(r,t){let e={modelTopology:r.modelTopology,format:r.format,generatedBy:r.generatedBy,convertedBy:r.convertedBy,weightsManifest:t};return r.signature!=null&&(e.signature=r.signature),r.userDefinedMetadata!=null&&(e.userDefinedMetadata=r.userDefinedMetadata),r.modelInitializer!=null&&(e.modelInitializer=r.modelInitializer),r.initializerSignature!=null&&(e.initializerSignature=r.initializerSignature),r.trainingConfig!=null&&(e.trainingConfig=r.trainingConfig),e}function H0(r,t,e){let n={modelTopology:r.modelTopology,format:r.format,generatedBy:r.generatedBy,convertedBy:r.convertedBy};if(r.trainingConfig!=null&&(n.trainingConfig=r.trainingConfig),r.weightsManifest!=null){if(!t)throw new Error("modelJSON has weightsManifest but weightSpecs is null");if(!e)throw new Error("modelJSON has weightsManifest but weightData is null");n.weightSpecs=t,n.weightData=e}return r.signature!=null&&(n.signature=r.signature),r.userDefinedMetadata!=null&&(n.userDefinedMetadata=r.userDefinedMetadata),r.modelInitializer!=null&&(n.modelInitializer=r.modelInitializer),r.initializerSignature!=null&&(n.initializerSignature=r.initializerSignature),n}async function Yp(r,t){let e,n;return r.weightsManifest!=null&&([e,n]=await t(r.weightsManifest)),H0(r,e,n)}function Qi(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:iE(JSON.stringify(r.modelTopology)),weightSpecsBytes:r.weightSpecs==null?0:iE(JSON.stringify(r.weightSpecs)),weightDataBytes:r.weightData==null?0:new Ur(r.weightData).byteLength}}function uh(r){let t=[];for(let e of r)t.push(...e.weights);return t}function zK(){let r=e=>{let n=e<<13,o=0;for(;!(n&8388608);)o-=8388608,n<<=1;return n&=-8388609,o+=947912704,n|o},t=new Uint32Array(2048);t[0]=0;for(let e=1;e<1024;e++)t[e]=r(e);for(let e=1024;e<2048;e++)t[e]=939524096+(e-1024<<13);return t}function BK(){let r=new Uint32Array(64);r[0]=0,r[31]=1199570944,r[32]=2147483648,r[63]=3347054592;for(let t=1;t<31;t++)r[t]=t<<23;for(let t=33;t<63;t++)r[t]=2147483648+(t-32<<23);return r}function VK(){let r=new Uint32Array(64);for(let t=0;t<64;t++)r[t]=1024;return r[0]=r[32]=0,r}function GK(){let r=zK(),t=BK(),e=VK();return n=>{let o=new ArrayBuffer(4*n.length),s=new Uint32Array(o);for(let i=0;i<n.length;i++){let a=n[i],u=r[e[a>>10]+(a&1023)]+t[a>>10];s[i]=u}return new Float32Array(o)}}var Cr=class r{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return r.instance==null&&(r.instance=new r),r.instance}static registerSaveRouter(t){r.getInstance().saveRouters.push(t)}static registerLoadRouter(t){r.getInstance().loadRouters.push(t)}static getSaveHandlers(t){return r.getHandlers(t,"save")}static getLoadHandlers(t,e){return r.getHandlers(t,"load",e)}static getHandlers(t,e,n){let o=[];return(e==="load"?r.getInstance().loadRouters:r.getInstance().saveRouters).forEach(i=>{let a=i(t,n);a!==null&&o.push(a)}),o}},mE=r=>Cr.registerSaveRouter(r),fE=r=>Cr.registerLoadRouter(r),dE=r=>Cr.getSaveHandlers(r),hE=(r,t)=>Cr.getLoadHandlers(r,t);var q0="tensorflowjs",K0=1,uc="models_store",gu="model_info_store";function gE(){if(!L().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,t=r.indexedDB||r.mozIndexedDB||r.webkitIndexedDB||r.msIndexedDB||r.shimIndexedDB;if(t==null)throw new Error("The current browser does not appear to support IndexedDB.");return t}function j0(r){let t=r.result;t.createObjectStore(uc,{keyPath:"modelPath"}),t.createObjectStore(gu,{keyPath:"modelPath"})}var ta=class{constructor(t){if(this.indexedDB=gE(),t==null||!t)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=t}async save(t){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,t)}async load(){return this.databaseAction(this.modelPath)}databaseAction(t,e){return new Promise((n,o)=>{let s=this.indexedDB.open(q0,K0);s.onupgradeneeded=()=>j0(s),s.onsuccess=()=>{let i=s.result;if(e==null){let a=i.transaction(uc,"readonly"),l=a.objectStore(uc).get(this.modelPath);l.onsuccess=()=>{if(l.result==null)return i.close(),o(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));n(l.result.modelArtifacts)},l.onerror=c=>(i.close(),o(l.error)),a.oncomplete=()=>i.close()}else{e.weightData=Ur.join(e.weightData);let a=Qi(e),u=i.transaction(gu,"readwrite"),l=u.objectStore(gu),c;try{c=l.put({modelPath:this.modelPath,modelArtifactsInfo:a})}catch(m){return o(m)}let p;c.onsuccess=()=>{p=i.transaction(uc,"readwrite");let m=p.objectStore(uc),f;try{f=m.put({modelPath:this.modelPath,modelArtifacts:e,modelArtifactsInfo:a})}catch(d){return o(d)}f.onsuccess=()=>n({modelArtifactsInfo:a}),f.onerror=d=>{l=u.objectStore(gu);let h=l.delete(this.modelPath);h.onsuccess=()=>(i.close(),o(f.error)),h.onerror=g=>(i.close(),o(f.error))}},c.onerror=m=>(i.close(),o(c.error)),u.oncomplete=()=>{p==null?i.close():p.oncomplete=()=>i.close()}}},s.onerror=i=>o(s.error)})}};ta.URL_SCHEME="indexeddb://";var xE=r=>L().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(ta.URL_SCHEME)?WK(r.slice(ta.URL_SCHEME.length)):null;Cr.registerSaveRouter(xE);Cr.registerLoadRouter(xE);function WK(r){return new ta(r)}function UK(r){return r.startsWith(ta.URL_SCHEME)?r.slice(ta.URL_SCHEME.length):r}var ux=class{constructor(){this.indexedDB=gE()}async listModels(){return new Promise((t,e)=>{let n=this.indexedDB.open(q0,K0);n.onupgradeneeded=()=>j0(n),n.onsuccess=()=>{let o=n.result,s=o.transaction(gu,"readonly"),a=s.objectStore(gu).getAll();a.onsuccess=()=>{let u={};for(let l of a.result)u[l.modelPath]=l.modelArtifactsInfo;t(u)},a.onerror=u=>(o.close(),e(a.error)),s.oncomplete=()=>o.close()},n.onerror=o=>e(n.error)})}async removeModel(t){return t=UK(t),new Promise((e,n)=>{let o=this.indexedDB.open(q0,K0);o.onupgradeneeded=()=>j0(o),o.onsuccess=()=>{let s=o.result,i=s.transaction(gu,"readwrite"),a=i.objectStore(gu),u=a.get(t),l;u.onsuccess=()=>{if(u.result==null)return s.close(),n(new Error(`Cannot find model with path '${t}' in IndexedDB.`));{let c=a.delete(t),p=()=>{l=s.transaction(uc,"readwrite");let f=l.objectStore(uc).delete(t);f.onsuccess=()=>e(u.result.modelArtifactsInfo),f.onerror=d=>n(u.error)};c.onsuccess=p,c.onerror=m=>(p(),s.close(),n(u.error))}},u.onerror=c=>(s.close(),n(u.error)),i.oncomplete=()=>{l==null?s.close():l.oncomplete=()=>s.close()}},o.onerror=s=>n(o.error)})}};var hl="/",Zp="tensorflowjs_models",yE="info",HK="model_topology",qK="weight_specs",KK="weight_data",jK="model_metadata";function bE(r){return{info:[Zp,r,yE].join(hl),topology:[Zp,r,HK].join(hl),weightSpecs:[Zp,r,qK].join(hl),weightData:[Zp,r,KK].join(hl),modelMetadata:[Zp,r,jK].join(hl)}}function wE(r){for(let t of Object.values(r))window.localStorage.removeItem(t)}function XK(r){let t=r.split(hl);if(t.length<3)throw new Error(`Invalid key format: ${r}`);return t.slice(1,t.length-1).join(hl)}function YK(r){return r.startsWith(ea.URL_SCHEME)?r.slice(ea.URL_SCHEME.length):r}var ea=class{constructor(t){if(!L().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,t==null||!t)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=t,this.keys=bE(this.modelPath)}async save(t){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let e=JSON.stringify(t.modelTopology),n=JSON.stringify(t.weightSpecs),o=Qi(t),s=Ur.join(t.weightData);try{this.LS.setItem(this.keys.info,JSON.stringify(o)),this.LS.setItem(this.keys.topology,e),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,uE(s));let i={format:t.format,generatedBy:t.generatedBy,convertedBy:t.convertedBy,signature:t.signature!=null?t.signature:void 0,userDefinedMetadata:t.userDefinedMetadata!=null?t.userDefinedMetadata:void 0,modelInitializer:t.modelInitializer!=null?t.modelInitializer:void 0,initializerSignature:t.initializerSignature!=null?t.initializerSignature:void 0,trainingConfig:t.trainingConfig!=null?t.trainingConfig:void 0};return this.LS.setItem(this.keys.modelMetadata,JSON.stringify(i)),{modelArtifactsInfo:o}}catch(i){throw wE(this.keys),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${o.modelTopologyBytes}, weightSpecsBytes=${o.weightSpecsBytes}, weightDataBytes=${o.weightDataBytes}.`)}}}async load(){let t=JSON.parse(this.LS.getItem(this.keys.info));if(t==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(t.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let e={},n=JSON.parse(this.LS.getItem(this.keys.topology));if(n==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);e.modelTopology=n;let o=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(o==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);e.weightSpecs=o;let s=this.LS.getItem(this.keys.modelMetadata);if(s!=null){let a=JSON.parse(s);e.format=a.format,e.generatedBy=a.generatedBy,e.convertedBy=a.convertedBy,a.signature!=null&&(e.signature=a.signature),a.userDefinedMetadata!=null&&(e.userDefinedMetadata=a.userDefinedMetadata),a.modelInitializer!=null&&(e.modelInitializer=a.modelInitializer),a.initializerSignature!=null&&(e.initializerSignature=a.initializerSignature),a.trainingConfig!=null&&(e.trainingConfig=a.trainingConfig)}let i=this.LS.getItem(this.keys.weightData);if(i==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return e.weightData=cE(i),e}};ea.URL_SCHEME="localstorage://";var IE=r=>L().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(ea.URL_SCHEME)?ZK(r.slice(ea.URL_SCHEME.length)):null;Cr.registerSaveRouter(IE);Cr.registerLoadRouter(IE);function ZK(r){return new ea(r)}var cx=class{constructor(){_(L().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),_(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let t={},e=Zp+hl,n=hl+yE;for(let o=0;o<this.LS.length;++o){let s=this.LS.key(o);if(s.startsWith(e)&&s.endsWith(n)){let i=XK(s);t[i]=JSON.parse(this.LS.getItem(s))}}return t}async removeModel(t){t=YK(t);let e=bE(t);if(this.LS.getItem(e.info)==null)throw new Error(`Cannot find model at path '${t}'`);let n=JSON.parse(this.LS.getItem(e.info));return wE(e),n}};var Jp="://",ai=class r{constructor(){this.managers={}}static getInstance(){return r.instance==null&&(r.instance=new r),r.instance}static registerManager(t,e){_(t!=null,()=>"scheme must not be undefined or null."),t.endsWith(Jp)&&(t=t.slice(0,t.indexOf(Jp))),_(t.length>0,()=>"scheme must not be an empty string.");let n=r.getInstance();_(n.managers[t]==null,()=>`A model store manager is already registered for scheme '${t}'.`),n.managers[t]=e}static getManager(t){let e=r.getInstance().managers[t];if(e==null)throw new Error(`Cannot find model manager for scheme '${t}'`);return e}static getSchemes(){return Object.keys(r.getInstance().managers)}};function px(r){if(r.indexOf(Jp)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ai.getSchemes().join(",")}`);return{scheme:r.split(Jp)[0],path:r.split(Jp)[1]}}async function CE(r,t,e=!1){_(r!==t,()=>`Old path and new path are the same: '${r}'`);let n=Cr.getLoadHandlers(r);_(n.length>0,()=>`Copying failed because no load handler is found for source URL ${r}.`),_(n.length<2,()=>`Copying failed because more than one (${n.length}) load handlers for source URL ${r}.`);let o=n[0],s=Cr.getSaveHandlers(t);_(s.length>0,()=>`Copying failed because no save handler is found for destination URL ${t}.`),_(s.length<2,()=>`Copying failed because more than one (${n.length}) save handlers for destination URL ${t}.`);let i=s[0],a=px(r).scheme,u=px(r).path,l=a===px(r).scheme,c=await o.load();e&&l&&await ai.getManager(a).removeModel(u);let p=await i.save(c);return e&&!l&&await ai.getManager(a).removeModel(u),p.modelArtifactsInfo}async function vE(){let r=ai.getSchemes(),t={};for(let e of r){let n=await ai.getManager(e).listModels();for(let o in n){let s=e+Jp+o;t[s]=n[o]}}return t}async function SE(r){let t=px(r);return ai.getManager(t.scheme).removeModel(t.path)}async function NE(r,t){return CE(r,t,!1)}async function kE(r,t){return CE(r,t,!0)}var X0=class{constructor(){this.messageName="setTimeoutCustom",this.functionRefs=[],this.handledMessageCount=0,this.hasEventListener=!1}fetch(t,e){return fetch(t,e)}now(){return performance.now()}encode(t,e){if(e!=="utf-8"&&e!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${e}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(t)}decode(t,e){return new TextDecoder(e).decode(t)}setTimeoutCustom(t,e){if(typeof window=="undefined"||!L().getBool("USE_SETTIMEOUTCUSTOM")){setTimeout(t,e);return}this.functionRefs.push(t),setTimeout(()=>{window.postMessage({name:this.messageName,index:this.functionRefs.length-1},"*")},e),this.hasEventListener||(this.hasEventListener=!0,window.addEventListener("message",n=>{if(n.source===window&&n.data.name===this.messageName){n.stopPropagation();let o=this.functionRefs[n.data.index];o(),this.handledMessageCount++,this.handledMessageCount===this.functionRefs.length&&(this.functionRefs=[],this.handledMessageCount=0)}},!0))}isTypedArray(t){return Zg(t)}};if(L().get("IS_BROWSER")){L().setPlatform("browser",new X0);try{ai.registerManager(ea.URL_SCHEME,new cx)}catch(r){}try{ai.registerManager(ta.URL_SCHEME,new ux)}catch(r){}}var JK={importFetch:()=>TE()},Y0;var Z0=class{constructor(){this.util=_E(),this.textEncoder=new this.util.TextEncoder}fetch(t,e){return L().global.fetch!=null?L().global.fetch(t,e):(Y0==null&&(Y0=JK.importFetch()),Y0(t,e))}now(){let t=process.hrtime();return t[0]*1e3+t[1]/1e6}encode(t,e){if(e!=="utf-8"&&e!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${e}`);return this.textEncoder.encode(t)}decode(t,e){return t.length===0?"":new this.util.TextDecoder(e).decode(t)}isTypedArray(t){return this.util.types.isFloat32Array(t)||this.util.types.isInt32Array(t)||this.util.types.isUint8Array(t)||this.util.types.isUint8ClampedArray(t)}};L().get("IS_NODE")&&!L().get("IS_BROWSER")&&L().setPlatform("node",new Z0);function wt(r,t="float32",e){return t=t||"float32",Le(r),new le(r,t,e)}function QK(r,t){let e=C(r,"x","cast");if(!y0(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&e.dtype!=="string"||t!=="string"&&e.dtype==="string")throw new Error("Only strings can be casted to strings");let n={x:e},o={dtype:t};return T.runKernel(fo,n,o)}var J=k({cast_:QK});function tj(r){let e={x:C(r,"x","clone","string_or_numeric")};return T.runKernel(go,e)}var un=k({clone_:tj});function mx(r,t=!1){console.log(r.toString(t))}L0();var ej={buffer:wt,cast:J,clone:un,print:mx};Q_(ej);function rj(r,t){let e=C(r,"a","add"),n=C(t,"b","add");[e,n]=Xt(e,n);let o={a:e,b:n};return T.runKernel(no,o)}var K=k({add_:rj});function nj(r,t){let e=C(r,"a","floorDiv"),n=C(t,"b","floorDiv");[e,n]=Xt(e,n);let o={a:e,b:n};return T.runKernel(fs,o)}var Qp=k({floorDiv_:nj});function oj(r,t){let e=C(r,"a","div"),n=C(t,"b","div");if([e,n]=Xt(e,n),e.dtype==="int32"&&n.dtype==="int32")return Qp(e,n);let o={a:e,b:n},s={};return T.runKernel(as,o,s)}var ut=k({div_:oj});function sj(r,t){let e=C(r,"a","mul"),n=C(t,"b","mul");[e,n]=Xt(e,n);let o={a:e,b:n};return T.runKernel(Ds,o)}var $=k({mul_:sj});function ij(r){let t=C(r,"x","abs");if(t.dtype==="complex64"){let e={x:t};return T.runKernel(Kl,e)}else{let e={x:t};return T.runKernel(Ai,e)}}var _e=k({abs_:ij});function aj(r){let e={x:C(r,"x","acos")};return T.runKernel(Go,e)}var fx=k({acos_:aj});function lj(r){let e={x:C(r,"x","acosh")};return T.runKernel(Wo,e)}var dx=k({acosh_:lj});function uj(r){_(Array.isArray(r),()=>"The argument passed to tf.addN() must be a list of tensors"),_(r.length>=1,()=>`Must pass at least one tensor to tf.addN(), but got ${r.length}`);let t=r.map((o,s)=>C(o,`tensors${s}`,"addN")),e=t[0];t.forEach(o=>{if(o.dtype!==e.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(o=>{if(!sn(o.shape,e.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});let n=t;return T.runKernel(Uo,n)}var EE=k({addN_:uj});function cj(r,t=null,e=!1){let o={x:C(r,"x","all","bool")},s={axis:t,keepDims:e};return T.runKernel(Ea,o,s)}var tm=k({all_:cj});function pj(r,t=null,e=!1){let o={x:C(r,"x","any","bool")},s={axis:t,keepDims:e};return T.runKernel(Aa,o,s)}var cc=k({any_:pj});function mj(r,t=0){let n={x:C(r,"x","argMax")},o={axis:t};return T.runKernel(Di,n,o)}var ra=k({argMax_:mj});function fj(r,t=0){let n={x:C(r,"x","argMin")},o={axis:t};return T.runKernel($i,n,o)}var hx=k({argMin_:fj});function dj(r){let e={x:C(r,"x","asin")};return T.runKernel(Ho,e)}var gx=k({asin_:dj});function hj(r){let e={x:C(r,"x","asinh")};return T.runKernel(qo,e)}var xx=k({asinh_:hj});function gj(r){let e={x:C(r,"x","atan")};return T.runKernel(Ko,e)}var yx=k({atan_:gj});function xj(r,t){let e=C(r,"a","atan2"),n=C(t,"b","atan2");[e,n]=Xt(e,n);let o={a:e,b:n};return T.runKernel(Xo,o)}var bx=k({atan2_:xj});function yj(r){let e={x:C(r,"x","atanh")};return T.runKernel(jo,e)}var wx=k({atanh_:yj});function bj(r,t,e,n,o="NHWC",s){let i=r[3],a=[...t,i],u=DE(o);return pc(r,a,e,s,n,null,null,u)}function Q0(r,t,e,n,o,s,i="channelsLast"){let[a,u]=ch(t),l;if(i==="channelsLast")l=[a,u,r[3],r[3]];else if(i==="channelsFirst")l=[a,u,r[1],r[1]];else throw new Error(`Unknown dataFormat ${i}`);return pc(r,l,e,n,o,s,!1,i)}function wj(r,t,e,n,o,s,i="NDHWC"){let[a,u,l]=J0(t),c,p;if(i==="NDHWC")p="channelsLast",c=[a,u,l,r[4],r[4]];else if(i==="NCDHW")p="channelsFirst",c=[a,u,l,r[1],r[1]];else throw new Error(`Unknown dataFormat ${i}`);return AE(r,c,e,n,o,!1,p,s)}function pc(r,t,e,n,o,s,i=!1,a="channelsLast"){let[u,l,c,p]=[-1,-1,-1,-1];if(a==="channelsLast")[u,l,c,p]=r;else if(a==="channelsFirst")[u,p,l,c]=r;else throw new Error(`Unknown dataFormat ${a}`);let[m,f,,d]=t,[h,g]=ch(e),[x,b]=ch(n),w=em(m,x),I=em(f,b),{padInfo:N,outHeight:E,outWidth:A}=vj(o,l,c,h,g,w,I,s,a),D=i?d*p:d,F;return a==="channelsFirst"?F=[u,D,E,A]:a==="channelsLast"&&(F=[u,E,A,D]),{batchSize:u,dataFormat:a,inHeight:l,inWidth:c,inChannels:p,outHeight:E,outWidth:A,outChannels:D,padInfo:N,strideHeight:h,strideWidth:g,filterHeight:m,filterWidth:f,effectiveFilterHeight:w,effectiveFilterWidth:I,dilationHeight:x,dilationWidth:b,inShape:r,outShape:F,filterShape:t}}function AE(r,t,e,n,o,s=!1,i="channelsLast",a){let[u,l,c,p,m]=[-1,-1,-1,-1,-1];if(i==="channelsLast")[u,l,c,p,m]=r;else if(i==="channelsFirst")[u,m,l,c,p]=r;else throw new Error(`Unknown dataFormat ${i}`);let[f,d,h,,g]=t,[x,b,w]=J0(e),[I,N,E]=J0(n),A=em(f,I),D=em(d,N),F=em(h,E),{padInfo:M,outDepth:V,outHeight:G,outWidth:W}=Sj(o,l,c,p,x,b,w,A,D,F,a),q=s?g*m:g,H;return i==="channelsFirst"?H=[u,q,V,G,W]:i==="channelsLast"&&(H=[u,V,G,W,q]),{batchSize:u,dataFormat:i,inDepth:l,inHeight:c,inWidth:p,inChannels:m,outDepth:V,outHeight:G,outWidth:W,outChannels:q,padInfo:M,strideDepth:x,strideHeight:b,strideWidth:w,filterDepth:f,filterHeight:d,filterWidth:h,effectiveFilterDepth:A,effectiveFilterHeight:D,effectiveFilterWidth:F,dilationDepth:I,dilationHeight:N,dilationWidth:E,inShape:r,outShape:H,filterShape:t}}function Ij(r,t,e,n,o){n==null&&(n=tN(r,t,e));let s=r[0],i=r[1],a=ph((s-t+2*n)/e+1,o),u=ph((i-t+2*n)/e+1,o);return[a,u]}function Cj(r,t,e,n,o,s){o==null&&(o=tN(r,t[0],n[0]));let i=[0,0,0,e];for(let a=0;a<3;a++)r[a]+2*o>=t[a]&&(i[a]=ph((r[a]-t[a]+2*o)/n[a]+1,s));return i}function tN(r,t,e,n=1){let o=em(t,n);return Math.floor((r[0]*(e-1)-e+o)/2)}function ch(r){return typeof r=="number"?[r,r,r]:r.length===2?[r[0],r[1],1]:r}function J0(r){return typeof r=="number"?[r,r,r]:r}function em(r,t){return t<=1?r:r+(r-1)*(t-1)}function vj(r,t,e,n,o,s,i,a,u){let l,c,p;if(typeof r=="number"){l={top:r,bottom:r,left:r,right:r,type:r===0?"VALID":"NUMBER"};let f=Ij([t,e],s,n,r,a);c=f[0],p=f[1]}else if(r==="same"){c=Math.ceil(t/n),p=Math.ceil(e/o);let m=Math.max(0,(c-1)*n+s-t),f=Math.max(0,(p-1)*o+i-e),d=Math.floor(m/2),h=m-d,g=Math.floor(f/2),x=f-g;l={top:d,bottom:h,left:g,right:x,type:"SAME"}}else if(r==="valid")l={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-s+1)/n),p=Math.ceil((e-i+1)/o);else if(typeof r=="object"){let m=u==="channelsLast"?r[1][0]:r[2][0],f=u==="channelsLast"?r[1][1]:r[2][1],d=u==="channelsLast"?r[2][0]:r[3][0],h=u==="channelsLast"?r[2][1]:r[3][1];l={top:m,bottom:f,left:d,right:h,type:m===0&&f===0&&d===0&&h===0?"VALID":"EXPLICIT"},c=ph((t-s+m+f)/n+1,a),p=ph((e-i+d+h)/o+1,a)}else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:l,outHeight:c,outWidth:p}}function Sj(r,t,e,n,o,s,i,a,u,l,c){let p,m,f,d;if(r==="valid"&&(r=0),typeof r=="number"){p={top:r,bottom:r,left:r,right:r,front:r,back:r,type:r===0?"VALID":"NUMBER"};let g=Cj([t,e,n,1],[a,u,l],1,[o,s,i],r,c);m=g[0],f=g[1],d=g[2]}else if(r==="same"){m=Math.ceil(t/o),f=Math.ceil(e/s),d=Math.ceil(n/i);let h=(m-1)*o+a-t,g=(f-1)*s+u-e,x=(d-1)*i+l-n,b=Math.floor(h/2),w=h-b,I=Math.floor(g/2),N=g-I,E=Math.floor(x/2),A=x-E;p={top:I,bottom:N,left:E,right:A,front:b,back:w,type:"SAME"}}else throw Error(`Unknown padding parameter: ${r}`);return{padInfo:p,outDepth:m,outHeight:f,outWidth:d}}function ph(r,t){if(!t)return Math.trunc(r);switch(t){case"round":return Math.round(r);case"ceil":return Math.ceil(r);case"floor":return Math.floor(r);default:throw new Error(`Unknown roundingMode ${t}`)}}function io(r){let[t,e,n]=ch(r);return t===1&&e===1&&n===1}function Dr(r,t){return io(r)||io(t)}function na(r){return ch(r).every(t=>t>0)}function DE(r){if(r==="NHWC")return"channelsLast";if(r==="NCHW")return"channelsFirst";throw new Error(`Unknown dataFormat ${r}`)}function Se(r,t,e){if(e!=null){if(typeof t=="string")throw Error(`Error in ${r}: pad must be an integer when using dimRoundingMode ${e} but got pad ${t}.`);if(typeof t=="number")_(_a(t),()=>`Error in ${r}: pad must be an integer when using dimRoundingMode ${e} but got pad ${t}.`);else if(typeof t=="object")t.forEach(n=>{n.forEach(o=>{_(_a(o),()=>`Error in ${r}: pad must be an integer when using dimRoundingMode ${e} but got pad ${o}.`)})});else throw Error(`Error in ${r}: Unknown padding parameter: ${t}`)}}function Nj(r,t){let n={x:C(r,"x","reshape","string_or_numeric")},o={shape:t};return T.runKernel(Gi,n,o)}var R=k({reshape_:Nj});function kj(r,t,e,n,o){let s=C(r,"x","avgPool","float32"),i=1;_(Dr(e,i),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${e} and dilations '${i}'`);let a=s,u=!1;s.rank===3&&(u=!0,a=R(s,[1,s.shape[0],s.shape[1],s.shape[2]])),_(a.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${a.rank}.`),Se("avgPool",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o},p=T.runKernel(Yo,l,c);return p=J(p,s.dtype),u?R(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var xu=k({avgPool_:kj});function Tj(r,t,e,n,o,s="NDHWC"){let i=C(r,"x","avgPool3d","float32"),a=i,u=!1;i.rank===4&&(u=!0,a=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),_(a.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${a.rank}.`),_(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),_(typeof e=="number"&&e>0||Array.isArray(e)&&e[0]>0&&e[1]>0&&e[2]>0,()=>`Error in avgPool3d: Stride must be > 0, but got '${e}'`),Se("avgPool3d",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o,dataFormat:s},p=T.runKernel(Ri,l,c);return p=J(p,a.dtype),u?R(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Ix=k({avgPool3d_:Tj});function _j(r,t=0){_(r.length>=1,()=>"Pass at least one tensor to concat");let e=fl(r,"tensors","concat","string_or_numeric");if(e[0].dtype==="complex64"&&e.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${s.dtype}. `)}),e.length===1)return un(e[0]);let n=e,o={axis:t};return T.runKernel(Oi,n,o)}var ie=k({concat_:_j});function Ej(r,t,e=!1,n=!1){let o=C(r,"a","matMul"),s=C(t,"b","matMul");[o,s]=Xt(o,s);let i={a:o,b:s},a={transposeA:e,transposeB:n};return T.runKernel(Zo,i,a)}var Bt=k({matMul_:Ej});function Aj(r){let e={x:C(r,"x","sigmoid","float32")};return T.runKernel(Xs,e)}var en=k({sigmoid_:Aj});function Dj(r,t,e){let n=C(r,"x","slice","string_or_numeric");if(n.rank===0)throw new Error("Slicing scalar is not possible");let o={x:n},s={begin:t,size:e};return T.runKernel(Ui,o,s)}var Ot=k({slice_:Dj});function $j(r){let e={x:C(r,"x","tanh","float32")};return T.runKernel(ni,e)}var li=k({tanh_:$j});function Rj(r,t,e,n,o,s){let i=C(r,"forgetBias","basicLSTMCell"),a=C(t,"lstmKernel","basicLSTMCell"),u=C(e,"lstmBias","basicLSTMCell"),l=C(n,"data","basicLSTMCell"),c=C(o,"c","basicLSTMCell"),p=C(s,"h","basicLSTMCell"),m=ie([l,p],1),f=Bt(m,a),d=K(f,u),h=d.shape[0],g=d.shape[1]/4,x=[h,g],b=Ot(d,[0,0],x),w=Ot(d,[0,g],x),I=Ot(d,[0,g*2],x),N=Ot(d,[0,g*3],x),E=K($(en(b),li(w)),$(c,en(K(i,I)))),A=$(li(E),en(N));return[E,A]}var $E=k({basicLSTMCell_:Rj});function Fj(r,t,e){let n=C(r,"x","batchToSpaceND"),o=t.reduce((a,u)=>a*u);_(n.rank>=1+t.length,()=>`input rank is ${n.rank} but should be > than blockShape.length ${t.length}`),_(e.length===t.length,()=>`crops.length is ${e.length} but should be equal to blockShape.length  ${t.length}`),_(n.shape[0]%o===0,()=>`input tensor batch is ${n.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${o}`);let s={x:n},i={blockShape:t,crops:e};return T.runKernel(Fi,s,i)}var yu=k({batchToSpaceND_:Fj});function RE(r){let t;return r.rank===0||r.rank===1?t=R(r,[1,1,1,r.size]):r.rank===2?t=R(r,[1,1,r.shape[0],r.shape[1]]):r.rank===3?t=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]):t=r,t}function Oj(r,t,e,n,o,s){s==null&&(s=.001);let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;n!=null&&(c=C(n,"offset","batchNorm")),_(a.rank===u.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_(c==null||a.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_(l==null||a.rank===l.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let m={x:RE(i),scale:l,offset:c,mean:a,variance:u},f={varianceEpsilon:s},d=T.runKernel(ds,m,f);return R(d,i.shape)}var oa=k({batchNorm_:Oj});function Mj(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),_(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),_(a.rank===2||a.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${a.rank}.`),_(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),oa(i,a,u,c,l,s)}var Cx=k({batchNorm2d_:Mj});function Pj(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),_(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),_(a.rank===3||a.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${a.rank}.`),_(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),oa(i,a,u,c,l,s)}var vx=k({batchNorm3d_:Pj});function Lj(r,t,e,n,o,s){let i=C(r,"x","batchNorm"),a=C(t,"mean","batchNorm"),u=C(e,"variance","batchNorm"),l;o!=null&&(l=C(o,"scale","batchNorm"));let c;return n!=null&&(c=C(n,"offset","batchNorm")),_(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),_(a.rank===4||a.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${a.rank}.`),_(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&_(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&_(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),oa(i,a,u,c,l,s)}var Sx=k({batchNorm4d_:Lj});function zj(r,t,e){let n=C(r,"x","bincount"),o=C(t,"weights","bincount");_(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(o.size===n.size||o.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${n.shape}, weights shape: ${o.shape}.`);let s={x:n,weights:o},i={size:e};return T.runKernel(Da,s,i)}var Nx=k({bincount_:zj});function Bj(r,t){let e=C(r,"x","bitwiseAnd"),n=C(t,"y","bitwiseAnd");if(!sn(e.shape,n.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${e.shape}, y: ${n.shape}`);if(e.dtype!=="int32"||n.dtype!=="int32")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${e.dtype} and type of y: ${n.dtype}`);let o={a:e,b:n};return T.runKernel($a,o)}var FE=k({bitwiseAnd_:Bj});function Vj(r,t){let e=C(r,"s0","broadcastArgs","int32"),n=C(t,"s1","broadcastArgs","int32");if(e.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${e.rank}`);if(n.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${n.rank}`);let o={s0:e,s1:n};return T.runKernel(ql,o)}var OE=k({broadcastArgs_:Vj});function Gj(r,t){let e=C(r,"broadcastTo","x"),n=e.shape;if(Le(t),t.length<e.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${e.rank}.`);if(t.length>e.rank){let l=e.shape.slice();for(;l.length<t.length;)l.unshift(1);e=R(e,l)}let o=e.shape,s=Array.from(t);for(let l=t.length-1;l>=0;l--)if(o[l]===t[l])s[l]=1;else if(e.shape[l]!==1)throw new Error(`broadcastTo(): [${n}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return un(e);let a={x:e},u={reps:s};return T.runKernel(oo,a,u)}var sa=k({broadcastTo_:Gj});function Wj(r){let e={x:C(r,"x","ceil","float32")};return T.runKernel(Jo,e)}var kx=k({ceil_:Wj});function Co(r,t,e){Le(r),e=e||Wl(t);let n={shape:r,value:t,dtype:e};return T.runKernel(Jl,{},n)}function Uj(r,t,e){let n=C(r,"x","clipByValue");if(_(t<=e,()=>`Error in clip: min (${t}) must be less than or equal to max (${e}).`),t===e)return Co(n.shape,t,n.dtype);let o={x:n},s={clipValueMin:t,clipValueMax:e};return T.runKernel(ho,o,s)}var vr=k({clipByValue_:Uj});function Hj(r){return ie(r,0)}var Tx=k({concat1d_:Hj});function qj(r,t){return ie(r,t)}var _x=k({concat2d_:qj});function Kj(r,t){return ie(r,t)}var Ex=k({concat3d_:Kj});function jj(r,t){return ie(r,t)}var Ax=k({concat4d_:jj});function Xj(r,t,e,n,o="NHWC",s=[1,1],i){let a=C(r,"x","conv2d","float32"),u=C(t,"filter","conv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(l.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${l.rank}.`),_(u.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`),Se("conv2d",n,i);let p=o==="NHWC"?l.shape[3]:l.shape[1];_(p===u.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`),_(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),_(na(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),_(na(e),()=>"Error in conv2D: Strides should be larger than 0.");let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=T.runKernel(Qo,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Nn=k({conv2d_:Xj});function Yj(r,t,e,n,o="NWC",s=1,i){let a=C(r,"x","conv1d"),u=C(t,"filter","conv1d"),l=a,c=!1;a.rank===2&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1]])),_(l.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${l.rank}.`),_(u.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`),Se("conv1d",n,i),_(l.shape[2]===u.shape[1],()=>`Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`),_(Dr(e,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${e} and dilation '${s}'`),_(na(s),()=>"Error in conv1D: Dilated rates should be larger than 0."),_(na(e),()=>"Error in conv1D: Stride should be larger than 0."),_(o==="NWC",()=>`Error in conv1d: got dataFormat of ${o} but only NWC is currently supported.`);let p=R(u,[1,u.shape[0],u.shape[1],u.shape[2]]),m=R(l,[l.shape[0],1,l.shape[1],l.shape[2]]),g=Nn(m,p,[1,e],n,"NHWC",[1,s],i);return c?R(g,[g.shape[2],g.shape[3]]):R(g,[g.shape[0],g.shape[2],g.shape[3]])}var rm=k({conv1d_:Yj});function Zj(r,t,e,n,o,s="NHWC",i){_(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let a=r,u=t,l=!1;t.rank===3&&(l=!0,u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]),a=[1,r[0],r[1],r[2]]),_(a.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${a.length}.`),_(u.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`),_(e.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${e.rank}`);let c=s==="NHWC"?a[3]:a[1],p=s==="NHWC"?u.shape[3]:u.shape[1];_(c===e.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${e.shape[2]}.`),_(p===e.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${e.shape[3]}.`),Se("conv2dDerInput",o,i);let m={dy:u,filter:e},f={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,inputShape:a},d=T.runKernel(ts,m,f);return l?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var nm=k({conv2DBackpropInput_:Zj});function Jj(r,t,e,n,o,s){let i=C(r,"x","conv2dTranspose"),a=C(t,"filter","conv2dTranspose");return nm(e,i,a,n,o,"NHWC",s)}var om=k({conv2dTranspose_:Jj});function Qj(r,t,e,n,o="NDHWC",s=[1,1,1]){let i=C(r,"x","conv3d"),a=C(t,"filter","conv3d"),u=i,l=!1;i.rank===4&&(l=!0,u=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),_(u.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`),_(a.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${a.rank}.`),_(u.shape[4]===a.shape[3],()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${a.shape[3]}.`),_(Dr(e,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),_(o==="NDHWC",()=>`Error in conv3d: got dataFormat of ${o} but only NDHWC is currently supported.`),_(na(s),()=>"Error in conv3D: Dilated rates should be larger than 0."),_(na(e),()=>"Error in conv3D: Strides should be larger than 0.");let c={x:u,filter:a},p={strides:e,pad:n,dataFormat:o,dilations:s},m=T.runKernel(es,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var Dx=k({conv3d_:Qj});function t6(r,t,e,n,o){_(r.length===t.rank,()=>`Length of inShape (${r.length}) and rank of dy (${t.rank}) must match`);let s=r,i=t,a=!1;t.rank===4&&(a=!0,i=R(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let u=s[4],l=i.shape[4];_(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),_(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),_(e.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${e.rank}`),_(u===e.shape[3],()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${e.shape[3]}.`),_(l===e.shape[4],()=>`Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${e.shape[4]}.`);let c={dy:i,filter:e},p={pad:o,strides:n,inputShape:s},m=T.runKernel(Fa,c,p);return a?R(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var $x=k({conv3DBackpropInput_:t6});function e6(r,t,e,n,o){let s=C(r,"x","conv3dTranspose"),i=C(t,"filter","conv3dTranspose");return $x(e,s,i,n,o)}var Rx=k({conv3dTranspose_:e6});function r6(r){let e={x:C(r,"x","cos","float32")};return T.runKernel(rs,e)}var bu=k({cos_:r6});function n6(r){let e={x:C(r,"x","cosh","float32")};return T.runKernel(ns,e)}var sm=k({cosh_:n6});function o6(r,t=0,e=!1,n=!1){let s={x:C(r,"x","cumprod")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(Oa,s,i)}var mc=k({cumprod_:o6});function s6(r,t=0,e=!1,n=!1){let s={x:C(r,"x","cumsum")},i={axis:t,exclusive:e,reverse:n};return T.runKernel(os,s,i)}var im=k({cumsum_:s6});function i6(r,t,e,n=!1){let o=C(r,"x","denseBincount"),s=C(t,"weights","denseBincount");_(o.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${o.dtype}`),_(o.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${o.rank}.`),_(e>=0,()=>`size must be non-negative, but got ${e}.`),_(s.size===o.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${o.shape}, weights shape: ${s.shape}.`);let i={x:o,weights:s},a={size:e,binaryOutput:n};return T.runKernel(jl,i,a)}var mh=k({denseBincount_:i6});function a6(r,t,e="NHWC"){let n=C(r,"x","depthToSpace","float32"),o=e==="NHWC"?n.shape[1]:n.shape[2],s=e==="NHWC"?n.shape[2]:n.shape[3],i=e==="NHWC"?n.shape[3]:n.shape[1];_(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),_(o*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${o} and ${t}  for depthToSpace with input shape
    ${n.shape}`),_(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
    ${s} and ${t} for depthToSpace with input shape
        ${n.shape}`),_(i%(t*t)===0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${n.shape}`);let a={x:n},u={blockSize:t,dataFormat:e};return T.runKernel(Pa,a,u)}var Fx=k({depthToSpace_:a6});function l6(r,t,e,n,o="NHWC",s=[1,1],i){let a=C(r,"x","depthwiseConv2d","float32"),u=C(t,"filter","depthwiseConv2d","float32"),l=a,c=!1;a.rank===3&&(c=!0,l=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(l.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`),_(u.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`);let p=o==="NHWC"?l.shape[3]:l.shape[1];_(p===u.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${p}) must match the inChannels dimension in filter ${u.shape[2]}.`),Se("depthwiseConv2d",n,i);let m={x:l,filter:u},f={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i},d=T.runKernel(ss,m,f);return c?R(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var ia=k({depthwiseConv2d_:l6});function u6(r){let e={x:C(r,"x","diag")};return T.runKernel(Xl,e)}var ME=k({diag_:u6});function c6(r,t,e,n,o=[1,1],s="NHWC"){let i=C(r,"x","dilation2d"),a=C(t,"filter","dilation2d");_(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),_(a.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${a.rank}.`),_(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let u=i,l=!1;i.rank===3&&(u=R(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=!0),_(u.shape[3]===a.shape[2],()=>`Error in dilation2d:  input and filter must have the same depth: ${u.shape[3]} vs ${a.shape[2]}`);let c={x:u,filter:a},p={strides:e,pad:n,dilations:o},m=T.runKernel(is,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Ox=k({dilation2d_:c6});var Hr={};Kt(Hr,{assertAndGetBroadcastShape:()=>Mt,getBroadcastDims:()=>PE,getReductionAxes:()=>we});function PE(r,t){let e=r.length,n=[];for(let o=0;o<e;o++){let s=e-1-o,i=r[s]||1;(t[t.length-1-o]||1)>1&&i===1&&n.unshift(s)}return n}function we(r,t){let e=[];for(let n=0;n<t.length;n++){let o=r[r.length-n-1],s=t.length-n-1,i=t[s];(o==null||o===1&&i>1)&&e.unshift(s)}return e}function Mt(r,t){let e=Math.max(r.length,t.length),n=new Array(e);for(let o=0;o<e;o++){let s=r[r.length-o-1];s==null&&(s=1);let i=t[t.length-o-1];if(i==null&&(i=1),s===1)n[e-o-1]=i;else if(i===1)n[e-o-1]=s;else if(s!==i){let a=`Operands could not be broadcast together with shapes ${r} and ${t}.`;throw Error(a)}else n[e-o-1]=s}return n}function p6(r,t){let e=C(r,"a","equal","string_or_numeric"),n=C(t,"b","equal","string_or_numeric");[e,n]=Xt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(za,o)}var $r=k({equal_:p6});function m6(r,t,e){let n=C(t,"a","where"),o=C(e,"b","where"),s=C(r,"condition","where","bool"),i=Mt(Mt(s.shape,n.shape),o.shape),a=sa(s,i),u=sa(n,i),l=sa(o,i),c={condition:a,t:u,e:l};return T.runKernel(Wi,c)}var Ie=k({where_:m6});function f6(r){let e={x:C(r,"x","zerosLike")};return T.runKernel(ji,e)}var vt=k({zerosLike_:f6});function d6(r,t){let e=C(r,"a","div"),n=C(t,"b","div");[e,n]=Xt(e,n);let o=ut(e,n),s=vt(o),i=$r(n,s);return Ie(i,s,o)}var Mx=k({divNoNan_:d6});function h6(r,t){let e=C(r,"t1","dot"),n=C(t,"t2","dot");_((e.rank===1||e.rank===2)&&(n.rank===1||n.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${e.rank} and ${n.rank}.`);let o=e.rank===1?e.size:e.shape[1],s=n.rank===1?n.size:n.shape[0];if(_(o===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${o} and ${s}.`),e.rank===1&&n.rank===1){let i=R(e,[1,-1]),a=R(n,[-1,1]),u=Bt(i,a);return R(u,[])}else if(e.rank===1&&n.rank===2){let i=R(e,[1,-1]),a=R(n,[n.shape[0],n.shape[1]]),u=Bt(i,a);return R(u,[u.size])}else if(e.rank===2&&n.rank===1){let i=R(n,[-1,1]),a=Bt(e,i);return R(a,[a.size])}else{let i=R(n,[n.shape[0],n.shape[1]]);return Bt(e,i)}}var Px=k({dot_:h6});function g6(r,...t){let e=t.map((o,s)=>C(o,`tensors${s}`,"einsum")),n={equation:r};return T.runKernel(Fp,e,n)}var wu=k({einsum_:g6});function x6(r){let e={x:C(r,"x","elu","float32")};return T.runKernel(ls,e)}var aa=k({elu_:x6});function y6(r,t){let e=C(r,"x","ensureShape","string_or_numeric");if(!d0(e.shape,t))throw new Error(`EnsureShape: Shape of tensor ${e.shape} is not compatible with expected shape ${t}`);return r}var LE=k({ensureShape_:y6});function b6(r){let t=C(r,"x","erf");_(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=J(t,"float32"));let e={x:t};return T.runKernel(us,e)}var am=k({erf_:b6});function eN(r,t){for(let e=0;e<r.length;++e)if(r[r.length-e-1]!==t-1-e)return!1;return!0}function zE(r,t,e){let n=r.length+t.length,o=[],s=0,i=0;for(let a=0;a<n;a++)e.indexOf(a)===-1?o.push(r[s++]):o.push(t[i++]);return o}function rN(r,t){let e=[],n=r.length;for(let s=0;s<n;s++)t.indexOf(s)===-1&&e.push(r[s]);let o=t.map(s=>r[s]);return[e,o]}function vo(r,t){let e=t.map(n=>1);return zE(r,e,t)}function w6(r,t,e){_(eN(t,e),()=>`${r} supports only inner-most axes for now. Got axes ${t} and rank-${e} input.`)}function nN(r,t){if(eN(r,t))return null;let e=[];for(let n=0;n<t;++n)r.indexOf(n)===-1&&e.push(n);return r.forEach(n=>e.push(n)),e}function fh(r){return r.map((t,e)=>[e,t]).sort((t,e)=>t[1]-e[1]).map(t=>t[0])}function I6(r,t){let e=[];for(let n=t-r;n<t;++n)e.push(n);return e}function C6(r,t=null,e=!1){let o={x:C(r,"x","max")},s={reductionIndices:t,keepDims:e};return T.runKernel(vs,o,s)}var Sr=k({max_:C6});function v6(r,t=null,e=!1){let o={x:C(r,"x","min")},s={axis:t,keepDims:e};return T.runKernel(Ts,o,s)}var gl=k({min_:v6});function S6(r,t){let e=C(r,"base","pow"),n=C(t,"exp","pow");[e,n]=Xt(e,n);let o={a:e,b:n};return T.runKernel(Fs,o)}var qr=k({pow_:S6});function pt(r,t){if((sr(r)&&t!=="string"||Array.isArray(r))&&t!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(t==="string"&&sr(r)&&!(r instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return ln(r,[],[],t)}function N6(r){let e={x:C(r,"x","sqrt","float32")};return T.runKernel(Zs,e)}var ge=k({sqrt_:N6});function k6(r){let t=C(r,"x","square"),e={};return T.runKernel("Square",{x:t},e)}var Wt=k({square_:k6});function T6(r,t=null,e=!1){let n=C(r,"x","sum");n.dtype==="bool"&&(n=J(n,"int32"));let o={x:n},s={axis:t,keepDims:e};return T.runKernel(Js,o,s)}var mt=k({sum_:T6});function _6(r,t="euclidean",e=null,n=!1){r=C(r,"x","norm");let o=BE(r,t,e),s=o.shape;if(n){let i=fr(e,r.shape);s=vo(o.shape,i)}return R(o,s)}function BE(r,t,e=null){if(r.rank===0)return _e(r);if(r.rank!==1&&e===null)return BE(R(r,[-1]),t,e);if(r.rank===1||typeof e=="number"||Array.isArray(e)&&e.length===1){if(t===1)return mt(_e(r),e);if(t===1/0)return Sr(_e(r),e);if(t===-1/0)return gl(_e(r),e);if(t==="euclidean"||t===2)return ge(mt(qr(_e(r),pt(2,"int32")),e));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(e)&&e.length===2){if(t===1)return Sr(mt(_e(r),e[0]),e[1]-1);if(t===1/0)return Sr(mt(_e(r),e[1]),e[0]);if(t===-1/0)return gl(mt(_e(r),e[1]),e[0]);if(t==="fro"||t==="euclidean")return ge(mt(Wt(r),e));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${e}`)}var xl=k({norm_:_6});function E6(r,t=null,e=!1){return xl(r,"euclidean",t,e)}var Lx=k({euclideanNorm_:E6});function A6(r){let e={x:C(r,"x","exp")};return T.runKernel(cs,e)}var Ke=k({exp_:A6});function D6(r,t=0){let e=C(r,"x","expandDims","string_or_numeric");_(t<=e.rank,()=>"Axis must be <= rank of the tensor");let n={input:e},o={dim:t};return T.runKernel(Mi,n,o)}var je=k({expandDims_:D6});function $6(r){let e={x:C(r,"x","expm1")};return T.runKernel(ps,e)}var zx=k({expm1_:$6});function R6(r,t){let e=C(r,"x","tile","string_or_numeric");_(e.rank===t.length,()=>`Error in transpose: rank of input ${e.rank} must match length of reps ${t}.`);let n={x:e},o={reps:t};return T.runKernel(oo,n,o)}var Rr=k({tile_:R6});function F6(r,t,e,n="float32"){t==null&&(t=r);let o=wt([r,t],n),s=r<=t?r:t;for(let a=0;a<s;++a)o.set(1,a,a);let i=R(o.toTensor(),[r,t]);if(e==null)return i;if(e.length===1)return Rr(je(i,0),[e[0],1,1]);if(e.length===2)return Rr(je(je(i,0),0),[e[0],e[1],1,1]);if(e.length===3)return Rr(je(je(je(i,0),0),0),[e[0],e[1],e[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${e.length}D.`)}var fc=k({eye_:F6});function O6(r){let e={x:C(r,"x","floor","float32")};return T.runKernel(ms,e)}var la=k({floor_:O6});function M6(r,t,e=0,n=0){let o=C(r,"x","gather"),s=C(t,"indices","gather","int32"),i={x:o,indices:s},a={axis:e,batchDims:n};return T.runKernel(Pi,i,a)}var ua=k({gather_:M6});function P6(r,t){let e=C(r,"a","greater","string_or_numeric"),n=C(t,"b","greater","string_or_numeric");[e,n]=Xt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(Ga,o)}var Re=k({greater_:P6});function L6(r,t){let e=C(r,"a","greaterEqual","string_or_numeric"),n=C(t,"b","greaterEqual","string_or_numeric");[e,n]=Xt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(hs,o)}var cn=k({greaterEqual_:L6});function z6(r){let e={input:C(r,"input","imag")};return T.runKernel(Pp,e)}var Iu=k({imag_:z6});function B6(r){let e={x:C(r,"x","isFinite")};return T.runKernel(gs,e)}var Bx=k({isFinite_:B6});function V6(r){let e={x:C(r,"x","isInf")};return T.runKernel(xs,e)}var Vx=k({isInf_:V6});function G6(r){let e={x:C(r,"x","isNaN")};return T.runKernel(ys,e)}var Gx=k({isNaN_:G6});function W6(r,t=.2){let n={x:C(r,"x","leakyRelu")},o={alpha:t};return T.runKernel(bs,n,o)}var Cu=k({leakyRelu_:W6});function U6(r,t){let e=C(r,"a","less","string_or_numeric"),n=C(t,"b","less","string_or_numeric");[e,n]=Xt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(Wa,o)}var yl=k({less_:U6});function H6(r,t){let e=C(r,"a","lessEqual","string_or_numeric"),n=C(t,"b","lessEqual","string_or_numeric");[e,n]=Xt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(Ua,o)}var Vn=k({lessEqual_:H6});function VE(r,t,e){if(e<=0)throw new Error("The number of values should be positive.");let n={start:r,stop:t,num:e};return T.runKernel(Ha,{},n)}function q6(r,t=5,e=1,n=1,o=.5){let s=C(r,"x","localResponseNormalization");_(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${s.rank}.`),_(_a(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,a=!1;s.rank===3&&(a=!0,i=R(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let u={x:i},l={depthRadius:t,bias:e,alpha:n,beta:o},c=T.runKernel(Cs,u,l);return a?R(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Wx=k({localResponseNormalization_:q6});function K6(r){let e={x:C(r,"x","log","float32")};return T.runKernel(ws,e)}var Nr=k({log_:K6});function j6(r){let e={x:C(r,"x","log1p")};return T.runKernel(Is,e)}var vu=k({log1p_:j6});function X6(r){return _(_i(r),()=>"The f passed in grad(f) must be a function"),(t,e)=>{let n=C(t,"x","tf.grad","string_or_numeric"),o=e!=null?C(e,"dy","tf.grad"):null;return T.tidy(()=>{let{value:s,grads:i}=T.gradients(()=>r(n),[n],o);return o!=null&&$e(s.shape,o.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Hx(i),i[0]})}}function Y6(r){return _(_i(r),()=>"The f passed in grads(f) must be a function"),(t,e)=>{_(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let n=fl(t,"args","tf.grads","string_or_numeric"),o=e!=null?C(e,"dy","tf.grads"):null;return T.tidy(()=>{let{value:s,grads:i}=T.gradients(()=>r(...n),n,o);return o!=null&&$e(s.shape,o.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Hx(i),i})}}function Z6(r){return _(_i(r),()=>"The f passed in valueAndGrad(f) must be a function"),(t,e)=>{_(t instanceof Lt,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),_(e==null||e instanceof Lt,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:n,value:o}=T.gradients(()=>r(t),[t],e);return Hx(n),{grad:n[0],value:o}}}function J6(r){return _(_i(r),()=>"The f passed in valueAndGrads(f) must be a function"),(t,e)=>{_(Array.isArray(t)&&t.every(o=>o instanceof Lt),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),_(e==null||e instanceof Lt,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let n=T.gradients(()=>r(...t),t,e);return e!=null&&$e(n.value.shape,e.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Hx(n.grads),n}}function Ux(r,t){_(_i(r),()=>"The f passed in variableGrads(f) must be a function"),_(t==null||Array.isArray(t)&&t.every(l=>l instanceof ml),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let e=t!=null;if(!e){t=[];for(let l in T.registeredVariables)t.push(T.registeredVariables[l])}let n=e?t.filter(l=>!l.trainable):null,o=t.length;t=t.filter(l=>l.trainable),_(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${o} variables is trainable.`);let s=!0,{value:i,grads:a}=T.gradients(r,t,null,s);_(a.some(l=>l!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),_(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let u={};return t.forEach((l,c)=>{a[c]!=null&&(u[l.name]=a[c])}),n!=null&&n.forEach(l=>u[l.name]=null),{value:i,grads:u}}function pn(r){return T.customGrad(r)}function Hx(r){if(r.filter(e=>e==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 Q6(r){let e={x:C(r,"x","neg")};return T.runKernel(zi,e)}var Ut=k({neg_:Q6});function tX(r){let e={x:C(r,"x","softplus")};return T.runKernel(Ys,e)}var ui=k({softplus_:tX});function eX(r){let t=C(r,"x","logSigmoid");return pn(n=>({value:Ut(ui(Ut(n))),gradFunc:i=>$(i,en(Ut(n)))}))(t)}var qx=k({logSigmoid_:eX});function rX(r,t){let e=C(r,"a","sub"),n=C(t,"b","sub");[e,n]=Xt(e,n);let o={a:e,b:n};return T.runKernel(ei,o)}var at=k({sub_:rX});function nX(r,t=-1){let e=C(r,"logits","logSoftmax");if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${e.rank} and axis was ${t}`);return pn((o,s)=>{let a=Sr(o,t,!0),u=at(o,a),l=at(J(u,"float32"),Nr(mt(Ke(u),t,!0)));return s([l]),{value:l,gradFunc:(p,m)=>{let[f]=m,d=!0,h=Ke(f);return at(p,$(mt(p,t,d),h))}}})(e)}var lm=k({logSoftmax_:nX});function oX(r,t=null,e=!1){let n=C(r,"x","logSumExp"),o=fr(t,n.shape),s=Sr(n,o,!0),i=at(n,s),a=Ke(i),u=mt(a,o),l=Nr(u),c=K(R(s,l.shape),l);if(e){let p=vo(c.shape,o);return R(c,p)}return c}var Su=k({logSumExp_:oX});function sX(r,t){let e=C(r,"a","logicalAnd","bool"),n=C(t,"b","logicalAnd","bool");Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(qa,o)}var Fr=k({logicalAnd_:sX});function iX(r){let e={x:C(r,"x","logicalNot","bool")};return T.runKernel(Ka,e)}var Nu=k({logicalNot_:iX});function aX(r,t){let e=C(r,"a","logicalOr","bool"),n=C(t,"b","logicalOr","bool");Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(ja,o)}var um=k({logicalOr_:aX});function lX(r,t){let e=C(r,"a","logicalXor","bool"),n=C(t,"b","logicalXor","bool");return Mt(e.shape,n.shape),Fr(um(r,t),Nu(Fr(r,t)))}var Kx=k({logicalXor_:lX});var jx=2147483648;function uX(r,t,e="left"){let n=C(r,"sortedSequence","searchSorted"),o=C(t,"values","searchSorted"),s=n.shape[n.shape.length-1],i=o.shape[o.shape.length-1],a=R(n,[-1,s]),u=R(o,[-1,i]);if(a.rank<2)throw new Error("Sorted input argument must be at least 2-dimensional");if(a.shape[0]!==u.shape[0])throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");if(jt(u.shape)>=jx)throw new Error(`values tensor size must less than ${jx}`);if(a.shape[1]>=jx)throw new Error(`trailing dim_size must less than ${jx} for int32 output type, was ${a.shape[1]}`);let l={sortedSequence:a,values:u},c={side:e};return T.runKernel(sl,l,c)}var dh=k({searchSorted_:uX});function GE(r,t){return dh(r,t,"left")}function cX(r,t,e,n,o){let s=C(r,"x","maxPool"),i=1,a=s,u=!1;s.rank===3&&(u=!0,a=R(s,[1,s.shape[0],s.shape[1],s.shape[2]])),_(a.rank===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.rank}.`),_(Dr(e,i),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${e} and dilations '${i}'`),Se("maxPool",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o},p=T.runKernel(Ns,l,c);return u?R(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ku=k({maxPool_:cX});function pX(r,t=[1,1,1],e,n,o,s="NDHWC"){let i=C(r,"x","maxPool3d"),a=i,u=!1;i.rank===4&&(u=!0,a=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),_(a.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${a.rank}.`),_(s==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),Se("maxPool3d",n,o);let l={x:a},c={filterSize:t,strides:e,pad:n,dimRoundingMode:o,dataFormat:s},p=T.runKernel(Li,l,c);return u?R(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Xx=k({maxPool3d_:pX});function mX(r,t,e,n,o=!1){let i={x:C(r,"x","maxPoolWithArgmax")},a={filterSize:t,strides:e,pad:n,includeBatchInIndex:o},u=T.runKernel(eu,i,a);return{result:u[0],indexes:u[1]}}var WE=k({maxPoolWithArgmax_:mX});function fX(r,t){let e=C(r,"a","maximum"),n=C(t,"b","maximum");[e,n]=Xt(e,n),e.dtype==="bool"&&(e=J(e,"int32"),n=J(n,"int32")),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(Ss,o)}var kn=k({maximum_:fX});function dX(r,t=null,e=!1){let o={x:C(r,"x","mean")},s={axis:t,keepDims:e};return T.runKernel(ks,o,s)}var Ne=k({mean_:dX});function ke(r,t="float32"){if(Le(r),t==="complex64"){let n=ke(r,"float32"),o=ke(r,"float32");return Sn(n,o)}let e=Ep(jt(r),t);return T.makeTensor(e,r,t)}function ar(r,t="float32"){if(Le(r),t==="complex64"){let n=ar(r,"float32"),o=ke(r,"float32");return Sn(n,o)}let e=Yd(jt(r),t);return T.makeTensor(e,r,t)}function UE(r,t,{indexing:e="xy"}={}){if(e!=="xy"&&e!=="ij")throw new TypeError(`${e} is not a valid third argument to meshgrid`);if(r===void 0)return[];let n=C(r,"x","meshgrid",r instanceof Lt?r.dtype:"float32");if(t===void 0)return[n];let o=C(t,"y","meshgrid",t instanceof Lt?t.dtype:"float32"),s=jt(n.shape),i=jt(o.shape);return e==="xy"?(n=R(n,[1,-1]),o=R(o,[-1,1]),[Bt(ar([i,1],n.dtype),n),Bt(o,ar([1,s],o.dtype))]):(n=R(n,[-1,1]),o=R(o,[1,-1]),[Bt(n,ar([1,i],n.dtype)),Bt(ar([s,1],o.dtype),o)])}function hX(r,t){let e=C(r,"a","minimum"),n=C(t,"b","minimum");[e,n]=Xt(e,n),e.dtype==="bool"&&(e=J(e,"int32"),n=J(n,"int32")),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(_s,o)}var lo=k({minimum_:hX});function gX(r,t,e){_(e==="reflect"||e==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${e}.`);let n=C(r,"x","mirrorPad");if(n.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");_(t.length===n.rank,()=>`Padding doesn't match input. Must be ${n.rank}. Got ${t.length}.`);let o=e==="reflect"?1:0;for(let a=0;a<n.rank;a++)_(t[a].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),_(t[a][0]>=0&&t[a][0]<=n.shape[a]-o&&t[a][1]>=0&&t[a][1]<=n.shape[a]-o,()=>`Padding in dimension ${a} cannot be greater than or equal to ${n.shape[a]-o} or less than 0 for input of shape ${n.shape}`);let s={paddings:t,mode:e},i={x:n};return T.runKernel(Es,i,s)}var Yx=k({mirrorPad_:gX});function xX(r,t){let e=C(r,"a","mod"),n=C(t,"b","mod");[e,n]=Xt(e,n);let o={a:e,b:n};return T.runKernel(As,o)}var Zx=k({mod_:xX});function yX(r,t=null,e=!1){r=C(r,"x","moments");let n=fr(t,r.shape),o=Ne(r,n,e),s=o.shape;e||(s=vo(o.shape,n));let i=Wt(at(J(r,"float32"),R(o,s))),a=Ne(i,n,e);return{mean:o,variance:a}}var dc=k({moments_:yX});function bX(r,t,e,n){let o=C(t,"data","multiRNNCell"),s=fl(e,"c","multiRNNCell"),i=fl(n,"h","multiRNNCell"),a=o,u=[];for(let p=0;p<r.length;p++){let m=r[p](a,s[p],i[p]);u.push(m[0]),u.push(m[1]),a=m[1]}let l=[],c=[];for(let p=0;p<u.length;p+=2)l.push(u[p]),c.push(u[p+1]);return[l,c]}var HE=k({multiRNNCell_:bX});function wX(r,t,e,n=!1){let o=C(r,"logits","multinomial"),s=o.size,i=o.rank;if(s<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${s}.`);if(i>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);e=e||Math.random();let u={logits:i===1?R(o,[1,-1]):o},l={numSamples:t,seed:e,normalized:n},c=T.runKernel(Ya,u,l);return i===1?R(c,[c.size]):c}var qE=k({multinomial_:wX});function IX(r,t){let e=C(r,"a","notEqual","string_or_numeric"),n=C(t,"b","notEqual","string_or_numeric");[e,n]=Xt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n};return T.runKernel(Za,o)}var ci=k({notEqual_:IX});function CX(r,t,e=1,n=0,o="int32"){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);let i={indices:C(r,"indices","oneHot","int32")},a={dtype:o,depth:t,onValue:e,offValue:n};return T.runKernel($s,i,a)}var ca=k({oneHot_:CX});function vX(r){let e={x:C(r,"x","onesLike")};return T.runKernel(Bi,e)}var wr=k({onesLike_:vX});function SX(r,t){let e=C(r,"v1","outerProduct"),n=C(t,"v2","outerProduct");_(e.rank===1&&n.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${e.rank} and ${n.rank}.`);let o=R(e,[-1,1]),s=R(n,[1,-1]);return Bt(o,s)}var KE=k({outerProduct_:SX});function NX(r,t,e=0){let n=C(r,"x","pad");if(n.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let o={paddings:t,constantValue:e},s={x:n};return T.runKernel(Rs,s,o)}var mn=k({pad_:NX});function kX(r,t,e=0){return _(t.length===2,()=>"Invalid number of paddings. Must be length of 2."),mn(r,[t],e)}var jE=k({pad1d_:kX});function TX(r,t,e=0){return _(t.length===2&&t[0].length===2&&t[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),mn(r,t,e)}var XE=k({pad2d_:TX});function _X(r,t,e=0){return _(t.length===3&&t[0].length===2&&t[1].length===2&&t[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),mn(r,t,e)}var YE=k({pad3d_:_X});function EX(r,t,e=0){return _(t.length===4&&t[0].length===2&&t[1].length===2&&t[2].length===2&&t[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),mn(r,t,e)}var ZE=k({pad4d_:EX});function AX(r,t,e){let n=C(r,"x","spaceToBatchND");_(n.rank>=1+t.length,()=>`input rank ${n.rank} should be > than [blockShape] ${t.length}`),_(e.length===t.length,()=>`paddings.shape[0] ${e.length} must be equal to [blockShape] ${t.length}`),_(n.shape.reduce((i,a,u)=>u>0&&u<=t.length?i&&(a+e[u-1][0]+e[u-1][1])%t[u-1]===0:i,!0),()=>`input spatial dimensions ${n.shape.slice(1)} with paddings ${e.toString()} must be divisible by blockShapes ${t.toString()}`);let o={x:n},s={blockShape:t,paddings:e};return T.runKernel(Hi,o,s)}var Tu=k({spaceToBatchND_:AX});function DX(r,t,e,n,o,s,i){o==null&&(o=[1,1]),s==null&&(s=1),n===0&&(n="valid");let a=C(r,"x","maxPool"),u=a,l=!1;a.rank===3&&(l=!0,u=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),_(Dr(s,o),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${s} and dilations '${o}'`);let c=Q0(u.shape,t,s,o,n),p=[c.dilationHeight,c.dilationWidth],m;n==="same"?m=RX([c.filterHeight,c.filterWidth],p):m=[[0,0],[0,0]];let f=p[0]===1&&p[1]===1,[d,h]=$X([c.inHeight,c.inWidth],p,m),g=f?n:"valid",x=f?u:Tu(u,p,d),w=(e==="avg"?()=>xu(x,t,s,g,i):()=>ku(x,t,s,g,i))(),I=f?w:yu(w,p,h);return l?R(I,[I.shape[1],I.shape[2],I.shape[3]]):I}function $X(r,t,e){let n=e.map(c=>c[0]),o=e.map(c=>c[1]),s=r.concat(n,o),i=t.map((c,p)=>(c-s[p]%c)%c),a=o.map((c,p)=>c+i[p]),u=t.map((c,p)=>[n[p],a[p]]),l=t.map((c,p)=>[0,i[p]]);return[u,l]}function RX(r,t){let n=r.map((i,a)=>i+(i-1)*(t[a]-1)).map(i=>i-1),o=n.map(i=>Math.floor(i/2)),s=n.map((i,a)=>i-o[a]);return n.map((i,a)=>[o[a],s[a]])}var Jx=k({pool_:DX});function FX(r,t){let e=C(r,"x","prelu"),n=C(t,"alpha","prelu"),o={x:e,alpha:n};return T.runKernel(Os,o)}var _u=k({prelu_:FX});function OX(r,t=null,e=!1){let n=C(r,"x","prod");n.dtype==="bool"&&(n=J(n,"int32"));let o={x:n},s={axis:t,keepDims:e};return T.runKernel(Ms,o,s)}var Qx=k({prod_:OX});function MX(r,t,e,n){let o=r.map((c,p)=>C(c,`tensors${p}`,"raggedGather","int32")),s=C(t,"paramsDenseValues","raggedGather"),i=C(e,"indices","raggedGather","int32"),a={paramsNestedSplits:o,paramsDenseValues:s,indices:i},u={outputRaggedRank:n},l=T.runKernel(Lp,a,u);return{outputNestedSplits:l.slice(0,l.length-1),outputDenseValues:l[l.length-1]}}var JE=k({raggedGather_:MX});function PX(r,t,e){let n=C(r,"starts","raggedRange"),o=C(t,"limits","raggedRange",n.dtype),s=C(e,"deltas","raggedRange",n.dtype),i={starts:n,limits:o,deltas:s},a=T.runKernel(zp,i);return{rtNestedSplits:a[0],rtDenseValues:a[1]}}var QE=k({raggedRange_:PX});function LX(r,t,e,n,o){let s=C(r,"shape","raggedTensorToTensor","int32"),i=C(t,"values","raggedTensorToTensor"),a=C(e,"defaultValue","raggedTensorToTensor",i.dtype),u=n.map((p,m)=>C(p,`tensors${m}`,"raggedTensorToTensor","int32")),l={shape:s,values:i,defaultValue:a,rowPartitionTensors:u},c={rowPartitionTypes:o};return T.runKernel(Bp,l,c)}var tA=k({raggedTensorToTensor_:LX});function zX(r,t,e){Le(r);let n=jt(r),o=null;if(e==null||e==="float32")o=new Float32Array(n);else if(e==="int32")o=new Int32Array(n);else if(e==="bool")o=new Uint8Array(n);else throw new Error(`Unknown data type ${e}`);for(let s=0;s<n;s++)o[s]=t();return T.makeTensor(o,r,e)}var eA=k({rand_:zX});var oy=Gl(hh());var IA={};Kt(IA,{TEST_EPSILON_FLOAT16:()=>yA,createVideoElement:()=>JX,encodeStrings:()=>wA,expectArrayBuffersEqual:()=>ZX,expectArraysClose:()=>KX,expectArraysEqual:()=>XX,expectNumbersClose:()=>bA,expectPromiseToFail:()=>jX,expectValuesInRange:()=>YX,play:()=>QX,testEpsilon:()=>ey});var qX=.001,yA=.1;function KX(r,t,e){return e==null&&(e=ey()),pN(r,t,(n,o)=>mN(n,o,e))}function ey(){return T.backend.floatPrecision()===32?qX:yA}function pN(r,t,e){let n=!0;if((sr(r)||sr(t))&&(n=!1),sr(r)&&sr(t)&&(n=!0),n){let i=r.constructor.name,a=t.constructor.name;if(i!==a)throw new Error(`Arrays are of different type. Actual: ${i}. Expected: ${a}`)}if(Array.isArray(r)&&Array.isArray(t)){let i=Wr(r),a=Wr(t);if(!sn(i,a))throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${a}]`)}let o=sr(r)?r:si(r),s=sr(t)?t:si(t);if(o.length!==s.length)throw new Error(`Arrays have different lengths actual: ${o.length} vs expected: ${s.length}.
Actual:   ${o}.
Expected: ${s}.`);for(let i=0;i<s.length;++i){let a=o[i],u=s[i];if(!e(a,u))throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${u}.
Actual:   ${o}.
Expected: ${s}.`)}typeof expect!="undefined"&&expect().nothing()}function jX(r,t){r().then(()=>t.fail(),()=>t()),typeof expect!="undefined"&&expect().nothing()}function XX(r,t){let e=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Vo(r)||Vo(r[0])||Vo(t)||Vo(t[0])?pN(r,e,(n,o)=>n==o):pN(r,t,(n,o)=>mN(n,o,0))}function bA(r,t,e){if(e==null&&(e=ey()),!mN(r,t,e))throw new Error(`Numbers differ: actual === ${r}, expected === ${t}`);typeof expect!="undefined"&&expect().nothing()}function mN(r,t,e){return!isFinite(r)&&!isFinite(t)?!0:!(isNaN(r)||isNaN(t)||Math.abs(r-t)>e)}function YX(r,t,e){for(let n=0;n<r.length;n++)if(r[n]<t||r[n]>e)throw new Error(`Value out of range:${r[n]} low: ${t}, high: ${e}`)}function ZX(r,t){let e=new Float32Array(r),n=new Float32Array(t);if(e.length!==n.length)throw new Error(`Expected ArrayBuffer to be of length ${n.length}, but it was ${e.length}`);for(let o=0;o<n.length;o++)if(e[o]!==n[o])throw new Error(`Expected ArrayBuffer value at ${o} to be ${n[o]} but got ${e[o]} instead`)}function wA(r){for(let t=0;t<r.length;t++){let e=r[t];Array.isArray(e)?wA(e):r[t]=fu(e)}return r}function JX(r){let t=document.createElement("video");return"playsInline"in t&&(t.playsInline=!0),t.muted=!0,t.loop=!0,t.style.position="fixed",t.style.left="0px",t.style.top="0px",t.preload="auto",t.appendChild(r),new Promise(e=>{t.addEventListener("loadeddata",n=>e(t)),t.load()})}async function QX(r){await r.play(),"requestVideoFrameCallback"in r&&await new Promise(t=>{r.requestVideoFrameCallback(t)})}var gc=class{constructor(t,e,n,o,s){this.mean=t,this.stdDev=e,this.dtype=n,this.nextVal=NaN,this.truncated=o,this.truncated&&(this.upper=this.mean+this.stdDev*2,this.lower=this.mean-this.stdDev*2);let i=s||Math.random();this.random=oy.alea(i.toString())}nextValue(){if(!isNaN(this.nextVal)){let o=this.nextVal;return this.nextVal=NaN,o}let t,e,n=!1;for(;!n;){let o,s,i;do o=2*this.random()-1,s=2*this.random()-1,i=o*o+s*s;while(i>=1||i===0);let a=Math.sqrt(-2*Math.log(i)/i);t=this.mean+this.stdDev*o*a,e=this.mean+this.stdDev*s*a,(!this.truncated||this.isValidTruncated(t))&&(n=!0)}return(!this.truncated||this.isValidTruncated(e))&&(this.nextVal=this.convertValue(e)),this.convertValue(t)}convertValue(t){return this.dtype==null||this.dtype==="float32"?t:Math.round(t)}isValidTruncated(t){return t<=this.upper&&t>=this.lower}},ry=class{constructor(t,e,n,o){this.alpha=t,this.beta=1/e,this.dtype=n;let s=o||Math.random();this.randu=oy.alea(s.toString()),this.randn=new gc(0,1,n,!1,this.randu()),t<1?this.d=t+2/3:this.d=t-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let t,e,n,o,s,i;for(;;){do o=this.randn.nextValue(),i=1+this.c*o;while(i<=0);if(i*=i*i,t=o*o,e=1-.331*t*t,n=.5*t+this.d*(1-i+Math.log(i)),s=this.randu(),s<e||Math.log(s)<n)break}return i=1/this.beta*this.d*i,this.alpha<1&&(i*=Math.pow(this.randu(),1/this.alpha)),this.convertValue(i)}convertValue(t){return this.dtype==="float32"?t:Math.round(t)}},ny=class{constructor(t=0,e=1,n,o){if(this.canReturnFloat=()=>this.dtype==null||this.dtype==="float32",this.min=t,this.range=e-t,this.dtype=n,o==null&&(o=Math.random()),typeof o=="number"&&(o=o.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${t} - ${e} <= 1 and dtype is not float`);this.random=oy.alea(o)}convertValue(t){return this.canReturnFloat()?t:Math.round(t)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function t5(r,t,e=1,n="float32",o){if(Le(r),e==null&&(e=1),n==null&&(n="float32"),n!=="float32"&&n!=="int32")throw new Error(`Unsupported data type ${n}`);let s=new ry(t,e,n,o),i=wt(r,n);for(let a=0;a<i.values.length;a++)i.values[a]=s.nextValue();return i.toTensor()}var CA=k({randomGamma_:t5});function e5(r,t=0,e=1,n,o){if(Le(r),n!=null&&n==="bool")throw new Error(`Unsupported data type ${n}`);let s=new gc(t,e,n,!1,o),i=wt(r,n);for(let a=0;a<i.values.length;a++)i.values[a]=s.nextValue();return i.toTensor()}var xc=k({randomNormal_:e5});function r5(r,t,e){if(t!=null&&t==="bool")throw new Error(`Unsupported data type ${t}`);return xc(r,0,1,t,e)}var vA=k({randomStandardNormal_:r5});function n5(r,t=0,e=1,n="float32",o){Le(r);let s=wt(r,n),i=new ny(t,e,null,o);for(let a=0;a<s.values.length;a++)s.values[a]=i.nextValue();return s.toTensor()}var Gn=k({randomUniform_:n5});function o5(r,t,e,n){return Gn(r,t,e,"int32",n)}var SA=k({randomUniformInt_:o5});function pa(r,t,e=1,n="float32"){if(e===0)throw new Error("Cannot have a step of zero");let o={start:r,stop:t,step:e,dtype:n};return T.runKernel(ru,{},o)}function s5(r){let e={input:C(r,"input","real")};return T.runKernel(Vp,e)}var bl=k({real_:s5});function i5(r){let e={x:C(r,"x","reciprocal")};return T.runKernel(Ps,e)}var sy=k({reciprocal_:i5});function a5(r){let e={x:C(r,"x","relu")};return T.runKernel(Ls,e)}var Or=k({relu_:a5});function l5(r){let e={x:C(r,"x","relu6")};return T.runKernel(Vs,e)}var cm=k({relu6_:l5});function u5(r,t){let n={x:C(r,"x","reverse")},o={dims:t};return T.runKernel(Gs,n,o)}var dr=k({reverse_:u5});function c5(r){let t=C(r,"x","reverse");return _(t.rank===1,()=>`Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`),dr(t,0)}var NA=k({reverse1d_:c5});function p5(r,t){let e=C(r,"x","reverse");return _(e.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${e.rank}.`),dr(e,t)}var kA=k({reverse2d_:p5});function m5(r,t){let e=C(r,"x","reverse");return _(e.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${e.rank}.`),dr(e,t)}var TA=k({reverse3d_:m5});function f5(r,t){let e=C(r,"x","reverse");return _(e.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${e.rank}.`),dr(e,t)}var _A=k({reverse4d_:f5});function d5(r){let e={x:C(r,"x","round")};return T.runKernel(Ws,e)}var pm=k({round_:d5});function h5(r){let e={x:C(r,"x","rsqrt","float32")};return T.runKernel(Us,e)}var mm=k({rsqrt_:h5});function g5(r){let e={x:C(r,"x","selu")};return T.runKernel(Hs,e)}var fm=k({selu_:g5});function x5(r,t,e,n,o,s=[1,1],i="NHWC"){let a=C(r,"x","separableConv2d"),u=C(t,"depthwiseFilter","separableConv2d"),l=C(e,"pointwiseFilter","separableConv2d"),c=a,p=!1;if(a.rank===3&&(p=!0,c=R(a,[1,a.shape[0],a.shape[1],a.shape[2]])),i==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");_(c.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`),_(u.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`),_(l.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`),_(l.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${l.shape[0]}.`),_(l.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${l.shape[1]}.`);let m=u.shape[2],f=u.shape[3];_(l.shape[2]===m*f,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${m*f}, but got ${l.shape[2]}.`);let d=ia(c,u,n,o,i,s),g=Nn(d,l,1,"valid",i);return p?R(g,[g.shape[1],g.shape[2],g.shape[3]]):g}var dm=k({separableConv2d_:x5});async function y5(r,t){let e=C(r,"x","setdiff1d"),n=C(t,"y","setdiff1d");_(e.dtype===n.dtype,()=>`x and y should have the same dtype, but got x (${e.dtype}) and y (${n.dtype}).`),_(e.rank===1,()=>`x should be 1D tensor, but got x (${e.shape}).`),_(n.rank===1,()=>`y should be 1D tensor, but got y (${n.shape}).`);let o=await e.data(),s=await n.data(),i=new Set(s),a=0;for(let c=0;c<o.length;c++)i.has(o[c])||a++;let u=new le([a],e.dtype),l=new le([a],"int32");for(let c=0,p=0;c<o.length;c++)i.has(o[c])||(u.values[p]=o[c],l.values[p]=c,p++);return[u.toTensor(),l.toTensor()]}var EA=y5;function b5(r){let e={x:C(r,"x","sign")};return T.runKernel(js,e)}var iy=k({sign_:b5});function w5(r){let e={x:C(r,"x","sin","float32")};return T.runKernel(qs,e)}var hm=k({sin_:w5});function I5(r){let e={x:C(r,"x","sinh")};return T.runKernel(Ks,e)}var gm=k({sinh_:I5});function C5(r,t,e){let n=C(r,"x","slice1d");return _(n.rank===1,()=>`slice1d expects a rank-1 tensor, but got a rank-${n.rank} tensor`),Ot(n,[t],[e])}var xm=k({slice1d_:C5});function v5(r,t,e){let n=C(r,"x","slice2d");return _(n.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${n.rank} tensor`),Ot(n,t,e)}var gh=k({slice2d_:v5});function S5(r,t,e){let n=C(r,"x","slice3d");return _(n.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${n.rank} tensor`),Ot(n,t,e)}var ym=k({slice3d_:S5});function N5(r,t,e){let n=C(r,"x","slice4d");return _(n.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${n.rank} tensor`),Ot(n,t,e)}var yc=k({slice4d_:N5});function k5(r,t=-1){let e=C(r,"logits","softmax","float32");if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${e.rank} and dim was ${t}`);let n={logits:e},o={dim:t};return T.runKernel(Qs,n,o)}var Eu=k({softmax_:k5});function T5(r){_(r.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${r.dtype}.`);let t={input:r};return T.runKernel(Op,t)}var Au=k({fft_:T5});function _5(r){_(r.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${r.dtype}.`);let t={input:r};return T.runKernel(Mp,t)}var wl=k({ifft_:_5});function E5(r){let t=r.shape[r.shape.length-1],e=r.size/t,n;if(t<=2){let o=R(r,[e,t]);n=wl(o)}else{let o=[e,2*(t-1)],s=R(bl(r),[e,t]),i=R(Iu(r),[e,t]),a=dr(Ot(s,[0,1],[e,t-2]),1),u=$(dr(Ot(i,[0,1],[e,t-2]),1),pt(-1)),l=ie([s,a],1),c=ie([i,u],1),p=R(Sn(l,c),[o[0],o[1]]);n=wl(p)}if(n=bl(n),r.rank===3&&r.shape[0]!==0){let o=n,s=r.shape[0];n=R(n,[s,n.shape[0]/s,n.shape[1]]),o.dispose()}return n}var bm=k({irfft_:E5});function A5(r,t,e=0){let o={x:C(r,"x","split")},s={numOrSizeSplits:t,axis:e};return T.runKernel(qi,o,s)}var hr=k({split_:A5});function D5(r,t){_(r.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${r.dtype}`);let e=r.shape[r.shape.length-1],n=r.size/e,o;if(t!=null&&t<e){let d=r.shape.map(g=>0),h=r.shape.map(g=>g);h[r.shape.length-1]=t,o=Ot(r,d,h),e=t}else if(t!=null&&t>e){let d=r.shape.map(h=>h);d[r.shape.length-1]=t-e,o=ie([r,ke(d)],r.shape.length-1),e=t}else o=r;let s=vt(o),i=R(Sn(o,s),[n,e]),a=Au(i),u=Math.floor(e/2)+1,l=bl(a),c=Iu(a),p=hr(l,[u,e-u],l.shape.length-1),m=hr(c,[u,e-u],c.shape.length-1),f=o.shape.slice();return f[o.shape.length-1]=u,R(Sn(p[0],m[0]),f)}var Du=k({rfft_:D5});function $5(r,t){let e=C(r,"a","squaredDifference"),n=C(t,"b","squaredDifference");[e,n]=Xt(e,n),Mt(e.shape,n.shape);let o={a:e,b:n},s={};return T.runKernel(ti,o,s)}var wm=k({squaredDifference_:$5});function R5(r,t){let e=C(r,"x","squeeze","string_or_numeric");return R(e,h0(e.shape,t).newShape)}var Wn=k({squeeze_:R5});function F5(r,t=0){let e=fl(r,"tensors","stack","string_or_numeric");_(e.length>=1,()=>"Pass at least one tensor to tf.stack"),e.length>0&&_(t<=e[0].rank,()=>"Axis must be <= rank of the tensor");let n=e,o={axis:t};return T.runKernel(Vi,n,o)}var Fe=k({stack_:F5});function O5(r,t=0){let n={x:C(r,"x","step")},o={alpha:t};return T.runKernel(xo,n,o)}var So=k({step_:O5});function M5(r,t,e,n,o=0,s=0,i=0,a=0,u=0){let c={x:C(r,"x","stridedSlice","string_or_numeric")},p={begin:t,end:e,strides:n,beginMask:o,endMask:s,ellipsisMask:i,newAxisMask:a,shrinkAxisMask:u};return T.runKernel(ll,c,p)}var ay=k({stridedSlice_:M5});function P5(r){let e={x:C(r,"x","tan","float32")};return T.runKernel(ri,e)}var ly=k({tan_:P5});function Oe(r,t){ro(r);let e=Wr(r,t);if(e.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return ln(r,null,e,t)}function pi(r,t,e){if(ro(r),t!=null&&t.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let n=Wr(r,e);if(n.length!==2&&n.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return ln(r,t,n,e)}function uy(r,t,e){if(ro(r),t!=null&&t.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let n=Wr(r,e);if(n.length!==3&&n.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return ln(r,t,n,e)}function AA(r,t,e){if(ro(r),t!=null&&t.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let n=Wr(r,e);if(n.length!==4&&n.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return ln(r,t,n,e)}function DA(r,t,e){if(ro(r),t!=null&&t.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let n=Wr(r,e);if(n.length!==5&&n.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return ln(r,t,n,e)}function $A(r,t,e){if(ro(r),t!=null&&t.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let n=Wr(r,e);if(n.length!==6&&n.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(n.length===1&&t==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return t=t||n,ln(r,t,n,e)}var $u={};Kt($u,{calculateShapes:()=>RA,validateInput:()=>Im,validateUpdateShape:()=>fN});function fN(r,t,e){let n=t.rank>1?t.shape[t.rank-1]:1,o=t.rank>1?t.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${e.shape}, indices.shape: ${t.shape}, shape: ${r}, sliceDim: ${n}, and batchDim: ${o}.`;if(e.rank<o)throw new Error(s+` update.rank < ${o}. `);if(r.length<n+(e.rank-o))throw new Error(s+` Output shape length < ${n+(e.rank-o)}`);if(e.rank!==o+r.length-n)throw new Error(s+` update.rank != ${o+r.length-n}`);for(let i=0;i<o;++i)if(e.shape[i]!==t.shape[i])throw new Error(s+` updates.shape[${i}] (${e.shape[i]}) != indices.shape[${i}] (${t.shape[i]}).`);for(let i=0;i<e.rank-o;++i)if(e.shape[i+o]!==r[i+n])throw new Error(s+` updates.shape[${i+o}] (${e.shape[i+o]}) != shape[${i+o}] (${r[i+o]})`)}function Im(r,t,e){if(t.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(r.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${r.rank}.`);if(t.dtype!=="int32")throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);if(e.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${e}`);if(e.length===0){if(t.size===0)throw new Error(`Indices specified for empty output. indices shape: ${t.shape}`);if(r.size===0)throw new Error(`Updates specified for empty output. updates shape: ${r.shape}`)}fN(e,t,r)}function RA(r,t,e){let n=t.shape.length,o=n>1?t.shape[n-1]:1,s=e.length,i=1;for(let p=o;p<s;++p)i*=e[p];let a=o<1?1:o,u=jt(t.shape)/a,l=[...Ei(e.slice(0,o)),1],c=jt(e);return{sliceRank:o,numUpdates:u,sliceSize:i,strides:l,outputSize:c}}function L5(r,t,e){let n=C(r,"tensor","tensorScatterupdate"),o=C(t,"indices","tensorScatterupdate","int32"),s=C(e,"updates","tensorScatterupdate");if(Im(s,o,n.shape),n.dtype!==s.dtype)throw new Error(`tensor and updates must have the same dtype, instead they are ${n.dtype} and ${s.dtype}.`);let i={tensor:n,indices:o,updates:s},a={};return T.runKernel(ol,i,a)}var FA=k({tensorScatterUpdate_:L5});function z5(r,t=1,e=!0){let n=C(r,"x","topk");if(n.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let o=n.shape[n.shape.length-1];if(t<0)throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);if(t>o)throw new Error(`'k' passed to topk() must be <= the last dimension (${o}) but got ${t}`);let s={x:n},i={k:t,sorted:e},[a,u]=T.runKernel(ul,s,i);return{values:a,indices:u}}var cy=k({topk_:z5});function B5(r,t=0,e=1,n,o){if(Le(r),n!=null&&n==="bool")throw new Error("Unsupported data type $ { dtype }");let s=new gc(t,e,n,!0,o),i=wt(r,n);for(let a=0;a<i.values.length;a++)i.values[a]=s.nextValue();return i.toTensor()}var Cm=k({truncatedNormal_:B5});function V5(r,t=0){let e=C(r,"x","unique","string_or_numeric");_(e.rank>0,()=>"The input tensor must be at least 1D");let n={x:e},o={axis:t},[s,i]=T.runKernel(cu,n,o);return{values:s,indices:i}}var py=k({unique_:V5});function G5(r,t,e){let n=C(r,"x","unsortedSegmentSum"),o=C(t,"segmentIds","unsortedSegmentSum","int32");_(_a(e),()=>"numSegments must be of dtype int");let s={x:n,segmentIds:o},i={numSegments:e};return T.runKernel(pu,s,i)}var vm=k({unsortedSegmentSum_:G5});function W5(r,t=0){let e=C(r,"x","unstack","string_or_numeric");_(t>=-e.shape.length&&t<e.shape.length,()=>`Axis = ${t} is not in [-${e.shape.length}, ${e.shape.length})`);let n={value:e},o={axis:t};return T.runKernel(Ki,n,o)}var gr=k({unstack_:W5});function OA(r,t){return dh(r,t,"right")}function my(r,t=!0,e,n){return T.makeVariable(r,t,e,n)}function fy(r,t){let e=[];for(let s=0;s<t.length;s++)t[s]&&e.push(s);let n=wt(r,"int32"),o=wt([e.length,r.length],"int32");for(let s=0;s<e.length;s++){let i=n.indexToLoc(e[s]),a=s*r.length;o.values.set(i,a)}return o.toTensor()}async function U5(r){let t=C(r,"condition","whereAsync","bool"),e=await t.data(),n=fy(t.shape,e);return r!==t&&t.dispose(),n}var dy=U5;async function H5(r,t,e){let n=C(r,"tensor","boolMask"),o=C(t,"mask","boolMask","bool"),s=e==null?0:e,i=o.rank,a=n.shape;_(i>0,()=>"mask cannot be scalar"),$e(a.slice(s,s+i),o.shape,"mask's shape must match the first K dimensions of tensor's shape,");let u=1;for(let h=s;h<s+i;h++)u*=a[h];let l=a.slice(0,s).concat([u],a.slice(s+i)),c=R(n,l),p=R(o,[-1]),m=await dy(p),f=Wn(m,[1]),d=ua(c,f,s);return r!==n&&n.dispose(),t!==o&&o.dispose(),f.dispose(),c.dispose(),p.dispose(),m.dispose(),d}var q5=H5;function K5(r,t,e){let n=C(r,"x","transpose");if(t==null&&(t=n.shape.map((i,a)=>a).reverse()),_(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`),t.forEach(i=>{_(i>=0&&i<n.rank,()=>`All entries in 'perm' must be between 0 and ${n.rank-1} but got ${t}`)}),n.rank<=1)return n.clone();let o={x:n},s={perm:t};return n.dtype==="complex64"?B(()=>{let i=bl(n),a=Iu(n);return i=T.runKernel(so,{x:i},s),a=T.runKernel(so,{x:a},s),e&&(a=Ut(a)),Sn(i,a)}):T.runKernel(so,o,s)}var Vt=k({transpose_:K5});function j5(r,t,e,n,o=!0){let s=C(r,"v","movingAverage"),i=C(t,"x","movingAverage"),a=C(e,"decay","movingAverage");M0(s,i),_(sn(s.shape,i.shape),()=>"Shape mismatch in v and x");let u=pt(1),l=at(u,a),c=$(at(i,s),l);if(o){_(n!=null,()=>"When using zeroDebias: true, step is required.");let p=C(n,"step","movingAverage");c=ut(c,at(u,qr(a,p)))}return K(s,c)}var X5=k({movingAverage_:j5});function Y5(r,t,e){Le(e);let n=C(r,"indices","scatterND","int32"),o=C(t,"updates","scatterND");Im(o,n,e);let s={indices:n,updates:o},i={shape:e};return T.runKernel(nl,s,i)}var Z5=k({scatterND_:Y5});function MA(r,t,e,n){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 o=r.rank>0?r.shape[0]:1,s=r.rank>1?r.shape[1]:1;if(e.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${e.length}, should be: ${s}.`);let i=t.size;if(!(t.rank===0||t.rank===1&&i===o))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${o}]`);if(t.dtype!==n.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function Q5(r,t,e,n=0){Le(e);let o=C(r,"sparseIndices","sparseToDense","int32"),s=C(t,"sparseValues","sparseToDense","string_or_numeric"),i=C(n,"defaultValue","sparseToDense",s.dtype);MA(o,s,e,i);let a={sparseIndices:o,sparseValues:s,defaultValue:i},u={outputShape:e};return T.runKernel(al,a,u)}var t8=k({sparseToDense_:Q5});function e8(r,t){let e=C(t,"indices","gatherND","int32"),o={params:C(r,"x","gatherND","string_or_numeric"),indices:e};return T.runKernel(Va,o)}var r8=k({gatherND_:e8});function PA(r,t){if(t==null)return r.shape.slice();if(sn(r.shape,t))return t;if(r.shape.length===t.length){let e=[];for(let n=0;n<r.shape.length;n++)t[n]==null&&r.shape[n]!=null?e.push(r.shape[n]):e.push(t[n]);return e}return t}function n8(r,t,e,n){let o=C(r,"x","dropout");if(_(o.dtype==="float32",()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${o.dtype} tensor instead.`),_(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),t===0)return r instanceof Lt?o.clone():o;let s=PA(o,e),i=1-t,a=ut(la(K(Gn(s,0,1,"float32",n),i)),i);return $(o,a)}var dN=k({dropout_:n8});function hN(r){return Math.floor(Math.pow(2,Math.ceil(Math.log(r)/Math.log(2))))}function xh(r,t,e){let n=1-r%2,o=new Float32Array(r);for(let s=0;s<r;++s){let i=2*Math.PI*s/(r+n-1);o[s]=t-e*Math.cos(i)}return Oe(o,"float32")}async function o8(r,t,e=1){let n=C(r,"predictions","inTopK"),o=C(t,"targets","inTopK");_(n.rank>1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${n.rank}`),_(n.rank-1===o.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${n.rank} and targets rank ${o.rank}`),$e(n.shape.slice(0,n.shape.length-1),o.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let s=n.shape[n.shape.length-1];_(e>0&&e<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${e}`);let i=await n.data(),a=await o.data(),[u,l]=[i.length/s,s],c=g0("bool",u);for(let p=0;p<u;p++){let m=p*l,f=i.subarray(m,m+l),d=[];for(let h=0;h<f.length;h++)d.push({value:f[h],index:h});d.sort((h,g)=>g.value-h.value),c[p]=0;for(let h=0;h<e;h++)if(d[h].index===a[p]){c[p]=1;break}}return r!==n&&n.dispose(),t!==o&&o.dispose(),ir(c,o.shape,"bool")}var s8=o8;var Ru={};Kt(Ru,{conv2d:()=>LA,depthwiseConv2d:()=>zA,matMul:()=>BA});function i8(r,t,e,n,o,s="NHWC",i){let a=r;r.rank===3&&(a=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let u=t;u.rank===3&&(u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]])),_(a.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${a.shape}.`),_(u.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`),_(e.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${e}.`);let l=s==="NHWC"?a.shape[3]:a.shape[1],c=s==="NHWC"?u.shape[3]:u.shape[1];_(l===e[2],()=>`Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${e[2]}.`),_(c===e[3],()=>`Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${e[3]}).`),Se("conv2dDerFilter",o,i);let p={x:a,dy:u},m={strides:n,pad:o,dataFormat:s,dimRoundingMode:i,filterShape:e};return T.runKernel(Dp,p,m)}var Sm=k({conv2DBackpropFilter_:i8});function bc(r,t,e){if(e==null||e==="linear")return r;if(e==="relu")return $(r,So(t));throw new Error(`Cannot compute gradient for fused activation ${e}.`)}function wc(r,t){let e=t,n=we(r.shape,t.shape);return n.length>0&&(e=mt(e,n)),R(e,r.shape)}function Ic(r,t,e,n){if(t==="linear")return r;if(t==="relu")return Or(r);if(t==="elu")return aa(r);if(t==="relu6")return cm(r);if(t==="prelu")return _u(r,e);if(t==="leakyrelu")return Cu(r,n);if(t==="sigmoid")return en(r);throw new Error(`Unknown fused activation ${t}.`)}var Cc=(r,t)=>!(r>0)||t==="linear";function a8({x:r,filter:t,strides:e,pad:n,dataFormat:o="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u="linear",preluActivationWeights:l,leakyreluAlpha:c}){if(u=u||"linear",Cc(T.state.gradientDepth,u)===!1){_(o==="NHWC",()=>`Error in fused conv2d: got dataFormat of ${o} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);let E=Nn(r,t,e,n,o,s,i);return a!=null&&(E=K(E,a)),Ic(E,u,l,c)}let p=C(r,"x","conv2d","float32"),m=C(t,"filter","conv2d","float32"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${m.rank}.`),Se("fused conv2d",n,i);let h=o==="NHWC"?f.shape[3]:f.shape[1];_(m.shape[2]===h,()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${m.shape[2]}.`),_(Dr(e,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`);let g=pc(f.shape,m.shape,e,s,n,i),x;a!=null&&(x=C(a,"bias","fused conv2d"),[x]=Xt(x,p),o==="NHWC"?Mt(g.outShape,x.shape):(_(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-${x.shape.length}.`),_(x.shape.length===0||x.shape[0]===g.outChannels||x.shape[0]===1,()=>`Error in fused conv2d: bias shape (${x.shape}) is not compatible with the number of output channels (${g.outChannels})`)));let b;if(l!=null){let E=l.shape;if(_(E.length<=1||E.length===3,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${E.length}.`),E.length===1)_(E[0]===1||E[0]===g.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the number of output channels (${g.outChannels}).`);else if(E.length===3)try{Mt(E,g.outShape)}catch(A){let D=`Error in fused conv2d: PReLU activation weights (${E}) is not compatible with the output shape of the conv2d (${g.outShape}).`;throw Error(D)}b=C(l,"prelu weights","fused conv2d")}let w=(E,A)=>{_(o==="NHWC",()=>`Error in gradient of fused conv2D: got dataFormat of ${o} but only NHWC is currently supported.`);let[D,F,M,V]=A,G=bc(E,M,u);_(io(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);let W=nm(F.shape,G,D,e,n),q=Sm(F,G,D.shape,e,n),H=[W,q];if(V!=null){let j=wc(V,G);H.push(j)}return H},I={x:f,filter:m,bias:x,preluActivationWeights:b},N={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?pn((A,D,F)=>{let M=T.runKernel(Yi,I,N);return F([D,A,M]),d&&(M=R(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:w}})(f,m):pn((A,D,F,M)=>{let V=T.runKernel(Yi,I,N);return M([D,A,V,F]),d&&(V=R(V,[V.shape[1],V.shape[2],V.shape[3]])),{value:V,gradFunc:w}})(f,m,x)}var LA=k({fusedConv2d_:a8});function l8(r,t,e,n,o,s=[1,1],i){let a=r;r.rank===3&&(a=R(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let u=t;u.rank===3&&(u=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let l={x:a,dy:u},c={strides:n,pad:o,dimRoundingMode:i,dilations:s,filterShape:e};return T.runKernel($p,l,c)}var hy=k({depthwiseConv2dNativeBackpropFilter_:l8});function u8(r,t,e,n,o,s=[1,1],i){let a=t,u=!1;t.rank===3&&(u=!0,a=R(t,[1,t.shape[0],t.shape[1],t.shape[2]]));let l={dy:a,filter:e},c={strides:n,pad:o,dimRoundingMode:i,dilations:s,inputShape:r},p=T.runKernel(Rp,l,c);return u?R(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var gy=k({depthwiseConv2dNativeBackpropInput_:u8});function c8({x:r,filter:t,strides:e,pad:n,dataFormat:o="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:a,activation:u="linear",preluActivationWeights:l,leakyreluAlpha:c}){if(Cc(T.state.gradientDepth,u)===!1){let N=ia(r,t,e,n,o,s,i);return a!=null&&(N=K(N,a)),Ic(N,u,l,c)}let p=C(r,"x","depthwiseConv2d","float32"),m=C(t,"filter","depthwiseConv2d","float32"),f=p,d=!1;p.rank===3&&(d=!0,f=R(p,[1,p.shape[0],p.shape[1],p.shape[2]])),_(f.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${f.rank}.`),_(m.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${m.rank}.`),_(f.shape[3]===m.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${f.shape[3]}) must match the inChannels dimension in filter ${m.shape[2]}.`),s==null&&(s=[1,1]),_(Dr(e,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${e} and dilations '${s}'`),Se("fused depthwiseConv2d",n,i);let h=pc(f.shape,m.shape,e,s,n,i,!0),g;a!=null&&(g=C(a,"bias","fused conv2d"),[g]=Xt(g,p),Mt(h.outShape,g.shape));let x;l!=null&&(x=C(l,"prelu weights","fused depthwiseConv2d"));let b=(N,E)=>{_(io(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);let[A,D,F,M]=E,V=bc(N,F,u),G=gy(D.shape,V,A,e,n,s,i),W=hy(D,V,A.shape,e,n,s,i);if(M!=null){let q=wc(g,V);return[G,W,q]}return[G,W]},w={x:f,filter:m,bias:g,preluActivationWeights:x},I={strides:e,pad:n,dataFormat:o,dilations:s,dimRoundingMode:i,activation:u,leakyreluAlpha:c};return a==null?pn((E,A,D)=>{let F=T.runKernel(Zi,w,I);return D([A,E,F]),d&&(F=R(F,[F.shape[1],F.shape[2],F.shape[3]])),{value:F,gradFunc:b}})(f,m):pn((E,A,D,F)=>{let M=T.runKernel(Zi,w,I);return F([A,E,M,D]),d&&(M=R(M,[M.shape[1],M.shape[2],M.shape[3]])),{value:M,gradFunc:b}})(f,m,g)}var zA=k({fusedDepthwiseConv2d_:c8});function p8({a:r,b:t,transposeA:e=!1,transposeB:n=!1,bias:o,activation:s="linear",preluActivationWeights:i,leakyreluAlpha:a=.2}){if(Cc(T.state.gradientDepth,s)===!1){let V=Bt(r,t,e,n);return o!=null&&(V=K(V,o)),Ic(V,s,i,a)}let u=C(r,"a","fused matMul"),l=C(t,"b","fused matMul");[u,l]=Xt(u,l);let c=e?u.shape[u.rank-2]:u.shape[u.rank-1],p=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=e?u.shape[u.rank-1]:u.shape[u.rank-2],f=n?l.shape[l.rank-2]:l.shape[l.rank-1],d=u.shape.slice(0,-2),h=l.shape.slice(0,-2),g=jt(d),x=jt(h);_(c===p,()=>`Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${e} and transposeB=${n} must match.`);let w=Mt(u.shape.slice(0,-2),l.shape.slice(0,-2)).concat([m,f]),I=e?R(u,[g,c,m]):R(u,[g,m,c]),N=n?R(l,[x,f,p]):R(l,[x,p,f]),E;o!=null&&(E=C(o,"bias","fused matMul"),[E]=Xt(E,u),Mt(w,E.shape));let A;i!=null&&(A=C(i,"prelu weights","fused matMul"));let D=(V,G)=>{let[W,q,H,j]=G,Y=bc(R(V,H.shape),H,s),Z,et;if(!e&&!n?(Z=Bt(Y,q,!1,!0),et=Bt(W,Y,!0,!1)):!e&&n?(Z=Bt(Y,q,!1,!1),et=Bt(Y,W,!0,!1)):e&&!n?(Z=Bt(q,Y,!1,!0),et=Bt(W,Y,!1,!1)):(Z=Bt(q,Y,!0,!0),et=Bt(Y,W,!0,!0)),o!=null){let nt=wc(j,Y);return[Z,et,nt]}else return[Z,et]},F={a:I,b:N,bias:E,preluActivationWeights:A},M={transposeA:e,transposeB:n,activation:s,leakyreluAlpha:a};return o==null?pn((G,W,q)=>{let H=T.runKernel(Xi,F,M);return q([G,W,H]),{value:R(H,w),gradFunc:D}})(I,N):pn((G,W,q,H)=>{let j=T.runKernel(Xi,F,M);return H([G,W,j,q]),{value:R(j,w),gradFunc:D}})(I,N,E)}var BA=k({fusedMatMul_:p8});function m8(r){return xh(r,.54,.46)}var VA=k({hammingWindow_:m8});function f8(r){return xh(r,.5,.5)}var xy=k({hannWindow_:f8});function d8(r,t,e,n=!1,o=0){let s=0,i=[];for(;s+t<=r.size;)i.push(Ot(r,s,t)),s+=e;if(n)for(;s<r.size;){let a=s+t-r.size,u=ie([Ot(r,s,t-a),Co([a],o)]);i.push(u),s+=e}return i.length===0?pi([],[0,t]):R(ie(i),[i.length,t])}var yy=k({frame_:d8});function h8(r,t,e,n,o=xy){n==null&&(n=hN(t));let s=yy(r,t,e),i=$(s,o(t));return Du(i,n)}var GA=k({stft_:h8});function g8(r,t,e,n,o="bilinear",s=0){let i=C(r,"image","cropAndResize"),a=C(t,"boxes","cropAndResize","float32"),u=C(e,"boxInd","cropAndResize","int32"),l=a.shape[0];_(i.rank===4,()=>`Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`),_(a.rank===2&&a.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${l},4] but had shape ${a.shape}.`),_(u.rank===1&&u.shape[0]===l,()=>`Error in cropAndResize: boxInd must be have size [${l}] but had shape ${a.shape}.`),_(n.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${n.length}.`),_(n[0]>=1&&n[1]>=1,()=>`cropSize must be atleast [1,1], but was ${n}`),_(o==="bilinear"||o==="nearest",()=>`method must be bilinear or nearest, but was ${o}`);let c={image:i,boxes:a,boxInd:u},p={method:o,extrapolationValue:s,cropSize:n};return T.runKernel(Ma,c,p)}var WA=k({cropAndResize_:g8});function x8(r){let t=C(r,"image","flipLeftRight","float32");_(t.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);let e={image:t};return T.runKernel(Ba,e,{})}var UA=k({flipLeftRight_:x8});function y8(r){let t=C(r,"image","grayscaleToRGB"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),_(n===1,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${n}.`);let o=new Array(t.rank);return o.fill(1,0,e),o[e]=3,Rr(t,o)}var HA=k({grayscaleToRGB_:y8});function b8(r){let t=C(r,"image","RGBToGrayscale"),e=t.rank-1,n=t.shape[e];_(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),_(n===3,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${n}.`);let o=t.dtype,s=J(t,"float32"),i=Oe([.2989,.587,.114]),a;switch(t.rank){case 2:a=wu("ij,j->i",s,i);break;case 3:a=wu("ijk,k->ij",s,i);break;case 4:a=wu("ijkl,l->ijk",s,i);break;case 5:a=wu("ijklm,m->ijkl",s,i);break;case 6:a=wu("ijklmn,n->ijklm",s,i);break;default:throw new Error("Not a valid tensor rank.")}return a=je(a,-1),J(a,o)}var qA=k({rgbToGrayscale_:b8});function w8(r,t,e=0,n=.5){let o=C(r,"image","rotateWithOffset","float32");_(o.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${o.rank}.`);let s={image:o},i={radians:t,fillValue:e,center:n};return T.runKernel(pl,s,i)}var KA=k({rotateWithOffset_:w8});function No(r,t,e,n,o,s){n==null&&(n=.5),o==null&&(o=Number.NEGATIVE_INFINITY),s==null&&(s=0);let i=r.shape[0];return e=Math.min(e,i),_(0<=n&&n<=1,()=>`iouThreshold must be in [0, 1], but was '${n}'`),_(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),_(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),_(t.rank===1,()=>"scores must be a 1D tensor"),_(t.shape[0]===i,()=>`scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`),_(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:e,iouThreshold:n,scoreThreshold:o,softNmsSigma:s}}function I8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY){let s=C(r,"boxes","nonMaxSuppression","float32"),i=C(t,"scores","nonMaxSuppression","float32"),a=No(s,i,e,n,o);e=a.maxOutputSize,n=a.iouThreshold,o=a.scoreThreshold;let u={maxOutputSize:e,iouThreshold:n,scoreThreshold:o};return T.runKernel(Ja,{boxes:s,scores:i},u)}var jA=k({nonMaxSuppression_:I8});function XA(r,t,e){let n=C8(r,t,e),o=n<0?-(n+1):n;r.splice(o,0,t)}function C8(r,t,e){return S8(r,t,e||v8)}function v8(r,t){return r>t?1:r<t?-1:0}function S8(r,t,e){let n=0,o=r.length,s=0,i=!1;for(;n<o;){s=n+(o-n>>>1);let a=e(t,r[s]);a>0?n=s+1:(o=s,i=!a)}return i?n:-n-1}function by(r,t,e,n,o){return gN(r,t,e,n,o,0)}function wy(r,t,e,n,o,s){return gN(r,t,e,n,o,0,!1,s,!0)}function Iy(r,t,e,n,o,s){return gN(r,t,e,n,o,s,!0)}function gN(r,t,e,n,o,s,i=!1,a=!1,u=!1){let l=[];for(let g=0;g<t.length;g++)t[g]>o&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(YA);let c=s>0?-.5/s:0,p=[],m=[];for(;p.length<e&&l.length>0;){let g=l.pop(),{score:x,boxIndex:b,suppressBeginIndex:w}=g;if(x<o)break;let I=!1;for(let N=p.length-1;N>=w;--N){let E=N8(r,b,p[N]);if(E>=n){I=!0;break}if(g.score=g.score*k8(n,c,E),g.score<=o)break}g.suppressBeginIndex=p.length,I||(g.score===x?(p.push(b),m.push(g.score)):g.score>o&&XA(l,g,YA))}let f=p.length,d=e-f;a&&d>0&&(p.push(...new Array(d).fill(0)),m.push(...new Array(d).fill(0)));let h={selectedIndices:p};return i&&(h.selectedScores=m),u&&(h.validOutputs=f),h}function N8(r,t,e){let n=r.subarray(t*4,t*4+4),o=r.subarray(e*4,e*4+4),s=Math.min(n[0],n[2]),i=Math.min(n[1],n[3]),a=Math.max(n[0],n[2]),u=Math.max(n[1],n[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),m=Math.max(o[1],o[3]),f=(a-s)*(u-i),d=(p-l)*(m-c);if(f<=0||d<=0)return 0;let h=Math.max(s,l),g=Math.max(i,c),x=Math.min(a,p),b=Math.min(u,m),w=Math.max(x-h,0)*Math.max(b-g,0);return w/(f+d-w)}function k8(r,t,e){let n=Math.exp(t*e*e);return e<=r?n:0}function YA(r,t){return r.score-t.score||r.score===t.score&&t.boxIndex-r.boxIndex}async function T8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY){let s=C(r,"boxes","nonMaxSuppressionAsync"),i=C(t,"scores","nonMaxSuppressionAsync"),a=No(s,i,e,n,o);e=a.maxOutputSize,n=a.iouThreshold,o=a.scoreThreshold;let u=await Promise.all([s.data(),i.data()]),l=u[0],c=u[1],{selectedIndices:p}=by(l,c,e,n,o);return s!==r&&s.dispose(),i!==t&&i.dispose(),Oe(p,"int32")}var ZA=T8;function _8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,"boxes","nonMaxSuppression"),a=C(t,"scores","nonMaxSuppression"),u=No(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l={boxes:i,scores:a},c={maxOutputSize:e,iouThreshold:n,scoreThreshold:o,softNmsSigma:s},p=T.runKernel(tl,l,c);return{selectedIndices:p[0],selectedScores:p[1]}}var JA=k({nonMaxSuppressionWithScore_:_8});async function E8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=0){let i=C(r,"boxes","nonMaxSuppressionAsync"),a=C(t,"scores","nonMaxSuppressionAsync"),u=No(i,a,e,n,o,s);e=u.maxOutputSize,n=u.iouThreshold,o=u.scoreThreshold,s=u.softNmsSigma;let l=await Promise.all([i.data(),a.data()]),c=l[0],p=l[1],{selectedIndices:m,selectedScores:f}=Iy(c,p,e,n,o,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Oe(m,"int32"),selectedScores:Oe(f)}}var QA=E8;function A8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,"boxes","nonMaxSuppression"),a=C(t,"scores","nonMaxSuppression"),u=No(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,m={boxes:i,scores:a},f={maxOutputSize:l,iouThreshold:c,scoreThreshold:p,padToMaxOutputSize:s},d=T.runKernel(Qa,m,f);return{selectedIndices:d[0],validOutputs:d[1]}}var t2=k({nonMaxSuppressionPadded_:A8});async function D8(r,t,e,n=.5,o=Number.NEGATIVE_INFINITY,s=!1){let i=C(r,"boxes","nonMaxSuppressionAsync"),a=C(t,"scores","nonMaxSuppressionAsync"),u=No(i,a,e,n,o,null),l=u.maxOutputSize,c=u.iouThreshold,p=u.scoreThreshold,[m,f]=await Promise.all([i.data(),a.data()]),{selectedIndices:d,validOutputs:h}=wy(m,f,l,c,p,s);return i!==r&&i.dispose(),a!==t&&a.dispose(),{selectedIndices:Oe(d,"int32"),validOutputs:pt(h,"int32")}}var e2=D8;function $8(r,t,e=!1,n=!1){let o=C(r,"images","resizeBilinear");_(o.rank===3||o.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${o.rank}.`),_(t.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),_(n===!1||e===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=o,i=!1;o.rank===3&&(i=!0,s=R(o,[1,o.shape[0],o.shape[1],o.shape[2]]));let[]=t,a={images:s},u={alignCorners:e,halfPixelCenters:n,size:t},l=T.runKernel(Bs,a,u);return i?R(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var Cy=k({resizeBilinear_:$8});function R8(r,t,e=!1,n=!1){let o=C(r,"images","resizeNearestNeighbor");_(o.rank===3||o.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${o.rank}.`),_(t.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),_(o.dtype==="float32"||o.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),_(n===!1||e===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=o,i=!1;o.rank===3&&(i=!0,s=R(o,[1,o.shape[0],o.shape[1],o.shape[2]]));let[]=t,a={images:s},u={alignCorners:e,halfPixelCenters:n,size:t},l=T.runKernel(zs,a,u);return i?R(l,[l.shape[1],l.shape[2],l.shape[3]]):l}var vy=k({resizeNearestNeighbor_:R8});function F8(r,t="binary",e=!1,n=.5){let o=C(r,"image","threshold"),s=.2989,i=.587,a=.114,u=o.shape[0]*o.shape[1],l=$(Oe([n]),255),c,p,m,f;if(_(o.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${o.rank}.`),_(o.shape[2]===3||o.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${o.shape[2]}.`),_(o.dtype==="int32"||o.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${o.dtype}.`),_(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),o.shape[2]===3){[c,p,m]=hr(o,[1,1,1],-1);let g=$(c,s),x=$(p,i),b=$(m,a);f=K(K(g,x),b)}else f=r;if(t==="otsu"){let g=Nx(J(pm(f),"int32"),ir([]),256);l=O8(g,u)}let d=e?Vn(f,l):Re(f,l);return J($(d,255),"int32")}function O8(r,t){let e=Oe([-1]),n=Oe([0]),o=Oe([0]),s,i,a,u,l,c;for(let p=0;p<r.size-1;p++){s=Ot(r,0,p+1),i=Ot(r,p+1),l=ut(mt(s),t),c=ut(mt(i),t);let m=mt($(s,pa(0,s.size)));a=ut(m,mt(s));let f=Co(i.shape,s.size),d=K(pa(0,i.size),f),h=$(i,d);u=ut(mt(h),mt(i));let g=at(a,u),x=at(a,u),b=$(l,c);o=$($(b,g),x);let w=Re(o,n);n=Ie(w,o,n),e=Ie(w,Oe([p]),e)}return e}var r2=k({threshold_:F8});function M8(r,t,e="nearest",n="constant",o=0,s){let i=C(r,"image","transform","float32"),a=C(t,"transforms","transform","float32");_(i.rank===4,()=>`Error in transform: image must be rank 4,but got rank ${i.rank}.`),_(a.rank===2&&(a.shape[0]===i.shape[0]||a.shape[0]===1)&&a.shape[1]===8,()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),_(s==null||s.length===2,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);let u={image:i,transforms:a},l={interpolation:e,fillMode:n,fillValue:o,outputShape:s};return T.runKernel(cl,u,l)}var n2=k({transform_:M8});function P8(r,t,e){let n=C(r,"a","bandPart");_(n.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${n.rank}.`);let o=n.shape,[s,i]=n.shape.slice(-2),a,u;typeof t=="number"?(_(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),_(t<=s,()=>`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`),a=C(t<0?s:t,"numLower","bandPart")):(_(t.dtype==="int32",()=>"bandPart(): numLower's dtype must be an int32."),a=Ie(yl(t,0),s,lo(t,s))),typeof e=="number"?(_(e%1===0,()=>`bandPart(): numUpper must be an integer, got ${e}.`),_(e<=i,()=>`bandPart(): numUpper (${e}) must not be greater than the number of columns (${i}).`),u=C(e<0?i:e,"numUpper","bandPart")):(_(e.dtype==="int32",()=>"bandPart(): numUpper's dtype must be an int32."),u=Ie(yl(e,0),i,lo(e,i)));let l=R(pa(0,s,1,"int32"),[-1,1]),c=pa(0,i,1,"int32"),p=at(l,c),m=Fr(Vn(p,a),cn(p,Ut(u))),f=ke([s,i],n.dtype);return R(Fe(gr(R(n,[-1,s,i])).map(d=>Ie(m,d,f))),o)}var o2=k({bandPart_:P8});function L8(r){let t;if(Array.isArray(r)){t=!1,_(r!=null&&r.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let o=r[0].shape[0];for(let s=1;s<r.length;++s)_(r[s].shape[0]===o,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${o})`)}else t=!0,r=hr(r,r.shape[0],0).map(o=>Wn(o,[0]));_(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let e=[],n=r;for(let o=0;o<r.length;++o)e.push(T.tidy(()=>{let s=n[o];if(o>0)for(let i=0;i<o;++i){let a=$(mt($(e[i],s)),e[i]);s=at(s,a)}return ut(s,xl(s,"euclidean"))}));return t?Fe(e,0):e}var s2=k({gramSchmidt_:L8});function z8(r,t=!1){if(_(r.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return i2(r,t);{let e=r.shape.slice(0,r.shape.length-2).reduce((u,l)=>u*l),n=gr(R(r,[e,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),o=[],s=[];n.forEach(u=>{let[l,c]=i2(u,t);o.push(l),s.push(c)});let i=R(Fe(o,0),r.shape),a=R(Fe(s,0),r.shape);return[i,a]}}function i2(r,t=!1){return T.tidy(()=>{_(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let e=r.shape[0],n=r.shape[1],o=fc(e),s=un(r),i=pi([[1]],[1,1]),a=un(i),u=e>=n?n:e;for(let l=0;l<u;++l){let c=s,p=a,m=o;[a,s,o]=T.tidy(()=>{let f=Ot(s,[l,l],[e-l,1]),d=xl(f),h=Ot(s,[l,l],[1,1]),g=Ie(Re(h,0),pi([[-1]]),pi([[1]])),x=at(h,$(g,d)),b=ut(f,x);b.shape[0]===1?a=un(i):a=ie([i,Ot(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let w=Ut(ut(Bt(g,x),d)),I=Ot(s,[l,0],[e-l,n]),N=$(w,a),E=Vt(a);if(l===0)s=at(I,Bt(N,Bt(E,I)));else{let F=at(I,Bt(N,Bt(E,I)));s=ie([Ot(s,[0,0],[l,n]),F],0)}let A=Vt(N),D=Ot(o,[0,l],[e,o.shape[1]-l]);if(l===0)o=at(D,Bt(Bt(D,a),A));else{let F=at(D,Bt(Bt(D,a),A));o=ie([Ot(o,[0,0],[e,l]),F],1)}return[a,s,o]}),Tt([c,p,m])}return!t&&e>n&&(o=Ot(o,[0,0],[e,n]),s=Ot(s,[0,0],[n,n])),[o,s]})}var a2=k({qr_:z8});var Je;(function(r){r[r.NONE=0]="NONE",r[r.MEAN=1]="MEAN",r[r.SUM=2]="SUM",r[r.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Je||(Je={}));function B8(r,t,e=Je.SUM_BY_NONZERO_WEIGHTS){let n=C(r,"losses","computeWeightedLoss"),o=null;t!=null&&(o=C(t,"weights","computeWeightedLoss"));let s=o==null?n:$(n,o);if(e===Je.NONE)return s;if(e===Je.SUM)return mt(s);if(e===Je.MEAN){if(o==null)return Ne(s);{let i=n.size/o.size,a=ut(mt(s),mt(o));return i>1?ut(a,pt(i)):a}}if(e===Je.SUM_BY_NONZERO_WEIGHTS){if(o==null)return ut(mt(s),pt(n.size));{let i=$(o,ar(n.shape)),a=J(mt(ci(i,pt(0))),"float32");return ut(mt(s),a)}}throw Error(`Unknown reduction: ${e}`)}var Kr=k({computeWeightedLoss_:B8});function V8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","absoluteDifference"),s=C(t,"predictions","absoluteDifference"),i=null;e!=null&&(i=C(e,"weights","absoluteDifference")),$e(o.shape,s.shape,"Error in absoluteDifference: ");let a=_e(at(o,s));return Kr(a,i,n)}var l2=k({absoluteDifference_:V8});function G8(r,t,e,n,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","cosineDistance"),i=C(t,"predictions","cosineDistance"),a=null;n!=null&&(a=C(n,"weights","cosineDistance")),$e(s.shape,i.shape,"Error in cosineDistance: ");let u=pt(1),l=at(u,mt($(s,i),e,!0));return Kr(l,a,o)}var u2=k({cosineDistance_:G8});function W8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","hingeLoss"),s=C(t,"predictions","hingeLoss"),i=null;e!=null&&(i=C(e,"weights","hingeLoss")),$e(o.shape,s.shape,"Error in hingeLoss: ");let a=pt(1);o=at($(pt(2),o),a);let u=Or(at(a,$(o,s)));return Kr(u,i,n)}var c2=k({hingeLoss_:W8});function U8(r,t,e,n=1,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","huberLoss"),i=C(t,"predictions","huberLoss"),a=null;e!=null&&(a=C(e,"weights","huberLoss")),$e(s.shape,i.shape,"Error in huberLoss: ");let u=pt(n),l=_e(at(i,s)),c=lo(l,u),p=at(l,c),m=K($(pt(.5),Wt(c)),$(u,p));return Kr(m,a,o)}var p2=k({huberLoss_:U8});function H8(r,t,e,n=1e-7,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"labels","logLoss"),i=C(t,"predictions","logLoss"),a=null;e!=null&&(a=C(e,"weights","logLoss")),$e(s.shape,i.shape,"Error in logLoss: ");let u=pt(1),l=pt(n),c=Ut($(s,Nr(K(i,l)))),p=$(at(u,s),Nr(K(at(u,i),l))),m=at(c,p);return Kr(m,a,o)}var m2=k({logLoss_:H8});function q8(r,t,e,n=Je.SUM_BY_NONZERO_WEIGHTS){let o=C(r,"labels","meanSquaredError"),s=C(t,"predictions","meanSquaredError"),i=null;e!=null&&(i=C(e,"weights","meanSquaredError")),$e(o.shape,s.shape,"Error in meanSquaredError: ");let a=wm(o,s);return Kr(a,i,n)}var f2=k({meanSquaredError_:q8});function K8(r,t){let e=C(r,"labels","sigmoidCrossEntropyWithLogits"),n=C(t,"logits","sigmoidCrossEntropyWithLogits");$e(e.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Or(n),s=$(n,e),i=vu(Ke(Ut(_e(n))));return K(at(o,s),i)}function j8(r,t,e,n=0,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"multiClassLabels","sigmoidCrossEntropy"),i=C(t,"logits","sigmoidCrossEntropy"),a=null;if(e!=null&&(a=C(e,"weights","sigmoidCrossEntropy")),$e(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),n>0){let l=pt(n),c=pt(1),p=pt(.5);s=K($(s,at(c,l)),$(p,l))}let u=K8(s,i);return Kr(u,a,o)}var d2=k({sigmoidCrossEntropy_:j8});function X8(r,t,e=-1){if(e===-1&&(e=t.rank-1),e!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${e}`);return pn((o,s,i)=>{let u=Su(s,[e],!0),l=at(J(s,"float32"),u);i([o,l]);let c=Ut($(l,o));return{value:mt(c,[e]),gradFunc:(f,d)=>{let[h,g]=d,x=vo(f.shape,[e]);return[$(R(f,x),at(J(h,"float32"),Ke(g))),$(R(f,x),at(Ke(g),J(h,"float32")))]}}})(r,t)}function Y8(r,t,e,n=0,o=Je.SUM_BY_NONZERO_WEIGHTS){let s=C(r,"onehotLabels","softmaxCrossEntropy"),i=C(t,"logits","softmaxCrossEntropy"),a=null;if(e!=null&&(a=C(e,"weights","softmaxCrossEntropy")),$e(s.shape,i.shape,"Error in softmaxCrossEntropy: "),n>0){let l=pt(n),c=pt(1),p=pt(s.shape[1]);s=K($(s,at(c,l)),ut(l,p))}let u=X8(s,i);return Kr(u,a,o)}var h2=k({softmaxCrossEntropy_:Y8});function Z8(r,t,e,n){let o=C(r,"indices","sparseFillEmptyRows","int32"),s=C(t,"values","sparseFillEmptyRows"),i=C(e,"denseShape","sparseFillEmptyRows","int32"),a=C(n,"defaultValue","sparseFillEmptyRows",s.dtype);if(o.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
        ${o.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(a.rank!==0)throw new Error(`Default value should be a scalar but received shape ${a.shape}`);let u={indices:o,values:s,denseShape:i,defaultValue:a},l=T.runKernel(nu,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}var g2=k({sparseFillEmptyRows_:Z8});function J8(r,t,e){let n=C(r,"inputIndices","sparseReshape","int32"),o=C(t,"inputShape","sparseReshape","int32"),s=C(e,"newShape","sparseReshape","int32");if(n.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
        ${n.shape}`);if(o.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${o.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:n,inputShape:o,newShape:s},a=T.runKernel(il,i);return{outputIndices:a[0],outputShape:a[1]}}var x2=k({sparseReshape_:J8});function Q8(r,t,e){let n=C(r,"data","sparseSegmentMean"),o=C(t,"indices","sparseSegmentMean","int32"),s=C(e,"segmentIds","sparseSegmentMean","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
          ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(ou,i)}var y2=k({sparseSegmentMean_:Q8});function tY(r,t,e){let n=C(r,"data","sparseSegmentSum"),o=C(t,"indices","sparseSegmentSum","int32"),s=C(e,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
         ${o.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
         ${s.shape}`);let i={data:n,indices:o,segmentIds:s};return T.runKernel(su,i)}var b2=k({sparseSegmentSum_:tY});function eY(r,t,e,n,o,s,i,a){let u=C(r,"data","stringNGrams","string");if(u.dtype!=="string")throw new Error("Data must be of datatype string");if(u.shape.length!==1)throw new Error(`Data must be a vector, saw: ${u.shape}`);let l=C(t,"dataSplits","stringNGrams");if(l.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:e,nGramWidths:n,leftPad:o,rightPad:s,padWidth:i,preserveShortSequences:a},p={data:u,dataSplits:l},m=T.runKernel(au,p,c);return{nGrams:m[0],nGramsSplits:m[1]}}var w2=k({stringNGrams_:eY});function rY(r,t,e=!0){let n=C(r,"input","stringSplit","string"),o=C(t,"delimiter","stringSplit","string");if(n.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${n.shape}`);if(o.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${o.shape}`);let s={skipEmpty:e},i={input:n,delimiter:o},a=T.runKernel(lu,i,s);return{indices:a[0],values:a[1],shape:a[2]}}var I2=k({stringSplit_:rY});function nY(r,t){let e=C(r,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let o={input:e};return T.runKernel(uu,o,n)}var C2=k({stringToHashBucketFast_:nY});function oY(r,t,e,n=!0){let o=C(r,"input","staticRegexReplace","string"),s={pattern:t,rewrite:e,replaceGlobal:n};return T.runKernel(ec,{x:o},s)}var v2=k({staticRegexReplace_:oY});var sY={fft:Au,ifft:wl,rfft:Du,irfft:bm},iY={hammingWindow:VA,hannWindow:xy,frame:yy,stft:GA},fn={flipLeftRight:UA,grayscaleToRGB:HA,resizeNearestNeighbor:vy,resizeBilinear:Cy,rgbToGrayscale:qA,rotateWithOffset:KA,cropAndResize:WA,nonMaxSuppression:jA,nonMaxSuppressionAsync:ZA,nonMaxSuppressionWithScore:JA,nonMaxSuppressionWithScoreAsync:QA,nonMaxSuppressionPadded:t2,nonMaxSuppressionPaddedAsync:e2,threshold:r2,transform:n2},xN={bandPart:o2,gramSchmidt:s2,qr:a2},aY={absoluteDifference:l2,computeWeightedLoss:Kr,cosineDistance:u2,hingeLoss:c2,huberLoss:p2,logLoss:m2,meanSquaredError:f2,sigmoidCrossEntropy:d2,softmaxCrossEntropy:h2},lY={sparseFillEmptyRows:g2,sparseReshape:x2,sparseSegmentMean:y2,sparseSegmentSum:b2},uY={stringNGrams:w2,stringSplit:I2,stringToHashBucketFast:C2,staticRegexReplace:v2};var Q={};Kt(Q,{Serializable:()=>yh,SerializationMap:()=>Sy,getRegisteredName:()=>pY,registerClass:()=>bN});var cY=new Map,yN=new Map,yh=class{getClassName(){return this.constructor.className}static fromConfig(t,e){return new t(e)}},Sy=class r{constructor(){this.classNameMap={}}static getMap(){return r.instance==null&&(r.instance=new r),r.instance}static register(t){r.getMap().classNameMap[t.className]=[t,t.fromConfig]}};function bN(r,t,e){_(r.className!=null,()=>"Class being registered does not have the static className property defined."),_(typeof r.className=="string",()=>"className is required to be a string, but got type "+typeof r.className),_(r.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),typeof t=="undefined"&&(t="Custom"),typeof e=="undefined"&&(e=r.className);let n=e,o=t+">"+n;return Sy.register(r),cY.set(o,r),yN.set(r,o),r}function pY(r){return yN.has(r)?yN.get(r):r.className}var jr=class extends yh{minimize(t,e=!1,n){let{value:o,grads:s}=this.computeGradients(t,n);if(n!=null){let i=n.map(a=>({name:a.name,tensor:s[a.name]}));this.applyGradients(i)}else this.applyGradients(s);return Tt(s),e?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(t,e){return Ux(t,e)}dispose(){this.iterations_!=null&&Tt(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:pt(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(t){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(t){return this.iterations_=(await t[0].tensor.data())[0],t.slice(1)}};Object.defineProperty(jr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var vc=class extends jr{static get className(){return"Adadelta"}constructor(t,e,n=null){super(),this.learningRate=t,this.rho=e,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:B(()=>vt(s).variable(i))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedGrads[o].variable,l=this.accumulatedUpdates[o].variable;B(()=>{let c=K($(u,this.rho),$(Wt(a),1-this.rho)),p=$(ut(ge(K(l,this.epsilon)),ge(K(u,this.epsilon))),a),m=K($(l,this.rho),$(Wt(p),1-this.rho));u.assign(c),l.assign(m);let f=K($(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Tt(this.accumulatedGrads.map(t=>t.variable)),Tt(this.accumulatedUpdates.map(t=>t.variable)))}async getWeights(){let t=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=t.length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.rho,e.epsilon)}};var Sc=class extends jr{static get className(){return"Adagrad"}constructor(t,e=.1){super(),this.learningRate=t,this.initialAccumulatorValue=e,this.accumulatedGrads=[]}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:B(()=>Co(s.shape,this.initialAccumulatorValue).variable(!1))});let i=Array.isArray(t)?t[o].tensor:t[n];if(i==null)return;let a=this.accumulatedGrads[o].variable;B(()=>{let u=K(a,Wt(i));a.assign(u);let l=K($(ut(i,ge(K(u,T.backend.epsilon()))),-this.learningRate),s);s.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Tt(this.accumulatedGrads.map(t=>t.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulatedGrads=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(t,e){return new t(e.learningRate,e.initialAccumulatorValue)}};var Nc=class extends jr{static get className(){return"Adam"}constructor(t,e,n,o=null){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],B(()=>{this.accBeta1=pt(e).variable(),this.accBeta2=pt(n).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=at(1,this.accBeta1),o=at(1,this.accBeta2);e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:B(()=>vt(a).variable(u))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${s}/v`,variable:B(()=>vt(a).variable(u))});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedSecondMoment[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=K($(p,this.beta2),$(Wt(l),1-this.beta2)),d=ut(m,n),h=ut(f,o);c.assign(m),p.assign(f);let g=K($(ut(d,K(ge(h),this.epsilon)),-this.learningRate),a);a.assign(g)}),this.accBeta1.assign($(this.accBeta1,this.beta1)),this.accBeta2.assign($(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedSecondMoment!=null&&Tt(this.accumulatedSecondMoment.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t),B(()=>{this.accBeta1.assign(qr(this.beta1,this.iterations_+1)),this.accBeta2.assign(qr(this.beta2,this.iterations_+1))});let e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon)}};var kc=class extends jr{static get className(){return"Adamax"}constructor(t,e,n,o=null,s=0){super(),this.learningRate=t,this.beta1=e,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],B(()=>{this.iteration=pt(0).variable(),this.accBeta1=pt(e).variable()}),o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(t){let e=Array.isArray(t)?t.map(n=>n.name):Object.keys(t);B(()=>{let n=at(1,this.accBeta1),o=ut(-this.learningRate,K($(this.iteration,this.decay),1));e.forEach((s,i)=>{let a=T.registeredVariables[s],u=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${s}/m`,variable:vt(a).variable(u)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${s}/v`,variable:vt(a).variable(u)});let l=Array.isArray(t)?t[i].tensor:t[s];if(l==null)return;let c=this.accumulatedFirstMoment[i].variable,p=this.accumulatedWeightedInfNorm[i].variable,m=K($(c,this.beta1),$(l,1-this.beta1)),f=$(p,this.beta2),d=_e(l),h=kn(f,d);c.assign(m),p.assign(h);let g=K($(ut(o,n),ut(m,K(h,this.epsilon))),a);a.assign(g)}),this.iteration.assign(K(this.iteration,1)),this.accBeta1.assign($(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Tt(this.accumulatedFirstMoment.map(t=>t.variable)),this.accumulatedWeightedInfNorm!=null&&Tt(this.accumulatedWeightedInfNorm.map(t=>t.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(t){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(t,e){return new t(e.learningRate,e.beta1,e.beta2,e.epsilon,e.decay)}};var Il=class extends jr{static get className(){return"SGD"}constructor(t){super(),this.learningRate=t,this.setLearningRate(t)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=Array.isArray(t)?t[o].tensor:t[n];if(s==null)return;let i=T.registeredVariables[n];B(()=>{let a=K($(this.c,s),i);i.assign(a)})}),this.incrementIterations()}setLearningRate(t){this.learningRate=t,this.c!=null&&this.c.dispose(),this.c=De(pt(-t))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(t){if(t=await this.extractIterations(t),t.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(t,e){return new t(e.learningRate)}};var Tc=class extends Il{static get className(){return"Momentum"}constructor(t,e,n=!1){super(t),this.learningRate=t,this.momentum=e,this.useNesterov=n,this.accumulations=[],this.m=pt(this.momentum)}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n];this.accumulations[o]==null&&(this.accumulations[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(!1))});let i=this.accumulations[o].variable,a=Array.isArray(t)?t[o].tensor:t[n];a!=null&&B(()=>{let u,l=K($(this.m,i),a);this.useNesterov?u=K($(this.c,K(a,$(l,this.m))),s):u=K($(this.c,l),s),i.assign(l),s.assign(u)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Tt(this.accumulations.map(t=>t.variable))}setMomentum(t){this.momentum=t}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=!1;this.accumulations=t.map(n=>({originalName:n.name,variable:n.tensor.variable(e)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(t,e){return new t(e.learningRate,e.momentum,e.useNesterov)}};var _c=class extends jr{static get className(){return"RMSProp"}constructor(t,e=.9,n=0,o=null,s=!1){if(super(),this.learningRate=t,this.decay=e,this.momentum=n,this.epsilon=o,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,o==null&&(this.epsilon=T.backend.epsilon()),t==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(t){(Array.isArray(t)?t.map(n=>n.name):Object.keys(t)).forEach((n,o)=>{let s=T.registeredVariables[n],i=!1;this.accumulatedMeanSquares[o]==null&&(this.accumulatedMeanSquares[o]={originalName:`${n}/rms`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMoments[o]==null&&(this.accumulatedMoments[o]={originalName:`${n}/momentum`,variable:B(()=>vt(s).variable(i))}),this.accumulatedMeanGrads[o]==null&&this.centered&&(this.accumulatedMeanGrads[o]={originalName:`${n}/mg`,variable:B(()=>vt(s).variable(i))});let a=Array.isArray(t)?t[o].tensor:t[n];if(a==null)return;let u=this.accumulatedMeanSquares[o].variable,l=this.accumulatedMoments[o].variable;B(()=>{let c=K($(u,this.decay),$(Wt(a),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[o].variable,m=K($(p,this.decay),$(a,1-this.decay)),f=ut($(a,this.learningRate),ge(at(c,K(Wt(m),this.epsilon)))),d=K($(l,this.momentum),f);u.assign(c),p.assign(m),l.assign(d);let h=at(s,d);s.assign(h)}else{let p=K($(u,this.decay),$(Wt(a),1-this.decay)),m=K($(l,this.momentum),ut($(a,this.learningRate),ge(K(p,this.epsilon))));u.assign(p),l.assign(m);let f=at(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Tt(this.accumulatedMeanSquares.map(t=>t.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Tt(this.accumulatedMeanGrads.map(t=>t.variable)),this.accumulatedMoments!=null&&Tt(this.accumulatedMoments.map(t=>t.variable))}async getWeights(){let t=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&t.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(t.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(t){t=await this.extractIterations(t);let e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,e*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(e*2,e*3).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(t,e){return new t(e.learningRate,e.decay,e.momentum,e.epsilon,e.centered)}};var mY=[vc,Sc,Nc,kc,Tc,_c,Il];function S2(){for(let r of mY)bN(r)}var Mr={};Kt(Mr,{CompositeArrayBuffer:()=>Ur,browserFiles:()=>k2,browserHTTPRequest:()=>A2,concatenateArrayBuffers:()=>pE,copyModel:()=>NE,decodeWeights:()=>ix,decodeWeightsStream:()=>ax,encodeWeights:()=>aE,fromMemory:()=>D2,fromMemorySync:()=>NN,getLoadHandlers:()=>hE,getModelArtifactsForJSON:()=>Yp,getModelArtifactsForJSONSync:()=>H0,getModelArtifactsInfoForJSON:()=>Qi,getSaveHandlers:()=>dE,getWeightSpecs:()=>uh,http:()=>ky,isHTTPScheme:()=>Ny,listModels:()=>vE,loadWeights:()=>_2,moveModel:()=>kE,registerLoadRouter:()=>fE,registerSaveRouter:()=>mE,removeModel:()=>SE,weightsLoaderFactory:()=>vN,withSaveHandler:()=>$2,withSaveHandlerSync:()=>R2});var fY="model",dY=".json",hY=".weights.bin";function N2(r){return new Promise(t=>setTimeout(t)).then(r)}var Nm=class r{constructor(t){if(!L().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");t.startsWith(r.URL_SCHEME)&&(t=t.slice(r.URL_SCHEME.length)),(t==null||t.length===0)&&(t=fY),this.modelJsonFileName=t+dY,this.weightDataFileName=t+hY}async save(t){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let e=Ur.join(t.weightData),n=window.URL.createObjectURL(new Blob([e],{type:"application/octet-stream"}));if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let o=[{paths:["./"+this.weightDataFileName],weights:t.weightSpecs}],s=lx(t,o),i=window.URL.createObjectURL(new Blob([JSON.stringify(s)],{type:"application/json"})),a=this.modelJsonAnchor==null?document.createElement("a"):this.modelJsonAnchor;if(a.download=this.modelJsonFileName,a.href=i,await N2(()=>a.dispatchEvent(new MouseEvent("click"))),t.weightData!=null){let u=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;u.download=this.weightDataFileName,u.href=n,await N2(()=>u.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:Qi(t)}}}};Nm.URL_SCHEME="downloads://";var wN=class{constructor(t){if(t==null||t.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${t}`);this.jsonFile=t[0],this.weightsFiles=t.slice(1)}async load(){return new Promise((t,e)=>{let n=new FileReader;n.onload=o=>{let s=JSON.parse(o.target.result),i=s.modelTopology;if(i==null){e(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));return}if(s.weightsManifest==null){e(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));return}if(this.weightsFiles.length===0){t({modelTopology:i});return}let u=Yp(s,l=>this.loadWeights(l));t(u)},n.onerror=o=>e(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),n.readAsText(this.jsonFile)})}loadWeights(t){let e=[],n=[];for(let i of t)e.push(...i.weights),n.push(...i.paths);let o=this.checkManifestAndWeightFiles(t),s=n.map(i=>this.loadWeightsFile(i,o[i]));return Promise.all(s).then(i=>[e,i])}loadWeightsFile(t,e){return new Promise((n,o)=>{let s=new FileReader;s.onload=i=>{let a=i.target.result;n(a)},s.onerror=i=>o(`Failed to weights data from file of path '${t}'.`),s.readAsArrayBuffer(e)})}checkManifestAndWeightFiles(t){let e=[],n=this.weightsFiles.map(s=>U0(s.name)),o={};for(let s of t)s.paths.forEach(i=>{let a=U0(i);if(e.indexOf(a)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${a}'`);if(e.push(a),n.indexOf(a)===-1)throw new Error(`Weight file with basename '${a}' is not provided.`);o[i]=this.weightsFiles[n.indexOf(a)]});if(e.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${e.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return o}},gY=r=>L().getBool("IS_BROWSER")&&!Array.isArray(r)&&r.startsWith(Nm.URL_SCHEME)?xY(r.slice(Nm.URL_SCHEME.length)):null;Cr.registerSaveRouter(gY);function xY(r="model"){return new Nm(r)}function k2(r){return new wN(r)}function IN(r,t,e,n){i(r),e=e==null?0:e,n=n==null?1:n,a(e,n);let o=0,s=u=>(u.then(l=>{let c=e+ ++o/r.length*(n-e);return t(c),l}),u);function i(u){_(u!=null&&Array.isArray(u)&&u.length>0,()=>"promises must be a none empty array")}function a(u,l){_(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${u}`),_(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${l}`),_(l>=u,()=>`startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${l}`)}return Promise.all(r.map(s))}async function CN(r,t){t==null&&(t={});let e=t.fetchFunc==null?L().platform.fetch:t.fetchFunc,n=r.map(p=>e(p,t.requestInit,{isBinary:!0})),a=(t.onProgress==null?await Promise.all(n):await IN(n,t.onProgress,0,.5)).map(p=>p.arrayBuffer());return t.onProgress==null?await Promise.all(a):await IN(a,t.onProgress,.5,1)}function T2(r,t){var e;let n=t.fetchFunc==null?L().platform.fetch:t.fetchFunc,o=0,s;return(e=t.onProgress)===null||e===void 0||e.call(t,0),new ReadableStream({pull:async i=>{for(var a;o<r.length;){s||(s=(await n(r[o],t.requestInit,{isBinary:!0})).body.getReader());let{done:u,value:l}=await s.read();if(u){o++,s=void 0,(a=t.onProgress)===null||a===void 0||a.call(t,o/r.length);continue}i.enqueue(l);return}i.close()}})}async function _2(r,t="",e,n){return vN(i=>CN(i,{requestInit:n}))(r,t,e)}function vN(r){return async(t,e="",n)=>{let o=t.map(()=>!1),s={},i=n!=null?n.map(()=>!1):[],a=[];if(t.forEach((f,d)=>{let h=0;f.weights.forEach(g=>{let x="quantization"in g?g.quantization.dtype:g.dtype,b=dl[x]*jt(g.shape),w=()=>{o[d]=!0,s[d]==null&&(s[d]=[]),s[d].push({manifestEntry:g,groupOffset:h,sizeBytes:b})};n!=null?n.forEach((I,N)=>{I===g.name&&(w(),i[N]=!0)}):w(),a.push(g.name),h+=b})}),!i.every(f=>f)){let f=n.filter((d,h)=>!i[h]);throw new Error(`Could not find weights in manifest with names: ${f.join(", ")}. 
Manifest JSON has weights with names: ${a.join(", ")}.`)}let u=o.reduce((f,d,h)=>(d&&f.push(h),f),[]),l=[];u.forEach(f=>{t[f].paths.forEach(d=>{let h=e+(e.endsWith("/")?"":"/")+d;l.push(h)})});let c=await r(l),p={},m=0;return u.forEach(f=>{let d=t[f].paths.length,h=new Ur(c.slice(m,m+d));s[f].forEach(x=>{let b=h.slice(x.groupOffset,x.groupOffset+x.sizeBytes),w=ix(b,[x.manifestEntry]);for(let I in w)p[I]=w[I]}),m+=d}),p}}var yY="application/octet-stream",bY="application/json",bh=class{constructor(t,e){if(this.DEFAULT_METHOD="POST",e==null&&(e={}),this.weightPathPrefix=e.weightPathPrefix,this.weightUrlConverter=e.weightUrlConverter,e.fetchFunc!=null?(_(typeof e.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=e.fetchFunc):this.fetch=L().platform.fetch,_(t!=null&&t.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(t)&&_(t.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${t.length}).`),this.path=t,e.requestInit!=null&&e.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=e.requestInit||{},this.loadOptions=e}async save(t){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let e=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);e.body=new FormData;let n=[{paths:["./model.weights.bin"],weights:t.weightSpecs}],o=lx(t,n);if(e.body.append("model.json",new Blob([JSON.stringify(o)],{type:bY}),"model.json"),t.weightData!=null){let i=Ur.join(t.weightData);e.body.append("model.weights.bin",new Blob([i],{type:yY}),"model.weights.bin")}let s=await this.fetch(this.path,e);if(s.ok)return{modelArtifactsInfo:Qi(t),responses:[s]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${s.status}.`)}async loadModelJSON(){let t=await this.fetch(this.path,this.requestInit);if(!t.ok)throw new Error(`Request to ${this.path} failed with status code ${t.status}. Please verify this URL points to the model JSON of the model to load.`);let e;try{e=await t.json()}catch(s){let i=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?i+=" 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.":i+=" Please make sure the server is serving valid JSON for this request.",new Error(i)}let n=e.modelTopology,o=e.weightsManifest;if(n==null&&o==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);return e}async load(){if(this.loadOptions.streamWeights)return this.loadStream();let t=await this.loadModelJSON();return Yp(t,e=>this.loadWeights(e))}async loadStream(){let t=await this.loadModelJSON(),e=await this.getWeightUrls(t.weightsManifest),n=uh(t.weightsManifest),o=()=>T2(e,this.loadOptions);return Object.assign(Object.assign({},t),{weightSpecs:n,getWeightStream:o})}async getWeightUrls(t){let e=Array.isArray(this.path)?this.path[1]:this.path,[n,o]=wY(e),s=this.weightPathPrefix||n,i=[],a=[];for(let u of t)for(let l of u.paths)this.weightUrlConverter!=null?a.push(this.weightUrlConverter(l)):i.push(s+l+o);return this.weightUrlConverter&&i.push(...await Promise.all(a)),i}async loadWeights(t){let e=await this.getWeightUrls(t),n=uh(t),o=await CN(e,this.loadOptions);return[n,o]}};bh.URL_SCHEME_REGEX=/^https?:\/\//;function wY(r){let t=r.lastIndexOf("/"),e=r.lastIndexOf("?"),n=r.substring(0,t),o=e>t?r.substring(e):"";return[n+"/",o]}function Ny(r){return r.match(bh.URL_SCHEME_REGEX)!=null}var E2=(r,t)=>{if(typeof fetch=="undefined"&&(t==null||t.fetchFunc==null))return null;{let e=!0;if(Array.isArray(r)?e=r.every(n=>Ny(n)):e=Ny(r),e)return ky(r,t)}return null};Cr.registerSaveRouter(E2);Cr.registerLoadRouter(E2);function ky(r,t){return new bh(r,t)}function A2(r,t){return ky(r,t)}var wh=class{constructor(t){this.modelArtifacts=t}load(){return this.modelArtifacts}},Ty=class{constructor(t){this.saveHandler=t}save(t){return this.saveHandler(t)}},SN=class{constructor(t){t.load&&(this.load=()=>Promise.resolve(t.load())),t.save&&(this.save=e=>Promise.resolve(t.save(e)))}};function D2(r,t,e,n){let o=arguments;return new SN(NN(...o))}function NN(r,t,e,n){return arguments.length===1?r.modelTopology!=null||r.weightSpecs!=null?new wh(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 wh({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 wh({modelTopology:r,weightSpecs:t,weightData:e,trainingConfig:n}))}function $2(r){return new Ty(r)}function R2(r){return new Ty(r)}var O2={};Kt(O2,{confusionMatrix:()=>F2});function IY(r,t,e){let n=C(r,"labels","confusionMatrix"),o=C(t,"predictions","confusionMatrix");_(e==null||e>0&&Number.isInteger(e),()=>`If provided, numClasses must be a positive integer, but got ${e}`),_(n.rank===1,()=>`Expected the rank of labels to be 1, but got ${n.rank}`),_(o.rank===1,()=>`Expected the rank of predictions to be 1, but got ${o.rank}`),_(n.shape[0]===o.shape[0],()=>`Mismatch in the number of examples: ${n.shape[0]} vs. ${o.shape[0]}. Labels and predictions should have the same number of elements.`),_(e>0&&Number.isInteger(e),()=>`numClasses is required to be a positive integer, but got ${e}`);let s=ca(J(n,"int32"),e),i=ca(J(o,"int32"),e),a=Vt(s),u=Bt(a,i);return J(u,"int32")}var F2=k({confusionMatrix_:IY});var _y={};Kt(_y,{draw:()=>EY,fromPixels:()=>AY,fromPixelsAsync:()=>kY,toPixels:()=>_Y});var Ec,M2=!1;function P2(r,t=3){if(t>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 e=!1,n=!1,o=!1,s=!1,i=!1,a=!1;if(r.data instanceof Uint8Array)e=!0;else if(typeof ImageData!="undefined"&&r instanceof ImageData)n=!0;else if(typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement)o=!0;else if(typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement)s=!0;else if(r.getContext!=null)i=!0;else if(typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap)a=!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(Wp(th,T.backendName)!=null){let d={pixels:r},h={numChannels:t};return T.runKernel(th,d,h)}let[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p;if(i)p=r.getContext("2d").getImageData(0,0,l,c).data;else if(n||e)p=r.data;else if(s||o||a){if(Ec==null)if(typeof document=="undefined")if(typeof OffscreenCanvas!="undefined"&&typeof OffscreenCanvasRenderingContext2D!="undefined")Ec=new OffscreenCanvas(1,1).getContext("2d");else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");else Ec=document.createElement("canvas").getContext("2d",{willReadFrequently:!0});Ec.canvas.width=l,Ec.canvas.height=c,Ec.drawImage(r,0,0,l,c),p=Ec.getImageData(0,0,l,c).data}let m;if(t===4)m=new Int32Array(p);else{let d=l*c;m=new Int32Array(d*t);for(let h=0;h<d;h++)for(let g=0;g<t;++g)m[h*t+g]=p[h*4+g]}return uy(m,[c,l,t],"int32")}function CY(r){return r!=null&&r.data instanceof Uint8Array}function vY(){return typeof window!="undefined"&&typeof ImageBitmap!="undefined"&&window.hasOwnProperty("createImageBitmap")}function SY(r){return r!=null&&r.width!==0&&r.height!==0}function NY(r){return vY()&&!(r instanceof ImageBitmap)&&SY(r)&&!CY(r)}async function kY(r,t=3){let e=null;if(L().getBool("WRAP_TO_IMAGEBITMAP")&&NY(r)){let n;try{n=await createImageBitmap(r,{premultiplyAlpha:"none"})}catch(o){n=null}n!=null&&n.width===r.width&&n.height===r.height?e=n:e=r}else e=r;return P2(e,t)}function L2(r){if(r.rank!==2&&r.rank!==3)throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${r.rank}.`);let t=r.rank===2?1:r.shape[2];if(t>4||t===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${t}`);if(r.dtype!=="float32"&&r.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${r.dtype}. Please use float32 or int32 tensors.`)}function TY(r){let t=(r==null?void 0:r.alpha)||1;if(t>1||t<0)throw new Error(`Alpha value ${t} is suppoed to be in range [0 - 1].`)}async function _Y(r,t){let e=C(r,"img","toPixels");if(!(r instanceof Lt)){let l=e;e=J(l,"int32"),l.dispose()}L2(e);let[n,o]=e.shape.slice(0,2),s=e.rank===2?1:e.shape[2],i=await e.data(),a=e.dtype==="float32"?255:1,u=new Uint8ClampedArray(o*n*4);for(let l=0;l<n*o;++l){let c=[0,0,0,255];for(let m=0;m<s;m++){let f=i[l*s+m];if(e.dtype==="float32"){if(f<0||f>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${f}.`)}else if(e.dtype==="int32"&&(f<0||f>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${f}.`);s===1?(c[0]=f*a,c[1]=f*a,c[2]=f*a):c[m]=f*a}let p=l*4;u[p+0]=Math.round(c[0]),u[p+1]=Math.round(c[1]),u[p+2]=Math.round(c[2]),u[p+3]=Math.round(c[3])}if(t!=null){M2||Wp(Qd,T.backendName)!=null&&(console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead."),M2=!0),t.width=o,t.height=n;let l=t.getContext("2d"),c=new ImageData(u,o,n);l.putImageData(c,0,0)}return e!==r&&e.dispose(),u}function EY(r,t,e){let n=C(r,"img","draw");if(!(r instanceof Lt)){let i=n;n=J(i,"int32"),i.dispose()}L2(n),TY(e==null?void 0:e.imageOptions);let o={image:n},s={canvas:t,options:e};T.runKernel(Qd,o,s)}var AY=k({fromPixels_:P2});var Ey={};Kt(Ey,{prepareAndValidate:()=>z2});function z2(r,t){let e=r.shape.length,n=t.shape.length;if(e<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${e}.`);if(n<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${n}.`);if(t.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[n-1]>e)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[n-1]} vs. ${e}`);if(jt(r.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${r.shape}.`);let o=t.shape,s=o[o.length-1],i=1;for(let p=0;p<o.length-1;++p)i*=o[p];let a=r.shape,u=o.slice();u.pop();let l=1;for(let p=s;p<e;++p)l*=a[p],u.push(a[p]);let c=[...Ei(r.shape).map(p=>p/l),1].slice(0,s);return[u,i,l,c]}var Be={};Kt(Be,{assertParamsValid:()=>$Y,computeFlatOffset:()=>PY,computeOutShape:()=>FY,getNormalizedAxes:()=>OY,isSliceContinous:()=>MY,maskToAxes:()=>RY,parseSliceParams:()=>TN,sliceInfo:()=>LY,startForAxis:()=>K2,startIndicesWithElidedDims:()=>U2,stopForAxis:()=>j2,stopIndicesWithElidedDims:()=>H2,stridesForAxis:()=>q2,stridesWithElidedDims:()=>V2});var kN=-2,DY=-1;function $Y(r,t,e){let n=r.shape.length;_(n===t.length,()=>`Error in slice${n}D: Length of begin ${t} must match the rank of the array (${n}).`),_(n===e.length,()=>`Error in slice${n}D: Length of size ${e} must match the rank of the array (${n}).`);for(let o=0;o<n;++o)_(t[o]+e[o]<=r.shape[o],()=>`Error in slice${n}D: begin[${o}] + size[${o}] (${t[o]+e[o]}) would overflow input.shape[${o}] (${r.shape[o]})`)}function RY(r){let t=[],e=0;for(;r>0;)r&1&&t.push(e),r/=2,e++;return t}function FY(r,t,e){let n=[];for(let o=0;o<r.length;o++)n[o]=Math.ceil((t[o]-r[o])/e[o]);return n}function V2(r,t,e,n){let o=[...r];for(let s=o.length;s<n.length;s++)o.push(1);for(let s=0;s<e;s++)s===0?o[t]=1:(o.splice(t,0,1),o.pop());return o}function G2(r,t,e){return e<=r?e:e-(t-1)}function W2(r,t){let e=[];for(let n=0;n<r;n++)e.push(t+n);return e}function OY(r,t,e,n,o,s,i,a,u){let l=r.length,c=new Array(l),p=new Array(l),m=new Array(l);if(t.length&&e>0){let f=t[0],d=e+1;c=U2(i,f,d,n,r),p=H2(a,f,d,o,r),m=V2(s,f,d,r)}else for(let f=0;f<l;f++)c[f]=K2(i,n,s,r,f,u),p[f]=j2(a,o,s,r,f,u),m[f]=q2(s,f,u);return{begin:c,end:p,strides:m}}function U2(r,t,e,n,o){let s=[...o],i=W2(e,t);for(let a=0;a<s.length;a++)if(i.indexOf(a)>-1)s[a]=0;else{let u=G2(t,e,a),l=n[u];r&1<<u&&(l=0),s[a]=l}return s}function H2(r,t,e,n,o){let s=[...o],i=W2(e,t);for(let a=0;a<s.length;a++)if(i.indexOf(a)>-1)s[a]=Number.MAX_SAFE_INTEGER;else{let u=G2(t,e,a),l=n[u];r&1<<u&&(l=Number.MAX_SAFE_INTEGER),s[a]=l}for(let a=0;a<s.length;a++){let u=o[a];s[a]<0&&(s[a]+=u),s[a]=kp(0,s[a],o[a])}return s}function q2(r,t,e){let n=r[t];return(e&1<<t||n==null)&&(n=1),n}function K2(r,t,e,n,o,s){let i=t[o],a=e[o]||1;(r&1<<o||s&1<<o||i==null)&&(a>0?i=Number.MIN_SAFE_INTEGER:i=Number.MAX_SAFE_INTEGER);let u=n[o];return i<0&&(i+=u),i=kp(0,i,u-1),i}function j2(r,t,e,n,o,s){let i=t[o],a=e[o]||1;(r&1<<o||s&1<<o||i==null)&&(a>0?i=Number.MAX_SAFE_INTEGER:i=Number.MIN_SAFE_INTEGER);let u=n[o];return i<0&&(i+=u),a>0?i=kp(0,i,u):i=kp(-1,i,u-1),i}function MY(r,t,e){let n=e.length;for(let o=0;o<e.length;o++)if(e[o]>1){n=o;break}for(let o=n+1;o<e.length;o++)if(t[o]>0||e[o]!==r[o])return!1;return!0}function PY(r,t){let e=r.length>0?r[r.length-1]:1;for(let n=0;n<r.length-1;n++)e+=r[n]*t[n];return e}function TN(r,t,e){let n,o=r.shape.length;typeof t=="number"?n=[t,...new Array(o-1).fill(0)]:t.length<o?n=t.concat(new Array(o-t.length).fill(0)):n=t.slice(),n.forEach(i=>{_(i!==-1,()=>"slice() does not support negative begin indexing.")});let s;return e==null?s=new Array(o).fill(-1):typeof e=="number"?s=[e,...new Array(o-1).fill(-1)]:e.length<o?s=e.concat(new Array(o-e.length).fill(-1)):s=e,s=s.map((i,a)=>i>=0?i:(_(i===-1,()=>`Negative size values should be exactly -1 but got ${i} for the slice() size at index ${a}.`),r.shape[a]-n[a])),[n,s]}function LY(r,t,e,n,o,s,i,a,u){let l;if(n==null?(l=new Array(t.length),l.fill(1)):l=n,i!=null&&i&i-1)throw new Error("Multiple ellipses in slice is not allowed.");let c=!1,p={dims:l.length,numAddAxisAfterEllipsis:0,begin:t.slice(),end:e.slice(),strides:l.slice(),beginMask:o,endMask:s,ellipsisMask:i,newAxisMask:a,shrinkAxisMask:u};for(let w=0;w<p.dims;w++)c&&1<<w&a&&p.numAddAxisAfterEllipsis++,1<<w&i&&(c=!0);c||(p.ellipsisMask|=1<<p.dims,p.dims++);let m={dims:r.length,beginMask:0,endMask:0,beginValid:!1,endValid:!1};zY(p,m);let f=!0,d=!0,h=!0,g=[],x=[];for(let w=0;w<r.length;++w){if(m.strides[w]===0)throw Error(`strides[${w}] must be non-zero`);let I=!!(m.shrinkAxisMask&1<<w),N=r[w];if(N===-1){g.push(I?1:-1);continue}let E=[m.beginMask&1<<w,m.endMask&1<<w],A=[m.strides[w]>0?0:-1,m.strides[w]>0?N:N-1];if(I&&m.strides[w]<=0)throw Error("only stride 1 allowed on non-range indexing.");h=h&&m.strides[w]===1;let D=!!(m.beginMask&1<<w&&m.endMask&1<<w);if(m.beginValid&&m.endValid){if(I){let G=m.begin[w]<0?N+m.begin[w]:m.begin[w];if(m.begin[w]=G,m.end[w]=m.begin[w]+1,G<0||G>=N)throw Error(`slice index ${m.begin[w]} of dimension ${w} out of bounds.`)}else m.begin[w]=B2(m.begin[w],0,m.strides[w],N,E,A),m.end[w]=B2(m.end[w],1,m.strides[w],N,E,A);let V=m.strides[w]===1&&m.begin[w]===0&&m.end[w]===N;f=f&&V,d=d&&(w===0&&m.strides[w]===1||V)}else f=f&&m.strides[w]===1&&D,d=d&&(w===0&&m.strides[w]===1||D);let F,M=!1;if(m.beginValid&&m.endValid?(F=m.end[w]-m.begin[w],M=!0):I?(F=1,M=!0):D&&N>=0&&(m.strides[w]<0?F=-N:F=N,M=!0),M){let V;F===0||F<0!=m.strides[w]<0?V=0:V=Math.trunc(F/m.strides[w])+(F%m.strides[w]!==0?1:0),g.push(V)}else g.push(-1)}for(let w=0;w<m.finalShapeGatherIndices.length;++w){let I=m.finalShapeGatherIndices[w];I>=0?x.push(g[I]):I===kN&&x.push(1)}return{finalShapeSparse:x.filter((w,I)=>m.finalShapeGatherIndices[I]!==kN),finalShape:x,isIdentity:f,sliceDim0:d,isSimpleSlice:h,begin:m.begin,end:m.end,strides:m.strides}}function zY(r,t){t.beginMask=0,t.endMask=0,t.shrinkAxisMask=0;let e=0;t.beginValid=r.begin!=null,t.endValid=r.end!=null,t.begin=new Array(t.dims),t.end=new Array(t.dims),t.strides=new Array(t.dims),t.finalShapeGatherIndices=[],t.finalShapeGatherIndicesSparse=[],t.inputShapeGatherIndicesSparse=new Array(t.dims);for(let n=0;n<r.dims;n++)if(1<<n&r.ellipsisMask){let o=Math.min(t.dims-(r.dims-n)+1+r.numAddAxisAfterEllipsis,t.dims);for(;e<o;e++)t.begin[e]=0,t.end[e]=0,t.strides[e]=1,t.beginMask|=1<<e,t.endMask|=1<<e,t.finalShapeGatherIndices.push(e),t.finalShapeGatherIndicesSparse.push(-1),t.inputShapeGatherIndicesSparse[e]=n}else if(1<<n&r.newAxisMask)t.finalShapeGatherIndices.push(kN),t.finalShapeGatherIndicesSparse.push(-1);else{if(e===t.begin.length)throw Error(`Index out of range using input dim ${e}; input has only ${t.dims} dims, ${t.begin.length}.`);r.begin!=null&&(t.begin[e]=r.begin[n]),r.end!=null&&(t.end[e]=r.end[n]),t.strides[e]=r.strides[n],r.beginMask&1<<n&&(t.beginMask|=1<<e),r.endMask&1<<n&&(t.endMask|=1<<e),r.shrinkAxisMask&1<<n?(t.finalShapeGatherIndices.push(DY),t.finalShapeGatherIndicesSparse.push(-1),t.shrinkAxisMask|=1<<e):(t.finalShapeGatherIndices.push(e),t.finalShapeGatherIndicesSparse.push(n)),t.inputShapeGatherIndicesSparse[e]=n,e++}}function B2(r,t,e,n,o,s){if(o[t])return e>0?s[t]:s[t+1&1];{let i=r<0?n+r:r;return i<s[0]?s[0]:i>s[1]?s[1]:i}}var X2="4.21.0";var Ih=class{static sgd(t){return new Il(t)}static momentum(t,e,n=!1){return new Tc(t,e,n)}static rmsprop(t,e=.9,n=0,o=null,s=!1){return new _c(t,e,n,o,s)}static adam(t=.001,e=.9,n=.999,o=null){return new Nc(t,e,n,o)}static adadelta(t=.001,e=.95,n=null){return new vc(t,e,n)}static adamax(t=.002,e=.9,n=.999,o=null,s=0){return new kc(t,e,n,o,s)}static adagrad(t,e=.1){return new Sc(t,e)}};var Ac=Ih;var BY=typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:r=>r();function Ch(){return new Promise(r=>BY(()=>r()))}var S={};Kt(S,{ERF_A1:()=>eZ,ERF_A2:()=>rZ,ERF_A3:()=>nZ,ERF_A4:()=>oZ,ERF_A5:()=>sZ,ERF_P:()=>tZ,PARALLELIZE_THRESHOLD:()=>Ay,RowPartitionType:()=>ma,SELU_SCALE:()=>EN,SELU_SCALEALPHA:()=>_N,applyActivation:()=>Ic,assertAndGetBroadcastShape:()=>Mt,assertAxesAreInnerMostDims:()=>w6,assertParamsConsistent:()=>VY,assignToTypedArray:()=>pZ,axesAreInnerMostDims:()=>eN,calculateShapes:()=>RA,checkEinsumDimSizes:()=>xZ,checkPadOnDimRoundingMode:()=>Se,combineLocations:()=>zE,combineRaggedTensorToTensorShapes:()=>WY,complexWithEvenIndex:()=>lZ,complexWithOddIndex:()=>uZ,computeConv2DInfo:()=>pc,computeConv3DInfo:()=>AE,computeDefaultPad:()=>tN,computeDilation2DInfo:()=>bj,computeOptimalWindowSize:()=>KY,computeOutAndReduceShapes:()=>rN,computeOutShape:()=>GY,computePool2DInfo:()=>Q0,computePool3DInfo:()=>wj,convertConv2DDataFormat:()=>DE,decodeEinsumEquation:()=>hZ,eitherStridesOrDilationsAreOne:()=>Dr,expandShapeToKeepDim:()=>vo,exponent:()=>fZ,exponents:()=>mZ,fromStringArrayToUint8:()=>LZ,fromUint8ToStringArray:()=>PZ,getAxesPermutation:()=>nN,getBroadcastDims:()=>PE,getComplexWithIndex:()=>cZ,getEinsumComputePath:()=>yZ,getEinsumPermutation:()=>gZ,getFusedBiasGradient:()=>wc,getFusedDyActivation:()=>bc,getImageCenter:()=>jY,getInnerMostAxes:()=>I6,getPermuted:()=>YY,getRaggedRank:()=>HY,getReductionAxes:()=>we,getReshaped:()=>XY,getReshapedPermuted:()=>ZY,getRowPartitionTypesHelper:()=>UY,getSliceBeginCoords:()=>JY,getSliceSize:()=>QY,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>CZ,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>vZ,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>SZ,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>TZ,getSparseReshapeInputOutputMismatchErrorMessage:()=>EZ,getSparseReshapeInputOutputMultipleErrorMessage:()=>_Z,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>NZ,getSparseReshapeNegativeOutputDimErrorMessage:()=>kZ,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>RZ,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>AZ,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>DZ,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>$Z,getUndoAxesPermutation:()=>fh,isIdentityPermutation:()=>bZ,log:()=>mK,mergeRealAndImagArrays:()=>iZ,prepareAndValidate:()=>z2,prepareSplitSize:()=>IZ,segment_util:()=>DN,shouldFuse:()=>Cc,slice_util:()=>Be,splitRealAndImagArrays:()=>aZ,stridesOrDilationsArePositive:()=>na,tupleValuesAreOne:()=>io,upcastType:()=>ur,validateDefaultValueShape:()=>qY,validateInput:()=>Im,validateUpdateShape:()=>fN,warn:()=>Ji});function VY(r,t){let e=r[0].length;r.forEach((o,s)=>{_(o.length===e,()=>`Error in concat${e}D: rank of tensors[${s}] must be the same as the rank of the rest (${e})`)}),_(t>=0&&t<e,()=>`Error in concat${e}D: axis must be between 0 and ${e-1}.`);let n=r[0];r.forEach((o,s)=>{for(let i=0;i<e;i++)_(i===t||o[i]===n[i],()=>`Error in concat${e}D: Shape of tensors[${s}] (${o}) does not match the shape of the rest (${n}) along the non-concatenated axis ${s}.`)})}function GY(r,t){let e=r[0].slice();for(let n=1;n<r.length;n++)e[t]+=r[n][t];return e}var ma;(function(r){r[r.FIRST_DIM_SIZE=0]="FIRST_DIM_SIZE",r[r.VALUE_ROWIDS=1]="VALUE_ROWIDS",r[r.ROW_LENGTHS=2]="ROW_LENGTHS",r[r.ROW_SPLITS=3]="ROW_SPLITS",r[r.ROW_LIMITS=4]="ROW_LIMITS",r[r.ROW_STARTS=5]="ROW_STARTS"})(ma||(ma={}));function WY(r,t,e){let n=new Array;if(e==null&&t==null)return n;if(t==null)for(;n.length<r+e.length;)n.push(-1);else n=t.slice();if(e==null)return n;if(r+e.length!==n.length)throw new Error(`rt input.shape and shape=${t} are incompatible: rt input.rank = ${r+e.length}, but shape.rank = ${n.length}`);for(let o=1;o<e.length;++o){let s=e[o],i=n[n.length-e.length+o],a=n[i];if(s>=0)if(a>=0){if(a!==s)throw new Error(`rt input.shape and shape=${t} are incompatible: rt input.shape[${o+r}] = ${s} but shape[${o+r}] = ${a}`)}else n[i]=s}return n}function UY(r){let t={FIRST_DIM_SIZE:ma.FIRST_DIM_SIZE,VALUE_ROWIDS:ma.VALUE_ROWIDS,ROW_LENGTHS:ma.ROW_LENGTHS,ROW_SPLITS:ma.ROW_SPLITS,ROW_LIMITS:ma.ROW_LIMITS,ROW_STARTS:ma.ROW_STARTS},e=[];for(let n of r)if(n in t)e.push(t[n]);else break;return e}function HY(r){return r.length===0?0:r[0]===ma.FIRST_DIM_SIZE?r.length-1:r.length}function qY(r,t){if(r==null||t==null)return;let e=r.length,n=t.length;if(e>=n)throw new Error(`defaultValue.shape=${r} and ragged tensor flatValues.shape=${t}, are incompatible: defaultValue.rank = ${e} must be less than ragged tensor input flatValues.rank = ${n})`);for(let o=0;o<Math.min(e,n-1);++o){let s=r[o],i=t[o+1];if(s>=0&&i>=0&&s!==1&&s!==i)throw new Error(`defaultValue.shape=${r}, and ragged tensor input flatValues.shape=${t} are incompatible: defaultValue.shape[${o-r.length}] = ${s} but ragged tensor input.flatValues.shape[${o-r.length}] = ${i}`)}}var Ay=30;function KY(r){return r<=Ay?r:_p(r,Math.floor(Math.sqrt(r)))}function jY(r,t,e){let n=e*(typeof r=="number"?r:r[0]),o=t*(typeof r=="number"?r:r[1]);return[n,o]}function XY(r,t,e,n=!0){let o=[];if(n)o=o.concat(t.slice(0)),o.push(r[0]/e),o=o.concat(r.slice(1));else{o=o.concat(r[0]);let s=t.length;for(let i=0;i<s;++i)o=o.concat([r[i+1]/t[i],t[i]]);o=o.concat(r.slice(s+1))}return o}function YY(r,t,e=!0){let n=[];if(e){n.push(t);for(let o=t+1;o<r;++o)o<=2*t?(n.push(o),n.push(o-(t+1))):n.push(o)}else{let o=[],s=[];for(let i=1;i<r;++i)i>=t*2+1||i%2===1?s.push(i):o.push(i);n.push(...o),n.push(0),n.push(...s)}return n}function ZY(r,t,e,n=!0){let o=[];n?o.push(r[0]/e):o.push(r[0]*e);for(let s=1;s<r.length;++s)s<=t.length?n?o.push(t[s-1]*r[s]):o.push(r[s]/t[s-1]):o.push(r[s]);return o}function JY(r,t){let e=[0];for(let n=0;n<t;++n)e.push(r[n][0]);return e}function QY(r,t,e){let n=r.slice(0,1);for(let o=0;o<e;++o)n.push(r[o+1]-t[o][0]-t[o][1]);return n}var _N=1.7580993408473768,EN=1.0507009873554805;var tZ=.3275911,eZ=.254829592,rZ=-.284496736,nZ=1.421413741,oZ=-1.453152027,sZ=1.061405429;function iZ(r,t){if(r.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${r.length}, imag: ${t.length}.`);let e=new Float32Array(r.length*2);for(let n=0;n<e.length;n+=2)e[n]=r[n/2],e[n+1]=t[n/2];return e}function aZ(r){let t=new Float32Array(r.length/2),e=new Float32Array(r.length/2);for(let n=0;n<r.length;n+=2)t[n/2]=r[n],e[n/2]=r[n+1];return{real:t,imag:e}}function lZ(r){let t=Math.ceil(r.length/4),e=new Float32Array(t),n=new Float32Array(t);for(let o=0;o<r.length;o+=4)e[Math.floor(o/4)]=r[o],n[Math.floor(o/4)]=r[o+1];return{real:e,imag:n}}function uZ(r){let t=Math.floor(r.length/4),e=new Float32Array(t),n=new Float32Array(t);for(let o=2;o<r.length;o+=4)e[Math.floor(o/4)]=r[o],n[Math.floor(o/4)]=r[o+1];return{real:e,imag:n}}function cZ(r,t){let e=r[t*2],n=r[t*2+1];return{real:e,imag:n}}function pZ(r,t,e,n){r[n*2]=t,r[n*2+1]=e}function mZ(r,t){let e=new Float32Array(r/2),n=new Float32Array(r/2);for(let o=0;o<Math.ceil(r/2);o++){let s=(t?2:-2)*Math.PI*(o/r);e[o]=Math.cos(s),n[o]=Math.sin(s)}return{real:e,imag:n}}function fZ(r,t,e){let n=(e?2:-2)*Math.PI*(r/t),o=Math.cos(n),s=Math.sin(n);return{real:o,imag:s}}var AN="->",dZ=/->/g,Y2=",",Z2="...";function hZ(r,t){r=r.replace(/\s/g,"");let e=(r.length-r.replace(dZ,"").length)/AN.length;if(e<1)throw new Error("Equations without an arrow are not supported.");if(e>1)throw new Error(`Equation must contain exactly one arrow ("${AN}").`);let[n,o]=r.split(AN);_(n.indexOf(Z2)===-1,()=>`The ellipsis notation ("${Z2}") is not supported yet.`);let s=n.split(Y2),i=s.length;if(t!==i)throw new Error(`Expected ${i} input tensors, received ${t}`);if(i>2)throw new Error("Support for more than 2 input tensors is not implemented yet.");let a=[];for(let m=0;m<o.length;++m){let f=o[m];if(!s.some(d=>d.indexOf(f)!==-1))throw new Error(`Output subscripts contain the label ${f} not present in the input subscripts.`);a.indexOf(f)===-1&&a.push(f)}for(let m=0;m<n.length;++m){let f=n[m];a.indexOf(f)===-1&&f!==Y2&&a.push(f)}let u=new Array(s.length);for(let m=0;m<i;++m){if(new Set(s[m].split("")).size!==s[m].length)throw new Error(`Found duplicate axes in input component ${s[m]}. Support for duplicate axes in input is not implemented yet.`);u[m]=[];for(let f=0;f<s[m].length;++f)u[m].push(a.indexOf(s[m][f]))}let l=a.length,c=o.length,p=[];for(let m=c;m<l;++m)p.push(m);return{allDims:a,summedDims:p,idDims:u}}function gZ(r,t){let e=new Array(r);e.fill(-1);for(let o=0;o<t.length;++o)e[t[o]]=o;let n=[];for(let o=0;o<r;++o)e[o]===-1&&n.push(o);return e=e.filter(o=>o!==-1),{permutationIndices:e,expandDims:n}}function xZ(r,t,e){let n=new Array(r);for(let o=0;o<e.length;++o){let s=e[o].shape;for(let i=0;i<t[o].length;++i)n[t[o][i]]===void 0?n[t[o][i]]=s[i]:_(n[t[o][i]]===s[i],()=>`Expected dimension ${n[t[o][i]]} at axis ${i} of input shaped ${JSON.stringify(s)}, but got dimension ${s[i]}`)}}function yZ(r,t){let e=r,n=[],o=0;r.length===0&&e.push(-1),o=r.length+1;for(let i=0;i<o;++i)n.push([]);let s=[];for(let i=0;i<e.length;++i){let a=e[i],u=wZ(t,a);for(let l of u)s.indexOf(l)===-1&&(n[i].push(l),s.push(l))}return{path:e,steps:n}}function bZ(r){return r.every((t,e)=>t===e)}function wZ(r,t){let e=[];for(let n=0;n<r.length;++n)(r[n].length===0||r[n].indexOf(t)!==-1||t===-1)&&e.push(n);return e}function IZ(r,t,e=0){let n=[];if(typeof t=="number")_(r.shape[e]%t===0,()=>"Number of splits must evenly divide the axis."),n=new Array(t).fill(r.shape[e]/t);else{let o=t.reduce((i,a)=>(a===-1&&(i+=1),i),0);_(o<=1,()=>"There should be only one negative value in split array.");let s=t.indexOf(-1);if(s!==-1){let i=t.reduce((a,u)=>u>0?a+u:a);t[s]=r.shape[e]-i}_(r.shape[e]===t.reduce((i,a)=>i+a),()=>"The sum of sizes must match the size of the axis dimension."),n=t}return n}function CZ(r){return`Received SparseTensor with denseShape[0] = 0 but
  indices.shape[0] = ${r}`}function vZ(r,t){return`indices(${r}, 0) is invalid: ${t} < 0`}function SZ(r,t,e){return`indices(${r}, 0) is invalid: ${t} >= ${e}`}function NZ(r,t){return`only one output dimension may be -1, not both ${r} and ${t}`}function kZ(r,t){return`size ${r} must be non-negative, not ${t}`}function TZ(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function _Z(r,t){let e=jt(r),n=jt(t);return`Input to reshape is a SparseTensor with ${e}
  dense values, but the requested shape requires a multiple of ${n}. inputShape=${r} outputShape= ${t}`}function EZ(r,t){let e=jt(r),n=jt(t);return`Input to reshape is a tensor with ${e} dense values, but the requested shape has ${n}. inputShape=${r} outputShape=${t}`}function AZ(){return"segment ids must be >= 0"}function DZ(){return"segment ids are not increasing"}function $Z(r,t){return`Segment id ${r} out of range [0, ${t}), possibly because segmentIds input is not sorted.`}function RZ(r,t,e){return`Bad: indices[${r}] == ${t} out of range [0, ${e})`}var DN={};Kt(DN,{collectGatherOpShapeInfo:()=>MZ,computeOutShape:()=>OZ,segOpComputeOptimalWindowSize:()=>FZ});function FZ(r,t){let e=!1,n;for(r<=Ay?(n=r,e=!0):n=_p(r,Math.floor(Math.sqrt(r)));!e;)n>t||n===r?e=!0:n=_p(r,n+1);return n}function OZ(r,t,e){let n=[],o=r.length;for(let s=0;s<o;s++)s!==t?n.push(r[s]):n.push(e);return n}function MZ(r,t,e,n){let o=t.shape.length,s=r.shape.length;if(n!==0&&(n<-o||n>o))throw new Error(`Expect batchDims in the range of [-${o}, ${o}], but got ${n}`);if(n<0&&(n+=o),n>s)throw new Error(`batchDims (${n}) must be less than rank(x) (
    ${s}).`);if(e<n)throw new Error(`batchDims (${n}) must be less than or equal to axis (${e}).`);for(let p=0;p<n;++p)if(r.shape[p]!==t.shape[p])throw new Error(`x.shape[${p}]: ${r.shape[p]} should be equal to indices.shape[${p}]: ${t.shape[p]}.`);let i=r.shape[e],a=[],u=1,l=1,c=1;for(let p=0;p<n;++p)a.push(r.shape[p]),u*=r.shape[p];for(let p=n;p<e;p++)a.push(r.shape[p]),l*=r.shape[p];for(let p=n;p<o;p++)a.push(t.shape[p]);for(let p=e+1;p<s;p++)a.push(r.shape[p]),c*=r.shape[p];return{batchSize:u,sliceSize:c,outerSize:l,dimSize:i,outputShape:a}}function PZ(r){try{return r.map(t=>Kp(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function LZ(r){return r.map(t=>fu(t))}var Xr={};Kt(Xr,{nonMaxSuppressionV3Impl:()=>by,nonMaxSuppressionV4Impl:()=>wy,nonMaxSuppressionV5Impl:()=>Iy,whereImpl:()=>fy});S2();var Dy={kernelName:Ai,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,So(J(e,"float32"),-1))}}};var J2={kernelName:Go,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>{let n=Wt(J(e,"float32")),o=ge(at(pt(1),n));return Ut(ut(r,o))}}}};var Q2={kernelName:Wo,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>{let n=ge(at(Wt(J(e,"float32")),1));return ut(r,n)}}}};var tD={kernelName:no,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=r,u=we(e.shape,o);return u.length>0&&(a=mt(a,u)),R(a,e.shape)},b:()=>{let a=r,u=we(n.shape,o);return u.length>0&&(a=mt(a,u)),R(a,n.shape)}}}};var eD={kernelName:Uo,saveAllInputs:!0,gradFunc:(r,t)=>{let e={};return t.forEach((n,o)=>{e[o]=()=>r.clone()}),e}};var rD={kernelName:Di,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>vt(e)}}};var nD={kernelName:$i,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>vt(e)}}};var oD={kernelName:Ho,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ut(r,ge(at(pt(1),Wt(J(e,"float32")))))}}};var sD={kernelName:qo,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>{let n=ge(K(pt(1),Wt(J(e,"float32"))));return ut(r,n)}}}};var iD={kernelName:Xo,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=K(Wt(e),Wt(n)),u=$(r,ut(n,a)),l=we(e.shape,o);return l.length>0&&(u=mt(u,l)),R(u,e.shape)},b:()=>{let a=K(Wt(e),Wt(n)),u=Ut($(r,ut(e,a))),l=we(n.shape,o);return l.length>0&&(u=mt(u,l)),R(u,n.shape)}}}};var aD={kernelName:Ko,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ut(r,K(Wt(J(e,"float32")),1))}}};var lD={kernelName:jo,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ut(r,at(pt(1),Wt(J(e,"float32"))))}}};function zZ(r,t,e,n,o,s){let i=C(r,"dy","avgPool3dGrad"),a=C(t,"input","avgPool3dGrad"),u=i,l=a,c=!1;a.rank===4&&(c=!0,u=R(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]]),l=R(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),_(u.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`),_(l.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`),Se("avgPool3dGrad",o,s);let p={dy:u,input:l},m={filterSize:e,strides:n,pad:o,dimRoundingMode:s},f=T.runKernel(Hl,p,m);return c?R(f,[f.shape[1],f.shape[2],f.shape[3],f.shape[4]]):f}var uD=k({avgPool3dGrad_:zZ});var cD={kernelName:Ri,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{filterSize:o,strides:s,pad:i,dimRoundingMode:a}=e;return{x:()=>uD(r,n,o,s,i,a)}}};function BZ(r,t,e,n,o){let s=C(r,"dy","avgPoolGrad"),i=C(t,"input","avgPoolGrad");_(i.rank===s.rank,()=>`Rank of input (${i.rank}) does not match rank of dy (${s.rank})`);let a=i,u=s,l=!1;i.rank===3&&(l=!0,a=R(i,[1,i.shape[0],i.shape[1],i.shape[2]]),u=R(s,[1,s.shape[0],s.shape[1],s.shape[2]])),_(u.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`),_(a.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${a.rank}.`);let c={dy:u,input:a},p={filterSize:e,strides:n,pad:o},m=T.runKernel(Ul,c,p);return l?R(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var pD=k({avgPoolGrad_:BZ});var mD={kernelName:Yo,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{filterSize:o,strides:s,pad:i}=e;return{x:()=>pD(r,n,o,s,i)}}};var fD={kernelName:Zo,inputsToSave:["a","b"],gradFunc:(r,t,e)=>{let[n,o]=t,{transposeA:s,transposeB:i}=e;return!s&&!i?{a:()=>Bt(r,o,!1,!0),b:()=>Bt(n,r,!0,!1)}:!s&&i?{a:()=>Bt(r,o,!1,!1),b:()=>Bt(r,n,!0,!1)}:s&&!i?{a:()=>Bt(o,r,!1,!0),b:()=>Bt(n,r,!1,!1)}:{a:()=>Bt(o,r,!0,!0),b:()=>Bt(r,n,!0,!0)}}};var dD={kernelName:Fi,gradFunc:(r,t,e)=>{let{blockShape:n,crops:o}=e;return{x:()=>Tu(r,n,o)}}};var hD={kernelName:__,gradFunc:(r,t,e)=>{let n=e,o=n.inputShape,s=n.shape,i=Array.from(s);for(let u=o.length-1;u>=0;u--)if(o[u]===s[u])i[u]=1;else if(o[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${s}].`);let a=[];for(let u=0;u<i.length;u++)i[u]>1&&a.push(u);return{x:()=>mt(r,a,!0)}}};var gD={kernelName:fo,gradFunc:r=>({x:()=>r.clone()})};var xD={kernelName:Jo,gradFunc:r=>({x:()=>vt(r)})};var yD={kernelName:ho,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{clipValueMin:o,clipValueMax:s}=e;return{x:()=>Ie(Fr(cn(n,o),Vn(n,s)),r,vt(r))}}};var bD={kernelName:Kl,inputsToSave:["x"],gradFunc:Dy.gradFunc};var wD={kernelName:Oi,saveAllInputs:!0,gradFunc:(r,t,e)=>{let n=t.map(u=>u.shape),{axis:o}=e,s=fr(o,t[0].shape)[0],i=n.map(u=>u[s]);return hr(r,i,s).map(u=>()=>u)}};var ID={kernelName:Qo,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let[n,o]=t,{dilations:s,strides:i,pad:a,dataFormat:u}=e;return _(io(s),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`),{x:()=>nm(n.shape,r,o,i,a,u),filter:()=>Sm(n,r,o.shape,i,a,u)}}};var CD={kernelName:ts,inputsToSave:["dy","filter"],gradFunc:(r,t,e)=>{let[n,o]=t,{strides:s,pad:i,dataFormat:a,dimRoundingMode:u}=e;return{dy:()=>Nn(r,o,s,i,a,1,u),filter:()=>Sm(r,n,o.shape,s,i,a,u)}}};function VZ(r,t,e,n,o){let s=r;r.rank===4&&(s=R(r,[1,r.shape[0],r.shape[1],r.shape[2],r.shape[3]]));let i=t;i.rank===4&&(i=R(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]])),_(s.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${s.shape}.`),_(i.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`),_(e.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${e}.`),_(s.shape[4]===e[3],()=>`Error in conv3dDerFilter: depth of input ${s.shape[4]}) must match input depth in filter (${e[3]}.`),_(i.shape[4]===e[4],()=>`Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${e[4]}).`);let a={x:s,dy:i},u={strides:n,pad:o,filterShape:e};return T.runKernel(Ra,a,u)}var vD=k({conv3DBackpropFilter_:VZ});var SD={kernelName:es,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let{dilations:n,strides:o,pad:s}=e;_(io(n),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${n}'`);let[i,a]=t;return{x:()=>$x(i.shape,r,a,o,s),filter:()=>vD(i,r,a.shape,o,s)}}};var ND={kernelName:rs,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(Ut(hm(J(e,"float32"))),r)}}};var kD={kernelName:ns,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(gm(J(e,"float32")),r)}}};var TD={kernelName:os,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{axis:o,exclusive:s,reverse:i}=e;return{x:()=>{let a=nN([o],n.rank),u=im(r,o,s,!i);return a!=null&&(u=Vt(u,a)),u}}}};var _D={kernelName:ss,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let{dilations:n,strides:o,pad:s,dimRoundingMode:i}=e,a=n==null?[1,1]:n;_(io(a),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);let[u,l]=t;return _(u.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`),_(l.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`),_(u.shape[3]===l.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),_(Dr(o,a),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${o} and dilations '${a}'.`),Se("depthwiseConv2d",s,i),{x:()=>gy(u.shape,r,l,o,s,a,i),filter:()=>hy(u,r,l.shape,o,s,a,i)}}};var ED={kernelName:is,inputsToSave:["x","filter"],gradFunc:(r,t,e)=>{let[n,o]=t,s={x:n,filter:o,dy:r},i={x:n,filter:o,dy:r};return{x:()=>T.runKernel(Yl,s,e),filter:()=>T.runKernel(Zl,i,e)}}};var AD={kernelName:ls,outputsToSave:[!0],gradFunc:(r,t)=>{let[e]=t,n={dy:r,y:e};return{x:()=>T.runKernel(La,n)}}};var DD={kernelName:us,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t,n=$(Ke(Ut(Wt(e))),2/Math.sqrt(Math.PI));return{x:()=>$(r,n)}}};var $D={kernelName:cs,outputsToSave:[!0],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,e)}}};var RD={kernelName:Mi,inputsToSave:["input"],gradFunc:(r,t)=>{let[e]=t;return{input:()=>R(r,e.shape)}}};var FD={kernelName:ps,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,Ke(e))}}};var OD={kernelName:ms,gradFunc:r=>({x:()=>vt(r)})};var MD={kernelName:fs,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=ut(r,J(n,"float32")),u=we(e.shape,o);return u.length>0?R(mt(a,u),e.shape):a},b:()=>{let a=$(r,J(e,"float32")),u=we(n.shape,o);u.length>0&&(a=R(mt(a,u),n.shape));let l=Wt(n);return Ut(ut(a,J(l,"float32")))}}}};var PD={kernelName:ds,inputsToSave:["x","mean","variance","scale"],gradFunc:(r,t,e)=>{let{varianceEpsilon:n}=e,[o,s,i,a]=t,u=a==null?pt(1):a,l=we(s.shape,o.shape),c=[];if(s.rank===1){for(let I=0;I<o.shape.length-1;++I)c.push(o.shape[I]);c.push(1)}let p=at(o,s),m=$(r,u),f=mm(K(i,pt(n))),d=$($($(f,f),f),pt(-.5));return{x:()=>s.rank===1?R($($(r,Rr(R(f,[1,1,1,s.shape[0]]),c)),u),o.shape):R($($(r,f),u),o.shape),mean:()=>{let I=$($(f,pt(-1)),m);return s.rank===1&&(I=mt(I,l)),R(I,s.shape)},variance:()=>{let I=$($(d,p),m);return s.rank===1&&(I=mt(I,l)),R(I,s.shape)},scale:()=>{let I=$(p,f),N=$(r,I);return s.rank===1&&(N=mt(N,l)),R(N,s.shape)},offset:()=>{let I=r;return s.rank===1&&(I=mt(I,l)),R(I,s.shape)}}}};var BD={kernelName:Pi,inputsToSave:["x","indices"],gradFunc:(r,t,e)=>{let[n,o]=t,{axis:s,batchDims:i}=e,a=fr(s,n.shape)[0],u=(l,c,p)=>()=>{let m=l.shape,f=c.size,d=m.slice(0,a),h=d.length,g=m.slice(s,m.length).slice(1),x=g.length,b=LD(0,h),w=LD(h+1,h+1+x),I=zD([d,[f],g]),N=R(p,I),E=R(c,[f]),A=zD([[h],b,w]),D=Vt(N,A),F=vm(D,E,l.shape[a]),M=fh(A);return F=Vt(F,M),F};if(i===1){let l=n.shape[0],c=n.split(l,0);return{x:()=>Fe(c.map((f,d)=>u(f,o.slice(d,1),r.slice(d,1))())).reshape(n.shape),indices:()=>o}}else return{x:u(n,o,r),indices:()=>o}}};function LD(r,t){let e=[];for(let n=r;n<t;++n)e.push(n);return e}function zD(r){let t=[];for(let e=0;e<r.length;++e)for(let n=0;n<r[e].length;++n)t.push(r[e][n]);return t}var VD={kernelName:hs,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t;return{a:()=>vt(e),b:()=>vt(n)}}};var GD={kernelName:go,gradFunc:r=>({x:()=>J(r,"float32")})};var WD={kernelName:gs,gradFunc:r=>({x:()=>vt(r)})};var UD={kernelName:xs,gradFunc:r=>({x:()=>vt(r)})};var HD={kernelName:ys,gradFunc:r=>({x:()=>vt(r)})};var qD={kernelName:bs,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{alpha:o}=e,s=Re(n,0);return{x:()=>Ie(s,r,$(r,o))}}};var KD={kernelName:Is,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ut(r,K(e,1))}}};var jD={kernelName:ws,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ut(r,J(e,"float32"))}}};var XD={kernelName:A_,inputsToSave:[],outputsToSave:[!0],gradFunc:(r,t,e)=>{let[n]=t,{axis:o}=e;return{logits:()=>{let i=Ke(n);return at(r,$(mt(r,o,!0),i))}}}};function GZ(r,t,e,n=5,o=1,s=1,i=.5){let a={x:r,y:t,dy:e},u={depthRadius:n,bias:o,alpha:s,beta:i};return T.runKernel(Xa,a,u)}var YD=k({localResponseNormalizationBackprop_:GZ});var ZD={kernelName:Cs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,t,e)=>{let[n,o]=t,{depthRadius:s,bias:i,alpha:a,beta:u}=e;return{x:()=>YD(n,o,r,s,i,a,u)}}};function $y(r,t,e,n){return t.rank<e.rank&&(t=R(t,vo(t.shape,n))),r.rank<e.rank&&(r=R(r,vo(r.shape,n))),{x:()=>$(r,J($r(e,t),r.dtype))}}var $N={kernelName:vs,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,t,e)=>{let n=e,{reductionIndices:o}=n,s=t[0],i=t[1],a=fr(o,s.shape),u=$y(r,i,s,a);return{x:()=>u.x()}}};var JD={kernelName:Ss,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t;return{a:()=>$(r,J(cn(e,n),"float32")),b:()=>$(r,J(yl(e,n),"float32"))}}};function WZ(r,t,e,n,o,s,i){let a=C(r,"dy","maxPool3dGrad"),u=C(t,"input","maxPool3dGrad"),l=C(e,"output","maxPool3dGrad"),c=a,p=u,m=l,f=!1;u.rank===4&&(f=!0,c=R(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]]),p=R(u,[1,u.shape[0],u.shape[1],u.shape[2],u.shape[3]]),m=R(l,[1,l.shape[0],l.shape[1],l.shape[2],l.shape[3]])),_(c.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`),_(p.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`),_(m.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${m.rank}.`),Se("maxPool3dGrad",s,i);let d={dy:c,input:p,output:m},h={filterSize:n,strides:o,pad:s,dimRoundingMode:i},g=T.runKernel(tu,d,h);return f?R(g,[g.shape[1],g.shape[2],g.shape[3],g.shape[4]]):g}var QD=k({maxPool3dGrad_:WZ});var t$={kernelName:Li,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,t,e)=>{let[n,o]=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=e;return{x:()=>QD(r,n,o,s,i,a,u)}}};function UZ(r,t,e,n,o,s,i){let a=C(r,"dy","maxPoolGrad"),u=C(t,"input","maxPoolGrad"),l=C(e,"output","maxPoolGrad");_(u.rank===a.rank,()=>`Rank of input (${u.rank}) does not match rank of dy (${a.rank})`),_(a.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${a.rank}.`),_(u.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${u.rank}.`),Se("maxPoolGrad",s,i);let c={dy:a,input:u,output:l},p={filterSize:n,strides:o,pad:s,dimRoundingMode:i};return T.runKernel(Ql,c,p)}var e$=k({maxPoolGrad_:UZ});var r$={kernelName:Ns,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,t,e)=>{let[n,o]=t,{filterSize:s,strides:i,pad:a}=e;return{x:()=>e$(r,n,o,s,i,a)}}};var n$={kernelName:ks,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{axis:o}=e,s=fr(o,n.shape),a=rN(n.shape,s)[1],u=jt(a);return{x:()=>{let c=n.shape.slice();s.forEach(f=>{c[f]=1});let p=R(r,c);return ut($(p,ar(n.shape,"float32")),u)}}}};var o$={kernelName:Ts,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(r,t,e)=>{let n=e,{axis:o}=n,[s,i]=t,a=fr(o,s.shape),u=$y(r,i,s,a);return{x:()=>u.x()}}};var s$={kernelName:_s,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t;return{a:()=>$(r,J(Vn(e,n),"float32")),b:()=>$(r,J(Re(e,n),"float32"))}}};var i$={kernelName:Es,inputsToSave:["x"],gradFunc:(r,t,e)=>{let n=t[0],{paddings:o}=e,s=o.map(i=>i[0]);return{x:()=>Ot(r,s,n.shape)}}};var a$={kernelName:As,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=we(e.shape,o);return a.length>0?R(mt(r,a),e.shape):r},b:()=>{let a=$(r,Ut(la(ut(e,n)))),u=we(n.shape,o);return u.length>0?R(mt(a,u),n.shape):a}}}};var l$={kernelName:Ds,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=$(r,J(n,"float32")),u=we(e.shape,o);return u.length>0?R(mt(a,u),e.shape):a},b:()=>{let a=$(r,J(e,"float32")),u=we(n.shape,o);return u.length>0?R(mt(a,u),n.shape):a}}}};var u$={kernelName:zi,gradFunc:r=>({x:()=>Ut(r)})};var c$={kernelName:$s,inputsToSave:["indices"],gradFunc:(r,t)=>{let e=t[0];return{indices:()=>ke(e.shape,"float32")}}};var p$={kernelName:Bi,gradFunc:r=>({x:()=>vt(r)})};var m$={kernelName:Vi,saveAllInputs:!0,gradFunc:(r,t,e)=>{let{axis:n}=e;return gr(r,n).map(s=>()=>s)}};var RN={kernelName:Rs,inputsToSave:["x"],gradFunc:(r,t,e)=>{let n=t[0],{paddings:o}=e,s=o.map(i=>i[0]);return{x:()=>Ot(r,s,n.shape)}}};var f$={kernelName:Fs,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(r,t)=>{let[e,n,o]=t,s=e,i=n,a=Mt(s.shape,i.shape);return{a:()=>{let c=J(i,"float32"),p=$(r,$(c,qr(s,at(c,pt(1))))),m=we(s.shape,a);return m.length>0&&(p=mt(p,m)),R(p,s.shape)},b:()=>{let c=Re(s,0),p=Ie(c,Nr(s),vt(s)),m=$(r,$(o,p)),f=we(i.shape,a);return f.length>0&&(m=mt(m,f)),R(m,i.shape)}}}};var d$={kernelName:Os,inputsToSave:["x","alpha"],gradFunc:(r,t)=>{let[e,n]=t,o=Re(e,0);return{x:()=>Ie(o,r,$(r,n)),alpha:()=>{let s=Ie(o,vt(r),$(r,e)),i=we(n.shape,r.shape);return i.length>0&&(s=mt(s,i)),R(s,n.shape)}}}};function HZ(r,t,e){let n=r.shape.slice();n[e]=1;let o=R(t,n),s=mc(r,e,!0,!1),i=mc(r,e,!0,!0),a=$(s,i);return $(o,a)}function qZ(r,t,e){let n=r.shape.length,o=n-e.length,s=S.getAxesPermutation(e,n),i=r;s!=null&&(i=Vt(r,s));let a=i.shape.slice(),l=a.splice(n-e.length,e.length).reduce((m,f)=>m*f,1);a.push(l);let c=i.reshape(a),p=HZ(c,t,o);if(p=p.reshape(i.shape),s!=null){let m=S.getUndoAxesPermutation(s);p=Vt(p,m)}return p}var h$={kernelName:Ms,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{axis:o}=e,s=[];return o==null?s=n.shape.map((i,a)=>a):typeof o=="number"?s=[o]:s=o,{x:()=>qZ(n,r,s)}}};var g$={kernelName:as,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=ut(r,J(n,"float32")),u=we(e.shape,o);return u.length>0?R(mt(a,u),e.shape):a},b:()=>{let a=$(r,J(e,"float32")),u=we(n.shape,o);u.length>0&&(a=R(mt(a,u),n.shape));let l=Wt(n);return Ut(ut(a,J(l,"float32")))}}}};var x$={kernelName:Ps,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ut(r,Ut(Wt(e)))}}};var y$={kernelName:Vs,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t,n=$(Vn(e,6),So(e));return{x:()=>$(r,J(n,"float32"))}}};var b$={kernelName:Ls,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,J(So(e),"float32"))}}};var w$={kernelName:Gi,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>R(r,e.shape)}}};var I$={kernelName:Bs,inputsToSave:["images"],gradFunc:(r,t,e)=>{let[n]=t,o={dy:r,images:n};return{images:()=>T.runKernel(rl,o,e)}}};var C$={kernelName:zs,inputsToSave:["images"],gradFunc:(r,t,e)=>{let[n]=t,o={dy:r,images:n};return{images:()=>T.runKernel(el,o,e)}}};var v$={kernelName:Gs,gradFunc:(r,t,e)=>{let{dims:n}=e,o=fr(n,r.shape);return{x:()=>dr(r,o)}}};var S$={kernelName:Ws,gradFunc:r=>({x:()=>vt(r)})};var N$={kernelName:Us,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>Ut(ut(r,$(qr(e,1.5),2)))}}};var k$={kernelName:Wi,inputsToSave:["condition"],gradFunc:(r,t)=>{let[e]=t;return{condition:()=>J(vt(e),"float32"),t:()=>$(r,J(e,r.dtype)),e:()=>$(r,J(Nu(e),r.dtype))}}};var T$={kernelName:Hs,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>{let n=Re(e,pt(0)),o=pt(_N),s=pt(EN),i=$(r,s),a=$($(r,o),Ke(J(e,"float32")));return Ie(n,i,a)}}}};var _$={kernelName:Xs,outputsToSave:[!0],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,$(e,at(pt(1),e)))}}};var E$={kernelName:js,gradFunc:r=>({x:()=>vt(r)})};var A$={kernelName:qs,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(bu(J(e,"float32")),r)}}};var D$={kernelName:Ks,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(sm(J(e,"float32")),r)}}};var $$={kernelName:Ui,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{begin:o,size:s}=e,i=n.shape,[a,u]=TN(n,o,s),l=[];for(let c=0;c<r.rank;c++)l.push([a[c],i[c]-a[c]-u[c]]);return{x:()=>mn(r,l)}}};var R$={kernelName:Qs,outputsToSave:[!0],gradFunc:(r,t,e)=>{let[n]=t,{dim:o}=e,s=!0,i=$(r,n);return{logits:()=>at(i,$(mt(i,[o],s),n))}}};var F$={kernelName:Ys,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,en(e))}}};var FN={kernelName:Hi,gradFunc:(r,t,e)=>{let{blockShape:n,paddings:o}=e;return{x:()=>yu(r,n,o)}}};var ON={kernelName:qi,gradFunc:(r,t,e)=>{let{axis:n}=e;return{x:()=>ie(r,n)}}};var O$={kernelName:Zs,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ut(r,$(ge(J(e,"float32")),2))}}};var M$={kernelName:iu,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(r,$(J(e,"float32"),2))}}};var P$={kernelName:ti,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=pt(2);return{a:()=>$(r,$(o,at(e,n))),b:()=>$(r,$(o,at(n,e)))}}};var L$={kernelName:xo,gradFunc:r=>({x:()=>vt(r)})};var z$={kernelName:ei,inputsToSave:["a","b"],gradFunc:(r,t)=>{let[e,n]=t,o=Mt(e.shape,n.shape);return{a:()=>{let a=r,u=we(e.shape,o);return u.length>0&&(a=mt(a,u)),R(a,e.shape)},b:()=>{let a=r,u=we(n.shape,o);return u.length>0&&(a=mt(a,u)),R(Ut(a),n.shape)}}}};var B$={kernelName:Js,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,o=n.shape.slice(),{axis:s}=e;fr(s,n.shape).forEach(l=>{o[l]=1});let a=R(r,o),u=$(a,ar(n.shape,"float32"));return{x:()=>u}}};var V$={kernelName:ri,inputsToSave:["x"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>ut(r,Wt(bu(e)))}}};var G$={kernelName:ni,outputsToSave:[!0],gradFunc:(r,t)=>{let[e]=t;return{x:()=>$(at(pt(1),Wt(e)),r)}}};var W$={kernelName:oo,inputsToSave:["x"],gradFunc:(r,t,e)=>{let[n]=t,{reps:o}=e;return{x:()=>{let i=vt(n);if(n.rank===1)for(let a=0;a<o[0];++a)i=K(i,Ot(r,[a*n.shape[0]],[n.shape[0]]));else if(n.rank===2)for(let a=0;a<o[0];++a)for(let u=0;u<o[1];++u)i=K(i,Ot(r,[a*n.shape[0],u*n.shape[1]],[n.shape[0],n.shape[1]]));else if(n.rank===3)for(let a=0;a<o[0];++a)for(let u=0;u<o[1];++u)for(let l=0;l<o[2];++l)i=K(i,Ot(r,[a*n.shape[0],u*n.shape[1],l*n.shape[2]],[n.shape[0],n.shape[1],n.shape[2]]));else if(n.rank===4)for(let a=0;a<o[0];++a)for(let u=0;u<o[1];++u)for(let l=0;l<o[2];++l)for(let c=0;c<o[3];++c)i=K(i,Ot(r,[a*n.shape[0],u*n.shape[1],l*n.shape[2],c*n.shape[3]],[n.shape[0],n.shape[1],n.shape[2],n.shape[3]]));else throw new Error(`Gradient for tile operation is not implemented for rank-${n.rank} tensors yet.`);return i}}}};var U$={kernelName:so,gradFunc:(r,t,e)=>{let n=e,{perm:o}=n,s=fh(o);return{x:()=>Vt(r,s)}}};var H$={kernelName:Ki,gradFunc:(r,t,e)=>{let n=e,{axis:o}=n;return{value:()=>Fe(r,o)}}};var q$={kernelName:pu,inputsToSave:["segmentIds"],gradFunc:(r,t)=>{let[e]=t;return{x:()=>KZ(r,e)}}};function KZ(r,t){let e=kn(t,vt(t)),n=ua(r,e),o=cn(t,pt(0,"int32")),s=n.rank-o.rank;for(let a=0;a<s;++a)o=je(o,a+1);o=Fr(o,ar(n.shape,"bool"));let i=vt(n);return Ie(o,n,i)}var K$={kernelName:ji,gradFunc:r=>({x:()=>vt(r)})};var jZ=[Dy,J2,Q2,tD,eD,rD,nD,oD,sD,iD,aD,lD,cD,mD,fD,dD,hD,gD,xD,yD,bD,wD,CD,ID,SD,ND,kD,TD,_D,ED,g$,AD,DD,$D,RD,FD,MD,OD,PD,BD,VD,GD,WD,UD,HD,qD,KD,jD,XD,ZD,$N,$N,JD,t$,r$,n$,o$,s$,i$,a$,l$,u$,c$,p$,m$,RN,RN,f$,d$,h$,x$,y$,b$,w$,I$,C$,v$,S$,N$,k$,T$,_$,E$,A$,D$,$$,R$,F$,FN,FN,ON,ON,O$,P$,M$,L$,z$,B$,V$,G$,W$,U$,H$,q$,K$];for(let r of jZ)$_(r);O().prototype.abs=function(){return this.throwIfDisposed(),_e(this)};O().prototype.acos=function(){return this.throwIfDisposed(),fx(this)};O().prototype.acosh=function(){return this.throwIfDisposed(),dx(this)};O().prototype.add=function(r){return this.throwIfDisposed(),K(this,r)};O().prototype.all=function(r,t){return this.throwIfDisposed(),tm(this,r,t)};O().prototype.any=function(r,t){return this.throwIfDisposed(),cc(this,r,t)};O().prototype.argMax=function(r){return this.throwIfDisposed(),ra(this,r)};O().prototype.argMin=function(r){return this.throwIfDisposed(),hx(this,r)};O().prototype.asScalar=function(){return this.throwIfDisposed(),_(this.size===1,()=>"The array must have only 1 element."),R(this,[])};O().prototype.asType=function(r){return this.throwIfDisposed(),J(this,r)};O().prototype.as1D=function(){return this.throwIfDisposed(),R(this,[this.size])};O().prototype.as2D=function(r,t){return this.throwIfDisposed(),R(this,[r,t])};O().prototype.as3D=function(r,t,e){return this.throwIfDisposed(),R(this,[r,t,e])};O().prototype.as4D=function(r,t,e,n){return this.throwIfDisposed(),R(this,[r,t,e,n])};O().prototype.as5D=function(r,t,e,n,o){return this.throwIfDisposed(),R(this,[r,t,e,n,o])};O().prototype.asin=function(){return this.throwIfDisposed(),gx(this)};O().prototype.asinh=function(){return this.throwIfDisposed(),xx(this)};O().prototype.atan=function(){return this.throwIfDisposed(),yx(this)};O().prototype.atan2=function(r){return this.throwIfDisposed(),bx(this,r)};O().prototype.atanh=function(){return this.throwIfDisposed(),wx(this)};O().prototype.avgPool=function(r,t,e,n){return this.throwIfDisposed(),xu(this,r,t,e,n)};O().prototype.batchToSpaceND=function(r,t){return this.throwIfDisposed(),yu(this,r,t)};O().prototype.batchNorm=function(r,t,e,n,o){return this.throwIfDisposed(),oa(this,r,t,e,n,o)};O().prototype.broadcastTo=function(r){return this.throwIfDisposed(),sa(this,r)};O().prototype.cast=function(r){return this.throwIfDisposed(),J(this,r)};O().prototype.ceil=function(){return this.throwIfDisposed(),kx(this)};O().prototype.clipByValue=function(r,t){return this.throwIfDisposed(),vr(this,r,t)};O().prototype.concat=function(r,t){return this.throwIfDisposed(),r instanceof Lt&&(r=[r]),ie([this,...r],t)};O().prototype.conv1d=function(r,t,e,n,o,s){return this.throwIfDisposed(),rm(this,r,t,e,n,o,s)};O().prototype.conv2dTranspose=function(r,t,e,n,o){return this.throwIfDisposed(),om(this,r,t,e,n,o)};O().prototype.conv2d=function(r,t,e,n,o,s){return this.throwIfDisposed(),Nn(this,r,t,e,n,o,s)};O().prototype.cos=function(){return this.throwIfDisposed(),bu(this)};O().prototype.cosh=function(){return this.throwIfDisposed(),sm(this)};O().prototype.cumprod=function(r,t,e){return this.throwIfDisposed(),mc(this,r,t,e)};O().prototype.cumsum=function(r,t,e){return this.throwIfDisposed(),im(this,r,t,e)};O().prototype.depthToSpace=function(r,t){return this.throwIfDisposed(),Fx(this,r,t)};O().prototype.depthwiseConv2d=function(r,t,e,n,o,s){return this.throwIfDisposed(),ia(this,r,t,e,n,o,s)};O().prototype.dilation2d=function(r,t,e,n,o){return this.throwIfDisposed(),Ox(this,r,t,e,n,o)};O().prototype.divNoNan=function(r){return this.throwIfDisposed(),Mx(this,r)};O().prototype.div=function(r){return this.throwIfDisposed(),ut(this,r)};O().prototype.dot=function(r){return this.throwIfDisposed(),Px(this,r)};O().prototype.elu=function(){return this.throwIfDisposed(),aa(this)};O().prototype.equal=function(r){return this.throwIfDisposed(),$r(this,r)};O().prototype.erf=function(){return this.throwIfDisposed(),am(this)};O().prototype.euclideanNorm=function(r,t){return this.throwIfDisposed(),Lx(this,r,t)};O().prototype.exp=function(){return this.throwIfDisposed(),Ke(this)};O().prototype.expandDims=function(r){return this.throwIfDisposed(),je(this,r)};O().prototype.expm1=function(){return this.throwIfDisposed(),zx(this)};O().prototype.fft=function(){return this.throwIfDisposed(),Au(this)};O().prototype.flatten=function(){return this.throwIfDisposed(),R(this,[this.size])};O().prototype.floor=function(){return this.throwIfDisposed(),la(this)};O().prototype.floorDiv=function(r){return this.throwIfDisposed(),Qp(this,r)};O().prototype.gather=function(r,t,e){return this.throwIfDisposed(),ua(this,r,t,e)};O().prototype.greaterEqual=function(r){return this.throwIfDisposed(),cn(this,r)};O().prototype.greater=function(r){return this.throwIfDisposed(),Re(this,r)};O().prototype.ifft=function(){return this.throwIfDisposed(),wl(this)};O().prototype.irfft=function(){return this.throwIfDisposed(),bm(this)};O().prototype.isFinite=function(){return this.throwIfDisposed(),Bx(this)};O().prototype.isInf=function(){return this.throwIfDisposed(),Vx(this)};O().prototype.isNaN=function(){return this.throwIfDisposed(),Gx(this)};O().prototype.leakyRelu=function(r){return this.throwIfDisposed(),Cu(this,r)};O().prototype.lessEqual=function(r){return this.throwIfDisposed(),Vn(this,r)};O().prototype.less=function(r){return this.throwIfDisposed(),yl(this,r)};O().prototype.localResponseNormalization=function(r,t,e,n){return this.throwIfDisposed(),Wx(this,r,t,e,n)};O().prototype.logSigmoid=function(){return this.throwIfDisposed(),qx(this)};O().prototype.logSoftmax=function(r){return this.throwIfDisposed(),lm(this,r)};O().prototype.logSumExp=function(r,t){return this.throwIfDisposed(),Su(this,r,t)};O().prototype.log=function(){return this.throwIfDisposed(),Nr(this)};O().prototype.log1p=function(){return this.throwIfDisposed(),vu(this)};O().prototype.logicalAnd=function(r){return this.throwIfDisposed(),Fr(this,r)};O().prototype.logicalNot=function(){return this.throwIfDisposed(),Nu(this)};O().prototype.logicalOr=function(r){return this.throwIfDisposed(),um(this,r)};O().prototype.logicalXor=function(r){return this.throwIfDisposed(),Kx(this,r)};O().prototype.matMul=function(r,t,e){return this.throwIfDisposed(),Bt(this,r,t,e)};O().prototype.maxPool=function(r,t,e,n){return this.throwIfDisposed(),ku(this,r,t,e,n)};O().prototype.max=function(r,t){return this.throwIfDisposed(),Sr(this,r,t)};O().prototype.maximum=function(r){return this.throwIfDisposed(),kn(this,r)};O().prototype.mean=function(r,t){return this.throwIfDisposed(),Ne(this,r,t)};O().prototype.min=function(r,t){return this.throwIfDisposed(),gl(this,r,t)};O().prototype.minimum=function(r){return this.throwIfDisposed(),lo(this,r)};O().prototype.mirrorPad=function(r,t){return this.throwIfDisposed(),Yx(this,r,t)};O().prototype.mod=function(r){return this.throwIfDisposed(),Zx(this,r)};O().prototype.mul=function(r){return this.throwIfDisposed(),$(this,r)};O().prototype.neg=function(){return this.throwIfDisposed(),Ut(this)};O().prototype.norm=function(r,t,e){return this.throwIfDisposed(),xl(this,r,t,e)};O().prototype.notEqual=function(r){return this.throwIfDisposed(),ci(this,r)};O().prototype.oneHot=function(r,t=1,e=0){return this.throwIfDisposed(),ca(this,r,t,e)};O().prototype.onesLike=function(){return this.throwIfDisposed(),wr(this)};O().prototype.pad=function(r,t){return this.throwIfDisposed(),mn(this,r,t)};O().prototype.pool=function(r,t,e,n,o,s){return this.throwIfDisposed(),Jx(this,r,t,e,n,o,s)};O().prototype.pow=function(r){return this.throwIfDisposed(),qr(this,r)};O().prototype.prelu=function(r){return this.throwIfDisposed(),_u(this,r)};O().prototype.prod=function(r,t){return this.throwIfDisposed(),Qx(this,r,t)};O().prototype.reciprocal=function(){return this.throwIfDisposed(),sy(this)};O().prototype.relu=function(){return this.throwIfDisposed(),Or(this)};O().prototype.relu6=function(){return this.throwIfDisposed(),cm(this)};O().prototype.reshapeAs=function(r){return this.throwIfDisposed(),R(this,r.shape)};O().prototype.reshape=function(r){return this.throwIfDisposed(),R(this,r)};O().prototype.resizeBilinear=function(r,t,e){return this.throwIfDisposed(),Cy(this,r,t,e)};O().prototype.resizeNearestNeighbor=function(r,t,e){return this.throwIfDisposed(),vy(this,r,t,e)};O().prototype.reverse=function(r){return this.throwIfDisposed(),dr(this,r)};O().prototype.rfft=function(){return this.throwIfDisposed(),Du(this)};O().prototype.round=function(){return this.throwIfDisposed(),pm(this)};O().prototype.rsqrt=function(){return this.throwIfDisposed(),mm(this)};O().prototype.selu=function(){return this.throwIfDisposed(),fm(this)};O().prototype.separableConv2d=function(r,t,e,n,o,s){return this.throwIfDisposed(),dm(this,r,t,e,n,o,s)};O().prototype.sigmoid=function(){return this.throwIfDisposed(),en(this)};O().prototype.sign=function(){return this.throwIfDisposed(),iy(this)};O().prototype.sin=function(){return this.throwIfDisposed(),hm(this)};O().prototype.sinh=function(){return this.throwIfDisposed(),gm(this)};O().prototype.slice=function(r,t){return this.throwIfDisposed(),Ot(this,r,t)};O().prototype.softmax=function(r){return this.throwIfDisposed(),Eu(this,r)};O().prototype.softplus=function(){return this.throwIfDisposed(),ui(this)};O().prototype.spaceToBatchND=function(r,t){return this.throwIfDisposed(),Tu(this,r,t)};O().prototype.split=function(r,t){return this.throwIfDisposed(),hr(this,r,t)};O().prototype.sqrt=function(){return this.throwIfDisposed(),ge(this)};O().prototype.square=function(){return this.throwIfDisposed(),Wt(this)};O().prototype.squaredDifference=function(r){return this.throwIfDisposed(),wm(this,r)};O().prototype.squeeze=function(r){return this.throwIfDisposed(),Wn(this,r)};O().prototype.stack=function(r,t){this.throwIfDisposed();let e=r instanceof Lt?[this,r]:[this,...r];return Fe(e,t)};O().prototype.step=function(r){return this.throwIfDisposed(),So(this,r)};O().prototype.stridedSlice=function(r,t,e,n,o,s,i,a){return this.throwIfDisposed(),ay(this,r,t,e,n,o,s,i,a)};O().prototype.sub=function(r){return this.throwIfDisposed(),at(this,r)};O().prototype.sum=function(r,t){return this.throwIfDisposed(),mt(this,r,t)};O().prototype.tan=function(){return this.throwIfDisposed(),ly(this)};O().prototype.tanh=function(){return this.throwIfDisposed(),li(this)};O().prototype.tile=function(r){return this.throwIfDisposed(),Rr(this,r)};O().prototype.toBool=function(){return this.throwIfDisposed(),J(this,"bool")};O().prototype.toFloat=function(){return this.throwIfDisposed(),J(this,"float32")};O().prototype.toInt=function(){return this.throwIfDisposed(),J(this,"int32")};O().prototype.topk=function(r,t){return this.throwIfDisposed(),cy(this,r,t)};O().prototype.transpose=function(r){return this.throwIfDisposed(),Vt(this,r)};O().prototype.unique=function(r){return this.throwIfDisposed(),py(this,r)};O().prototype.unsortedSegmentSum=function(r,t){return this.throwIfDisposed(),vm(this,r,t)};O().prototype.unstack=function(r){return this.throwIfDisposed(),gr(this,r)};O().prototype.where=function(r,t){return this.throwIfDisposed(),Ie(r,this,t)};O().prototype.zerosLike=function(){return this.throwIfDisposed(),vt(this)};var uo=class r extends Error{constructor(t){super(t),Object.setPrototypeOf(this,r.prototype)}},dn=class r extends Error{constructor(t){super(t),Object.setPrototypeOf(this,r.prototype)}},z=class r extends Error{constructor(t){super(t),Object.setPrototypeOf(this,r.prototype)}},_t=class r extends Error{constructor(t){super(t),Object.setPrototypeOf(this,r.prototype)}},Ry=class r extends Error{constructor(t){super(t),Object.setPrototypeOf(this,r.prototype)}};var vh=class{constructor(t){this.maxEntries=t||100,this.cache=new Map}get(t){let e;return this.cache.has(t)&&(e=this.cache.get(t),this.cache.delete(t),this.cache.set(t,e)),e}put(t,e){if(this.cache.has(t))this.cache.delete(t);else if(this.cache.size>=this.maxEntries){let n=this.cache.keys().next().value;this.cache.delete(n)}this.cache.set(t,e)}getMaxEntries(){return this.maxEntries}setMaxEntries(t){if(t<0)throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${t}.`);if(this.maxEntries>t)for(let e=0;e<this.maxEntries-t;e++){let n=this.cache.keys().next().value;this.cache.delete(n)}this.maxEntries=t}};function To(r,t){if(Array.isArray(r)){let e=[];for(let n=0;n<t;n++)e=e.concat(r);return e}else{let e=new Array(t);return e.fill(r),e}}function co(r,t){if(!r)throw new Ry(t)}function PN(r,t){let e=0;for(let n of r)n===t&&e++;return e}function kr(r){return r.length===1?r[0]:r}function ue(r){return Array.isArray(r)?r:[r]}function _o(r){let e=r.replace(/(.)([A-Z][a-z0-9]+)/g,"$1_$2").replace(/([a-z])([A-Z])/g,"$1_$2").toLowerCase();return e[0]!=="_"?e:"private"+e}function Cl(r){return r.length<=1||r.indexOf("_")===-1?r:r.replace(/[_]+(\w|$)/g,(t,e)=>e.toUpperCase())}var ko={};function km(r){if(r==null)return null;let t={};return t.className=r.getClassName(),t.config=r.getConfig(),t}function MN(r){if(!(r==null||typeof r!="object"))if(Array.isArray(r))r.forEach(t=>MN(t));else{let t=Object.keys(r);for(let e of t){let n=r[e];n!=null&&typeof n=="object"&&(!Array.isArray(n)&&n.type==="ndarray"&&typeof n.value=="number"?r[e]=n.value:MN(n))}}}function fa(r,t={},e={},n="object",o=!1){if(typeof r=="string"){let s=r,i;if(s in e)i=e[s];else if(s in ko)i=ko[s];else if(i=t[s],i==null)throw new z(`Unknown ${n}: ${r}. This may be due to one of the following reasons:
1. The ${n} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${n} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return i}else{let s=r;if(s.className==null||s.config==null)throw new z(`${n}: Improper config format: ${JSON.stringify(s)}.
'className' and 'config' must set.`);let i=s.className,a,u;if(i in e?[a,u]=e[i]:i in ko?[a,u]=ko.className:i in t&&([a,u]=t[i]),a==null)throw new z(`Unknown ${n}: ${i}. This may be due to one of the following reasons:
1. The ${n} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
2. The custom ${n} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(u!=null){let l={};for(let f of Object.keys(ko))l[f]=ko[f];for(let f of Object.keys(e))l[f]=e[f];let c=s.config;c.customObjects=l;let p=Object.assign({},ko);for(let f of Object.keys(e))ko[f]=e[f];MN(s.config);let m=u(a,s.config,e,o);return ko=Object.assign({},p),m}else{let l=Object.assign({},ko);for(let p of Object.keys(e))ko[p]=e[p];let c=new a(s.config);return ko=Object.assign({},l),c}}}function XZ(r,t){return r<t?-1:r>t?1:0}function Sh(r,t){return-1*XZ(r,t)}function Eo(r){if(r==null)return r;let t=[];for(let e of r)t.indexOf(e)===-1&&t.push(e);return t}function j$(r){if(r==null)throw new z(`Invalid value in obj: ${JSON.stringify(r)}`);for(let t in r)if(r.hasOwnProperty(t))return!1;return!0}function da(r,t,e){if(e!=null&&r.indexOf(e)<0)throw new z(`${e} is not a valid ${t}.  Valid values are ${r} or null/undefined.`)}function Fy(r,t,e=0,n=1/0){return co(e>=0),co(n>=e),Array.isArray(r)&&r.length>=e&&r.length<=n&&r.every(o=>typeof o===t)}function tr(r,t){Array.isArray(r)?(y.assert(r.length>0,()=>`${t} is unexpectedly an empty array.`),r.forEach((e,n)=>tr(e,`element ${n+1} of ${t}`))):y.assert(Number.isInteger(r)&&r>0,()=>`Expected ${t} to be a positive integer, but got ${X$(r)}.`)}function X$(r){return r===null?"null":Array.isArray(r)?"["+r.map(t=>X$(t)).join(",")+"]":typeof r=="string"?`"${r}"`:`${r}`}function Y$(r,t,e){let n=e!=null?e():y.now(),o;return(...i)=>{let a=e!=null?e():y.now();return a-n<t||(n=a,o=r(...i)),o}}function Oy(r){return r==="relu"?"relu":r==="linear"?"linear":r==="elu"?"elu":null}var YZ=0;function Py(){return YZ++}var My={};function Fu(r=""){return r in My||(My[r]=0),My[r]+=1,r+My[r].toString()}var Z$=["channelsFirst","channelsLast"],J$=["nearest","bilinear"],Q$=["valid","same","causal"],tR=["max","avg"],eR=["sum","mul","concat","ave"];var Tm=new Map;function Me(r){da(Z$,"DataFormat",r)}function nR(r){da(J$,"InterpolationFormat",r)}function hn(r){da(Q$,"PaddingMode",r)}function LN(r){da(tR,"PoolMode",r)}var Nh=[],rR="/";function fi(r,t){Nh.push(r);try{let e=t();return Nh.pop(),e}catch(e){throw Nh.pop(),e}}function ZZ(){return Nh.length===0?"":Nh.join(rR)+rR}function Ly(r){if(!oR(r))throw new Error("Not a valid tensor name: '"+r+"'");return ZZ()+r}function zy(r){if(!oR(r))throw new Error("Not a valid tensor name: '"+r+"'");Tm.has(r)||Tm.set(r,0);let t=Tm.get(r);if(Tm.set(r,Tm.get(r)+1),t>0){let e=`${r}_${t}`;return Tm.set(e,1),e}else return r}var JZ=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function oR(r){return!!r.match(JZ)}function sR(r){return r===parseInt(r.toString(),10)}function Ao(r,t,e){t==null&&(t=0),e==null&&(e=r.length);let n=1;for(let o=t;o<e;++o)n*=r[o];return n}function Dc(r){if(r.length===0)return Number.NaN;let t=Number.POSITIVE_INFINITY;for(let e=0;e<r.length;e++){let n=r[e];n<t&&(t=n)}return t}function di(r){if(r.length===0)return Number.NaN;let t=Number.NEGATIVE_INFINITY;for(let e=0;e<r.length;e++){let n=r[e];n>t&&(t=n)}return t}function gn(r,t){if(t<r)throw new z(`end (${t}) < begin (${r}) is forbidden.`);let e=[];for(let n=r;n<t;++n)e.push(n);return e}var zN;function cr(){return zN==null&&(zN=sx().epsilon()),zN}function xn(){return"channelsLast"}function rn(r,t){return J(r,t)}function Sl(r,t=-1){let e=r.shape.slice();return t<0&&(t=e.length+t+1),e.splice(t,0,1),R(r,e)}function iR(r,t){return B(()=>{if(r.shape.length!==2)throw new z(`repeat() expects a rank-2 tensor, but received a rank-${r.shape.length} tensor.`);let e=Sl(r,1);return Vy(e,[1,t,1])})}function aR(r){let t=[Ao(r.shape)];return R(r,t)}function lR(r){if(r.rank<=1)throw new z(`batchFlatten requires a minimum rank of 2. Got rank: ${r.rank}.`);let t=[r.shape[0],Ao(r.shape,1)];return R(r,t)}function vl(r,t,e){return B(()=>{switch(r.rank){case 1:return xm(r,t,e);case 2:return gh(r,[t,0],[e,r.shape[1]]);case 3:return ym(r,[t,0,0],[e,r.shape[1],r.shape[2]]);case 4:return yc(r,[t,0,0,0],[e,r.shape[1],r.shape[2],r.shape[3]]);case 5:return Ot(r,[t,0,0,0,0],[e,r.shape[1],r.shape[2],r.shape[3],r.shape[4]]);case 6:return Ot(r,[t,0,0,0,0,0],[e,r.shape[1],r.shape[2],r.shape[3],r.shape[4],r.shape[5]]);default:throw new z(`sliceAlongFirstAxis() received an unsupported tensor rank: ${r.rank}`)}})}function BN(r,t,e){return B(()=>{switch(r.rank){case 1:return xm(r,t,e);case 2:return gh(r,[0,t],[r.shape[0],e]);case 3:return ym(r,[0,0,t],[r.shape[0],r.shape[1],e]);case 4:return yc(r,[0,0,0,t],[r.shape[0],r.shape[1],r.shape[2],e]);default:throw new z(`sliceAlongLastAxis() received an unsupported tensor rank: ${r.rank}`)}})}function kh(r,t,e,n){return B(()=>{switch(r.rank){case 1:return xm(r,t,e);case 2:switch(n){case 1:return vl(r,t,e);case 2:return BN(r,t,e);default:throw new z(`The axis is not within the rank of the tensor ${n}`)}case 3:switch(n){case 1:return vl(r,t,e);case 2:return ym(r,[0,t,0],[r.shape[0],e,r.shape[2]]);case 3:return BN(r,t,e);default:throw new z(`The axis is not within the rank of the tensor ${n}`)}case 4:switch(n){case 1:return vl(r,t,e);case 2:return yc(r,[0,t,0,0],[r.shape[0],e,r.shape[2],r.shape[3]]);case 3:return yc(r,[0,0,t,0],[r.shape[0],r.shape[1],e,r.shape[3]]);case 4:return BN(r,t,e);default:throw new z(`The axis is not within the rank of the tensor ${n}`)}default:throw new z(`sliceAlongLastAxis() received an unsupported tensor rank: ${r.rank}`)}})}function _m(r,t=-1){let e;return t<0&&(e=r[0].rank,e!==0?t=e:t=0),t===r[0].rank&&(t=-1),ie(r,t)}function GN(r,t){switch(r.rank){case 1:return Tx([r,t]);case 2:return _x([r,t],0);case 3:return Ex([r,t],0);case 4:return Ax([r,t],0);default:throw new z(`concatAlongFirstAxis() received an unsupported tensor rank: ${r.rank}`)}}function Vy(r,t){if(Array.isArray(t)||(t=[t]),r.rank!==t.length)throw new z(`The length of input n (${t.length}) does not match the number of dimensions in input x (${r.rank})`);return Rr(r,t)}function Em(r,t=0,e=1,n,o){return xc(r,t,e,n,o)}function Do(r,t,e,n){if(r.rank<2||t.rank<2)throw new _t(`dot requires both inputs to be rank >= 2 but got x shape = ${r.shape} and y shape = ${t.shape}`);if(t.rank>=3){let o=r.shape.slice(-1)[0],s=t.shape.slice(-2)[0];if(o!==s)throw new _t(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${r.shape} and  y shape = ${t.shape}`)}if(r.rank===2&&t.rank===2)return Ru.matMul({a:r,b:t,transposeA:!1,transposeB:!1,bias:n?VN(r.rank,n,xn()):null,activation:e});{let o=r.shape.slice(),s=o.pop();r=R(r,[-1,s]);let i=t.shape.slice(),a=i.pop(),u=i.pop(),l=[...i,a],c=Array.from({length:t.rank},(d,h)=>h===0?t.rank-2:h<=t.rank-2?h-1:h);t=R(Vt(t,c),[u,-1]);let p=[...o,...l];return R(Ru.matMul({a:r,b:t,transposeA:!1,transposeB:!1,bias:n?VN(r.rank,n,xn()):null,activation:e}),p)}}function Gy(r,t,e){return B(()=>(Array.isArray(t)?t=Oe(t,"int32"):t=J(t,"int32"),ua(r,t,e)))}function $c(r){return $(r,r)}function VN(r,t,e){let n=t.shape;if(t.rank!==1&&t.rank!==r)throw new z(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${r}`);if(r===5){if(e==="channelsFirst")return n.length===1?R(t,[1,n[0],1,1,1]):R(t,[1,n[3],n[0],n[1],n[2]]);if(e==="channelsLast")return n.length===1?R(t,[1,1,1,1,n[0]]):R(t,[1].concat(n))}else if(r===4){if(e==="channelsFirst")return n.length===1?R(t,[1,n[0],1,1]):R(t,[1,n[2],n[0],n[1]]);if(e==="channelsLast")return n.length===1?R(t,[1,1,1,n[0]]):R(t,[1].concat(n))}else if(r===3){if(e==="channelsFirst")return n.length===1?R(t,[1,n[0],1]):R(t,[1,n[1],n[0]]);if(e==="channelsLast")return n.length===1?R(t,[1,1,n[0]]):R(t,[1].concat(n))}else if(r<3)return t;throw new z(`Unsupported input rank by biasAdd: ${t.rank}`)}function yn(r,t,e){return B(()=>(e==null&&(e=xn()),Me(e),K(r,VN(r.rank,t,e))))}function uR(r,t=1){if(t!==1)throw new _t(`Support for alpha values other than 1 (${t}) is not implemented yet.`);return aa(r)}function cR(r){return B(()=>ut(r,K(_e(r),1)))}function Wy(r,t,e,n){return B(()=>dN(r,t,e,n))}function pR(r){return B(()=>{let t=K(.5,$(.2,r));return vr(t,0,1)})}function Ou(r,t,e=!1){return e?r():t()}var mR=["fanIn","fanOut","fanAvg"],fR=["normal","uniform","truncatedNormal"];function QZ(r){da(mR,"FanMode",r)}function t7(r){da(fR,"Distribution",r)}var bn=class extends Q.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Am=class extends bn{apply(t,e){return ke(t,e)}};Am.className="Zeros";Q.registerClass(Am);var Mu=class extends bn{apply(t,e){return ar(t,e)}};Mu.className="Ones";Q.registerClass(Mu);var Dm=class extends bn{constructor(t){if(super(),typeof t!="object")throw new z(`Expected argument of type ConstantConfig but got ${t}`);if(t.value===void 0)throw new z(`config must have value set but got ${t}`);this.value=t.value}apply(t,e){return B(()=>$(pt(this.value),ar(t,e)))}getConfig(){return{value:this.value}}};Dm.className="Constant";Q.registerClass(Dm);var $m=class extends bn{constructor(t){super(),this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=t.minval||this.DEFAULT_MINVAL,this.maxval=t.maxval||this.DEFAULT_MAXVAL,this.seed=t.seed}apply(t,e){return Gn(t,this.minval,this.maxval,e,this.seed)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};$m.className="RandomUniform";Q.registerClass($m);var Rm=class extends bn{constructor(t){super(),this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=t.mean||this.DEFAULT_MEAN,this.stddev=t.stddev||this.DEFAULT_STDDEV,this.seed=t.seed}apply(t,e){if(e=e||"float32",e!=="float32"&&e!=="int32")throw new _t(`randomNormal does not support dType ${e}.`);return Em(t,this.mean,this.stddev,e,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Rm.className="RandomNormal";Q.registerClass(Rm);var Fm=class extends bn{constructor(t){super(),this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=t.mean||this.DEFAULT_MEAN,this.stddev=t.stddev||this.DEFAULT_STDDEV,this.seed=t.seed}apply(t,e){if(e=e||"float32",e!=="float32"&&e!=="int32")throw new _t(`truncatedNormal does not support dType ${e}.`);return Cm(t,this.mean,this.stddev,e,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};Fm.className="TruncatedNormal";Q.registerClass(Fm);var Om=class extends bn{constructor(t){super(),this.gain=t.gain!=null?t.gain:1}apply(t,e){return B(()=>{if(t.length!==2||t[0]!==t[1])throw new z("Identity matrix initializer can only be used for 2D square matrices.");return $(this.gain,fc(t[0]))})}getConfig(){return{gain:this.gain}}};Om.className="Identity";Q.registerClass(Om);function e7(r,t="channelsLast"){let e,n;if(Me(t),r.length===2)e=r[0],n=r[1];else if([3,4,5].indexOf(r.length)!==-1){if(t==="channelsFirst"){let o=Ao(r,2);e=r[1]*o,n=r[0]*o}else if(t==="channelsLast"){let o=Ao(r,0,r.length-2);e=r[r.length-2]*o,n=r[r.length-1]*o}}else{let o=Ao(r);e=Math.sqrt(o),n=Math.sqrt(o)}return[e,n]}var Yr=class extends bn{constructor(t){if(super(),t.scale<0)throw new z(`scale must be a positive float. Got: ${t.scale}`);this.scale=t.scale==null?1:t.scale,this.mode=t.mode==null?"fanIn":t.mode,QZ(this.mode),this.distribution=t.distribution==null?"normal":t.distribution,t7(this.distribution),this.seed=t.seed}apply(t,e){let n=e7(t),o=n[0],s=n[1],i=this.scale;if(this.mode==="fanIn"?i/=Math.max(1,o):this.mode==="fanOut"?i/=Math.max(1,s):i/=Math.max(1,(o+s)/2),this.distribution==="normal"){let a=Math.sqrt(i);if(e=e||"float32",e!=="float32"&&e!=="int32")throw new _t(`${this.getClassName()} does not support dType ${e}.`);return Cm(t,0,a,e,this.seed)}else{let a=Math.sqrt(3*i);return Gn(t,-a,a,e,this.seed)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};Yr.className="VarianceScaling";Q.registerClass(Yr);var Rc=class extends Yr{constructor(t){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Rc.className="GlorotUniform";Q.registerClass(Rc);var Fc=class extends Yr{constructor(t){super({scale:1,mode:"fanAvg",distribution:"normal",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Fc.className="GlorotNormal";Q.registerClass(Fc);var Oc=class extends Yr{constructor(t){super({scale:2,mode:"fanIn",distribution:"normal",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Oc.className="HeNormal";Q.registerClass(Oc);var Mc=class extends Yr{constructor(t){super({scale:2,mode:"fanIn",distribution:"uniform",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Mc.className="HeUniform";Q.registerClass(Mc);var Pc=class extends Yr{constructor(t){super({scale:1,mode:"fanIn",distribution:"normal",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Pc.className="LeCunNormal";Q.registerClass(Pc);var Lc=class extends Yr{constructor(t){super({scale:1,mode:"fanIn",distribution:"uniform",seed:t==null?null:t.seed})}getClassName(){return Yr.className}};Lc.className="LeCunUniform";Q.registerClass(Lc);var Mm=class extends bn{constructor(t){super(),this.DEFAULT_GAIN=1,this.ELEMENTS_WARN_SLOW=2e3,this.gain=t.gain==null?this.DEFAULT_GAIN:t.gain,this.seed=t.seed}apply(t,e){return B(()=>{if(t.length<2)throw new _t("Shape must be at least 2D.");if(e!=="int32"&&e!=="float32"&&e!==void 0)throw new TypeError(`Unsupported data type ${e}.`);e=e;let n=y.sizeFromShape(t.slice(0,-1)),o=t[t.length-1],s=n*o;s>this.ELEMENTS_WARN_SLOW&&console.warn(`Orthogonal initializer is being called on a matrix with more than ${this.ELEMENTS_WARN_SLOW} (${s}) elements: Slowness may result.`);let i=[Math.max(o,n),Math.min(o,n)],a=Em(i,0,1,e,this.seed),u=xN.qr(a,!1),l=u[0],p=u[1].flatten().stridedSlice([0],[Math.min(o,n)*Math.min(o,n)],[Math.min(o,n)+1]);return l=$(l,p.sign()),n<o&&(l=l.transpose()),$(pt(this.gain),l.reshape(t))})}getConfig(){return{gain:this.gain,seed:this.seed}}};Mm.className="Orthogonal";Q.registerClass(Mm);var dR={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function hR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"initializer")}function Te(r){return km(r)}function xe(r){if(typeof r=="string"){let t=r in dR?dR[r]:r;if(t==="GlorotNormal")return new Fc;if(t==="GlorotUniform")return new Rc;if(t==="HeNormal")return new Oc;if(t==="HeUniform")return new Mc;if(t==="LeCunNormal")return new Pc;if(t==="LeCunUniform")return new Lc;{let e={};return e.className=t,e.config={},hR(e)}}else return r instanceof bn?r:hR(r)}function Uy(r){return Array.isArray(r)&&Array.isArray(r[0])}function Pm(r){return r.length===0?[]:Array.isArray(r[0])?r:[r]}function St(r){let t;if(Array.isArray(r)){if(r.length!==1)throw new z(`Expected Tensor length to be 1; got ${r.length}`);t=r[0]}else t=r;return t}function Gt(r){if(Array.isArray(r)&&Array.isArray(r[0])){if(r.length===1)return r=r,r[0];throw new z(`Expected exactly 1 Shape; got ${r.length}`)}else return r}function Lm(r){let t=0;for(let e of r)e.shape.length===0?t+=1:t+=e.shape.reduce((n,o)=>n*o);return t}var xR="Variable",Th=class{constructor(t,e="float32",n=xR,o=!0,s=null){this.dtype=e==null?"float32":e,this.shape=t.shape,this.id=Py(),n=n==null?xR:n,this.originalName=Ly(n),this.name=zy(this.originalName),this.trainable_=o,this.constraint=s,this.val=my(t,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(t){return this.assertNotDisposed(),n7(this.val,t),this.val.id!==t.id&&(this.val.assign(t),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(t){this.trainable_=t,this.val.trainable=t}};function n7(r,t){if(r.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(r.shape)+" vs. "+JSON.stringify(t.shape))}function _h(r){return r.map(t=>t.read())}function zm(r){r.forEach(t=>{t[0].write(t[1])})}var Ce=class{constructor(t){this.dtype=t.dtype,this.shape=t.shape,t.shape!=null?this.ndim=t.shape.length:this.ndim=t.ndim,this.maxNDim=t.maxNDim,this.minNDim=t.minNDim,this.axes=t.axes||{}}},nn=class{constructor(t,e,n,o,s,i,a){this.dtype=t,this.shape=e,this.sourceLayer=n,this.inputs=o,this.callArgs=s,this.outputTensorIndex=a,this.id=Py(),i!=null&&(this.originalName=Ly(i),this.name=zy(this.originalName)),this.rank=e.length}},o7=0,Nl=class{constructor(t,e){this.callArgs=e,this.id=o7++,this.outboundLayer=t.outboundLayer,this.inboundLayers=t.inboundLayers,this.nodeIndices=t.nodeIndices,this.tensorIndices=t.tensorIndices,this.inputTensors=t.inputTensors,this.outputTensors=t.outputTensors,this.inputMasks=t.inputMasks,this.outputMasks=t.outputMasks,this.inputShapes=t.inputShapes,this.outputShapes=t.outputShapes;for(let n of t.inboundLayers)n!=null&&n.outboundNodes.push(this);t.outboundLayer.inboundNodes.push(this)}getConfig(){let t=[];for(let e of this.inboundLayers)e!=null?t.push(e.name):t.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:t,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},s7=0,Et=class extends Q.Serializable{constructor(t={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=s7++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let e=t.name;if(!e){let n=this.getClassName();e=_o(n)+"_"+Fu(n)}if(this.name=e,this.trainable_=t.trainable==null?!0:t.trainable,t.inputShape!=null||t.batchInputShape!=null){let n;if(t.batchInputShape!=null)n=t.batchInputShape;else if(t.inputShape!=null){let s=null;t.batchSize!=null&&(s=t.batchSize),n=[s].concat(t.inputShape)}this.batchInputShape=n;let o=t.dtype;o==null&&(o=t.inputDType),o==null&&(o="float32"),this.dtype=o}t.weights!=null?this.initialWeights=t.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(t,e){return t.name+"_ib-"+e.toString()}getNodeAtIndex(t,e){if(this.inboundNodes.length===0)throw new dn(`The layer has never been called and thus has no defined ${e}.`);if(this.inboundNodes.length<=t)throw new z(`Asked to get ${e} at node ${t}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[t]}getInputAt(t){return kr(this.getNodeAtIndex(t,"input").inputTensors)}getOutputAt(t){return kr(this.getNodeAtIndex(t,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new uo(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new uo(`Layer ${this.name} is not connected, no input to return.`);return kr(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new uo(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new uo(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return kr(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(t=>t())}get updates(){return this._updates}get built(){return this._built}set built(t){this._built=t}get trainable(){return this.trainable_}set trainable(t){this._trainableWeights.forEach(e=>e.trainable=t),this.trainable_=t}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(t=>t.trainable):[]}set trainableWeights(t){this._trainableWeights=t}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(t=>!t.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(t){this._nonTrainableWeights=t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(t){let e=ue(t);if(this.inputSpec==null||this.inputSpec.length===0)return;let n=ue(this.inputSpec);if(e.length!==n.length)throw new z(`Layer ${this.name} expects ${n.length} inputs, but it received ${e.length} input tensors. Input received: ${t}`);for(let o=0;o<e.length;o++){let s=e[o],i=n[o];if(i==null)continue;let a=s.rank;if(i.ndim!=null&&a!==i.ndim)throw new z(`Input ${o} is incompatible with layer ${this.name}: expected ndim=${i.ndim}, found ndim=${a}`);if(i.maxNDim!=null&&a>i.maxNDim)throw new z(`Input ${o} is incompatible with layer ${this.name}: expected max_ndim=${i.maxNDim}, found ndim=${a}`);if(i.minNDim!=null&&a<i.minNDim)throw new z(`Input ${o} is incompatible with layer ${this.name}: expected min_ndim=${i.minNDim}, found ndim=${a}.`);if(i.dtype!=null&&s.dtype!==i.dtype)throw new z(`Input ${o} is incompatible with layer ${this.name} : expected dtype=${i.dtype}, found dtype=${s.dtype}.`);if(i.axes){let u=s.shape;for(let l in i.axes){let c=Number(l),p=i.axes[l],m=c>=0?u[c]:u[u.length+c];if(p!=null&&[p,null].indexOf(m)===-1)throw new z(`Input ${o} is incompatible with layer ${this.name}: expected axis ${c} of input shape to have value ${p} but got shape ${u}.`)}}if(i.shape!=null)for(let u=0;u<i.shape.length;++u){let l=i.shape[u],c=s.shape[u];if(l!=null&&c!=null&&l!==c)throw new z(`Input ${o} is incompatible with layer ${this.name}: expected shape=${i.shape}, found shape=${s.shape}.`)}}}call(t,e){return t}invokeCallHook(t,e){this._callHook!=null&&this._callHook(t,e)}setCallHook(t){this._callHook=t}clearCallHook(){this._callHook=null}apply(t,e){e=e||{},this.assertNotDisposed();let n=ue(t),o=l7(t),s=u7(t);if(o===s)throw new z("Arguments to apply() must be all SymbolicTensors or all Tensors");return fi(this.name,()=>{if(!this.built){this.assertInputCompatibility(t);let i=[];for(let a of ue(t))i.push(a.shape);this.build(kr(i)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&s&&(this._refCount=1)}if(this.assertInputCompatibility(t),s){let i=this.call(t,e);this.supportsMasking&&this.setMaskMetadata(t,i);let a=ue(i),u=[];for(let l of a)n.indexOf(l)!==-1&&(l=l.clone()),u.push(l);if(i=kr(u),this.activityRegularizer!=null)throw new _t("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}else{let i=i7(t),a=this.computeOutputShape(i),u,l=a7(t);if(this.warnOnIncompatibleInputShape(Array.isArray(t)?i[0]:i),a!=null&&a.length>0&&Array.isArray(a[0])?u=a.map((c,p)=>new nn(l,c,this,ue(t),e,this.name,p)):u=new nn(l,a,this,ue(t),e,this.name),this.addInboundNode(t,u,null,null,i,a,e),this._refCount++,this.activityRegularizer!=null)throw new _t("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return u}})}warnOnIncompatibleInputShape(t){if(this.batchInputShape!=null)if(t.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(t)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let e=!1;this.batchInputShape.forEach((n,o)=>{n!=null&&t[o]!=null&&t[o]!==n&&(e=!0)}),e&&console.warn(`The shape of the input tensor (${JSON.stringify(t)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new uo(`The layer ${this.name} has never been called and thus has no defined output shape.`);let t=[];for(let e of this.inboundNodes){let n=JSON.stringify(e.outputShapes);t.indexOf(n)===-1&&t.push(n)}if(t.length===1){let e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&e.length===1?e[0]:e}else throw new uo(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new dn(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Lm(this.weights)}build(t){this.built=!0}getWeights(t=!1){return _h(t?this.trainableWeights:this.weights)}setWeights(t){B(()=>{let e=this.weights;if(e.length!==t.length)throw new z(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${t.length}, but the layer was expecting ${e.length} weights. Provided weights: ${t}...`);if(e.length===0)return;let n=[],o=_h(e);for(let s=0;s<o.length;++s){let i=o[s],a=e[s],u=t[s];if(!y.arraysEqual(i.shape,u.shape))throw new z(`Layer weight shape ${i.shape} not compatible with provided weight shape ${u.shape}`);n.push([a,u])}zm(n)})}addWeight(t,e,n,o,s,i,a,u){if(this._addedWeightNames.indexOf(t)!==-1)throw new z(`Duplicate weight name ${t} for layer ${this.name}`);this._addedWeightNames.push(t),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(o=u!=null?u():xe("zeros"));let l=o.apply(e,n),c=new Th(l,n,t,i,a);return l.dispose(),s!=null&&this.addLoss(()=>s.apply(c.read())),i==null&&(i=!0),i?this._trainableWeights.push(c):this._nonTrainableWeights.push(c),c}setFastWeightInitDuringBuild(t){this.fastWeightInitDuringBuild=t}addLoss(t){t==null||Array.isArray(t)&&t.length===0||(t=ue(t),this._losses!==void 0&&this._losses!==null&&this.losses.push(...t))}computeOutputShape(t){return t}computeMask(t,e){if(!this.supportsMasking){if(e!=null)if(Array.isArray(e))e.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return e}setMaskMetadata(t,e,n){if(!this.supportsMasking)return;let o=this.computeMask(t,n),s=ue(e),i=ue(o);if(s.length!==i.length)throw new Error(`${this.name} outputs ${s.length} tensors but ${s.length} masks for those tensors`);for(let a=0;a<s.length;a++)s[a].kerasMask=i[a]}addInboundNode(t,e,n,o,s,i,a=null){let u=ue(t);e=ue(e),n=ue(n),o=ue(o),s=Pm(s),i=Pm(i);let l=[],c=[],p=[];for(let m of u)l.push(m.sourceLayer),c.push(m.nodeIndex),p.push(m.tensorIndex);new Nl({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:p,inputTensors:u,outputTensors:e,inputMasks:n,outputMasks:o,inputShapes:s,outputShapes:i},a);for(let m=0;m<e.length;m++)e[m].sourceLayer=this,e[m].nodeIndex=this.inboundNodes.length-1,e[m].tensorIndex=m}getConfig(){let t={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(t.batchInputShape=this.batchInputShape),this.dtype!=null&&(t.dtype=this.dtype),t}disposeWeights(){return this.weights.forEach(t=>t.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let t=0;return--this._refCount===0&&(t=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:t}}};function i7(r){r=ue(r);let t=[];for(let e of r)t.push(e.shape);return kr(t)}function a7(r){return"float32"}function WN(r,t,e){if((t==null||e!=null&&e>0)&&(t=r.sourceLayer,e=r.nodeIndex),t.inboundNodes.length===0)return[r];{let n=t.inboundNodes[e];if(n.inboundLayers.length===0)return n.inputTensors;{let o=[];for(let s=0;s<n.inboundLayers.length;s++){let i=n.inputTensors[s],a=n.inboundLayers[s],u=n.nodeIndices[s],l=WN(i,a,u);for(let c of l)o.indexOf(c)===-1&&o.push(c)}return o}}}function l7(r){let t=!0;for(let e of ue(r))if(!(e instanceof nn)){t=!1;break}return t}function u7(r){let t=!0;for(let e of ue(r))if(e instanceof nn){t=!1;break}return t}var hi=class extends Et{constructor(t){if(super({dtype:t.dtype,name:t.name!=null?t.name:Fu("input").toString()}),t.batchSize==null&&(t.batchSize=null),t.sparse==null&&(t.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=t.sparse,t.inputShape!=null&&t.batchInputShape!=null)throw new z("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let e=t.batchInputShape;if(e==null){if(t.inputShape==null)throw new z("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");e=[t.batchSize].concat(t.inputShape)}else if(t.batchSize!=null)throw new z("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=t.dtype||"float32";this.batchInputShape=e,this.dtype=n,this.inputSpec=[{shape:e}];let o=new nn(this.dtype,this.batchInputShape,this,[],{},this.name);o.nodeIndex=0,o.tensorIndex=0,new Nl({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[o],outputTensors:[o],inputMasks:[null],outputMasks:[null],inputShapes:[e],outputShapes:[e]})}apply(t,e){throw new z(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};hi.className="InputLayer";Q.registerClass(hi);function Hy(r){if(r.batchShape==null&&r.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(r.batchShape!=null&&r.shape!=null)throw new z("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=r.batchShape;r.shape!=null&&t==null&&(t=[null].concat(r.shape));let e=r.dtype;return e==null&&(e="float32"),new hi({batchInputShape:t,name:r.name,dtype:e,sparse:r.sparse}).inboundNodes[0].outputTensors[0]}function c7(r,t){if(r.dtype==null||r.dtype===t.dtype)return t;try{return J(t,r.dtype)}catch(e){throw new z(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${r.name}' (${r.dtype}).`)}}var ha=class r{constructor(t){if(this.id2Value={},this.id2Mask={},this.name2Id={},t instanceof r)for(let e in t.id2Value)this.id2Value[e]=t.id2Value[e],e in t.id2Mask&&(this.id2Mask[e]=t.id2Mask[e]);else{if(t==null)return;for(let e of t)this.add(e.key,e.value)}}add(t,e,n){if(this.id2Value[t.id]==null)this.id2Value[t.id]=c7(t,e),this.name2Id[t.name]=t.id,n!=null&&(this.id2Mask[t.id]=n);else throw new z(`Duplicate key: name=${t.name}, id=${t.id}`);return this}addFeed(t){this.add(t.key,t.value)}hasKey(t){return this.id2Value[t.id]!=null}names(){return Object.keys(this.name2Id)}getValue(t){if(t instanceof nn){if(this.id2Value[t.id]==null)throw new z(`Nonexistent key: ${t.name}`);return this.id2Value[t.id]}else{let e=this.name2Id[t];if(e==null)throw new z(`Feed dict has no SymbolicTensor name: ${t}`);return this.id2Value[e]}}getMask(t){if(t instanceof nn){if(this.id2Value[t.id]==null)throw new z(`Nonexistent key: ${t.name}`);return this.id2Mask[t.id]}else{let e=this.name2Id[t];if(e==null)throw new z(`Feed dict has no SymbolicTensor name: ${t}`);return this.id2Mask[e]}}disposeMasks(){this.id2Mask!=null&&Tt(this.id2Mask)}},qy=new vh,Ky=new vh;function bR(r){qy!=null&&qy.setMaxEntries(r),Ky!=null&&Ky.setMaxEntries(r)}function zc(r,t,e,n){let o=e==null?!1:e.training,s=Array.isArray(r),i=s?r:[r],a=i.map(d=>d.name),u=[],l=t.names();for(let d of a)l.indexOf(d)!==-1?u.push(t.getValue(d)):u.push(null);n!=null&&(n.maxNumTensors=-1/0,n.minNumTensors=1/0);let c=a.join(",")+"|"+t.names().sort().join(","),p=qy.get(c),m;if(p==null){let d=p7(i,t);p=d.sorted,m=d.recipientCounts,qy.put(c,p),Ky.put(c,m)}m={},o||Object.assign(m,Ky.get(c));let f=new ha(t);for(let d=0;d<p.length;++d){if(n!=null){let F=lh().numTensors;F>n.maxNumTensors&&(n.maxNumTensors=F),F<n.minNumTensors&&(n.minNumTensors=F)}let h=p[d],g=h.sourceLayer;if(g instanceof hi)continue;let x=[],b=[],w=[],I=!1;for(let F of h.inputs){let M=f.getValue(F),V=f.getMask(F);x.push(M),b.push(V),V!=null&&(I=!0),o||(m[F.name]--,m[F.name]===0&&!t.hasKey(F)&&a.indexOf(F.name)===-1&&!M.isDisposed&&F.sourceLayer.stateful!==!0&&w.push(M))}I&&(e=e||{},e.mask=b[0]);let N=ue(g.apply(x,e)),E=null;g.supportsMasking&&(E=g.computeMask(x,b));let A=f7(h),D=Array.isArray(A)?A:[A];for(let F=0;F<D.length;++F){f.hasKey(D[F])||f.add(D[F],N[F],Array.isArray(E)?E[0]:E);let M=a.indexOf(D[F].name);M!==-1&&(u[M]=N[F])}o||Tt(w)}return f.disposeMasks(),s?u:u[0]}function p7(r,t){y.assert(r!=null&&r.length>0,()=>"Expected at least one fetch, got none");let e=[],n={};if(r.length===1){let o=yR(r[0],t);e=o.sorted,n=o.recipientMap}else{let o=new Set;for(let s of r){let{sorted:i,recipientMap:a}=yR(s,t);for(let u of i)o.has(u.name)||(e.push(u),o.add(u.name));for(let u in a)n[u]==null&&(n[u]=new Set),a[u].forEach(l=>n[u].add(l))}}return{sorted:e,recipientCounts:m7(n)}}function m7(r){let t={};for(let e in r)t[e]=r[e].size;return t}function yR(r,t){let e=new Set,n=[],o={};for(let a of t.names())e.add(a);let s=[],i=[];for(s.push(r);s.length>0;){let a=s[s.length-1];if(e.has(a.name)){s.pop();continue}let u=i[i.length-1]===s.length-1;if(a.inputs.length===0||u)s.pop(),n.push(a),e.add(a.name),u&&i.pop();else{i.push(s.length-1);for(let l of a.inputs)o[l.name]==null&&(o[l.name]=new Set),o[l.name].add(a.name),!e.has(l.name)&&s.push(l)}}return{sorted:n,recipientMap:o}}function f7(r){let t;if(r.sourceLayer.inboundNodes.length===1)t=r.sourceLayer.output;else{let e=null;for(let n=0;n<r.sourceLayer.inboundNodes.length;++n)for(let o of r.sourceLayer.inboundNodes[n].outputTensors)if(o.id===r.id){e=n;break}t=r.sourceLayer.getOutputAt(e)}return t}var d7=L();d7.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES",()=>100,bR);var CR={};Kt(CR,{maxNorm:()=>h7,minMaxNorm:()=>y7,nonNeg:()=>x7,unitNorm:()=>g7});function UN(r,t){return B(()=>ge(mt($(r,r),t,!0)))}var Bc=class extends Q.Serializable{getConfig(){return{}}},Bm=class extends Bc{constructor(t){super(),this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=t.maxValue!=null?t.maxValue:this.defaultMaxValue,this.axis=t.axis!=null?t.axis:this.defaultAxis}apply(t){return B(()=>{let e=UN(t,this.axis),n=vr(e,0,this.maxValue);return $(t,ut(n,K(cr(),e)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};Bm.className="MaxNorm";Q.registerClass(Bm);var Vm=class extends Bc{constructor(t){super(),this.defaultAxis=0,this.axis=t.axis!=null?t.axis:this.defaultAxis}apply(t){return B(()=>ut(t,K(cr(),UN(t,this.axis))))}getConfig(){return{axis:this.axis}}};Vm.className="UnitNorm";Q.registerClass(Vm);var Gm=class extends Bc{apply(t){return Or(t)}};Gm.className="NonNeg";Q.registerClass(Gm);var Wm=class extends Bc{constructor(t){super(),this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=t.minValue!=null?t.minValue:this.defaultMinValue,this.maxValue=t.maxValue!=null?t.maxValue:this.defaultMaxValue,this.rate=t.rate!=null?t.rate:this.defaultRate,this.axis=t.axis!=null?t.axis:this.defaultAxis}apply(t){return B(()=>{let e=UN(t,this.axis),n=K($(this.rate,vr(e,this.minValue,this.maxValue)),$(1-this.rate,e));return $(t,ut(n,K(cr(),e)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};Wm.className="MinMaxNorm";Q.registerClass(Wm);var wR={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function Ve(r){return km(r)}function IR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"constraint")}function Ge(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in wR?wR[r]:r,config:{}};return IR(e)}else return r instanceof Bc?r:IR(r)}function h7(r){return new Bm(r)}function g7(r){return new Vm(r)}function x7(){return new Gm}function y7(r){return new Wm(r)}var vR={};Kt(vR,{constant:()=>I7,glorotNormal:()=>_7,glorotUniform:()=>T7,heNormal:()=>E7,heUniform:()=>A7,identity:()=>N7,leCunNormal:()=>D7,leCunUniform:()=>$7,ones:()=>w7,orthogonal:()=>R7,randomNormal:()=>v7,randomUniform:()=>C7,truncatedNormal:()=>S7,varianceScaling:()=>k7,zeros:()=>b7});function b7(){return new Am}function w7(){return new Mu}function I7(r){return new Dm(r)}function C7(r){return new $m(r)}function v7(r){return new Rm(r)}function S7(r){return new Fm(r)}function N7(r){return new Om(r)}function k7(r){return new Yr(r)}function T7(r){return new Rc(r)}function _7(r){return new Fc(r)}function E7(r){return new Oc(r)}function A7(r){return new Mc(r)}function D7(r){return new Pc(r)}function $7(r){return new Lc(r)}function R7(r){return new Mm(r)}var oF={};Kt(oF,{Layer:()=>Et,RNN:()=>po,RNNCell:()=>Tl,activation:()=>JJ,add:()=>a9,alphaDropout:()=>U9,average:()=>l9,averagePooling1d:()=>ok,averagePooling2d:()=>sk,averagePooling3d:()=>ik,avgPool1d:()=>x9,avgPool2d:()=>b9,avgPool3d:()=>I9,avgPooling1d:()=>y9,avgPooling2d:()=>w9,avgPooling3d:()=>C9,batchNormalization:()=>d9,bidirectional:()=>M9,categoryEncoding:()=>X9,centerCrop:()=>K9,concatenate:()=>u9,conv1d:()=>WJ,conv2d:()=>UJ,conv2dTranspose:()=>HJ,conv3d:()=>qJ,conv3dTranspose:()=>KJ,convLstm2d:()=>$9,convLstm2dCell:()=>R9,cropping2D:()=>XJ,dense:()=>QJ,depthwiseConv2d:()=>ZJ,dot:()=>f9,dropout:()=>t9,elu:()=>PJ,embedding:()=>i9,flatten:()=>r9,gaussianDropout:()=>W9,gaussianNoise:()=>G9,globalAveragePooling1d:()=>v9,globalAveragePooling2d:()=>S9,globalMaxPool1d:()=>L9,globalMaxPool2d:()=>z9,globalMaxPooling1d:()=>tF,globalMaxPooling2d:()=>eF,gru:()=>k9,gruCell:()=>T9,input:()=>JN,inputLayer:()=>MJ,layerNormalization:()=>h9,leakyReLU:()=>zJ,lstm:()=>_9,lstmCell:()=>E9,masking:()=>H9,maxPool1d:()=>B9,maxPool2d:()=>V9,maxPooling1d:()=>rF,maxPooling2d:()=>nF,maxPooling3d:()=>N9,maximum:()=>c9,minimum:()=>p9,multiply:()=>m9,permute:()=>s9,prelu:()=>BJ,randomWidth:()=>Y9,reLU:()=>LJ,repeatVector:()=>n9,rescaling:()=>q9,reshape:()=>o9,resizing:()=>j9,rnn:()=>F9,separableConv2d:()=>jJ,simpleRNN:()=>A9,simpleRNNCell:()=>D9,softmax:()=>VJ,spatialDropout1d:()=>e9,stackedRNNCells:()=>O9,thresholdedReLU:()=>GJ,timeDistributed:()=>P9,upSampling2d:()=>YJ,zeroPadding2d:()=>g9});async function ga(r){if(r==null)return;let t=[],e=[],n=[];for(let o in r){let s=r[o];if(typeof s!="number"){let i=s;t.push(i.data()),e.push(o),n.push(i)}}if(t.length>0){let o=await Promise.all(t);for(let s=0;s<o.length;++s)r[e[s]]=o[s][0];Tt(n)}}function jy(r){if(r!=null)for(let t in r){let e=r[t];typeof e!="number"&&e.dispose()}}var SR;(function(r){r[r.SILENT=0]="SILENT",r[r.VERBOSE=1]="VERBOSE"})(SR||(SR={}));var F7=125,kl=class{constructor(){this.validationData=null}setParams(t){this.params=t}async onEpochBegin(t,e){}async onEpochEnd(t,e){}async onBatchBegin(t,e){}async onBatchEnd(t,e){}async onTrainBegin(t){}async onTrainEnd(t){}setModel(t){}},Xy=class{constructor(t,e=10){t==null&&(t=[]),this.callbacks=t,this.queueLength=e}append(t){this.callbacks.push(t)}setParams(t){for(let e of this.callbacks)e.setParams(t)}setModel(t){for(let e of this.callbacks)e.setModel(t)}async onEpochBegin(t,e){e==null&&(e={});for(let n of this.callbacks)await n.onEpochBegin(t,e)}async onEpochEnd(t,e){e==null&&(e={});for(let n of this.callbacks)await n.onEpochEnd(t,e)}async onBatchBegin(t,e){e==null&&(e={});for(let n of this.callbacks)await n.onBatchBegin(t,e)}async onBatchEnd(t,e){e==null&&(e={});for(let n of this.callbacks)await n.onBatchEnd(t,e)}async onTrainBegin(t){t==null&&(t={});for(let e of this.callbacks)await e.onTrainBegin(t)}async onTrainEnd(t){t==null&&(t={});for(let e of this.callbacks)await e.onTrainEnd(t)}},HN=class extends kl{constructor(){super()}async onEpochBegin(t){this.seen=0,this.totals={}}async onBatchEnd(t,e){e==null&&(e={});let n=e.size==null?0:e.size;this.seen+=n;for(let o in e){let s=e[o];if(typeof s=="number")this.totals.hasOwnProperty(o)||(this.totals[o]=0),this.totals[o]=this.totals[o]+s*n;else{let i;o in this.totals?i=this.totals[o]:this.totals[o]=0;let a=B(()=>K(this.totals[o],$(s,n)));this.totals[o]=a,i!=null&&i.dispose()}}}async onEpochEnd(t,e){if(e!=null)for(let n of this.params.metrics)this.totals[n]!=null&&(typeof this.totals[n]=="number"?e[n]=this.totals[n]/this.seen:B(()=>{let o=$(ut(1,this.seen),this.totals[n]);e[n]=o,this.totals[n].dispose(),De(e[n])}))}},Yy=class extends kl{async onTrainBegin(t){this.epoch=[],this.history={}}async onEpochEnd(t,e){e==null&&(e={}),this.epoch.push(t);for(let n in e)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(e[n])}async syncData(){let t=[],e=[],n=[];for(let s in this.history){let i=this.history[s];for(let a=0;a<i.length;++a)if(typeof i[a]!="number"){let u=i[a];t.push(u.data()),e.push(s),n.push(a)}}let o=await Promise.all(t);for(let s=0;s<o.length;++s)this.history[e[s]][n[s]].dispose(),this.history[e[s]][n[s]]=o[s][0]}},Zy=class extends kl{constructor(t,e){if(super(),this.currentEpoch=0,this.nowFunc=t.nowFunc,this.nextFrameFunc=t.nextFrameFunc||Ch,this.yieldEvery=e||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=F7),this.yieldEvery==="never"&&t.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");y.isNumber(this.yieldEvery)&&(this.maybeWait=Y$(this.maybeWait.bind(this),this.yieldEvery,this.nowFunc)),this.trainBegin=t.onTrainBegin,this.trainEnd=t.onTrainEnd,this.epochBegin=t.onEpochBegin,this.epochEnd=t.onEpochEnd,this.batchBegin=t.onBatchBegin,this.batchEnd=t.onBatchEnd,this.yield=t.onYield}async maybeWait(t,e,n){let o=[];this.yield!=null&&(await ga(n),o.push(this.yield(t,e,n))),o.push(this.nextFrameFunc()),await Promise.all(o)}async onEpochBegin(t,e){this.currentEpoch=t,this.epochBegin!=null&&(await ga(e),await this.epochBegin(t,e))}async onEpochEnd(t,e){let n=[];this.epochEnd!=null&&(await ga(e),n.push(this.epochEnd(t,e))),this.yieldEvery==="epoch"&&n.push(this.nextFrameFunc()),await Promise.all(n)}async onBatchBegin(t,e){this.batchBegin!=null&&(await ga(e),await this.batchBegin(t,e))}async onBatchEnd(t,e){let n=[];this.batchEnd!=null&&(await ga(e),n.push(this.batchEnd(t,e))),this.yieldEvery==="batch"?n.push(this.nextFrameFunc()):y.isNumber(this.yieldEvery)&&n.push(this.maybeWait(this.currentEpoch,t,e)),await Promise.all(n)}async onTrainBegin(t){this.trainBegin!=null&&(await ga(t),await this.trainBegin(t))}async onTrainEnd(t){this.trainEnd!=null&&(await ga(t),await this.trainEnd(t))}};function Jy(r,t){return r==null&&(r={}),r instanceof kl?[r]:Array.isArray(r)&&r[0]instanceof kl?r:ue(r).map(n=>new Zy(n,t))}var Um=class r{constructor(){}static registerCallbackConstructor(t,e){y.assert(t>=0&&Number.isInteger(t),()=>`Verbosity level is expected to be an integer >= 0, but got ${t}`),r.checkForDuplicate(e),r.constructors[t]==null&&(r.constructors[t]=[]),r.constructors[t].push(e)}static checkForDuplicate(t){for(let e in r.constructors)r.constructors[+e].forEach(o=>{if(o===t)throw new z("Duplicate callback constructor.")})}static clear(){r.constructors={}}static createCallbacks(t){let e=[];for(let n in r.constructors){let o=+n;t>=o&&e.push(...r.constructors[o])}return e.map(n=>new n)}};Um.constructors={};function Qy(r,t,e,n,o,s,i,a,u){let l=new Yy,c=[new HN,...Um.createCallbacks(t)];r!=null&&c.push(...r),c.push(l);let p=new Xy(c);return p.setParams({epochs:e,initialEpoch:n,samples:o,steps:s,batchSize:i,verbose:t,doValidation:a,metrics:u}),{callbackList:p,history:l}}function wn(r,t={},e=!1){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"layer",e)}function Eh(r,t){return B(()=>{r.dtype!=="float32"&&(r=J(r,"float32"));let e=mt($c(r),t,!0),n=Co(e.shape,cr()),o=ge(kn(e,n));return ut(r,o)})}function xa(r,t){return B(()=>Ne($c(at(t,r)),-1))}function Hm(r,t){return B(()=>Ne(_e(at(t,r)),-1))}function Pu(r,t){return B(()=>{let e=at(r,t),n=vr(_e(r),cr(),Number.MAX_VALUE),o=_e(ut(e,n));return $(100,Ne(o,-1))})}function O7(r,t){return B(()=>{let e=vr(t,cr(),Number.MAX_VALUE),n=Nr(K(1,e)),o=vr(r,cr(),Number.MAX_VALUE),s=Nr(K(1,o));return Ne($c(at(n,s)),-1)})}function M7(r,t){return B(()=>{let e=kn(0,at(1,$(r,t)));return Ne($c(e),-1)})}function P7(r,t){return B(()=>{let e=kn(0,at(1,$(r,t)));return Ne(e,-1)})}function L7(r,t){return B(()=>{let e=mt($(r,t),-1),n=Sr($(at(1,r),t),-1);return kn(0,K(1,at(n,e)))})}function z7(r,t){return B(()=>{let e=Math.log(2),n=at(t,r),o=at(K(n,ui($(-2,n))),e);return Ne(o,-1)})}function Vc(r,t,e=!1){return B(()=>{if(e)t=Eu(t);else{let n=mt(t,t.shape.length-1,!0);t=ut(t,n)}return t=vr(t,cr(),1-cr()),Ut(mt($(J(r,"float32"),Nr(t)),t.shape.length-1))})}function qm(r,t,e=!1){return B(()=>{let n=J(la(aR(r)),"int32");t=vr(t,cr(),1-cr());let o=t.shape,s=R(ca(n,o[o.length-1]),o);return Vc(s,t,e)})}function B7(r,t){if(!y.arraysEqual(r.shape,t.shape))throw new z(`logits and labels must have the same shape, but got shapes ${JSON.stringify(r.shape)} and ${JSON.stringify(t.shape)}`);return B(()=>{let e=Or(t),n=Ut(_e(t));return K(at(e,$(t,r)),vu(Ke(n)))})}function Km(r,t){return B(()=>{let e;return e=vr(t,cr(),1-cr()),e=Nr(ut(e,at(1,e))),Ne(B7(r,e),-1)})}function V7(r,t){return B(()=>{let e=vr(r,cr(),1),n=vr(t,cr(),1);return mt($(r,Nr(ut(e,n))),-1)})}function G7(r,t){return B(()=>{let e=Nr(K(cr(),t));return Ne(at(t,$(r,e)),-1)})}function Dh(r,t){return B(()=>{let e=Eh(r,-1),n=Eh(t,-1),o=$(e,n);return Ut(mt(o,-1))})}var Ah={meanSquaredError:xa,meanAbsoluteError:Hm,meanAbsolutePercentageError:Pu,meanSquaredLogarithmicError:O7,squaredHinge:M7,hinge:P7,categoricalHinge:L7,logcosh:z7,categoricalCrossentropy:Vc,sparseCategoricalCrossentropy:qm,binaryCrossentropy:Km,kullbackLeiblerDivergence:V7,poisson:G7,cosineProximity:Dh};function tb(r){if(typeof r=="string"){if(r in Ah)return Ah[r];let t=`Unknown loss ${r}`;throw r.toLowerCase().includes("softmaxcrossentropy")&&(t=`Unknown loss ${r}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new z(t)}else return r}function $h(r,t){return B(()=>{let e=$(.5,wr(t)),n=rn(Re(t,e),r.dtype);return Ne($r(r,n),-1)})}function Rh(r,t){return B(()=>rn($r(ra(r,-1),ra(t,-1)),"float32"))}function kR(r,t){return B(()=>J(mt(Fr($r(r,1),$r(t,1))),"float32"))}function W7(r,t){return B(()=>J(mt(Fr($r(r,1),$r(t,0))),"float32"))}function U7(r,t){return B(()=>J(mt(Fr($r(r,0),$r(t,1))),"float32"))}function qN(r,t){return B(()=>{let e=kR(r,t),n=U7(r,t),o=K(e,n);return J(Ie(Re(o,0),ut(e,o),0),"float32")})}function TR(r,t){return B(()=>{let e=kR(r,t),n=W7(r,t),o=K(e,n);return J(Ie(Re(o,0),ut(e,o),0),"float32")})}function rb(r,t){return Km(r,t)}function nb(r,t){return r.rank===t.rank&&(r=Wn(r,[r.rank-1])),t=ra(t,-1),t.dtype!==r.dtype&&(t=J(t,r.dtype)),J($r(r,t),"float32")}function _R(r,t){return B(()=>{let e=r.sub(t).square().sum(),n=r.sub(r.mean()).square().sum();return pt(1).sub(e.div(n))})}var H7=xa,q7=xa,K7=Hm,j7=Hm,X7=Pu,Y7=Pu,Fh=Vc,Z7=Dh,KN=qm,eb={binaryAccuracy:$h,categoricalAccuracy:Rh,precision:qN,categoricalCrossentropy:Fh,sparseCategoricalCrossentropy:KN,mse:H7,MSE:q7,mae:K7,MAE:j7,mape:X7,MAPE:Y7,cosine:Z7};function ER(r){if(typeof r=="string"&&r in eb)return eb[r];if(typeof r!="string"&&r!=null)return r;throw new z(`Unknown metric ${r}`)}function Oh(r){if(co(r!==null,`Unknown LossOrMetricFn ${r}`),typeof r=="string")return r;{let t;for(let e of Object.keys(Ah))if(Ah[e]===r){t=e;break}if(t!==void 0)return t;for(let e of Object.keys(eb))if(eb[e]===r){t=e;break}return t!==void 0?t:r.name}}function DR(r){let t={Adagrad:()=>Ac.adagrad(.01),Adadelta:()=>Ac.adadelta(1,.95,cr()),Adam:()=>Ac.adam(.001,.9,.999,cr()),Adamax:()=>Ac.adamax(.002,.9,.999,cr(),0),RMSProp:()=>Ac.rmsprop(.001,.9,0,cr()),SGD:()=>Ac.sgd(.01)};if(t.adagrad=t.Adagrad,t.adadelta=t.Adadelta,t.adam=t.Adam,t.adamax=t.Adamax,t.rmsprop=t.RMSProp,t.sgd=t.SGD,r in t)return t[r]();throw new z(`Unknown Optimizer ${r}`)}function XN(r,t,e=!1){if(r==null||typeof r!="object"||Object.getPrototypeOf(r)!==Object.prototype||!jN(r))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(e){let n=JSON.stringify(r);n.length>1048576&&console.warn(`User-defined metadata of model "${t}" is too large in size (length=${n.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= 1048576.`)}}function jN(r){if(r===null)return!0;if(typeof r=="object")if(Object.getPrototypeOf(r)===Object.prototype){let t=Object.keys(r);for(let e of t)if(typeof e!="string"||!jN(r[e]))return!1;return!0}else if(Array.isArray(r)){for(let t of r)if(!jN(t))return!1;return!0}else return!1;else{let t=typeof r;return t==="string"||t==="number"||t==="boolean"}}function $R(r,t,e,n=console.log){let o=tJ(r),s=["Layer (type)","Input Shape","Output shape","Param #"];o?(t=t||90,e=e||[.32,.61,.89,1]):(t=t||115,e=e||[.24,.48,.7,.8,1]),e[e.length-1]<=1&&(e=e.map(c=>Math.floor(t*c)));let i;if(!o){s.push("Receives inputs"),i=[];for(let c in r.nodesByDepth)i.push(...r.nodesByDepth[c])}n("_".repeat(t)),ob(s,e,n),n("=".repeat(t));let a=r.layers;for(let c=0;c<a.length;++c)o?eJ(a[c],e,n):rJ(a[c],e,i,n),n((c===a.length-1?"=":"_").repeat(t));r.checkTrainableWeightsConsistency();let u=Q7(r),l=Lm(r.nonTrainableWeights);n(`Total params: ${u+l}`),n(`Trainable params: ${u}`),n(`Non-trainable params: ${l}`),n("_".repeat(t))}function Q7(r){let t;return r.collectedTrainableWeights!=null?t=Lm(r.collectedTrainableWeights):t=Lm(r.trainableWeights),t}function tJ(r){let t=!0,e=[],n=[];for(let o in r.nodesByDepth)e.push(r.nodesByDepth[o]);for(let o of e){if(o.length>1||o.length===1&&o[0].inboundLayers.length>1){t=!1;break}n.push(...o)}if(t)for(let o of r.layers){let s=!1;for(let i of o.inboundNodes)if(n.indexOf(i)!==-1)if(s){t=!1;break}else s=!0;if(!t)break}return t}function ob(r,t,e=console.log){let n="";for(let o=0;o<r.length;++o)o>0&&(n=n.slice(0,n.length-1)+" "),n+=r[o],n=n.slice(0,t[o]),n+=" ".repeat(t[o]-n.length);e(n)}function eJ(r,t,e){let n,o;try{o=r.inboundNodes.map(u=>JSON.stringify(u.inputShapes)).join(",")}catch(u){o="multiple"}try{n=JSON.stringify(r.outputShape)}catch(u){n="multiple"}let s=r.name,i=r.getClassName(),a=[`${s} (${i})`,o,n,r.countParams().toString()];ob(a,t,e)}function rJ(r,t,e,n){let o,s;try{s=r.inboundNodes.map(p=>JSON.stringify(p.inputShapes)).join(",")}catch(p){s="multiple"}try{o=JSON.stringify(r.outputShape)}catch(p){o="multiple"}let i=[];for(let p of r.inboundNodes)if(!(e!=null&&e.length>0&&e.indexOf(p)===-1))for(let m=0;m<p.inboundLayers.length;++m){let f=p.inboundLayers[m].name,d=p.nodeIndices[m],h=p.tensorIndices[m];i.push(`${f}[${d}][${h}]`)}let a=r.name,u=r.getClassName(),l=i.length===0?"":i[0],c=[`${a} (${u})`,s,o,r.countParams().toString(),l];ob(c,t,n);for(let p=1;p<i.length;++p)ob(["","","","",i[p]],t,n)}function RR(r,t,e){return(r==="inboundNodes"||r==="outputLayers"||r==="inputLayers")&&t===0&&typeof e=="string"}function Gc(r,t){if(r===null)return null;if(typeof r=="string")return Cl(r);if(typeof r=="number"||typeof r=="boolean")return r;if(r instanceof Array){let e=[],n=r.length;for(let o=0;o<n;++o){let s=r[o];RR(t,o,s)?e.push(s):e.push(Gc(s,t))}return e}else{let e={};for(let n of Object.keys(r)){let o=r[n];if(n==="name"&&typeof o=="string")e[n]=o;else{let s=Cl(n);e[s]=Gc(o,s)}}return e}}function sb(r,t){if(r==null)return null;if(typeof r=="string")return _o(r);if(typeof r=="number"||typeof r=="boolean")return r;if(r instanceof Array){let e=[],n=r.length;for(let o=0;o<n;++o){let s=r[o];RR(t,o,s)?e.push(s):e.push(sb(s,t))}return e}else{let e={};for(let n of Object.keys(r)){let o=r[n],s=_o(n);(n==="name"||n==="className")&&typeof o=="string"?e[s]=o:e[s]=sb(o,n)}return e}}var jm="4.21.0";var nJ=r=>{let t=Object.keys(r);if(t.length===0)return!1;let e=t[0].split("/");return!isNaN(parseInt(e[e.length-1],10))},ib=class r extends Et{constructor(t){if(super({}),this.containerNodes=new Set,this.name=t.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Fu(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(t.inputs)?this.inputs=t.inputs.slice():this.inputs=[t.inputs],Array.isArray(t.outputs)?this.outputs=t.outputs.slice():this.outputs=[t.outputs],Eo(this.inputs).length!==this.inputs.length)throw new z(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);Eo(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let w=b.sourceLayer,I=b.nodeIndex,N=b.tensorIndex;this.outputLayers.push(w),this.outputLayersNodeIndices.push(I),this.outputLayersTensorIndices.push(N)}for(let b of this.inputs){let w=b.sourceLayer,I=b.nodeIndex,N=b.tensorIndex;co(I===0,"input layer has >1 nodes"),co(N===0,"input layer has >1 tensors"),this.inputLayers.push(w),this.inputLayersNodeIndices.push(I),this.inputLayersTensorIndices.push(N)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let w=this.inputLayers[b];if(!(w instanceof hi))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${t.inputs}. Input ${b} (0-based) originates from layer type ${w.getClassName()}.`);this.inputNames.push(w.name),this.feedInputShapes.push(w.batchInputShape),this.feedInputNames.push(w.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let e={},n={},o={},s={},i={},a=[],u=(b,w,I,N,E,A)=>{(N==null||E==null||A==null)&&(N=b.sourceLayer,E=b.nodeIndex,A=b.tensorIndex);let D=N.inboundNodes[E];if(I.indexOf(D)!==-1)throw new dn(`The tensor ${b.name} at layer "${N.name}" is part of a cycle.`);if(w.indexOf(D)!==-1)return;this.containerNodes.add(r.nodeKey(N,E)),N.id in i||(i[N.id]=Object.keys(i).length),I.indexOf(D)===-1&&I.push(D);let F=D.inboundLayers.length;for(let M=0;M<F;M++){let V=D.inputTensors[M],G=D.inboundLayers[M],W=D.nodeIndices[M],q=D.tensorIndices[M];u(V,w,I,G,W,q)}for(w.push(D);I.indexOf(D)>=0;)I.splice(I.indexOf(D),1);a.push(D)},l=[],c=[];for(let b of this.outputs)u(b,l,c);let p=a.slice().reverse();for(let b of p){n[b.id]=b,b.id in e||(e[b.id]=0);let w=e[b.id],I=o[b.outboundLayer.id]==null?0:o[b.outboundLayer.id];w=Math.max(w,I),o[b.outboundLayer.id]=w,s[b.outboundLayer.id]=b.outboundLayer,e[b.id]=w;for(let N=0;N<b.inboundLayers.length;N++){let E=b.inboundLayers[N],A=b.nodeIndices[N],D=E.inboundNodes[A],F=e[D.id]==null?0:e[D.id];e[D.id]=Math.max(w+1,F),n[D.id]=D}}let m={};for(let b in e){let w=e[b];w in m||(m[w]=[]),m[w].push(n[b])}let f={};for(let b in o){let w=o[b];w in f||(f[w]=[]),f[w].push(s[b])}let d=Object.keys(f).map(b=>parseInt(b,10)).sort(Sh);this.layers=[];for(let b of d){let w=f[b];w.sort((I,N)=>{let E=i[I.id],A=i[N.id];return E<A?-1:E>A?1:0});for(let I of w)I instanceof r&&this.internalContainerRefs.push(I),this.layers.push(I)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(Sh);let h=this.inputs.slice(),g=[];for(let b of d)for(let w of m[b]){let I=w.outboundLayer;if(I!=null){for(let N of w.inputTensors)if(h.indexOf(N)===-1)throw new dn(`Graph disconnected: cannot obtain value for tensor ${N} at layer "${I.name}". The following previous layers were accessed without issue: ${g}`);for(let N of w.outputTensors)h.push(N);g.push(I.name)}}this.nodesByDepth=m;let x=this.layers.map(b=>b.name);for(let b of x){let w=x.filter(I=>I===b).length;if(w!==1)throw new dn(`The name "${b}" is used ${w} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(x))}this.outboundNodes=[],this.inboundNodes=[],new Nl({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let t={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let e of this.layers)t.numDisposedVariables+=e.dispose().numDisposedVariables;for(let e of this.internalContainerRefs)t.numDisposedVariables+=e.dispose().numDisposedVariables}return t.refCountAfterDispose=this._refCount,t}get trainable(){return this.trainable_}set trainable(t){this.layers.forEach(e=>{e._trainableWeights.forEach(n=>n.trainable=t)}),this.trainable_=t}get trainableWeights(){if(this._trainableWeights.length>0)throw new z("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let t=[];for(let e of this.layers)t=t.concat(e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.layers)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.layers)e.push(...n.trainableWeights);return e.concat(t)}return t}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(t,e=!0){let n={},o=0,s=nJ(t);s&&this.parseWeights(t);for(let a of this.layers)for(let[u,l]of a.weights.entries()){let c=s?`${l.name.split("/").slice(0,-1).join("/")+"/"}${u}`:l.originalName;if(n[c]!=null)throw new z(`Duplicate weight name: ${c}`);n[c]=l,o++}let i=[];for(let a in t){let u=a;if(n[a]==null){let l=a.split("/");u=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(n[u]!=null)i.push([n[u],t[a]]);else if(e)throw new z(`Provided weight data has no target variable: ${a}`);delete n[u]}if(e){let a=[];for(let u in n)a.push(u);if(a.length>0)throw new z(`${a.length} of ${o} weights are not set: ${a}`)}zm(i)}parseWeights(t){for(let e in Object.keys(t)){let n=e.split("/"),o=["vars","layer_checkpoint_dependencies"],s=n.map(i=>i.startsWith("_")?i.slice(1):i).filter(i=>!o.includes(i)).join("/");s!==e&&(t[s]=t[e],delete t[e])}}updatedConfig(){let t=this.getConfig(),e={};return e.className=this.getClassName(),e.config=t,e.kerasVersion=`tfjs-layers ${jm}`,e.backend="TensorFlow.js",e}toJSON(t,e=!0){let n=sb(this.updatedConfig());return e?JSON.stringify(n):n}call(t,e){return B(()=>{t=ue(t);let n=new ha;for(let o=0;o<this.inputs.length;++o)n.add(this.inputs[o],t[o]);return zc(this.outputs,n,e)})}computeMask(t,e){return B(()=>{t=ue(t);let n;return e==null?n=To(null,t.length):n=ue(e),this.runInternalGraph(t,n)[1]})}computeOutputShape(t){let e=Pm(t);if(e.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${t}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let a=0;a<e.length;a++){let u=this.inputLayers[a],l=e[a],c=u.name+"_0_0";n[c]=l}let o=Object.keys(this.nodesByDepth).map(a=>parseInt(a,10)).sort(Sh);if(o.length>1)for(let a of o){let u=this.nodesByDepth[a];for(let l of u){let c=l.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<l.inboundLayers.length;h++){let g=l.inboundLayers[h],x=l.nodeIndices[h],b=l.tensorIndices[h],w=`${g.name}_${x}_${b}`,I=n[w];p.push(I)}let m=c.computeOutputShape(kr(p)),f=Pm(m),d=c.inboundNodes.indexOf(l);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;n[g]=f[h]}}}let s=[],i=[];for(let a=0;a<this.outputLayers.length;a++){let u=this.outputLayers[a],l=this.outputLayersNodeIndices[a],c=this.outputLayersTensorIndices[a],p=`${u.name}_${l}_${c}`;i.push(p)}for(let a=0;a<i.length;a++){let u=i[a];co(u in n),s.push(n[u])}return kr(s)}runInternalGraph(t,e){e==null&&(e=To(null,t.length));let n={};for(let u=0;u<this.inputs.length;++u){let l=this.inputs[u],c=t[u],p=e[u];n[l.id]=[c,p]}let o=Object.keys(this.nodesByDepth).map(u=>parseInt(u,10)).sort(Sh);for(let u of o){let l=this.nodesByDepth[u];for(let c of l){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in n&&d.push(n[h.id]);if(d.length===m.length){let h={},g,x,b,w;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[I,N]=d[0];h.mask==null&&(h.mask=N),b=ue(p.call(I,h)),w=ue(p.computeMask(I,N)),g=[I],x=[N]}else g=d.map(I=>I[0]),x=d.map(I=>I[1]),h.mask==null&&(h.mask=x),b=ue(p.call(g,h)),w=ue(p.computeMask(g,x));if(p.activityRegularizer)throw new _t("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let I=0;I<f.length;++I){let N=f[I],E=b[I],A=w[I];n[N.id]=[E,A]}}}}let s=[],i=[],a=[];for(let u of this.outputs){co(u.id in n,`Could not compute output ${u.name} : ${u.id}`);let[l,c]=n[u.id];a.push(l.shape),s.push(l),i.push(c)}return[s,i,a]}buildNodeConversionMap(t){let e={},n;for(let o of this.layers){n=o instanceof r?1:0;for(let s=0;s<o.inboundNodes.length;s++){let i=r.nodeKey(o,s);this.containerNodes.has(i)&&(e[i]=n,n+=1)}}return e}getLayer(t,e){if(e!=null)return this.findLayer(e);if(t==null)throw new z("Provide either a layer name or layer index");if(typeof t=="number")return this.findLayer(t);for(let n of this.layers)if(n.name===t)return n;throw new z(`No such layer: ${t}`)}findLayer(t){if(this.layers.length<=t)throw new z(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}calculateLosses(){return B(()=>{let t=[];for(let e of this.layers)for(let n=0;n<e.inboundNodes.length;++n){let o=r.nodeKey(e,n);this.containerNodes.has(o)&&t.push(...e.calculateLosses())}return t})}getConfig(){let t={name:this.name},e=this.buildNodeConversionMap(this.layers),n=[];for(let i of this.layers){let a=i.getClassName(),u=i.getConfig(),l=[];for(let p=0;p<i.inboundNodes.length;p++){let m=i.inboundNodes[p],f=r.nodeKey(i,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${i.name} was passed non-serializable keyword arguments: ${m.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),d={}}if(m.inboundLayers.length>0){let h=[];for(let g=0;g<m.inboundLayers.length;g++){let x=m.inboundLayers[g],b=m.nodeIndices[g],w=m.tensorIndices[g],I=r.nodeKey(x,b),N=e[I];N==null&&(N=0),h.push([x.name,N,w,d])}l.push(h)}}}let c={};c.name=i.name,c.className=a,c.config=u,c.inboundNodes=l,n.push(c)}t.layers=n;let o=[];for(let i=0;i<this.inputLayers.length;i++){let a=this.inputLayers[i],u=this.inputLayersNodeIndices[i],l=r.nodeKey(a,u);if(!this.containerNodes.has(l))continue;let c=e[l];c==null&&(c=0);let p=this.inputLayersTensorIndices[i];o.push([a.name,c,p])}t.inputLayers=o;let s=[];for(let i=0;i<this.outputLayers.length;i++){let a=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=r.nodeKey(a,u);if(!this.containerNodes.has(l))continue;let c=e[l];c==null&&(c=0);let p=this.outputLayersTensorIndices[i];s.push([a.name,c,p])}return t.outputLayers=s,t}static fromConfig(t,e,n={},o=!1){let s={},i={};function a(g,x){g.name in i?i[g.name].push(x):i[g.name]=[x]}function u(g,x){let b=[],w;for(let I of x){let N=I[0],E=I[1],A=I[2];if(w=I[3]==null?{}:I[3],!(N in s)){a(g,x);return}let D=s[N];if(D.inboundNodes.length<=E){a(g,x);return}let F=D.inboundNodes[E];b.push(F.outputTensors[A])}b.length>0&&g.apply(kr(b),w)}function l(g){let x=g.name,b=wn(g,e.customObjects!=null?e.customObjects:{});b.setFastWeightInitDuringBuild(o),s[x]=b,g.inboundNodes.forEach(I=>{if(!(I instanceof Array))throw new z(`Corrupted configuration, expected array for nodeData: ${I}`);a(b,I)})}let c=e.name,p=e.layers;for(let g of p)l(g);for(;!j$(i);)for(let g of p){let x=s[g.name];if(x.name in i){let b=i[x.name];delete i[x.name];for(let w of b)u(x,w)}}let m=[],f=[],d=e.inputLayers;for(let g of d){let x=g[0],b=g[1],w=g[2];co(x in s);let N=s[x].inboundNodes[b].outputTensors;m.push(N[w])}let h=e.outputLayers;for(let g of h){let x=g[0],b=g[1],w=g[2];co(x in s);let N=s[x].inboundNodes[b].outputTensors;f.push(N[w])}return new t({inputs:m,outputs:f,name:c})}get stateful(){if(this._stateful)throw new z("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let t of this.layers)if(t.stateful)return!0;return!1}resetStates(){B(()=>{this.layers.forEach(t=>{t.stateful&&t.resetStates()})})}};function oJ(r,t,e){let n=t.length;if(r==null||Array.isArray(r)&&r.length===0)return t.map(o=>null);if(n===1)return Array.isArray(r)&&r.length===1?r:typeof r=="object"&&t[0]in r?[r[t[0]]]:[r];if(Array.isArray(r)){if(r.length!==n)throw new Error(`Provided ${e} is an array of ${r.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return r}else if(typeof r=="object"&&Object.keys(r).length>0&&typeof r[Object.keys(r)[0]]=="object"){let o=[];return t.forEach(s=>{s in r?o.push(r[s]):o.push(null)}),o}else throw new Error(`The model has multiple (${n}) outputs, so ${e} must be either an array with ${n} elements or an object with ${t} keys. Provided ${e} not understood: ${JSON.stringify(r)}`)}function ab(r,t){return oJ(r,t,"classWeight")}async function lb(r,t,e,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(e!=null){let o=B(()=>{if(r.shape.length===1)return un(r);if(r.shape.length===2){if(r.shape[1]>1)return ra(r,1);if(r.shape[1]===1)return R(r,[r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await o.data());Tt(o);let i=[];return s.forEach(a=>{if(e[a]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${a} exists in the data but not in classWeight`);i.push(e[a])}),Oe(i,"float32")}else return null}function FR(r,t){return $(r,t)}var sJ=32;function PR(r,t){let e,n,o=t;e=o.xs,n=o.ys,y.assert(e!=null&&n!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let s=OR("input",r.inputNames,e),i=OR("output",r.outputNames,n),a=s[0].shape[0];y.assert(s.length===r.inputs.length,()=>`LayersModel has ${r.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(r.inputNames)})`),y.assert(i.length===r.outputs.length,()=>`LayersModel has ${r.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(r.outputNames)})`);for(let u=0;u<s.length;u++)y.assert(s[u].shape[0]===a,()=>`Batch size mismatch: input ${r.inputNames[u]} has ${s[u].shape[0]}; expected  ${a} based on input ${r.inputNames[0]}.`);for(let u=0;u<i.length;u++)y.assert(i[u].shape[0]===a,()=>`Batch size mismatch: output ${r.outputNames[u]} has ${i[u].shape[0]}; expected  ${a} based on input ${r.inputNames[0]}.`);return{xs:s,ys:i}}function OR(r,t,e){if(e instanceof Lt)return[e];if(Array.isArray(e))return y.assert(e.length===t.length,()=>`Received an array of ${e.length} Tensors, but expected ${t.length} to match the ${r} keys ${t}.`),e;{let n=[];for(let o of t){if(e[o]==null)throw new z(`The feature data generated by the dataset lacks the required ${r} key '${o}'.`);n.push(e[o])}return n}}function iJ(r){if(r.length===3)throw new _t("Validation with sample weights is not implemented yet.");return{xs:r[0],ys:r[1]}}async function LR(r,t,e){let n=e.batchesPerEpoch!=null;if(y.assert(r.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),y.assert(e!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),y.assert(e.epochs!=null&&e.epochs>0&&Number.isInteger(e.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${e.epochs}`),y.assert(!n||e.batchesPerEpoch>0&&Number.isInteger(e.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${e.batchesPerEpoch}`),y.assert(e.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let o=e.validationData!=null,s,i;if(o)if(MR(e.validationData))y.assert(e.validationBatches==null||e.validationBatches>0&&Number.isInteger(e.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${e.validationBatches}`);else{let g=iJ(e.validationData);s=g.xs,i=g.ys}let a=r.makeTrainFunction(),u=r.getDedupedMetricsNames(),l;o?l=u.slice().concat(u.map(g=>"val_"+g)):l=u.slice();let c=Jy(e.callbacks,e.yieldEvery),p=e.verbose==null?1:e.verbose,{callbackList:m,history:f}=Qy(c,p,e.epochs,null,null,aJ(t,e),null,o,l);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=e.initialEpoch==null?0:e.initialEpoch,h=await t.iterator();for(;d<e.epochs;){let g={};await m.onEpochBegin(d);let x=0,b=0;for(n||(h=await t.iterator());!n||x<e.batchesPerEpoch;){let w=await h.next();if(n&&w.done){console.warn(`You provided \`batchesPerEpoch\` as ${e.batchesPerEpoch}, but your dataset iterator ran out of data after ${x} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${e.batchesPerEpoch*e.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(w.value!=null){let{xs:I,ys:N}=PR(r,w.value),E={};E.batch=b,E.size=I[0].shape[0],await m.onBatchBegin(b,E);let A=[];if(e.classWeight!=null){let M=ab(e.classWeight,r.outputNames);for(let V=0;V<M.length;++V)A.push(await lb(N[V],null,M[V]))}let D=I.concat(N).concat(A),F=a(D);Tt(D);for(let M=0;M<u.length;++M){let V=u[M],G=F[M];E[V]=G,De(G)}await m.onBatchEnd(b,E),jy(E),b++,x++}if(n?x>=e.batchesPerEpoch:w.done){if(o){let I;MR(e.validationData)?I=ue(await r.evaluateDataset(e.validationData,{batches:e.validationBatches})):I=ue(r.evaluate(s,i,{batchSize:e.validationBatchSize==null?sJ:e.validationBatchSize,verbose:0}));for(let N=0;N<r.metricsNames.length;++N)g[`val_${r.metricsNames[N]}`]=I[N]}break}if(r.stopTraining_)break}if(await m.onEpochEnd(d,g),d++,r.stopTraining_)break}return await m.onTrainEnd(),await r.history.syncData(),r.history}finally{r.isTraining=!1}}function aJ(r,t){let e=null;return t.batchesPerEpoch!=null?e=t.batchesPerEpoch:Number.isFinite(r.size)&&(e=r.size),e}function MR(r){return typeof r.iterator=="function"}function lJ(r){return typeof r.next=="function"}async function zR(r,t,e){e=e||{};let n=e.batches!=null,o=r.testFunction,s=[];if(e.verbose>0)throw new _t("Verbose mode is not implemented yet.");y.assert(!n||e.batches>0&&Number.isInteger(e.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(e.batches)}`);let i=lJ(t)?t:await t.iterator(),a=0,u=0;for(;!n||u<e.batches;){let l=await i.next();if(s=B(()=>{if(l.value){let{xs:c,ys:p}=PR(r,l.value),m=c.concat(p),f=B(()=>o(m));if(Tt(m),u===0)for(let h=0;h<f.length;++h)s.push(pt(0));let d=m[0].shape[0];for(let h=0;h<f.length;++h){let g=f[h],x=s[h];s[h]=B(()=>K(s[h],$(d,g))),u>0&&Tt(x)}Tt(f),a+=d,++u}return s}),l.done){n&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${e.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let l=0;l<s.length;++l){let c=s[l];s[l]=ut(s[l],a),Tt(c)}return kr(s)}function ub(r){y.assert(r>0&&Number.isInteger(r),()=>`batchSize is required to be a positive integer, but got ${r}`)}function Xm(r,t,e){return r==null?[null]:Array.isArray(r)?r.map(n=>vl(n,t,e-t)):vl(r,t,e-t)}function cb(r,t){return B(()=>r==null?null:Array.isArray(r)?r.map(e=>cb(e,t)):Gy(r,t.dtype==="int32"?t:J(t,"int32")))}function pb(r,t){let e=[],n=0,o=null;for(;n<r;)o=n+t,o>=r&&(o=r),e.push([n,o]),n=o;return e}function YN(r){let t=[];r instanceof Lt&&(r=[r]);for(let e=0;e<r.length;++e){let n=r[e];if(n.rank===1)t.push(Sl(n,1));else{if(n.rank===0)throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");t.push(n)}}return t}function $o(r,t){if(r==null)return;let e=[];if(t instanceof Lt)e.push(t.id);else if(Array.isArray(t))t.forEach(o=>e.push(o.id));else if(t!=null)for(let o in t){let s=t[o];e.push(s.id)}let n=[];if(r instanceof Lt)e.indexOf(r.id)===-1&&n.push(r);else if(Array.isArray(r))r.forEach(o=>{e.indexOf(o.id)===-1&&n.push(o)});else if(r!=null)for(let o in r){let s=r[o];e.indexOf(s.id)===-1&&n.push(s)}n.forEach(o=>{o.isDisposed||o.dispose()})}function uJ(r){return r instanceof Lt}function ZN(r){return Array.isArray(r)}function BR(r){return!uJ(r)&&!ZN(r)}function VR(r,t,e,n=!0,o=""){if(t==null||t.length===0){if(r!=null){let i=!1;if(ZN(r)&&r.length>0)i=!0;else if(BR(r)){for(let a in r)if(r.hasOwnProperty(a)){i=!0;break}}else i=!0;if(i)throw new z(`Error when checking model ${o} expected no data, but got ${r}`)}return[]}if(r==null)return t.map(i=>null);let s;if(BR(r)){r=r,s=[];for(let i of t){if(r[i]==null)throw new z(`No data provided for "${i}". Need data for each key in: ${t}`);s.push(r[i])}}else if(ZN(r)){if(r=r,r.length!==t.length)throw new z(`Error when checking model ${o}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${r}`);s=r}else{if(r=r,t.length>1)throw new z(`The model ${o} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${r.shape}`);s=[r]}if(s=YN(s),e!=null)for(let i=0;i<t.length;++i){if(e[i]==null)continue;let a=s[i];if(a.shape.length!==e[i].length)throw new z(`Error when checking ${o}: expected ${t[i]} to have ${e[i].length} dimension(s). but got array with shape ${a.shape}`);for(let u=0;u<e[i].length;++u){if(u===0&&!n)continue;let l=a.shape[u],c=e[i][u];if(c!=null&&c>=0&&l!==c)throw new z(`${o} expected a batch of elements where each example has shape [${e[i].slice(1,e[i].length)}] (i.e.,tensor shape [*,${e[i].slice(1,e[i].length)}]) but the ${o} received an input with ${a.shape[0]} examples, each with shape [${a.shape.slice(1,a.shape.length)}] (tensor shape [${a.shape}])`)}}return s}function cJ(r,t,e){let n=Eo(r.map(s=>s.shape[0]));n.sort();let o=Eo(t.map(s=>s.shape[0]));if(o.sort(),n.length>1)throw new z(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(r.map(s=>s.shape))}`);if(o.length>1)throw new z(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(t.map(s=>s.shape))}`);if(n.length>0&&o.length>0&&!y.arraysEqual(n,o))throw new z(`Input Tensors should have the same number of samples as target Tensors. Found ${n[0]} input sample(s) and ${o[0]} target sample(s).`)}function pJ(r,t,e){let n=[xa,Km,Vc];for(let o=0;o<r.length;++o){let s=r[o],i=t[o],a=e[o];if(i!=null){if(i===Vc&&s.shape[s.shape.length-1]===1)throw new z(`You are passing a target array of shape ${s.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);if(n.indexOf(i)!==-1){let u=s.shape.slice(1),l=a.slice(1);for(let c=0;c<u.length;++c){let p=u[c],m=l[c];if(m!=null&&p!==m)throw new z(`A target Tensor with shape ${s.shape} was passed for an output of shape ${a}, while using a loss function that expects targets to have the same shape as the output.`)}}}}}function GR(r,t,e,n=!0,o=""){let s;if(Array.isArray(r)){if(r.length!==t.length)throw new z(`Error when checking model ${o}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${t.length} Tensor(s), but instead got ${r.length} Tensors(s).`);s=r}else{if(t.length>1)throw new z(`The model expects ${t.length} ${o} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(r.shape)}.`);s=[r]}if(e!=null)for(let i=0;i<t.length;++i){if(e[i]==null)continue;let a=s[i];if(a.shape.length!==e[i].length)throw new z(`Error when checking ${o}: expected ${t[i]} to have ${e[i].length} dimension(s), but got array with shape ${JSON.stringify(a.shape)}`);for(let u=0;u<e[i].length;++u){if(u===0&&!n)continue;let l=a.shape[u],c=e[i][u];if(c!=null&&c!==l)throw new z(`Error when checking ${o}: expected ${t[i]} to have shape ${JSON.stringify(e[i])} but got array with shape ${JSON.stringify(a.shape)}.`)}}}function mJ(r,t){if(r==null||Array.isArray(r)&&r.length===0)return t.map(n=>[]);let e;if(typeof r=="string"||typeof r=="function")e=[r];else if(Array.isArray(r)||typeof r=="object")e=r;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${r}`);if(Array.isArray(e))return t.map(n=>e);{let n=[];for(let o of t){let s=e.hasOwnProperty(o)?e[o]:[];Array.isArray(s)||(s=[s]),n.push(s)}return n}}var fJ="layers-model",Un=class extends ib{constructor(t){super(t),this.isTraining=!1}summary(t,e,n=console.log){if(!this.built)throw new z("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");$R(this,t,e,n)}compile(t){if(t.loss==null&&(t.loss=[]),this.loss=t.loss,typeof t.optimizer=="string")this.optimizer_=DR(t.optimizer),this.isOptimizerOwned=!0;else{if(!(t.optimizer instanceof jr))throw new z("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=t.optimizer,this.isOptimizerOwned=!1}let e=[];if(!Array.isArray(t.loss)&&typeof t.loss!="string"&&typeof t.loss!="function"){t.loss=t.loss;for(let i in t.loss)if(this.outputNames.indexOf(i)===-1)throw new z(`Unknown entry in loss dictionary: "${i}". Only expected the following keys: ${this.outputNames}`);for(let i of this.outputNames)t.loss[i]==null&&console.warn(`Output "${i}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${i} during training`),e.push(tb(t.loss[i]))}else if(Array.isArray(t.loss)){if(t.loss.length!==this.outputs.length)throw new z(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${t.loss}.`);e=t.loss.map(a=>tb(a))}else{let i=tb(t.loss);this.outputs.forEach(a=>{e.push(i)})}this.lossFunctions=e,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i<this.outputs.length;++i){let a=this.internalOutputShapes[i],u=this.outputNames[i];this.feedOutputNames.push(u),this.feedOutputShapes.push(a),this.feedLossFns.push(this.lossFunctions[i])}let n=[];this.metrics=t.metrics,this.metricsNames=["loss"],this.metricsTensors=[],fi("loss",()=>{for(let i=0;i<this.outputs.length;++i){if(n.indexOf(i)!==-1)continue;let a=this.lossFunctions[i];this.outputs.length>1&&(this.metricsTensors.push([a,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let o=mJ(t.metrics,this.outputNames),s=(i,a,u)=>{this.outputNames.length>1&&(a=this.outputNames[i]+"_"+a),this.metricsNames.push(a),this.metricsTensors.push([u,i])};fi("metric",()=>{for(let i=0;i<this.outputs.length;++i){if(n.indexOf(i)!==-1)continue;let a=o[i];(l=>{let c="",p,m,f;for(let d of l){if(typeof d=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(d)!==-1){let g=this.internalOutputShapes[i];g[g.length-1]===1||this.lossFunctions[i]===Km?["accuracy","acc"].indexOf(d)!==-1?m=$h:["crossentropy","ce"].indexOf(d)!==-1&&(m=rb):this.lossFunctions[i]===qm?["accuracy","acc"].indexOf(d)!==-1?m=nb:["crossentropy","ce"].indexOf(d)!==-1&&(m=KN):["accuracy","acc"].indexOf(d)!==-1?m=Rh:["crossentropy","ce"].indexOf(d)!==-1&&(m=Fh);let x;["accuracy","acc"].indexOf(d)!==-1?x="acc":["crossentropy","ce"].indexOf(d)!==-1&&(x="ce"),f=m,p=c+x}else f=ER(d),p=c+Oh(d);let h;fi(p,()=>{h=f}),s(i,p,h)}})(a)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(t,e,n={}){let o=n.batchSize==null?32:n.batchSize;ub(o);let i=this.standardizeUserDataXY(t,e,!0,o);try{let a=i[0].concat(i[1]);this.makeTestFunction();let u=this.testFunction,l=this.testLoop(u,a,o,n.verbose,n.steps);return kr(l)}finally{$o(i[0],t),$o(i[1],e)}}async evaluateDataset(t,e){return this.makeTestFunction(),zR(this,t,e)}checkNumSamples(t,e,n,o="steps"){let s;if(n!=null){if(s=null,e!=null)throw new z(`If ${o} is set, batchSize must be null or undefined.Got batchSize = ${e}`)}else if(t!=null)Array.isArray(t)?s=t[0].shape[0]:s=t.shape[0];else throw new z(`Either the input data should have a defined shape, or ${o} shoud be specified.`);return s}execute(t,e){if(Array.isArray(e)&&e.length===0)throw new z("`outputs` is an empty Array, which is not allowed.");let n=Array.isArray(e),o=n?e:[e],s=this.retrieveSymbolicTensors(o),i=new ha;if(t instanceof Lt&&(t=[t]),Array.isArray(t)){if(t.length!==this.inputs.length)throw new z(`The number of inputs provided (${t.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let u=0;u<this.inputs.length;++u)i.add(this.inputs[u],t[u])}else for(let u of this.inputs){let l=t[u.name];if(l==null)throw new z(`No value is provided for the model's input ${u.name}`);i.add(u,l)}let a=zc(s,i);return n?a:a[0]}retrieveSymbolicTensors(t){let e=To(null,t.length),n=t.length;for(let o of this.layers){let s=Array.isArray(o.output)?o.output:[o.output],i=s.map(a=>a.name);for(let a=0;a<t.length;++a){let u=i.indexOf(t[a]);if(u!==-1&&(e[a]=s[u],n--),n===0)break}if(n===0)break}if(n>0){let o=[];throw e.forEach((s,i)=>{s==null&&o.push(t[i])}),new z(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(o)}`)}return e}predictLoop(t,e=32,n=!1){return B(()=>{let o=this.checkNumSamples(t);if(n)throw new _t("Verbose predictLoop() is not implemented yet.");let s=pb(o,e),i=this.outputs.map(a=>[]);for(let a=0;a<s.length;++a)B(()=>{let l=s[a][0],c=s[a][1],p=Xm(t,l,c),m=[];if(Array.isArray(p))for(let d=0;d<p.length;++d)m.push({key:this.inputs[d],value:p[d]});else m.push({key:this.inputs[0],value:p});let f=new ha(m);return zc(this.outputs,f)}).forEach((l,c)=>i[c].push(l));return kr(i.map(a=>ie(a,0)))})}predict(t,e={}){let n=YN(t);GR(n,this.inputNames,this.feedInputShapes,!1);try{let o=e.batchSize==null?32:e.batchSize;return ub(o),this.predictLoop(n,o)}finally{$o(n,t)}}predictOnBatch(t){GR(t,this.inputNames,this.feedInputShapes,!0);let e=(Array.isArray(t)?t[0]:t).shape[0];return this.predictLoop(t,e)}standardizeUserDataXY(t,e,n=!0,o){if(this.optimizer_==null)throw new dn("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let s=[];for(let i=0;i<this.feedOutputShapes.length;++i){let a=this.feedOutputShapes[i];this.feedLossFns[i]===qm?s.push(a.slice(0,a.length-1).concat([1])):s.push(a)}if(t=VR(t,this.feedInputNames,this.feedInputShapes,!1,"input"),e=VR(e,this.feedOutputNames,s,!1,"target"),cJ(t,e,null),pJ(e,this.feedLossFns,this.feedOutputShapes),this.stateful&&o!=null&&o>0&&t[0].shape[0]%o!==0)throw new z(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${o}. Found: ${t[0].shape[0]} sample(s).`);return[t,e]}async standardizeUserData(t,e,n,o,s=!0,i){let[a,u]=this.standardizeUserDataXY(t,e,s,i);if(n!=null)throw new Error("sample weight is not supported yet.");let l=null;if(o!=null){let c=ab(o,this.outputNames);l=[];for(let p=0;p<c.length;++p)l.push(await lb(u[p],null,c[p]))}return[a,u,l]}testLoop(t,e,n,o=0,s){return B(()=>{let i=this.checkNumSamples(e,n,s,"steps"),a=[];if(o>0)throw new _t("Verbose mode is not implemented yet.");if(s!=null)throw new _t("steps mode in testLoop() is not implemented yet");{let u=pb(i,n),l=Oe(gn(0,i));for(let c=0;c<u.length;++c){let p=u[c][0],m=u[c][1],f=vl(l,p,m-p),d=cb(e,f),h=t(d);if(c===0)for(let g=0;g<h.length;++g)a.push(pt(0));for(let g=0;g<h.length;++g){let x=h[g];a[g]=K(a[g],$(m-p,x))}}for(let c=0;c<a.length;++c)a[c]=ut(a[c],i)}return a})}getDedupedMetricsNames(){let t=this.metricsNames,e=[];for(let n=0;n<t.length;++n){let o=t[n],s=o;if(PN(t,o)>1){let i=PN(t.slice(0,n),o);s+=`_${i}`}e.push(s)}return e}makeTrainFunction(){return t=>{let e=[],n=t.slice(0,this.inputs.length),o=t.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=t.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),i=[],a=()=>{let p=[];for(let h=0;h<this.inputs.length;++h)p.push({key:this.inputs[h],value:n[h]});let m=new ha(p),f=zc(this.outputs,m,{training:!0}),d;for(let h=0;h<this.lossFunctions.length;++h){let g=this.lossFunctions[h],x=g(o[h],f[h]);s[h]!=null&&(x=FR(x,s[h]));let b=Ne(x);e.push(b),h===0?d=x:d=K(d,x)}for(let h=0;h<this.metricsTensors.length;++h){let g;if(this.outputs.length>1&&h<this.outputs.length)g=e[h];else{let x=this.metricsTensors[h][0],b=this.metricsTensors[h][1];g=Ne(x(o[b],f[b]))}De(g),i.push(g)}return d=Ne(d),this.calculateLosses().forEach(h=>{d=K(d,h)}),d},u=this.collectedTrainableWeights.map(p=>p.read());return[this.optimizer_.minimize(a,!0,u)].concat(i)}}makeTestFunction(){this.testFunction=t=>B(()=>{let e=[],n,o=t.slice(0,this.inputs.length),s=t.slice(this.inputs.length,this.inputs.length+this.outputs.length),i=[];for(let l=0;l<this.inputs.length;++l)i.push({key:this.inputs[l],value:o[l]});let a=new ha(i),u=zc(this.outputs,a);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],p=Ne(c(s[l],u[l]));l===0?n=p:n=K(n,p),e.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],p=this.metricsTensors[l][1],m=Ne(c(s[p],u[p]));e.push(m)}return e})}async fit(t,e,n={}){if(this.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");this.isTraining=!0;let o,s,i,a,u,l,c,p,m;try{let f=n.batchSize==null?32:n.batchSize;ub(f);let h=await this.standardizeUserData(t,e,n.sampleWeight,n.classWeight,!1,f);o=h[0],s=h[1],m=h[2];let g=!1,x;if(n.validationData!=null&&n.validationData.length>0){if(g=!0,n.validationData.length===2)u=n.validationData[0],l=n.validationData[1];else throw n.validationData.length===3?new _t("validationData including sample weights is not supported yet."):new z(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${n.validationData} is invalid.`);let M=await this.standardizeUserData(u,l,null,null,!0,f);c=M[0],p=M[1],x=c.concat(p)}else if(n.validationSplit!=null&&n.validationSplit>0&&n.validationSplit<1){g=!0;let F=Math.floor(o[0].shape[0]*(1-n.validationSplit)),M=o[0].shape[0];c=Xm(o,F,M),i=o,o=Xm(o,0,F),p=Xm(s,F,M),a=s,s=Xm(s,0,F),x=c.concat(p)}else n.validationSteps!=null&&(g=!0);let b=o.concat(s).concat(m);this.checkTrainableWeightsConsistency();let w=this.makeTrainFunction(),I=this.getDedupedMetricsNames(),N,E;g?(this.makeTestFunction(),N=this.testFunction,E=I.slice().concat(I.map(F=>"val_"+F))):(N=null,x=[],E=I.slice());let A=Jy(n.callbacks,n.yieldEvery);return await this.fitLoop(w,b,I,f,n.epochs,n.verbose,A,N,x,n.shuffle,E,n.initialEpoch,null,null)}finally{this.isTraining=!1,$o(o,t),$o(s,e),$o(i,t),$o(a,e),$o(c,u),$o(p,l),m!=null&&Tt(m)}}async fitLoop(t,e,n,o,s,i,a,u,l,c,p,m,f,d){o==null&&(o=32),s==null&&(s=1),c==null&&(c=!0),m==null&&(m=0);let h=!1;if(u!=null&&l!=null&&(h=!0),d!=null&&(h=!0,f==null))throw new z("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let g=this.checkNumSamples(e,o,f,"steps_per_epoch"),x;g!=null&&(x=gn(0,g)),i==null&&(i=1);let{callbackList:b,history:w}=Qy(a,i,s,m,g,f,o,h,p);b.setModel(this),this.history=w,await b.onTrainBegin(),this.stopTraining_=!1;for(let I=m;I<s;++I){await b.onEpochBegin(I);let N={};if(f!=null)throw new _t("stepsPerEpoch mode is not implemented yet.");{if(c==="batch")throw new _t("batch shuffling is not implemneted yet");c&&y.shuffle(x);let E=Oe(x),A=pb(g,o);for(let D=0;D<A.length;++D){let F={};if(await b.onBatchBegin(D,F),B(()=>{let M=A[D][0],V=A[D][1],G=vl(E,M,V-M);F.batch=D,F.size=V-M;let W=cb(e,G),q=t(W);for(let H=0;H<n.length;++H){let j=n[H],Y=q[H];F[j]=Y,De(Y)}if(D===A.length-1&&h){let H=this.testLoop(u,l,o);for(let j=0;j<n.length;++j){let Y=n[j],Z=H[j];De(Z),N["val_"+Y]=Z}}}),await b.onBatchEnd(D,F),jy(F),this.stopTraining_)break}E.dispose()}if(await b.onEpochEnd(I,N),this.stopTraining_)break}return await b.onTrainEnd(),await this.history.syncData(),this.history}async fitDataset(t,e){return LR(this,t,e)}async trainOnBatch(t,e){let n=await this.standardizeUserData(t,e),o=n[0],s=n[1],a=this.makeTrainFunction()(o.concat(s)),u=[];for(let l of a){let c=await l.data();u.push(c[0])}return Tt(a),$o(n[0],t),$o(n[1],e),kr(u)}getNamedWeights(t){let e=[],n=t!=null&&t.trainableOnly,o=n?this.trainableWeights:this.weights,s=this.getWeights(n);for(let i=0;i<o.length;++i)n&&!o[i].trainable||e.push({name:o[i].originalName,tensor:s[i]});return e}set stopTraining(t){this.stopTraining_=t}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(t){this.optimizer_!==t&&(this.optimizer_=t,this.isOptimizerOwned=!1)}dispose(){let t=super.dispose();if(t.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let e=lh().numTensors;this.optimizer_.dispose(),t.numDisposedVariables+=e-lh().numTensors}return t}getLossIdentifiers(){let t;if(typeof this.loss=="string")t=_o(this.loss);else if(Array.isArray(this.loss)){for(let e of this.loss)if(typeof e!="string")throw new Error("Serialization of non-string loss is not supported.");t=this.loss.map(e=>_o(e))}else{let e=Object.keys(this.loss);t={};let n=this.loss;for(let o of e)if(typeof n[o]=="string")t[o]=_o(n[o]);else throw new Error("Serialization of non-string loss is not supported.")}return t}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[_o(Oh(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(t=>_o(Oh(t)));{let t={};for(let e in this.metrics)t[e]=_o(Oh(this.metrics[e]));return t}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(t){if(t.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(t.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(t.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let e=Gc(t.optimizer_config),n=wn(e),o;if(typeof t.loss=="string")o=Cl(t.loss);else if(Array.isArray(t.loss))o=t.loss.map(i=>Cl(i));else if(t.loss!=null){o={};for(let i in t.loss)o[i]=Cl(t.loss[i])}let s;if(Array.isArray(t.metrics))s=t.metrics.map(i=>Cl(i));else if(t.metrics!=null){s={};for(let i in t.metrics)s[i]=Cl(t.metrics[i])}this.compile({loss:o,metrics:s,optimizer:n})}async save(t,e){if(typeof t=="string"){let l=Mr.getSaveHandlers(t);if(l.length===0)throw new z(`Cannot find any save handlers for URL '${t}'`);if(l.length>1)throw new z(`Found more than one (${l.length}) save handlers for URL '${t}'`);t=l[0]}if(t.save==null)throw new z("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Mr.encodeWeights(this.getNamedWeights(e)),a={modelTopology:this.toJSON(null,!1),format:fJ,generatedBy:`TensorFlow.js tfjs-layers v${jm}`,convertedBy:null};if((e==null?!1:e.includeOptimizer)&&this.optimizer!=null){a.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:p}=await Mr.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...p),n.data=Mr.concatenateArrayBuffers([n.data,c])}return this.userDefinedMetadata!=null&&(XN(this.userDefinedMetadata,this.name,!0),a.userDefinedMetadata=this.userDefinedMetadata),a.weightData=n.data,a.weightSpecs=n.specs,t.save(a)}setUserDefinedMetadata(t){XN(t,this.name),this.userDefinedMetadata=t}getUserDefinedMetadata(){return this.userDefinedMetadata}};Un.className="Model";Q.registerClass(Un);var mb=class extends Un{};mb.className="Functional";Q.registerClass(mb);async function WR(r,t){"modelTopology"in r||(r={modelTopology:r}),r=r;let e=r.modelTopology;e.model_config!=null&&(e=e.model_config);let n=Gc(e),o=wn(n,t);if(r.weightsManifest!=null){let s=await Mr.loadWeights(r.weightsManifest,r.pathPrefix,o.weights.map(a=>a.originalName)),i={};for(let a of o.weights)i[a.originalName]=s[a.originalName];o.loadWeights(i),Tt(s)}return o}async function UR(r,t){if(t==null&&(t={}),typeof r=="string"){let e=Mr.getLoadHandlers(r,t);if(e.length===0)e.push(Mr.browserHTTPRequest(r,t));else if(e.length>1)throw new z(`Found more than one (${e.length}) load handlers for URL '${r}'`);r=e[0]}return dJ(r,void 0,t)}async function dJ(r,t,e){if(e==null&&(e={}),r.load==null)throw new z("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let n=await r.load(),o=n.modelTopology;o.model_config!=null&&(o=o.model_config);let s=e.strict==null?!0:e.strict,i=n.weightData!=null&&n.weightSpecs!=null&&s,a=wn(Gc(o),t,i),u=n.trainingConfig;if(u!=null&&a.loadTrainingConfig(u),n.userDefinedMetadata!=null&&a.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new z("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:c}=hJ(n.weightData,n.weightSpecs);a.loadWeights(l,s),a.optimizer!=null&&c.length>0&&await a.optimizer.setWeights(c),Tt(l),Tt(c.map(p=>p.tensor))}return a}function hJ(r,t){let e=Mr.decodeWeights(r,t),n={},o=[];return t.forEach(s=>{s.group==="optimizer"?o.push({name:s.name,tensor:e[s.name]}):n[s.name]=e[s.name]}),{modelWeights:n,optimizerWeights:o}}var Wc=class r extends Un{constructor(t){if(super({inputs:[],outputs:[]}),t=t||{},this.trainable=!0,this.built=!1,this.name=t.name!=null?t.name:Fu("sequential_"),t.layers!=null)for(let e of t.layers)this.add(e)}checkShape(t){if(t.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new z(`Negative dimension size caused by adding layer ${t.name} with input shape [${t.inboundNodes[0].inputTensors[0].shape}]`)}add(t){let e=t instanceof r||t instanceof Un,n;if(e){if(n=t,n.outputs.length!==1)throw new z("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new z("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(t.inboundNodes.length===0){if(t.batchInputShape==null)throw new z("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let o=Hy({batchShape:t.batchInputShape,dtype:t.dtype,name:t.name+"_input"});t.apply(o)}if(e)this.outputs=n.outputs,this.inputs=n.inputs;else{if(t.inboundNodes.length!==1)throw new z(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${t.name} which has ${t.inboundNodes.length} pre-existing inbound connections.`);if(t.inboundNodes[0].outputTensors.length!==1)throw new z("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[t.inboundNodes[0].outputTensors[0]],this.inputs=WN(this.outputs[0])}this.inboundNodes=[],new Nl({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:To(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(o=>o.shape),outputShapes:this.outputs[0].shape})}else{let o=t.apply(this.outputs[0]);if(Array.isArray(o))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(t),this.outputs=[o],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(t),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let t=this.layers.length-1;this.layers[t].outboundNodes=[],this.outputs=[this.layers[t].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(t,e){return this.model==null&&this.build(),this.model.call(t,e)}build(t){if(Gt(t),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Un({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(t,e,n=console.log){this.built||this.build(),super.summary(t,e,n)}setWeights(t){this.model==null&&this.build(),this.model.setWeights(t)}evaluate(t,e,n={}){if(!this.built)throw new dn("The model needs to be compiled before being used.");return this.model.evaluate(t,e,n)}async evaluateDataset(t,e){if(!this.built)throw new dn("The model needs to be compiled before being used.");return this.model.evaluateDataset(t,e)}predict(t,e={}){return this.model==null&&this.build(),this.model.predict(t,e)}predictOnBatch(t){return this.model==null&&this.build(),this.model.predictOnBatch(t)}compile(t){this.build(),this.model.compile(t),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(t){this.model.optimizer=t}async fit(t,e,n={}){if(!this.built)throw new dn("The model needs to be compiled before being used.");return this.model.fit(t,e,n)}async fitDataset(t,e){if(!this.built)throw new dn("The model needs to be compiled before being used.");return this.model.fitDataset(t,e)}async trainOnBatch(t,e){return this.model.trainOnBatch(t,e)}static fromConfig(t,e,n={},o=!1){let s,i={};if(e instanceof Array){if(e[0].className==null||e[0].className==="Merge")throw new z("Legacy serialization format not supported yet.");s=e}else y.assert(e.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),s=e.layers,delete e.layers,i=e;let a=new t(i);if(!(a instanceof r))throw new _t(`Sequential.fromConfig called on non-Sequential input: ${a}`);for(let u of s){let c=wn(u,void 0,o);o&&c.setFastWeightInitDuringBuild(!0),a.add(c)}return a}set stopTraining(t){if(this.model==null)throw new z("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=t}get stopTraining(){if(this.model==null)throw new z("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let t=[];for(let e of this.layers){let n={};n.className=e.getClassName(),n.config=e.getConfig(),t.push(n)}return{name:this.name,layers:t}}};Wc.className="Sequential";Q.registerClass(Wc);function gJ(r){return new Un(r)}function xJ(r){return new Wc(r)}function JN(r){return Hy(r)}function yJ(r,t){Um.registerCallbackConstructor(r,t)}var Pr=class extends Q.Serializable{getConfig(){return{}}},fb=class extends Pr{apply(t,e=1){return uR(t,e)}};fb.className="elu";Q.registerClass(fb);var db=class extends Pr{apply(t){return fm(t)}};db.className="selu";Q.registerClass(db);var hb=class extends Pr{apply(t){return Or(t)}};hb.className="relu";Q.registerClass(hb);var gb=class extends Pr{apply(t){return B(()=>lo(6,Or(t)))}};gb.className="relu6";Q.registerClass(gb);var xb=class extends Pr{apply(t){return t}};xb.className="linear";Q.registerClass(xb);var yb=class extends Pr{apply(t){return en(t)}};yb.className="sigmoid";Q.registerClass(yb);var bb=class extends Pr{apply(t){return pR(t)}};bb.className="hardSigmoid";Q.registerClass(bb);var wb=class extends Pr{apply(t){return ui(t)}};wb.className="softplus";Q.registerClass(wb);var Ib=class extends Pr{apply(t){return cR(t)}};Ib.className="softsign";Q.registerClass(Ib);var Cb=class extends Pr{apply(t){return li(t)}};Cb.className="tanh";Q.registerClass(Cb);var Ym=class extends Pr{apply(t,e=-1){return Eu(t,e)}};Ym.className="softmax";Q.registerClass(Ym);var vb=class extends Pr{apply(t,e=-1){return lm(t,e)}};vb.className="logSoftmax";Q.registerClass(vb);var Sb=class extends Pr{apply(t){return B(()=>B(()=>{let e=Math.sqrt(2),n=$(.5,K(1,am(ut(t,e))));return $(t,n)}))}};Sb.className="gelu";Q.registerClass(Sb);var Nb=class extends Pr{apply(t){return B(()=>$(.5,$(t,K(1,li($(ge(ut(2,Math.PI)),K(t,$(.044715,qr(t,3)))))))))}};Nb.className="gelu_new";Q.registerClass(Nb);var kb=class extends Pr{apply(t){return B(()=>$(t,li(ui(t))))}};kb.className="mish";Q.registerClass(kb);var Tb=class extends Pr{apply(t,e=1){return B(()=>$(en($(t,e)),t))}};Tb.className="swish";Q.registerClass(Tb);function gi(r){return r.getClassName()}function QN(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"activation")}function xi(r){if(r==null){let t={};return t.className="linear",t.config={},QN(t)}if(typeof r=="string"){let t={};return t.className=r,t.config={},QN(t)}else return r instanceof Pr?r:QN(r)}function tk(r){if(r!=null&&typeof r!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${r}`)}var _b=class extends Q.Serializable{},Lu=class extends _b{constructor(t){super(),tk(t),this.l1=t==null||t.l1==null?.01:t.l1,this.l2=t==null||t.l2==null?.01:t.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(t){return B(()=>{let e=ke([1]);return this.hasL1&&(e=K(e,mt($(this.l1,_e(t))))),this.hasL2&&(e=K(e,mt($(this.l2,$c(t))))),R(e,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(t,e){return new t({l1:e.l1,l2:e.l2})}};Lu.className="L1L2";Q.registerClass(Lu);function KR(r){return tk(r),new Lu({l1:r!=null?r.l1:null,l2:0})}function jR(r){return tk(r),new Lu({l2:r!=null?r.l2:null,l1:0})}var HR={l1l2:"L1L2"};function fe(r){return km(r)}function qR(r,t={}){return fa(r,Q.SerializationMap.getMap().classNameMap,t,"regularizer")}function ve(r){if(r==null)return null;if(typeof r=="string"){let e={className:r in HR?HR[r]:r,config:{}};return qR(e)}else return r instanceof _b?r:qR(r)}var Zm=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null&&(this.maxValue=t.maxValue)}call(t,e){t=St(t);let n=Or(t);return this.maxValue!=null&&(n=vr(n,0,this.maxValue)),n}computeOutputShape(t){return t}getConfig(){let t={maxValue:this.maxValue},e=super.getConfig();return Object.assign(t,e),t}};Zm.className="ReLU";Q.registerClass(Zm);var Jm=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_ALPHA=.3,t==null&&(t={}),this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return Cu(n,this.alpha)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};Jm.className="LeakyReLU";Q.registerClass(Jm);var Qm=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA_INITIALIZER="zeros",t==null&&(t={}),this.supportsMasking=!0,this.alphaInitializer=xe(t.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ve(t.alphaRegularizer),this.alphaConstraint=Ge(t.alphaConstraint),t.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(t.sharedAxes))this.sharedAxes=t.sharedAxes;else if(typeof t.sharedAxes=="number")this.sharedAxes=[t.sharedAxes];else throw new z(`Expected sharedAxes to be a number or an array of numbers, but got ${t.sharedAxes}`)}build(t){t=Gt(t);let e=t.slice(1);if(this.sharedAxes!=null)for(let o of this.sharedAxes)e[o-1]=1;this.alpha=this.addWeight("alpha",e,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let o=1;o<t.length;++o)n[o]=t[o];this.inputSpec=[new Ce({ndim:t.length,axes:n})],this.built=!0}call(t,e){return t=St(t),_u(t,this.alpha.read())}getConfig(){let t={alphaInitializer:Te(this.alphaInitializer),alphaRegularizer:fe(this.alphaRegularizer),alphaConstraint:Ve(this.alphaConstraint),sharedAxes:this.sharedAxes},e=super.getConfig();return Object.assign(t,e),t}};Qm.className="PReLU";Q.registerClass(Qm);var tf=class extends Et{constructor(t){if(super(t==null?{}:t),this.DEFAULT_ALPHA=1,t==null&&(t={}),t.alpha!=null&&t.alpha!==this.DEFAULT_ALPHA)throw new _t(`Non-default alpha value (${t.alpha}) is not supported by the ELU layer yet.`);this.alpha=t.alpha==null?this.DEFAULT_ALPHA:t.alpha}call(t,e){let n=St(t);return aa(n)}computeOutputShape(t){return t}getConfig(){let t={alpha:this.alpha},e=super.getConfig();return Object.assign(t,e),t}};tf.className="ELU";Q.registerClass(tf);var ef=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_THETA=1,t==null&&(t={}),this.theta=t.theta==null?this.DEFAULT_THETA:t.theta}call(t,e){let n=St(t);return $(n,J(Re(n,this.theta),"float32"))}computeOutputShape(t){return t}getConfig(){let t={theta:this.theta},e=super.getConfig();return Object.assign(t,e),t}};ef.className="ThresholdedReLU";Q.registerClass(ef);var rf=class extends Et{constructor(t){super(t==null?{}:t),this.DEFAULT_AXIS=1,t==null&&(t={}),this.softmax=new Ym().apply,this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis}call(t,e){return B(()=>{let n=St(t),o=e.mask;if(o!=null){let s=$(at(ar(n.shape),J(o,n.dtype)),pt(-1e9));n=K(n,s)}return this.axis instanceof Array?this.axis.length>1?Ke(at(n,Su(n,this.axis,!0))):this.softmax(n,this.axis[0]):this.softmax(n,this.axis)})}computeOutputShape(t){return t}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};rf.className="Softmax";Q.registerClass(rf);function zu(r,t,e){if(typeof r=="number")return To(r,t);if(r.length!==t)throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${r.length} elements.`);for(let n=0;n<t;++n){let o=r[n];if(!sR(o))throw new z(`The ${e} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(r)} including a non-integer number ${o}`)}return r}function Tn(r,t,e,n,o=1){if(r==null)return r;let s=t+(t-1)*(o-1),i;return e==="same"?i=r:i=r-s+1,Math.floor((i+n-1)/n)}function yi(r,t,e,n){if(r==null)return null;if(n==="valid")r=r*t+di([e-t,0]);else if(n==="same")r=r*t;else throw new z(`Unsupport padding mode: ${n}.`);return r}function Ph(r,t){return B(()=>(Me(t),t==="channelsFirst"?Vt(r,[0,2,3,1]):r))}function ek(r,t){return B(()=>(Me(t),t==="channelsFirst"?Vt(r,[0,2,3,4,1]):r))}function wJ(r,t,e,n=1,o="valid",s,i=1){return B(()=>{if(s==null&&(s=xn()),Me(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(t.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(e!=null&&e.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s==="channelsFirst"&&(r=Vt(r,[0,2,1])),o==="causal")throw new _t("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let a=rm(r,t,n,o==="same"?"same":"valid","NWC",i);return e!=null&&(a=yn(a,e)),a})}function XR(r,t,e,n=[1,1],o="valid",s,i,a=null){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(t.rank!==3&&t.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let u=Ph(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Ru.conv2d({x:u,filter:t,strides:n,pad:o==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:e,activation:a}),s==="channelsFirst"&&(u=Vt(u,[0,3,1,2])),u})}function IJ(r,t,e,n=[1,1,1],o="valid",s,i){return B(()=>{if(s==null&&(s=xn()),Me(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(t.rank!==4&&t.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let a=ek(r,s);if(o==="causal")throw new _t("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return a=Dx(a,t,n,o==="same"?"same":"valid","NDHWC",i),e!=null&&(a=yn(a,e)),s==="channelsFirst"&&(a=Vt(a,[0,4,1,2,3])),a})}var Mh=class r extends Et{constructor(t,e){if(super(e),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",r.verifyArgs(e),this.rank=t,tr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new _t(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=zu(e.kernelSize,t,"kernelSize"),this.strides=zu(e.strides==null?1:e.strides,t,"strides"),this.padding=e.padding==null?"valid":e.padding,hn(this.padding),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Me(this.dataFormat),this.activation=xi(e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.biasInitializer=xe(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ge(e.biasConstraint),this.biasRegularizer=ve(e.biasRegularizer),this.activityRegularizer=ve(e.activityRegularizer),this.dilationRate=zu(e.dilationRate==null?1:e.dilationRate,t,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(t){if(co("kernelSize"in t,"required key 'kernelSize' not in config"),typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(t.kernelSize)}.`)}getConfig(){let t={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:gi(this.activation),useBias:this.useBias,biasInitializer:Te(this.biasInitializer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}},nf=class r extends Mh{constructor(t,e){super(t,e),this.kernel=null,r.verifyArgs(e),this.filters=e.filters,tr(this.filters,"filters"),this.kernelInitializer=xe(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ge(e.kernelConstraint),this.kernelRegularizer=ve(e.kernelRegularizer)}build(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[e]}`);let n=t[e],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[e]:n}}],this.built=!0}call(t,e){return B(()=>{t=St(t);let n,o=this.bias==null?null:this.bias.read(),s=Oy(this.activation.getClassName());if(s!=null&&this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=wJ(t,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=XR(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=IJ(t,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new _t("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(t){t=Gt(t);let e=[],n=this.dataFormat==="channelsLast"?t.slice(1,t.length-1):t.slice(2);for(let s=0;s<n.length;++s){let i=Tn(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);e.push(i)}let o=[t[0]];return this.dataFormat==="channelsLast"?(o=o.concat(e),o.push(this.filters)):(o.push(this.filters),o=o.concat(e)),o}getConfig(){let t={filters:this.filters,kernelInitializer:Te(this.kernelInitializer),kernelRegularizer:fe(this.kernelRegularizer),kernelConstraint:Ve(this.kernelConstraint)},e=super.getConfig();return Object.assign(t,e),t}static verifyArgs(t){if(!("filters"in t)||typeof t.filters!="number"||t.filters<1)throw new z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(t.filters)}`)}},Uc=class r extends nf{constructor(t){super(2,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(t.kernelSize)}.`)}};Uc.className="Conv2D";Q.registerClass(Uc);var Hc=class r extends nf{constructor(t){super(3,t),r.verifyArgs(t)}getConfig(){let t=super.getConfig();return delete t.rank,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!(Array.isArray(t.kernelSize)&&(t.kernelSize.length===1||t.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(t.kernelSize)}.`)}};Hc.className="Conv3D";Q.registerClass(Hc);var of=class extends Uc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:4,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a;this.dataFormat==="channelsFirst"?(i=2,a=3):(i=1,a=2);let u=o[i],l=o[a],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=yi(u,m,c,this.padding),h=yi(l,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,1]));let x=om(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Vt(x,[0,3,1,2])),this.bias!=null&&(x=yn(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let i=this.kernelSize[0],a=this.kernelSize[1],u=this.strides[0],l=this.strides[1];return e[n]=this.filters,e[o]=yi(e[o],u,i,this.padding),e[s]=yi(e[s],l,a,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};of.className="Conv2DTranspose";Q.registerClass(of);var sf=class extends Hc{constructor(t){if(super(t),this.inputSpec=[new Ce({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(t){if(t=Gt(t),t.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(t));let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=t[e],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ce({ndim:5,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{let n=St(t);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],i,a,u;this.dataFormat==="channelsFirst"?(u=2,i=3,a=4):(u=1,i=2,a=3);let l=o[u],c=o[i],p=o[a],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],x=this.strides[2],b=yi(l,h,m,this.padding),w=yi(c,g,f,this.padding),I=yi(p,x,d,this.padding),N=[s,b,w,I,this.filters];this.dataFormat!=="channelsLast"&&(n=Vt(n,[0,2,3,4,1]));let E=Rx(n,this.kernel.read(),N,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Vt(E,[0,4,1,2,3])),this.bias!==null&&(E=yn(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(t){t=Gt(t);let e=t.slice(),n,o,s,i;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,i=4):(n=4,o=1,s=2,i=3);let a=this.kernelSize[0],u=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return e[n]=this.filters,e[o]=yi(e[o],c,a,this.padding),e[s]=yi(e[s],p,u,this.padding),e[i]=yi(e[i],m,l,this.padding),e}getConfig(){let t=super.getConfig();return delete t.dilationRate,t}};sf.className="Conv3DTranspose";Q.registerClass(sf);var Eb=class extends nf{constructor(t,e){if(super(t,e),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,e.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(e.kernelInitializer!=null||e.kernelRegularizer!=null||e.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(e.padding!=null&&e.padding!=="same"&&e.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(e.padding)}`);this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ve(e.depthwiseRegularizer),this.depthwiseConstraint=Ge(e.depthwiseConstraint),this.pointwiseInitializer=xe(e.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ve(e.pointwiseRegularizer),this.pointwiseConstraint=Ge(e.pointwiseConstraint)}build(t){if(t=Gt(t),t.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(t)}`);let e=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(t[e])}`);let n=t[e],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let a=0;a<this.rank;++a)s.push(1);s.push(n*this.depthMultiplier,this.filters);let i=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,i,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,i,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,i,this.biasConstraint):this.bias=null,this.inputSpec=[new Ce({ndim:this.rank+2,axes:{[e]:n}})],this.built=!0}call(t,e){return B(()=>{t=St(t);let n;if(this.rank===1)throw new _t("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(t=Vt(t,[0,2,3,1])),n=dm(t,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Vt(n,[0,3,1,2])),n})}getConfig(){let t=super.getConfig();return delete t.rank,delete t.kernelInitializer,delete t.kernelRegularizer,delete t.kernelConstraint,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.pointwiseInitializer=Te(this.pointwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.pointwiseRegularizer=fe(this.pointwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseConstraint),t.pointwiseConstraint=Ve(this.pointwiseConstraint),t}};Eb.className="SeparableConv";var af=class extends Eb{constructor(t){super(2,t)}};af.className="SeparableConv2D";Q.registerClass(af);var lf=class r extends nf{constructor(t){super(1,t),r.verifyArgs(t),this.inputSpec=[{ndim:3}]}getConfig(){let t=super.getConfig();return delete t.rank,delete t.dataFormat,t}static verifyArgs(t){if(typeof t.kernelSize!="number"&&!Fy(t.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(t.kernelSize)}.`)}};lf.className="Conv1D";Q.registerClass(lf);var uf=class extends Et{constructor(t){super(t),typeof t.cropping=="number"?this.cropping=[[t.cropping,t.cropping],[t.cropping,t.cropping]]:typeof t.cropping[0]=="number"?this.cropping=[[t.cropping[0],t.cropping[0]],[t.cropping[1],t.cropping[1]]]:this.cropping=t.cropping,this.dataFormat=t.dataFormat===void 0?"channelsLast":t.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(t){return this.dataFormat==="channelsFirst"?[t[0],t[1],t[2]-this.cropping[0][0]-this.cropping[0][1],t[3]-this.cropping[1][0]-this.cropping[1][1]]:[t[0],t[1]-this.cropping[0][0]-this.cropping[0][1],t[2]-this.cropping[1][0]-this.cropping[1][1],t[3]]}call(t,e){return B(()=>{if(t=St(t),this.dataFormat==="channelsLast"){let n=kh(t,this.cropping[0][0],t.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return kh(n,this.cropping[1][0],t.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=kh(t,this.cropping[0][0],t.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return kh(n,this.cropping[1][0],t.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let t={cropping:this.cropping,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};uf.className="Cropping2D";Q.registerClass(uf);var cf=class extends Et{constructor(t){super(t),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=t.size==null?this.DEFAULT_SIZE:t.size,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),this.interpolation=t.interpolation==null?"nearest":t.interpolation,nR(this.interpolation)}computeOutputShape(t){if(this.dataFormat==="channelsFirst"){let e=t[2]==null?null:this.size[0]*t[2],n=t[3]==null?null:this.size[1]*t[3];return[t[0],t[1],e,n]}else{let e=t[1]==null?null:this.size[0]*t[1],n=t[2]==null?null:this.size[1]*t[2];return[t[0],e,n,t[3]]}}call(t,e){return B(()=>{let n=St(t),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Vt(n,[0,2,3,1]);let s=this.size[0]*o[2],i=this.size[1]*o[3],a=this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i]);return Vt(a,[0,3,1,2])}else{let s=this.size[0]*o[1],i=this.size[1]*o[2];return this.interpolation==="nearest"?fn.resizeNearestNeighbor(n,[s,i]):fn.resizeBilinear(n,[s,i])}})}getConfig(){let t={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}};cf.className="UpSampling2D";Q.registerClass(cf);function CJ(r,t,e=[1,1],n="valid",o,s){return B(()=>{o==null&&(o=xn()),Me(o);let i=Ph(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(t.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=ia(i,t,e,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}var pf=class extends Mh{constructor(t){super(2,t),this.depthwiseKernel=null,this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=xe(t.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ge(t.depthwiseConstraint),this.depthwiseRegularizer=ve(t.depthwiseRegularizer)}build(t){if(t=Gt(t),t.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(t)}.`);let e=this.dataFormat==="channelsFirst"?1:3;if(t[e]==null||t[e]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${t[e]}).`);let n=t[e],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{t=St(t);let n=CJ(t,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=yn(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[1]*this.depthMultiplier:t[3]*this.depthMultiplier,s=Tn(e,this.kernelSize[0],this.padding,this.strides[0]),i=Tn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[t[0],o,s,i]:[t[0],s,i,o]}getConfig(){let t=super.getConfig();return t.depthMultiplier=this.depthMultiplier,t.depthwiseInitializer=Te(this.depthwiseInitializer),t.depthwiseRegularizer=fe(this.depthwiseRegularizer),t.depthwiseConstraint=Ve(this.depthwiseRegularizer),t}};pf.className="DepthwiseConv2D";Q.registerClass(pf);function rk(r,t,e,n){if(Array.isArray(r)){if(t!=null||e!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(e=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(t=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return t=o(t),e=o(e),{inputs:r,initialState:t,constants:e}}function nk(r,t,e,n=!1,o,s,i=!1,a=!1){return B(()=>{let u=t.shape.length;if(u<3)throw new z(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(gn(2,u));if(t=Vt(t,l),s!=null)throw new _t("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=J(J(o,"bool"),"float32"),o.rank===u-1&&(o=je(o,-1)),o=Vt(o,l)),n&&(t=dr(t,0),o!=null&&(o=dr(o,0)));let c=[],p,m=e,f=t.shape[0],d=gr(t),h;o!=null&&(h=gr(o));for(let x=0;x<f;++x){let b=d[x],w=B(()=>r(b,m));if(o==null)p=w[0],m=w[1];else{let I=B(()=>{let N=h[x],E=at(wr(N),N),A=K($(w[0],N),$(m[0],E)),D=m.map((F,M)=>K($(w[1][M],N),$(F,E)));return{output:A,newStates:D}});p=I.output,m=I.newStates}a&&c.push(p)}let g;return a&&(g=Fe(c,1)),[p,g,m]})}var po=class r extends Et{constructor(t){super(t);let e;if(t.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(t.cell)?e=new jc({cells:t.cell}):e=t.cell,e.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=e,this.returnSequences=t.returnSequences==null?!1:t.returnSequences,this.returnState=t.returnState==null?!1:t.returnState,this.goBackwards=t.goBackwards==null?!1:t.goBackwards,this._stateful=t.stateful==null?!1:t.stateful,this.unroll=t.unroll==null?!1:t.unroll,this.supportsMasking=!0,this.inputSpec=[new Ce({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return gn(0,t).map(e=>null)}else return this.states_}setStates(t){this.states_=t}computeOutputShape(t){Uy(t)&&(t=t[0]),t=t;let e=this.cell.stateSize;Array.isArray(e)||(e=[e]);let n=e[0],o;if(this.returnSequences?o=[t[0],t[1],n]:o=[t[0],n],this.returnState){let s=[];for(let i of e)s.push([t[0],i]);return[o].concat(s)}else return o}computeMask(t,e){return B(()=>{Array.isArray(e)&&(e=e[0]);let n=this.returnSequences?e:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let t=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,e=[];for(let n=0;n<t;++n)e.push(null);return e}else return this.states_}set states(t){this.states_=t}build(t){if(this.numConstants!=null)throw new _t("Constants support is not implemented in RNN yet.");Uy(t)&&(t=t[0]),t=t;let n=this.stateful?t[0]:null,o=t.slice(2);this.inputSpec[0]=new Ce({shape:[n,null,...o]});let s=[t[0]].concat(t.slice(2));this.cell.build(s);let i;if(Array.isArray(this.cell.stateSize)?i=this.cell.stateSize:i=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(a=>a.shape[a.shape.length-1]),i))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=i.map(a=>new Ce({shape:[null,a]}));this.stateful&&this.resetStates()}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_=[ke([n,this.cell.stateSize])];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ke([n,o])):this.states_[0]=ke([n,this.cell.stateSize]);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e===!0?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let o=0;o<this.states_.length;++o){let s=t[o],i=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,a=[n,i];if(!y.arraysEqual(s.shape,a))throw new z(`State ${o} is incompatible with layer ${this.name}: expected shape=${a}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>De(o.clone()))})}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);t=s.inputs,n=s.initialState,o=s.constants;let i=[],a=[];if(n!=null){e.initialState=n,i=i.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Ce({shape:l.shape}));a=a.concat(this.stateSpec)}if(o!=null&&(e.constants=o,i=i.concat(o),this.numConstants=o.length),i[0]instanceof nn){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;t=St(t),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(t));let i=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==i)throw new z(`RNN Layer has ${i} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let a={training:o},l=nk((d,h)=>{let g=this.cell.call([d].concat(h),a);return[g[0],g.slice(1)]},t,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],p=l[1],m=l[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(t){return B(()=>{let e=ke(t.shape);return e=mt(e,[1,2]),e=Sl(e),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Vy(e,[1,n]):e):this.cell.stateSize>1?[Vy(e,[1,this.cell.stateSize])]:[e]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(t)}getConfig(){let t=super.getConfig(),e={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(e.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===r.className&&(e.cell={className:this.cell.getClassName(),config:n}),Object.assign(Object.assign(Object.assign({},n),t),e)}static fromConfig(t,e,n={}){let o=e.cell,s=wn(o,n);return new t(Object.assign(e,{cell:s}))}};po.className="RNN";Q.registerClass(po);var Tl=class extends Et{},qc=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation==null?this.DEFAULT_ACTIVATION:t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t),this.kernel=this.addWeight("kernel",[t[t.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${t.length}.`);let n=t[1];t=t[0];let o=e.training==null?!1:e.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=El({ones:()=>wr(t),rate:this.dropout,training:o,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=El({ones:()=>wr(n),rate:this.recurrentDropout,training:o,dropoutFunc:this.dropoutFunc}));let s,i=this.dropoutMask,a=this.recurrentDropoutMask;i!=null?s=Do($(t,i),this.kernel.read()):s=Do(t,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),a!=null&&(n=$(n,a));let u=K(s,Do(n,this.recurrentKernel.read()));return this.activation!=null&&(u=this.activation.apply(u)),[u,u]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign(Object.assign({},t),e)}};qc.className="SimpleRNNCell";Q.registerClass(qc);var mf=class extends po{constructor(t){t.cell=new qc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return new t(e)}};mf.className="SimpleRNN";Q.registerClass(mf);var Kc=class extends Tl{constructor(t){if(super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){t=Gt(t);let e=t[t.length-1];this.kernel=this.addWeight("kernel",[e,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(t,e){return B(()=>{if(t=t,t.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training==null?!1:e.training,o=t[1];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=El({ones:()=>wr(t),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=El({ones:()=>wr(o),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,a,u,l;0<this.dropout&&this.dropout<1&&(t=$(t,s[0]));let c=Do(t,this.kernel.read());this.useBias&&(c=yn(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=$(o,i[0]));let p=this.recurrentKernel.read(),[m,f]=hr(p,[2*this.units,this.units],p.rank-1),d=Do(o,m),[h,g,x]=hr(c,3,c.rank-1),[b,w]=hr(d,2,d.rank-1);a=this.recurrentActivation.apply(K(h,b)),u=this.recurrentActivation.apply(K(g,w));let I=Do($(u,o),f);l=this.activation.apply(K(x,I));let N=K($(a,o),$(K(1,Ut(a)),l));return[N,N]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:gi(this.activation),recurrentActivation:gi(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign(Object.assign({},t),e)}};Kc.className="GRUCell";Q.registerClass(Kc);var ff=class extends po{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new Kc(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};ff.className="GRU";Q.registerClass(ff);var _l=class extends Tl{constructor(t){super(t),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation===void 0?this.DEFAULT_ACTIVATION:t.activation),this.recurrentActivation=xi(t.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:t.recurrentActivation),this.useBias=t.useBias==null?!0:t.useBias,this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=xe(t.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=t.unitForgetBias,this.kernelRegularizer=ve(t.kernelRegularizer),this.recurrentRegularizer=ve(t.recurrentRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.kernelConstraint=Ge(t.kernelConstraint),this.recurrentConstraint=Ge(t.recurrentConstraint),this.biasConstraint=Ge(t.biasConstraint),this.dropout=Dc([1,di([0,t.dropout==null?0:t.dropout])]),this.recurrentDropout=Dc([1,di([0,t.recurrentDropout==null?0:t.recurrentDropout])]),this.dropoutFunc=t.dropoutFunc,this.implementation=t.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(t){var e;t=Gt(t);let n=t[t.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,i=this.units;o=new(e=class extends bn{apply(u,l){let c=s.apply([i]),p=new Mu().apply([i]),m=s.apply([i*2]);return GN(GN(c,p),m)}},e.className="CustomInit",e)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training;if(t=t,t.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let o=t[1],s=t[2];t=t[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=El({ones:()=>wr(t),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=El({ones:()=>wr(o),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,a=this.recurrentDropoutMask,u,l,c,p;0<this.dropout&&this.dropout<1&&(t=$(t,i[0]));let m=Do(t,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=$(o,a[0])),m=K(m,Do(o,this.recurrentKernel.read())),this.useBias&&(m=yn(m,this.bias.read()));let[f,d,h,g]=hr(m,4,m.rank-1);u=this.recurrentActivation.apply(f),l=this.recurrentActivation.apply(d),c=K($(l,s),$(u,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=$(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let t=super.getConfig(),e={units:this.units,activation:gi(this.activation),recurrentActivation:gi(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),recurrentInitializer:Te(this.recurrentInitializer),biasInitializer:Te(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:fe(this.kernelRegularizer),recurrentRegularizer:fe(this.recurrentRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),recurrentConstraint:Ve(this.recurrentConstraint),biasConstraint:Ve(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign(Object.assign({},t),e)}};_l.className="LSTMCell";Q.registerClass(_l);var df=class extends po{constructor(t){t.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),t.cell=new _l(t),super(t)}call(t,e){return B(()=>{this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}static fromConfig(t,e){return e.implmentation===0&&(e.implementation=1),new t(e)}};df.className="LSTM";Q.registerClass(df);var jc=class extends Tl{constructor(t){super(t),this.cells=t.cells}get stateSize(){let t=[];for(let e of this.cells.slice().reverse())Array.isArray(e.stateSize)?t.push(...e.stateSize):t.push(e.stateSize);return t}call(t,e){return B(()=>{t=t;let n=t.slice(1),o=[];for(let a of this.cells.slice().reverse())Array.isArray(a.stateSize)?o.push(n.splice(0,a.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],i;for(let a=0;a<this.cells.length;++a){let u=this.cells[a];n=o[a],a===0?i=[t[0]].concat(n):i=[i[0]].concat(n),i=u.call(i,e),s.push(i.slice(1))}n=[];for(let a of s.slice().reverse())n.push(...a);return[i[0]].concat(n)})}build(t){Uy(t)&&(t=t[0]),t=t;let e;this.cells.forEach((n,o)=>{fi(`RNNCell_${o}`,()=>{n.build(t),Array.isArray(n.stateSize)?e=n.stateSize[0]:e=n.stateSize,t=[t[0],e]})}),this.built=!0}getConfig(){let t=super.getConfig(),e=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(e)};return Object.assign(Object.assign({},t),o)}static fromConfig(t,e,n={}){let o=[];for(let s of e.cells)o.push(wn(s,n));return new t({cells:o})}get trainableWeights(){if(!this.trainable)return[];let t=[];for(let e of this.cells)t.push(...e.trainableWeights);return t}get nonTrainableWeights(){let t=[];for(let e of this.cells)t.push(...e.nonTrainableWeights);if(!this.trainable){let e=[];for(let n of this.cells)e.push(...n.trainableWeights);return e.concat(t)}return t}getWeights(){let t=[];for(let e of this.cells)t.push(...e.weights);return _h(t)}setWeights(t){let e=[];for(let n of this.cells){let o=n.weights.length,s=t.splice(o);for(let i=0;i<n.weights.length;++i)e.push([n.weights[i],s[i]])}zm(e)}};jc.className="StackedRNNCells";Q.registerClass(jc);function El(r){let{ones:t,rate:e,training:n=!1,count:o=1,dropoutFunc:s}=r,i=()=>s!=null?s(t(),e):Wy(t(),e),a=()=>Ou(i,t,n);return!o||o<=1?De(a().clone()):Array(o).fill(void 0).map(a).map(l=>De(l.clone()))}var vJ=function(r,t){var e={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&t.indexOf(n)<0&&(e[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)t.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(e[n[o]]=r[n[o]]);return e};var Ab=class extends po{constructor(t){if(t.unroll)throw new _t("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(t.cell))throw new _t("It is not possible at the moment to stack convolutional cells.");super(t),this.inputSpec=[new Ce({ndim:5})]}call(t,e){return B(()=>{if(this.cell.dropoutMask!=null&&(Tt(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Tt(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),e&&e.constants)throw new z("ConvRNN2D cell does not support constants");let n=e==null?null:e.mask,o=e==null?null:e.training,s=e==null?null:e.initialState;return super.call(t,{mask:n,training:o,initialState:s})})}computeOutputShape(t){let e=this.computeSingleOutputShape(t);return this.returnSequences||(e=[e[0],...e.slice(2)]),this.returnState&&(e=[e,...Array(2).fill([t[0],...e.slice(-3)])]),e}getInitialState(t){return B(()=>{let{stateSize:e}=this.cell,n=t.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],i=ke(s);return Array.isArray(e)?Array(e.length).fill(i):[i]})}resetStates(t,e=!1){B(()=>{if(!this.stateful)throw new uo("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_=[ke(s)];else if(t==null)Tt(this.states_),this.keptStates!=null&&(Tt(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ke(s)):this.states_[0]=ke(s);else{if(Array.isArray(t)||(t=[t]),t.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${t.length} state value(s). Input received: ${t}`);e?this.keptStates.push(this.states_.slice()):Tt(this.states_);for(let a=0;a<this.states_.length;++a){let u=t[a],l=s;if(!y.arraysEqual(u.shape,l))throw new z(`State ${a} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${u.shape}`);this.states_[a]=u}}this.states_=this.states_.map(a=>De(a.clone()))})}computeSingleOutputShape(t){let{dataFormat:e,filters:n,kernelSize:o,padding:s,strides:i,dilationRate:a}=this.cell,u=e==="channelsFirst",l=t[u?3:2],c=t[u?4:3],p=Tn(l,o[0],s,i[0],a[0]),m=Tn(c,o[1],s,i[1],a[1]);return[...t.slice(0,2),...u?[n,p,m]:[p,m,n]]}};Ab.className="ConvRNN2D";var Xc=class extends _l{constructor(t){let{filters:e,kernelSize:n,strides:o,padding:s,dataFormat:i,dilationRate:a}=t;super(Object.assign(Object.assign({},t),{units:e})),this.filters=e,tr(this.filters,"filters"),this.kernelSize=zu(n,2,"kernelSize"),this.kernelSize.forEach(u=>tr(u,"kernelSize")),this.strides=zu(o||1,2,"strides"),this.strides.forEach(u=>tr(u,"strides")),this.padding=s||"valid",hn(this.padding),this.dataFormat=i||"channelsLast",Me(this.dataFormat),this.dilationRate=zu(a||1,2,"dilationRate"),this.dilationRate.forEach(u=>tr(u,"dilationRate"))}build(t){var e;t=Gt(t);let n=this.dataFormat==="channelsFirst"?1:t.length-1;if(t[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${t[n]}`);let o=t[n],s=4,i=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",i,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let a=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",a,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let u;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;u=new(e=class extends bn{apply(m,f){let d=l.apply([c]),h=ar([c]),g=l.apply([c*2]);return _m([d,h,g])}},e.className="CustomInit",e)}else u=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,u,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(t,e){return B(()=>{if(t.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${t.length}.`);let n=e.training||!1,o=t[0],s=t[1],i=t[2],a=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=El({ones:()=>wr(o),rate:this.dropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let u=this.dropoutMask,l=(nt,st,lt)=>!st||!st[lt]?nt:$(st[lt],nt),c=l(o,u,0),p=l(o,u,1),m=l(o,u,2),f=l(o,u,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=El({ones:()=>wr(s),rate:this.recurrentDropout,training:n,count:a,dropoutFunc:this.dropoutFunc}));let d=this.recurrentDropoutMask,h=l(s,d,0),g=l(s,d,1),x=l(s,d,2),b=l(s,d,3),w=3,[I,N,E,A]=hr(this.kernel.read(),a,w),[D,F,M,V]=this.useBias?hr(this.bias.read(),a):[null,null,null,null];c=this.inputConv(c,I,D,this.padding),p=this.inputConv(p,N,F,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,A,V,this.padding);let[G,W,q,H]=hr(this.recurrentKernel.read(),a,w);h=this.recurrentConv(h,G),g=this.recurrentConv(g,W),x=this.recurrentConv(x,q),b=this.recurrentConv(b,H);let j=this.recurrentActivation.apply(K(c,h)),Y=this.recurrentActivation.apply(K(p,g)),Z=K($(Y,i),$(j,this.activation.apply(K(m,x)))),et=$(this.recurrentActivation.apply(K(f,b)),this.activation.apply(Z));return[et,et,Z]})}getConfig(){let t=super.getConfig(),{units:e}=t,n=vJ(t,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign(Object.assign({},n),o)}inputConv(t,e,n,o){let s=Nn(t,e,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?yn(s,n,this.dataFormat):s}recurrentConv(t,e){return Nn(t,e,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Xc.className="ConvLSTM2DCell";Q.registerClass(Xc);var hf=class extends Ab{constructor(t){let e=new Xc(t);super(Object.assign(Object.assign({},t),{cell:e}))}static fromConfig(t,e){return new t(e)}};hf.className="ConvLSTM2D";Q.registerClass(hf);var Yc=class extends Et{constructor(t){super(t),this.rate=Math.max(Math.min(t.rate,1),0),this.noiseShape=t.noiseShape,this.seed=t.seed,this.supportsMasking=!0}getNoiseShape(t){if(this.noiseShape==null)return this.noiseShape;let e=t.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?e[o]:this.noiseShape[o]);return n}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(0<this.rate&&this.rate<1){let o=e.training==null?!1:e.training,s=this.getNoiseShape(n);return Ou(()=>Wy(n,this.rate,s,this.seed),()=>n,o)}return t})}getConfig(){let t={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},e=super.getConfig();return Object.assign(t,e),t}dispose(){return super.dispose()}};Yc.className="Dropout";Q.registerClass(Yc);var gf=class extends Yc{constructor(t){super(t),this.inputSpec=[{ndim:3}]}getNoiseShape(t){let e=t.shape;return[e[0],1,e[2]]}};gf.className="SpatialDropout1D";Q.registerClass(gf);var xf=class extends Et{constructor(t){if(super(t),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",t.batchInputShape==null&&t.inputShape==null&&t.inputDim!=null){let e=null;t.batchSize!=null&&(e=t.batchSize),this.batchInputShape=[e,t.inputDim]}this.units=t.units,tr(this.units,"units"),this.activation=xi(t.activation),t.useBias!=null&&(this.useBias=t.useBias),this.kernelInitializer=xe(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=xe(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ge(t.kernelConstraint),this.biasConstraint=Ge(t.biasConstraint),this.kernelRegularizer=ve(t.kernelRegularizer),this.biasRegularizer=ve(t.biasRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(t){t=Gt(t);let e=t[t.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[e,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:e}}],this.built=!0}computeOutputShape(t){t=Gt(t);let e=t.slice();return e[e.length-1]=this.units,e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=Oy(this.activation.getClassName()),s;return o!=null?s=Do(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=Do(n,this.kernel.read()),this.bias!=null&&(s=yn(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let t={units:this.units,activation:gi(this.activation),useBias:this.useBias,kernelInitializer:Te(this.kernelInitializer),biasInitializer:Te(this.biasInitializer),kernelRegularizer:fe(this.kernelRegularizer),biasRegularizer:fe(this.biasRegularizer),activityRegularizer:fe(this.activityRegularizer),kernelConstraint:Ve(this.kernelConstraint),biasConstraint:Ve(this.biasConstraint)},e=super.getConfig();return Object.assign(t,e),t}};xf.className="Dense";Q.registerClass(xf);var yf=class extends Et{constructor(t){t=t||{},super(t),this.inputSpec=[{minNDim:3}],this.dataFormat=t.dataFormat}computeOutputShape(t){t=Gt(t);for(let e of t.slice(1))if(e==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${t.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[t[0],Ao(t,1)]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s<n.rank;++s)o.push(s);o.push(1),n=Vt(n,o)}return lR(n)})}getConfig(){let t={};this.dataFormat!=null&&(t.dataFormat=this.dataFormat);let e=super.getConfig();return Object.assign(t,e),t}};yf.className="Flatten";Q.registerClass(yf);var bf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.activation=xi(t.activation)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.activation.apply(n)})}getConfig(){let t={activation:gi(this.activation)},e=super.getConfig();return Object.assign(t,e),t}};bf.className="Activation";Q.registerClass(bf);var wf=class extends Et{constructor(t){super(t),this.n=t.n,this.inputSpec=[{ndim:2}]}computeOutputShape(t){return[t[0],this.n,t[1]]}call(t,e){return B(()=>(t=St(t),iR(t,this.n)))}getConfig(){let t={n:this.n},e=super.getConfig();return Object.assign(t,e),t}};wf.className="RepeatVector";Q.registerClass(wf);var If=class extends Et{constructor(t){super(t),this.targetShape=t.targetShape;for(let e=0;e<this.targetShape.length;++e)this.isUnknown(this.targetShape[e])&&(this.targetShape[e]=null)}isUnknown(t){return t<0||t==null}fixUnknownDimension(t,e){let n="Total size of new array must be unchanged.",o=e.slice(),s=1,i=null;for(let u=0;u<o.length;++u){let l=o[u];if(this.isUnknown(l))if(i===null)i=u;else throw new z("Can only specifiy one unknown dimension.");else s*=l}let a=Ao(t);if(i!==null){if(s===0||a%s!==0)throw new z(n);o[i]=a/s}else if(a!==s)throw new z(n);return o}computeOutputShape(t){let e=!1;for(let n=0;n<t.length;++n)if(this.isUnknown(t[n])){e=!0;break}return e?t.slice(0,1).concat(this.targetShape):t.slice(0,1).concat(this.fixUnknownDimension(t.slice(1),this.targetShape))}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return R(n,s)})}getConfig(){let t={targetShape:this.targetShape},e=super.getConfig();return Object.assign(t,e),t}};If.className="Reshape";Q.registerClass(If);var Cf=class extends Et{constructor(t){if(super(t),t.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(t.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${t.dims} instead.`);let e=gn(1,t.dims.length+1);if(!y.arraysEqual(t.dims.slice().sort(),e))throw new Error("Invalid permutation `dims`: "+JSON.stringify(t.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=t.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ce({ndim:this.dims.length+1})]}computeOutputShape(t){t=Gt(t);let e=t.slice();return this.dims.forEach((n,o)=>{e[o+1]=t[n]}),e}call(t,e){return Vt(St(t),this.dimsIncludingBatch)}getConfig(){let t={dims:this.dims},e=super.getConfig();return Object.assign(t,e),t}};Cf.className="Permute";Q.registerClass(Cf);var vf=class extends Et{constructor(t){super(t==null?{}:t),this.supportsMasking=!0,t!=null?this.maskValue=t.maskValue==null?0:t.maskValue:this.maskValue=0}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={maskValue:this.maskValue};return Object.assign(e,t),e}computeMask(t,e){let n=St(t);return cc(ci(n,this.maskValue),-1)}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t),i=cc(ci(n,this.maskValue),-1,!0);return $(n,J(i,n.dtype))})}};vf.className="Masking";Q.registerClass(vf);var Sf=class extends Et{constructor(t){if(super(t),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",t.batchInputShape==null&&t.inputShape==null){let e=null;t.batchSize!=null&&(e=t.batchSize),t.inputLength==null?this.batchInputShape=[e,null]:this.batchInputShape=[e].concat(ue(t.inputLength))}this.inputDim=t.inputDim,tr(this.inputDim,"inputDim"),this.outputDim=t.outputDim,tr(this.outputDim,"outputDim"),this.embeddingsInitializer=xe(t.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ve(t.embeddingsRegularizer),this.activityRegularizer=ve(t.activityRegularizer),this.embeddingsConstraint=Ge(t.embeddingsConstraint),this.maskZero=t.maskZero,this.supportsMasking=t.maskZero,this.inputLength=t.inputLength}build(t){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(t){}computeMask(t,e){return B(()=>this.maskZero?(t=St(t),ci(t,vt(t))):null)}computeOutputShape(t){if(t=Gt(t),this.inputLength==null)return[...t,this.outputDim];let e=ue(this.inputLength);if(e.length!==t.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);{let n=0;for(let o=0;o<e.length;++o){let s=e[o],i=t[o+1];if(s!=null&&i!=null&&s!==i)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${t}`);s==null&&(e[n]=i),n++}}return[t[0],...e,this.outputDim]}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);n.dtype!=="int32"&&(n=rn(n,"int32"));let o=Gy(this.embeddings.read(),R(n,[n.size]));return R(o,Gt(this.computeOutputShape(n.shape)))})}getConfig(){let t={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Te(this.embeddingsInitializer),embeddingsRegularizer:fe(this.embeddingsRegularizer),activityRegularizer:fe(this.activityRegularizer),embeddingsConstraint:Ve(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},e=super.getConfig();return Object.assign(t,e),t}};Sf.className="Embedding";Q.registerClass(Sf);var Al=class extends Et{constructor(t){super(t||{}),this.supportsMasking=!0}mergeFunction(t){throw new _t}computeElementwiseOpOutputShape(t,e){if(t==null||e==null)return null;if(t.length<e.length)return this.computeElementwiseOpOutputShape(e,t);if(e.length===0)return t;let n=t.slice(0,t.length-e.length);for(let o=0;o<e.length;++o){let s=t[t.length-e.length+o],i=e[o];if(s==null||i==null||s<0||i<0)n.push(null);else if(s===1)n.push(i);else if(i===1)n.push(s);else{if(s!==i)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(t)+" "+JSON.stringify(e));n.push(s)}}return n}build(t){if(Array.isArray(t)&&!Array.isArray(t[0])&&(t=[Gt(t)]),t=t,t.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${t.length} input(s).`);let e=[];for(let s of t)s!=null&&s[0]!==null&&e.push(s[0]);if(e=Eo(e),e.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(t)}.`);let n=t[0]==null?null:t[0].slice(1);for(let s=1;s<t.length;++s){let i=t[s]==null?null:t[s].slice(1);n=this.computeElementwiseOpOutputShape(n,i)}let o=t.map(s=>s.length);t.indexOf(null)===-1&&Eo(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(t,e){return B(()=>{if(t=t,this.reshapeRequired){let n=[],o=t.map(s=>s.rank);if(o.indexOf(null)===-1){let s=di(o);for(let i of t){let a=i.rank;for(let u=0;u<s-a;++u)i=Sl(i,1);n.push(i)}return this.mergeFunction(n)}else{let s=!1;for(let u of t){let l=u.rank;if(l==null){let c=u.shape,p=c[0],m=c.slice(1).concat([p]),f=R(u,[p].concat(Ao(c.slice(1))));f=Vt(f,[1,0]),f=R(f,m),n.push(f),s=!0}else if(l>1){let c=gn(1,l).concat([0]);n.push(Vt(u,c)),s=!0}else n.push(u)}let i=this.mergeFunction(n),a=i.rank;if(s){if(a==null){let u=i.shape,l=u.length,c=u[l-1],p=[c].concat(u.slice(0,u.length-1));i=R(Vt(R(i,[-1,c]),[1,0]),p)}else if(a>1){let u=[a-1].concat(gn(0,a-1));i=Vt(i,u)}}return i}}else return this.mergeFunction(t)})}computeOutputShape(t){t=t;let e;t[0]==null?e=null:e=t[0].slice(1);for(let o=1;o<t.length;++o){let s=t[o]==null?null:t[o].slice(1);e=this.computeElementwiseOpOutputShape(e,s)}let n=[];for(let o of t)o!=null&&o[0]!==null&&n.push(o[0]);return n=Eo(n),n.length===1?e=n.concat(e):e=[null].concat(e),e}computeMask(t,e){return B(()=>{if(e==null)return null;if(!Array.isArray(e))throw new z("`mask` should be an Array");if(!Array.isArray(t))throw new z("`inputs` should be an Array");if(e.length!==t.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${t.length} vs ${e.length})`);if(e.every(o=>o==null))return null;e=e.map(o=>o==null?o:je(o,0));let n=e[0];for(let o=1;o<e.length-1;++o)n=Fr(n,e[o]);return n})}},Nf=class extends Al{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0].clone();for(let n=1;n<t.length;++n)e=K(e,t[n]);return e})}};Nf.className="Add";Q.registerClass(Nf);var kf=class extends Al{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0].clone();for(let n=1;n<t.length;++n)e=$(e,t[n]);return e})}};kf.className="Multiply";Q.registerClass(kf);var Tf=class extends Al{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0].clone();for(let n=1;n<t.length;++n)e=K(e,t[n]);return $(1/t.length,e)})}};Tf.className="Average";Q.registerClass(Tf);var _f=class extends Al{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0];for(let n=1;n<t.length;++n)e=kn(e,t[n]);return e})}};_f.className="Maximum";Q.registerClass(_f);var Ef=class extends Al{constructor(t){super(t)}mergeFunction(t){return B(()=>{let e=t[0];for(let n=1;n<t.length;++n)e=lo(e,t[n]);return e})}};Ef.className="Minimum";Q.registerClass(Ef);var Af=class extends Al{constructor(t){super(t),this.DEFAULT_AXIS=-1,t==null&&(t={}),this.axis=t.axis==null?this.DEFAULT_AXIS:t.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){if(!(Array.isArray(t)&&Array.isArray(t[0]))||t.length===1)throw new z("A `Concatenate` layer should be called on a list of at least 2 inputs");t=t;let e=!0;for(let o of t)if(o!=null){e=!1;break}if(e)return;let n=[];for(let o=0;o<t.length;++o){let s=t[o].slice();s.splice(this.axis,1);let i=!1;for(let a of n)if(y.arraysEqual(a,s)){i=!0;break}i||n.push(s)}if(n.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(t))}mergeFunction(t){return B(()=>_m(t,this.axis))}computeOutputShape(t){if(!(Array.isArray(t)&&Array.isArray(t[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let e=t,n=e[0].slice(),o=this.axis<0?n.length+this.axis:this.axis;for(let s of e.slice(1)){if(n[o]==null||s[o]==null){n[o]=null;break}n[o]+=s[o]}return n}computeMask(t,e){if(e==null)return null;if(!Array.isArray(e))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(t))throw new z("`inputs` should be an array for Concatenate");if(e.length!==t.length)throw new z(`Mismatch in the length of mask (${e.length}) and the legnth of inputs (${t.length})`);return B(()=>{let n=!0;if(e.forEach(i=>{if(i!=null){n=!1;return}}),n)return null;let o=[];for(let i=0;i<t.length;++i)e[i]==null?o.push(J(wr(t[i]),"bool")):e[i].rank<t[i].rank?o.push(je(e[i],-1)):o.push(e[i]);let s=ie(o,this.axis);return tm(s,-1,!1)})}getConfig(){let t={axis:this.axis},e=super.getConfig();return Object.assign(t,e),t}};Af.className="Concatenate";Q.registerClass(Af);function Lh(r,t){for(;r<0;)r+=t;return r}function SJ(r,t,e){if(r.shape.length>3||t.shape.length>3)throw new _t("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof e=="number"&&(e=[e,e]),r.dtype==="complex64"||t.dtype==="complex64")throw new _t("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=t.shape.length;e==null&&(e=[n-1,o-2]);let s=e;return B(()=>{let i;if(n>o){i=n-o;let u=[];for(let l=0;l<i;++l)u.push(1);t=R(t,t.shape.concat(u))}else if(o>n){i=o-n;let u=[];for(let l=0;l<i;++l)u.push(1);r=R(r,r.shape.concat(u))}else i=0;let a;if(r.shape.length===2&&t.shape.length===2)s[0]===s[1]?a=mt($(r,t),s[0]):a=mt($(Vt(r,[1,0]),t),s[1]);else{let u=s[0]!==r.shape.length-1,l=s[1]===t.shape.length-1;a=Bt(r,t,u,l)}if(i>0){let u;n>o?u=n+o-3:u=n-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);a=Wn(a,l)}return a.shape.length===1&&(a=je(a,1)),a})}var Df=class extends Al{constructor(t){super(t),this.axes=t.axes,this.normalize=t.normalize==null?!1:t.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0],n=t[1];if(e.length>3||n.length>3)throw new _t("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);if(e[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${e[o[0]]} !== ${n[o[1]]}`)}mergeFunction(t){if(t.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${t.length} input(s).`);let e=t[0],n=t[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,i)=>Lh(s,t[i].shape.length)):o=[Lh(this.axes,e.shape.length),Lh(this.axes,n.shape.length)],this.normalize&&(e=Eh(e,o[0]),n=Eh(n,o[1])),SJ(e,n,o)}interpretAxes(t,e){let n;return Array.isArray(this.axes)?n=this.axes:n=[Lh(this.axes,t.length),Lh(this.axes,e.length)],n}computeOutputShape(t){y.assert(Array.isArray(t)&&t.length===2&&Array.isArray(t[0])&&Array.isArray(t[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let e=t[0].slice(),n=t[1].slice();if(e.length>3||n.length>3)throw new _t("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(e,n);e.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=e.concat(n);return s.length===1&&s.push(1),s}computeMask(t,e){return null}getConfig(){let t={axes:this.axes,normalize:this.normalize},e=super.getConfig();return Object.assign(t,e),t}};Df.className="Dot";Q.registerClass(Df);var $f=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.stddev=t.stddev}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={stddev:this.stddev};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return Ou(()=>K(Em(n.shape,0,this.stddev),n),()=>n,e.training||!1)})}};$f.className="GaussianNoise";Q.registerClass($f);var Rf=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{this.invokeCallHook(t,e);let n=St(t);return this.rate>0&&this.rate<1?Ou(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return $(n,Em(n.shape,1,s))},()=>n,e.training||!1):n})}};Rf.className="GaussianDropout";Q.registerClass(Rf);var Ff=class extends Et{constructor(t){super(t),this.supportsMasking=!0,this.rate=t.rate,this.noiseShape=t.noiseShape}_getNoiseShape(t){return this.noiseShape||St(t).shape}computeOutputShape(t){return t}getConfig(){let t=super.getConfig(),e={rate:this.rate};return Object.assign(e,t),e}call(t,e){return B(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(t);return Ou(()=>{let s=St(t),u=-1.6732632423543772*1.0507009873554805,l=cn(Gn(n),this.rate);l=rn(l,"float32");let c=((1-this.rate)*(1+this.rate*u**2))**-.5,p=-c*u*this.rate,m=K($(s,l),$(K(l,-1),u));return K($(m,c),p)},()=>St(t),e.training||!1)}return t})}};Ff.className="AlphaDropout";Q.registerClass(Ff);function zh(r,t,e,n,o,s=.001){let i;if(r.rank===2)i=Cx(r,t,e,n,o,s);else if(r.rank===3)i=vx(r,t,e,n,o,s);else if(r.rank===4)i=Sx(r,t,e,n,o,s);else throw new _t(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return i}function NJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance;return[zh(r,i,a,e,t,o),i,a]})}function kJ(r,t,e,n,o=.001){return B(()=>{let s=dc(r,n),i=s.mean,a=s.variance,u=[];for(let d of gn(0,r.rank))n.indexOf(d)!==-1?u.push(1):u.push(r.shape[d]);let l=R(i,u),c=R(a,u),p=t==null?null:R(t,u),m=e==null?null:R(e,u);return[zh(r,l,c,m,p,o),i,a]})}function TJ(r,t,e,n,o=.001){return y.arraysEqual(n.slice().sort(),gn(0,r.rank-1))?NJ(r,t,e,n,o):kJ(r,t,e,n,o)}var Of=class extends Et{constructor(t){t==null&&(t={}),super(t),this.supportsMasking=!0,this.axis=t.axis==null?-1:t.axis,this.momentum=t.momentum==null?.99:t.momentum,this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=xe(t.betaInitializer||"zeros"),this.gammaInitializer=xe(t.gammaInitializer||"ones"),this.movingMeanInitializer=xe(t.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=xe(t.movingVarianceInitializer||"ones"),this.betaConstraint=Ge(t.betaConstraint),this.gammaConstraint=Ge(t.gammaConstraint),this.betaRegularizer=ve(t.betaRegularizer),this.gammaRegularizer=ve(t.gammaRegularizer)}build(t){t=Gt(t);let e=this.axis>=0?this.axis:this.axis+t.length,n=t[e];if(n==null)throw new z(`Axis ${e} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(t)}.`);this.inputSpec=[new Ce({ndim:t.length,axes:{[e]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(t,e){return B(()=>{let n=e.training==null?!1:e.training,o=St(t),s=o.shape,i=s.length,a=gn(0,i),u=this.axis>=0?this.axis:this.axis+i;a.splice(u,1);let l=To(1,i);l[u]=s[u];let c=a.slice();c.sort();let p=!y.arraysEqual(c,gn(0,i).slice(0,i-1)),m=()=>{if(p){let b=R(this.movingMean.read(),l),w=R(this.movingVariance.read(),l),I=this.center?R(this.beta.read(),l):null,N=this.scale?R(this.gamma.read(),l):null;return zh(o,b,w,I,N,this.epsilon)}else return zh(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=TJ(o,this.gamma.read(),this.beta.read(),a,this.epsilon),g=(b,w,I)=>{B(()=>{let N=1-I,E=b.read(),A=$(at(E,w),N);b.write(at(E,A))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let t={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),movingMeanInitializer:Te(this.movingMeanInitializer),movingVarianceInitializer:Te(this.movingVarianceInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer),betaConstraint:Ve(this.betaConstraint),gammaConstraint:Ve(this.gammaConstraint)},e=super.getConfig();return Object.assign(t,e),t}};Of.className="BatchNormalization";Q.registerClass(Of);var Mf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.axis=t.axis==null?-1:t.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=t.epsilon==null?.001:t.epsilon,this.center=t.center==null?!0:t.center,this.scale=t.scale==null?!0:t.scale,this.betaInitializer=xe(t.betaInitializer||"zeros"),this.gammaInitializer=xe(t.gammaInitializer||"ones"),this.betaRegularizer=ve(t.betaRegularizer),this.gammaRegularizer=ve(t.gammaRegularizer),this.supportsMasking=!0}build(t){t=Gt(t);let e=t.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=e);for(let s of this.axis)if(s<0||s>=e)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==Eo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>t[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(t,e){let n=St(t),o=n.shape,s=o.length;return B(()=>{let{mean:a,variance:u}=dc(n,this.axis,!0),l=To(1,s);for(let h of this.axis)l[h]=o[h];let c=h=>h!=null&&h.shape.length!==s?R(h,l):h,p=this.scale?c(this.gamma.read()):null,m=this.center?c(this.beta.read()):null,f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(o[h]),d.push(1)):(f.push(1),d.push(o[h]));return a=Rr(a,f),u=Rr(u,f),p!=null&&(p=Rr(p,d)),m!=null&&(m=Rr(m,d)),zh(n,a,u,m,p,this.epsilon)})}getConfig(){let t={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Te(this.betaInitializer),gammaInitializer:Te(this.gammaInitializer),betaRegularizer:fe(this.betaRegularizer),gammaRegularizer:fe(this.gammaRegularizer)},e=super.getConfig();return Object.assign(t,e),t}};Mf.className="LayerNormalization";Q.registerClass(Mf);function _J(r,t,e){return B(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(e==null&&(e=xn()),e!=="channelsLast"&&e!=="channelsFirst")throw new z(`Unknown data format: ${e}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return e==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],mn(r,n)})}var Pf=class extends Et{constructor(t){if(t==null&&(t={}),super(t),this.dataFormat=t.dataFormat==null?xn():t.dataFormat,t.padding==null)this.padding=[[1,1],[1,1]];else if(typeof t.padding=="number")this.padding=[[t.padding,t.padding],[t.padding,t.padding]];else{if(t.padding=t.padding,t.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${t.padding.length} array.`);let e,n;if(typeof t.padding[0]=="number")e=[t.padding[0],t.padding[0]],n=[t.padding[1],t.padding[1]];else{if(t.padding=t.padding,t.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${t.padding[0].length} array.`);if(e=t.padding[0],t.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${t.padding[1].length} array.`);n=t.padding[1]}this.padding=[e,n]}this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e,n;return this.dataFormat==="channelsFirst"?(t[2]!=null&&t[2]>=0?e=t[2]+this.padding[0][0]+this.padding[0][1]:e=null,t[3]!=null&&t[3]>=0?n=t[3]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],t[1],e,n]):(t[1]!=null&&t[1]>=0?e=t[1]+this.padding[0][0]+this.padding[0][1]:e=null,t[2]!=null&&t[2]>=0?n=t[2]+this.padding[1][0]+this.padding[1][1]:n=null,[t[0],e,n,t[3]])}call(t,e){return B(()=>_J(St(t),this.padding,this.dataFormat))}getConfig(){let t={padding:this.padding,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}};Pf.className="ZeroPadding2D";Q.registerClass(Pf);function Mb(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=Ph(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=ku(r,t,e,a):i=xu(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,3,1,2])),i})}function YR(r,t,e,n,o,s){return B(()=>{Me(o),LN(s),hn(n),e==null&&(e=[1,1,1]),n==null&&(n="valid"),o==null&&(o=xn()),s==null&&(s="max"),r=ek(r,o);let i,a=n==="same"?"same":"valid";return s==="max"?i=Xx(r,t,e,a):i=Ix(r,t,e,a),o==="channelsFirst"&&(i=Vt(i,[0,4,1,2,3])),i})}var Db=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=2),super(t),typeof t.poolSize=="number")this.poolSize=[t.poolSize];else if(Array.isArray(t.poolSize)&&t.poolSize.length===1&&typeof t.poolSize[0]=="number")this.poolSize=t.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.poolSize)}`);if(tr(this.poolSize,"poolSize"),t.strides==null)this.strides=this.poolSize;else if(typeof t.strides=="number")this.strides=[t.strides];else if(Array.isArray(t.strides)&&t.strides.length===1&&typeof t.strides[0]=="number")this.strides=t.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(t.strides)}`);tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,hn(this.padding),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){t=Gt(t);let e=Tn(t[1],this.poolSize[0],this.padding,this.strides[0]);return[t[0],e,t[2]]}call(t,e){return B(()=>{this.invokeCallHook(t,e),t=Sl(St(t),2);let n=this.poolingFunction(St(t),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Wn(n,[2])})}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides},e=super.getConfig();return Object.assign(t,e),t}},Lf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"max")}};Lf.className="MaxPooling1D";Q.registerClass(Lf);var zf=class extends Db{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"avg")}};zf.className="AveragePooling1D";Q.registerClass(zf);var $b=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n]:[t[0],e,n,t[3]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Bf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"max")}};Bf.className="MaxPooling2D";Q.registerClass(Bf);var Vf=class extends $b{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),Mb(t,e,n,o,s,"avg")}};Vf.className="AveragePooling2D";Q.registerClass(Vf);var Rb=class extends Et{constructor(t){if(t.poolSize==null&&(t.poolSize=[2,2,2]),super(t),this.poolSize=Array.isArray(t.poolSize)?t.poolSize:[t.poolSize,t.poolSize,t.poolSize],t.strides==null)this.strides=this.poolSize;else if(Array.isArray(t.strides)){if(t.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${t.strides.length}.`);this.strides=t.strides}else this.strides=[t.strides,t.strides,t.strides];tr(this.poolSize,"poolSize"),tr(this.strides,"strides"),this.padding=t.padding==null?"valid":t.padding,this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),hn(this.padding),this.inputSpec=[new Ce({ndim:5})]}computeOutputShape(t){t=Gt(t);let e=this.dataFormat==="channelsFirst"?t[2]:t[1],n=this.dataFormat==="channelsFirst"?t[3]:t[2],o=this.dataFormat==="channelsFirst"?t[4]:t[3];return e=Tn(e,this.poolSize[0],this.padding,this.strides[0]),n=Tn(n,this.poolSize[1],this.padding,this.strides[1]),o=Tn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[t[0],t[1],e,n,o]:[t[0],e,n,o,t[4]]}call(t,e){return B(()=>(this.invokeCallHook(t,e),this.poolingFunction(St(t),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let t={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},Gf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,"max")}};Gf.className="MaxPooling3D";Q.registerClass(Gf);var Wf=class extends Rb{constructor(t){super(t)}poolingFunction(t,e,n,o,s){return Me(s),hn(o),YR(t,e,n,o,s,"avg")}};Wf.className="AveragePooling3D";Q.registerClass(Wf);var Fb=class extends Et{constructor(t){super(t),this.inputSpec=[new Ce({ndim:3})]}computeOutputShape(t){return[t[0],t[2]]}call(t,e){throw new _t}},Uf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Ne(n,1)})}};Uf.className="GlobalAveragePooling1D";Q.registerClass(Uf);var Hf=class extends Fb{constructor(t){super(t||{})}call(t,e){return B(()=>{let n=St(t);return Sr(n,1)})}};Hf.className="GlobalMaxPooling1D";Q.registerClass(Hf);var Ob=class extends Et{constructor(t){super(t),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Me(this.dataFormat),this.inputSpec=[new Ce({ndim:4})]}computeOutputShape(t){return t=t,this.dataFormat==="channelsLast"?[t[0],t[3]]:[t[0],t[1]]}call(t,e){throw new _t}getConfig(){let t={dataFormat:this.dataFormat},e=super.getConfig();return Object.assign(t,e),t}},qf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Ne(n,[1,2]):Ne(n,[2,3])})}};qf.className="GlobalAveragePooling2D";Q.registerClass(qf);var Kf=class extends Ob{call(t,e){return B(()=>{let n=St(t);return this.dataFormat==="channelsLast"?Sr(n,[1,2]):Sr(n,[2,3])})}};Kf.className="GlobalMaxPooling2D";Q.registerClass(Kf);var Pb=class extends Et{constructor(t){super(t),this.layer=t.layer}build(t){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(t){this.layer!=null&&(this.layer.trainable=t)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(t){this.layer.setWeights(t)}getConfig(){let t={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},e=super.getConfig();return Object.assign(t,e),t}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(t)}static fromConfig(t,e,n={}){let o=e.layer,s=wn(o,n);delete e.layer;let i={layer:s};return Object.assign(i,e),new t(i)}},jf=class extends Pb{constructor(t){super(t),this.supportsMasking=!0}build(t){if(t=Gt(t),t.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(t)}`);this.inputSpec=[{shape:t}];let e=[t[0]].concat(t.slice(2));this.layer.built||(this.layer.build(e),this.layer.built=!0),super.build(t)}computeOutputShape(t){t=Gt(t);let e=[t[0]].concat(t.slice(2)),n=this.layer.computeOutputShape(e),o=t[1];return[n[0],o].concat(n.slice(1))}call(t,e){return B(()=>(t=St(t),nk((i,a)=>[St(this.layer.call(i,e)),[]],t,[],!1,null,null,!1,!0)[1]))}};jf.className="TimeDistributed";Q.registerClass(jf);function EJ(r){da(eR,"BidirectionalMergeMode",r)}var AJ="concat",Xf=class extends Pb{constructor(t){super(t);let e=t.layer.getConfig(),n={};n.className=t.layer.getClassName(),n.config=e,this.forwardLayer=wn(n),e.goBackwards=e.goBackwards!==!0;let o={};if(o.className=t.layer.getClassName(),o.config=e,this.backwardLayer=wn(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=t.mergeMode===void 0?AJ:t.mergeMode,EJ(this.mergeMode),t.weights)throw new _t("weights support is not implemented for Bidirectional layer yet.");this._stateful=t.layer.stateful,this.returnSequences=t.layer.returnSequences,this.returnState=t.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=t.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(t){this._trainable=t,this.forwardLayer!=null&&(this.forwardLayer.trainable=t),this.backwardLayer!=null&&(this.backwardLayer.trainable=t)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(t){let e=t.length,n=Math.floor(e/2);this.forwardLayer.setWeights(t.slice(0,n)),this.backwardLayer.setWeights(t.slice(n))}computeOutputShape(t){let e=this.forwardLayer.computeOutputShape(t);Array.isArray(e)&&Array.isArray(e[0])||(e=[e]),e=e;let n,o,s;return this.returnState&&(s=e.slice(1)),n=e[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):kr(o)}apply(t,e){let n=e==null?null:e.initialState,o=e==null?null:e.constants;e==null&&(e={});let s=rk(t,n,o,this.numConstants);if(t=s.inputs,n=s.initialState,o=s.constants,Array.isArray(t)&&(n=t.slice(1),t=t[0]),(n==null||n.length===0)&&o==null)return super.apply(t,e);let i=[],a=[];if(n!=null){let l=n.length;if(l%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");e.initialState=n,i.push(...n);let c=n.map(p=>new Ce({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),a.push(...c)}if(o!=null)throw new _t("Support for constants in Bidirectional layers is not implemented yet.");let u=i[0]instanceof nn;for(let l of i)if(l instanceof nn!==u)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(u){let l=[t].concat(i),c=this.inputSpec.concat(a),p=this.inputSpec;this.inputSpec=c;let m=super.apply(l,e);return this.inputSpec=p,m}else return super.apply(t,e)}call(t,e){return B(()=>{let n=e.initialState,o,s;if(n==null)o=this.forwardLayer.call(t,e),s=this.backwardLayer.call(t,e);else{let u=n.slice(0,n.length/2),l=n.slice(n.length/2);o=this.forwardLayer.call(t,Object.assign(e,{initialState:u})),s=this.backwardLayer.call(t,Object.assign(e,{initialState:l}))}let i;this.returnState&&(Array.isArray(o)&&(i=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=dr(s,1));let a;return this.mergeMode==="concat"?a=_m([o,s]):this.mergeMode==="sum"?a=K(o,s):this.mergeMode==="ave"?a=$(.5,K(o,s)):this.mergeMode==="mul"?a=$(o,s):this.mergeMode==null&&(a=[o,s]),this.returnState?this.mergeMode==null?a.concat(i):[a].concat(i):a})}resetStates(t){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(t){fi(this.forwardLayer.name,()=>{this.forwardLayer.build(t)}),fi(this.backwardLayer.name,()=>{this.backwardLayer.build(t)}),this.built=!0}computeMask(t,e){Array.isArray(e)&&(e=e[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[e,e]:n=e:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(i=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(t){super.setFastWeightInitDuringBuild(t),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(t),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(t)}getConfig(){let t={mergeMode:this.mergeMode},e=super.getConfig();return Object.assign(t,e),t}static fromConfig(t,e){let n=wn(e.layer);if(delete e.layer,e.numConstants!=null)throw new _t("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let o=e;return o.layer=n,new t(o)}};Xf.className="Bidirectional";Q.registerClass(Xf);var Yf=class extends Et{constructor(t){super(t),this.scale=t.scale,t.offset?this.offset=t.offset:this.offset=0}getConfig(){let t={scale:this.scale,offset:this.offset},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>(t=St(t),t.dtype!=="float32"&&(t=rn(t,"float32")),K($(t,this.scale),this.offset)))}};Yf.className="Rescaling";Q.registerClass(Yf);var{resizeBilinear:DJ,cropAndResize:$J}=fn,Zf=class extends Et{constructor(t){super(t),this.height=t.height,this.width=t.width}centerCrop(t,e,n,o,s,i,a,u){return B(()=>{let l,c=!1,p=e/i,m=n/a,f=(o+e)/i,d=(s+n)/a,h=[p,m,f,d],g=[];t.rank===3?(c=!0,l=Fe([t])):l=t;for(let N=0;N<l.shape[0];N++)g.push(h);let x=ir(g,[g.length,4]),b=pa(0,g.length,1,"int32"),I=$J(l,x,b,[o,s],"nearest");return c?rn(St(gr(I)),u):rn(I,u)})}upsize(t,e,n,o){return B(()=>{let s=DJ(t,[e,n]);return rn(s,o)})}call(t,e){return B(()=>{let n=St(t),o=n.dtype,s=n.shape,i=s[s.length-3],a=s[s.length-2],u=0;i!==this.height&&(u=Math.floor((i-this.height)/2));let l=0;return a!==this.width&&(l=Math.floor((a-this.width)/2),l===0&&(l=1)),u>=0&&l>=0?this.centerCrop(n,u,l,this.height,this.width,i,a,o):this.upsize(t,this.height,this.width,o)})}getConfig(){let t={height:this.height,width:this.width},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t.length-3,n=t.length-2;return t[e]=this.height,t[n]=this.width,t}};Zf.className="CenterCrop";Q.registerClass(Zf);function ZR(r,t,e,n){let o=St(r);if(o.dtype!=="int32"&&(o=rn(o,"int32")),t==="int")return o;let s=o.shape;if(o.rank===0&&(o=je(o,-1)),t==="oneHot"&&o.shape[o.shape.length-1]!==1&&(o=je(o,-1)),o.rank>2)throw new z(`When outputMode is not int, maximum output rank is 2 Received outputMode ${t} and input shape ${s} which would result in output rank ${o.rank}.`);let i=["multiHot","oneHot"].includes(t),a=o,u;if(typeof n!="undefined"&&t==="count"?u=mh(a,n,e,i):u=mh(a,[],e,i),t!=="tfIdf")return u;if(n)return $(u,n);throw new z("When outputMode is 'tfIdf', weights must be provided.")}var Jf=class extends Et{constructor(t){super(t),this.numTokens=t.numTokens,t.outputMode?this.outputMode=t.outputMode:this.outputMode="multiHot"}getConfig(){let t={numTokens:this.numTokens,outputMode:this.outputMode},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){return t=Gt(t),t==null?[this.numTokens]:this.outputMode==="oneHot"&&t[t.length-1]!==1?(t.push(this.numTokens),t):(t[t.length-1]=this.numTokens,t)}call(t,e){return B(()=>{t=St(t),t.dtype!=="int32"&&(t=rn(t,"int32"));let n;if(typeof e.countWeights!="undefined"){if(this.outputMode!=="count")throw new z(`countWeights is not used when outputMode !== count.
              Received countWeights=${e.countWeights}`);n=St(e.countWeights)}let o=Sr(t),s=gl(t),i=Re(this.numTokens,o).bufferSync().get(0),a=cn(s,0).bufferSync().get(0);if(!(i&&a))throw new z(`Input values must be between 0 < values <= numTokens with numTokens=${this.numTokens}`);return ZR(t,this.outputMode,this.numTokens,n)})}};Jf.className="CategoryEncoding";Q.registerClass(Jf);var FJ=["bilinear","nearest"],JR=new Set(FJ),Qf=class extends Et{constructor(t){if(super(t),this.height=t.height,this.width=t.width,t.interpolation)if(JR.has(t.interpolation))this.interpolation=t.interpolation;else throw new z(`Invalid interpolation parameter: ${t.interpolation} is not implemented`);else this.interpolation="bilinear";this.cropToAspectRatio=!!t.cropToAspectRatio}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.height,this.width,e]}getConfig(){let t={height:this.height,width:this.width,interpolation:this.interpolation,cropToAspectRatio:this.cropToAspectRatio},e=super.getConfig();return Object.assign(t,e),t}call(t,e){return B(()=>{let n=[this.height,this.width];if(this.interpolation==="bilinear")return fn.resizeBilinear(t,n,!this.cropToAspectRatio);if(this.interpolation==="nearest")return fn.resizeNearestNeighbor(t,n,!this.cropToAspectRatio);throw new Error(`Interpolation is ${this.interpolation} but only ${[...JR]} are supported`)})}};Qf.className="Resizing";Q.registerClass(Qf);var Bh=class{constructor(t){this.seed=t}next(){if(this.seed!==void 0)return this.seed++}};Bh.className="RandomSeed";var Vh=class extends Et{constructor(t){super(t),this.randomGenerator=new Bh(t.seed)}getConfig(){let t={seed:this.randomGenerator.seed},e=super.getConfig();return Object.assign(t,e),t}};Vh.className="BaseRandomLayer";var OJ=["bilinear","nearest"],QR=new Set(OJ),td=class extends Vh{constructor(t){super(t);let{factor:e,interpolation:n="bilinear"}=t;if(this.factor=e,Array.isArray(this.factor)&&this.factor.length===2)this.widthLower=this.factor[0],this.widthUpper=this.factor[1];else if(!Array.isArray(this.factor)&&this.factor>0)this.widthLower=-this.factor,this.widthUpper=this.factor;else throw new z(`Invalid factor: ${this.factor}. Must be positive number or tuple of 2 numbers`);if(this.widthLower<-1||this.widthUpper<-1)throw new z(`factor must have values larger than -1. Got: ${this.factor}`);if(this.widthUpper<this.widthLower)throw new z(`factor cannot have upper bound less than lower bound.
        Got upper bound: ${this.widthUpper}.
        Got lower bound: ${this.widthLower}
      `);if(n)if(QR.has(n))this.interpolation=n;else throw new z(`Invalid interpolation parameter: ${n} is not implemented`)}getConfig(){let t={factor:this.factor,interpolation:this.interpolation},e=super.getConfig();return Object.assign(t,e),t}computeOutputShape(t){t=Gt(t);let e=t[2];return[this.imgHeight,-1,e]}call(t,e){return B(()=>{let n=St(t);this.imgHeight=n.shape[n.shape.length-3];let o=n.shape[n.shape.length-2];this.widthFactor=Gn([1],1+this.widthLower,1+this.widthUpper,"float32",this.randomGenerator.next());let s=this.widthFactor.dataSync()[0]*o;s=Math.round(s);let i=[this.imgHeight,s];switch(this.interpolation){case"bilinear":return fn.resizeBilinear(t,i);case"nearest":return fn.resizeNearestNeighbor(t,i);default:throw new Error(`Interpolation is ${this.interpolation}
          but only ${[...QR]} are supported`)}})}};td.className="RandomWidth";Q.registerClass(td);function MJ(r){return new hi(r)}function PJ(r){return new tf(r)}function LJ(r){return new Zm(r)}function zJ(r){return new Jm(r)}function BJ(r){return new Qm(r)}function VJ(r){return new rf(r)}function GJ(r){return new ef(r)}function WJ(r){return new lf(r)}function UJ(r){return new Uc(r)}function HJ(r){return new of(r)}function qJ(r){return new Hc(r)}function KJ(r){return new sf(r)}function jJ(r){return new af(r)}function XJ(r){return new uf(r)}function YJ(r){return new cf(r)}function ZJ(r){return new pf(r)}function JJ(r){return new bf(r)}function QJ(r){return new xf(r)}function t9(r){return new Yc(r)}function e9(r){return new gf(r)}function r9(r){return new yf(r)}function n9(r){return new wf(r)}function o9(r){return new If(r)}function s9(r){return new Cf(r)}function i9(r){return new Sf(r)}function a9(r){return new Nf(r)}function l9(r){return new Tf(r)}function u9(r){return new Af(r)}function c9(r){return new _f(r)}function p9(r){return new Ef(r)}function m9(r){return new kf(r)}function f9(r){return new Df(r)}function d9(r){return new Of(r)}function h9(r){return new Mf(r)}function g9(r){return new Pf(r)}function ok(r){return new zf(r)}function x9(r){return ok(r)}function y9(r){return ok(r)}function sk(r){return new Vf(r)}function b9(r){return sk(r)}function w9(r){return sk(r)}function ik(r){return new Wf(r)}function I9(r){return ik(r)}function C9(r){return ik(r)}function v9(r){return new Uf(r)}function S9(r){return new qf(r)}function tF(r){return new Hf(r)}function eF(r){return new Kf(r)}function rF(r){return new Lf(r)}function nF(r){return new Bf(r)}function N9(r){return new Gf(r)}function k9(r){return new ff(r)}function T9(r){return new Kc(r)}function _9(r){return new df(r)}function E9(r){return new _l(r)}function A9(r){return new mf(r)}function D9(r){return new qc(r)}function $9(r){return new hf(r)}function R9(r){return new Xc(r)}function F9(r){return new po(r)}function O9(r){return new jc(r)}function M9(r){return new Xf(r)}function P9(r){return new jf(r)}var L9=tF,z9=eF,B9=rF,V9=nF;function G9(r){return new $f(r)}function W9(r){return new Rf(r)}function U9(r){return new Ff(r)}function H9(r){return new vf(r)}function q9(r){return new Yf(r)}function K9(r){return new Zf(r)}function j9(r){return new Qf(r)}function X9(r){return new Jf(r)}function Y9(r){return new td(r)}var sF={};Kt(sF,{MAPE:()=>aQ,MSE:()=>cQ,binaryAccuracy:()=>Z9,binaryCrossentropy:()=>J9,categoricalAccuracy:()=>tQ,categoricalCrossentropy:()=>eQ,cosineProximity:()=>oQ,mape:()=>lQ,meanAbsoluteError:()=>sQ,meanAbsolutePercentageError:()=>iQ,meanSquaredError:()=>uQ,mse:()=>pQ,precision:()=>rQ,r2Score:()=>mQ,recall:()=>nQ,sparseCategoricalAccuracy:()=>Q9});function Z9(r,t){return $h(r,t)}function J9(r,t){return rb(r,t)}function Q9(r,t){return nb(r,t)}function tQ(r,t){return Rh(r,t)}function eQ(r,t){return Fh(r,t)}function rQ(r,t){return qN(r,t)}function nQ(r,t){return TR(r,t)}function oQ(r,t){return Dh(r,t)}function sQ(r,t){return Hm(r,t)}function iQ(r,t){return Pu(r,t)}function aQ(r,t){return Pu(r,t)}function lQ(r,t){return Pu(r,t)}function uQ(r,t){return xa(r,t)}function cQ(r,t){return xa(r,t)}function pQ(r,t){return xa(r,t)}function mQ(r,t){return _R(r,t)}var iF={};Kt(iF,{modelFromJSON:()=>WR});var aF={};Kt(aF,{l1:()=>dQ,l1l2:()=>fQ,l2:()=>hQ});function fQ(r){return new Lu(r)}function dQ(r){return KR(r)}function hQ(r){return jR(r)}var zb=class extends kl{constructor(){super(...arguments),this.model=null}setModel(t){if(!(t instanceof Un))throw new Error("model must be a LayersModel, not some other Container");this.model=t}};function Lb(r,t){return r<t}function lF(r,t){return r>t}var Bb=class extends zb{constructor(t){if(super(),t==null&&(t={}),t.restoreBestWeights)throw new _t("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=t.monitor||"val_loss",this.minDelta=Math.abs(t.minDelta||0),this.patience=t.patience||0,this.verbose=t.verbose||0,this.mode=t.mode||"auto",this.baseline=t.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=Lb:this.mode==="max"?this.monitorFunc=lF:this.monitor.indexOf("acc")!==-1?this.monitorFunc=lF:this.monitorFunc=Lb,this.monitorFunc===Lb&&(this.minDelta*=-1)}async onTrainBegin(t){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===Lb?1/0:-1/0}async onEpochEnd(t,e){await ga(e);let n=this.getMonitorValue(e);n!=null&&(this.monitorFunc(n-this.minDelta,this.best)?(this.best=n,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=t,this.model.stopTraining=!0)))}async onTrainEnd(t){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(t){t==null&&(t={});let e=t[this.monitor];return e==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(t)}`),e}};function gQ(r){return new Bb(r)}var xQ={earlyStopping:gQ};var yQ=L();yQ.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 _n;(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"})(_n||(_n={}));var uF;(function(r){let t;(function(e){e[e.LEGACY=0]="LEGACY",e[e.V1=1]="V1",e[e.V2=2]="V2"})(t=r.CheckpointFormatVersion||(r.CheckpointFormatVersion={}))})(uF||(uF={}));var ak={};function wQ(r,t){let e={tfOpName:r,category:"custom",inputs:[],attrs:[],customExecutor:t};ak[r]=e}function Vb(r){return ak[r]}function IQ(r){delete ak[r]}function v(r,t,e,n,o){let s=t.inputParams[r];if(s&&s.inputIndexStart!==void 0){let a=s.inputIndexStart,u=s.inputIndexEnd===0?void 0:s.inputIndexEnd===void 0?a+1:s.inputIndexEnd,l=a<0?t.inputNames.length+a:a;if(s.type==="tensor")return pr(t.inputNames[l],e,n,o);if(s.type==="tensors"){let m=t.inputs.slice(a,u);return t.inputNames.slice(a,u).filter((d,h)=>{var g;return((g=m[h])===null||g===void 0?void 0:g.op)!=="NoOp"}).map(d=>pr(d,e,n,o))}let c=pr(t.inputNames[l],e,n,o),p=c.dataSync();return s.type==="number"?p[0]:y.toNestedArray(c.shape,p)}let i=t.attrParams[r];return i&&i.value}function pr(r,t,e,n){let[o,s]=In(r,e);if(n!=null){let a=n.getHashTableHandleByName(o);if(a!=null)return a}let i=e.currentContextIds.find(a=>!!t[Gb(o,a)]);return i!==void 0?t[Gb(o,i)][s]:void 0}function lk(r,t,e){return t[Gb(r,e.currentContextId)]}function bi(r,t){let[e,n,o]=In(r,t);return[Gb(e,t&&t.currentContextId),n,o]}function Gb(r,t){return t?`${r}-${t}`:r}function In(r,t){if(r==="")return["",0,void 0];let e=t!=null&&t.parseNodeNameCache!=null;if(e){let s=t.parseNodeNameCache.get(r);if(s!=null)return s}let n=r.split(":"),o;if(n.length===1)o=[r,0,void 0];else{let s=n[0],i=n.length===3?n[1]:void 0,a=Number(n[n.length-1]);o=[s,a,i]}return e&&t.parseNodeNameCache.set(r,o),o}function Gh(r,t,e){let n=v("pad",r,t,e);if(n==="explicit"){n=v("explicitPaddings",r,t,e);let o=[[0,0],[0,0],[0,0],[0,0]];for(let s=0;s<4;s++)o[s][0]=n[s*2],o[s][1]=n[s*2+1];return o}return n}function wi(r){return r.kept?r:un(r)}var uk={};Kt(uk,{json:()=>CQ});var CQ=[{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 ck={};Kt(ck,{json:()=>vQ});var vQ=[{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:"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}]},{tfOpName:"IsFinite",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"IsInf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}];var pk={};Kt(pk,{json:()=>SQ});var SQ=[{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 mk={};Kt(mk,{json:()=>NQ});var NQ=[{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 fk={};Kt(fk,{json:()=>kQ});var kQ=[{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:"RandomUniformInt",category:"creation",inputs:[{start:0,name:"shape",type:"number[]"}],attrs:[{tfName:"minval",name:"minval",type:"number"},{tfName:"maxval",name:"maxval",type:"number"},{tfName:"seed",name:"seed",type:"number",defaultValue:0},{tfName:"seed2",name:"seed2",type:"number",defaultValue:0,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 dk={};Kt(dk,{json:()=>TQ});var TQ=[{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 hk={};Kt(hk,{json:()=>_Q});var _Q=[{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 gk={};Kt(gk,{json:()=>EQ});var EQ=[{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 xk={};Kt(xk,{json:()=>AQ});var AQ=[{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 yk={};Kt(yk,{json:()=>DQ});var DQ=[{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 bk={};Kt(bk,{json:()=>$Q});var $Q=[{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}]},{tfOpName:"BitwiseAnd",category:"logical",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}]}];var wk={};Kt(wk,{json:()=>RQ});var RQ=[{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"}]},{tfOpName:"MatrixBandPart",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"numLower",type:"tensor"},{start:1,name:"numUpper",type:"tensor"}]}];var Ik={};Kt(Ik,{json:()=>FQ});var FQ=[{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"}]}];var Ck={};Kt(Ck,{json:()=>OQ});var OQ=[{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"},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{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 vk={};Kt(vk,{json:()=>MQ});var MQ=[{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}]},{tfOpName:"TensorScatterUpdate",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"values",type:"tensor"}]}];var Sk={};Kt(Sk,{json:()=>PQ});var PQ=[{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 Nk={};Kt(Nk,{json:()=>LQ});var LQ=[{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 kk={};Kt(kk,{json:()=>zQ});var zQ=[{tfOpName:"StaticRegexReplace",category:"string",inputs:[{start:0,name:"input",type:"tensor"}],attrs:[{tfName:"pattern",name:"pattern",type:"string"},{tfName:"rewrite",name:"rewrite",type:"string"},{tfName:"replace_global",name:"replaceGlobal",type:"bool"}]},{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 Tk={};Kt(Tk,{json:()=>BQ});var BQ=[{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:"EnsureShape",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 Wh=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let t=[uk,ck,pk,mk,fk,dk,hk,gk,xk,yk,bk,wk,Ik,Ck,vk,Sk,Nk,kk,Tk],e=[].concat(...t.map(n=>n.json));this.opMappers=e.reduce((n,o)=>(n[o.tfOpName]=o,n),{})}transformGraph(t,e={}){let n=t.node,o=[],s=[],i=[],a=n.reduce((h,g)=>(h[g.name]=this.mapNode(g),g.op.startsWith("Placeholder")?o.push(h[g.name]):g.op==="Const"?s.push(h[g.name]):(g.input==null||g.input.length===0)&&i.push(h[g.name]),h),{}),u=[],l=[],c={},p={};e!=null&&(c=this.mapSignatureEntries(e.inputs),p=this.mapSignatureEntries(e.outputs));let m=Object.keys(a);m.forEach(h=>{let g=a[h];g.inputNames.forEach((x,b)=>{let[w,,I]=bi(x),N=a[w];if(N.outputs!=null){let E=N.outputs.indexOf(I);if(E!==-1){let A=`${w}:${E}`;g.inputNames[b]=A}}g.inputs.push(N),N.children.push(g)})}),Object.keys(p).length===0?m.forEach(h=>{let g=a[h];g.children.length===0&&l.push(g)}):Object.keys(p).forEach(h=>{let[g]=bi(h),x=a[g];x!=null&&(x.signatureKey=p[h],l.push(x))}),Object.keys(c).length>0?Object.keys(c).forEach(h=>{let[g]=bi(h),x=a[g];x&&(x.signatureKey=c[h],u.push(x))}):u=o;let f={};t.library!=null&&t.library.function!=null&&(f=t.library.function.reduce((h,g)=>(h[g.signature.name]=this.mapFunction(g),h),{}));let d={nodes:a,inputs:u,outputs:l,weights:s,placeholders:o,signature:e,functions:f};return i.length>0&&(d.initNodes=i),d}mapSignatureEntries(t){return Object.keys(t||{}).reduce((e,n)=>(e[t[n].name]=n,e),{})}mapNode(t){let e=Vb(t.op)||this.opMappers[t.op]||{};t.attr==null&&(t.attr={});let n={name:t.name,op:t.op,category:e.category,inputNames:(t.input||[]).map(o=>o.startsWith("^")?o.slice(1):o),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:t.attr,outputs:e.outputs};return e.inputs!=null&&(n.inputParams=e.inputs.reduce((o,s)=>(o[s.name]={type:s.type,inputIndexStart:s.start,inputIndexEnd:s.end},o),{})),e.attrs!=null&&(n.attrParams=e.attrs.reduce((o,s)=>{let i=s.type,a;switch(s.type){case"string":a=Wb(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=Wb(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"string[]":a=Yb(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=Yb(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"number":a=Hb(t.attr,s.tfName,s.defaultValue||0),a===void 0&&s.tfDeprecatedName&&(a=Hb(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"number[]":a=Xb(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=Xb(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool":a=Ub(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=Ub(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"bool[]":a=Jb(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=Jb(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape":a=jb(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=jb(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"shape[]":a=Zb(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=Zb(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype":a=qb(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=qb(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"dtype[]":a=Kb(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=Kb(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"func":a=cF(t.attr,s.tfName,s.defaultValue),a===void 0&&s.tfDeprecatedName&&(a=cF(t.attr,s.tfDeprecatedName,s.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${s.type} for op: ${t.op}`)}return o[s.name]={value:a,type:i},o},{})),n}mapFunction(t){let e=t.nodeDef,n=[],o=[],s={};e!=null&&(s=e.reduce((p,m)=>(p[m.name]=this.mapNode(m),m.op==="Const"&&o.push(p[m.name]),p),{}));let i=[],a=[];t.signature.inputArg.forEach(p=>{let[m]=bi(p.name),f={name:m,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:_k(p.type),type:"dtype"}},children:[]};f.signatureKey=p.name,i.push(f),s[m]=f}),Object.keys(s).forEach(p=>{let m=s[p];m.inputNames.forEach((f,d)=>{let[h,,g]=bi(f),x=s[h];if(x.outputs!=null){let b=x.outputs.indexOf(g);if(b!==-1){let w=`${h}:${b}`;m.inputNames[d]=w}}m.inputs.push(x),x.children.push(m)})});let l=t.ret;t.signature.outputArg.forEach(p=>{let[m,f]=bi(l[p.name]),d=s[m];d!=null&&(d.defaultOutput=f,a.push(d))});let c=this.mapArgsToSignature(t);return{nodes:s,inputs:i,outputs:a,weights:o,placeholders:n,signature:c}}mapArgsToSignature(t){return{methodName:t.signature.name,inputs:t.signature.inputArg.reduce((e,n)=>(e[n.name]=this.mapArgToTensorInfo(n),e),{}),outputs:t.signature.outputArg.reduce((e,n)=>(e[n.name]=this.mapArgToTensorInfo(n,t.ret),e),{})}}mapArgToTensorInfo(t,e){let n=t.name;return e!=null&&(n=e[n]),{name:n,dtype:t.type}}};function VQ(r){let t=L().global;if(typeof t.atob!="undefined")return t.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 pF(r,t){let e=Array.isArray(r)?String.fromCharCode.apply(null,r):VQ(r);return t?e:e.toLowerCase()}function Wb(r,t,e,n=!1){let o=r[t];return o!=null?pF(o.s,n):e}function Ub(r,t,e){let n=r[t];return n?n.b:e}function Hb(r,t,e){let n=r[t]||{},o=n.i!=null?n.i:n.f!=null?n.f:e;return typeof o=="number"?o:parseInt(o,10)}function _k(r){switch(typeof r=="string"&&(r=_n[r]),r){case _n.DT_FLOAT:case _n.DT_HALF:return"float32";case _n.DT_INT32:case _n.DT_INT64:case _n.DT_INT8:case _n.DT_UINT8:return"int32";case _n.DT_BOOL:return"bool";case _n.DT_DOUBLE:return"float32";case _n.DT_STRING:return"string";case _n.DT_COMPLEX64:case _n.DT_COMPLEX128:return"complex64";default:return null}}function cF(r,t,e){let n=r[t];return n&&n.func?n.func.name:e}function qb(r,t,e){let n=r[t];return n&&n.type?_k(n.type):e}function Kb(r,t,e){let n=r[t];return n&&n.list&&n.list.type?n.list.type.map(o=>_k(o)):e}function mF(r){if(!r.unknownRank)return r.dim!=null?r.dim.map(t=>typeof t.size=="number"?t.size:parseInt(t.size,10)):[]}function jb(r,t,e){let n=r[t];return n&&n.shape?mF(n.shape):e}function Xb(r,t,e){let n=r[t];return n?((n.list.f&&n.list.f.length?n.list.f:n.list.i)||[]).map(o=>typeof o=="number"?o:parseInt(o,10)):e}function Yb(r,t,e,n=!1){let o=r[t];return o&&o.list&&o.list.s?o.list.s.map(s=>pF(s,n)):e}function Zb(r,t,e){let n=r[t];return n&&n.list&&n.list.shape?n.list.shape.map(o=>mF(o)):e}function Jb(r,t,e){let n=r[t];return n&&n.list&&n.list.b?n.list.b:e}var Qb=class{constructor(t,e,n){this.node=t,this.tensorMap=e,this.context=n,this.inputs=[],this.attrs={},this.inputs=t.inputNames.map(o=>this.getInput(o)),t.rawAttrs!=null&&(this.attrs=Object.keys(t.rawAttrs).reduce((o,s)=>(o[s]=this.getAttr(s),o),{}))}getInput(t){return pr(t,this.tensorMap,this.context)}getAttr(t,e){let n=this.node.rawAttrs[t];if(n.tensor!=null)return pr(t,this.tensorMap,this.context);if(n.i!=null||n.f!=null)return Hb(this.node.rawAttrs,t,e);if(n.s!=null)return Wb(this.node.rawAttrs,t,e);if(n.b!=null)return Ub(this.node.rawAttrs,t,e);if(n.shape!=null)return jb(this.node.rawAttrs,t,e);if(n.type!=null)return qb(this.node.rawAttrs,t,e);if(n.list!=null){if(n.list.i!=null||n.list.f!=null)return Xb(this.node.rawAttrs,t,e);if(n.list.s!=null)return Yb(this.node.rawAttrs,t,e);if(n.list.shape!=null)return Zb(this.node.rawAttrs,t,e);if(n.list.b!=null)return Jb(this.node.rawAttrs,t,e);if(n.list.type!=null)return Kb(this.node.rawAttrs,t,e)}return e}};var ae={};Kt(ae,{OP_SCOPE_SUFFIX:()=>V0,abs:()=>_e,acos:()=>fx,acosh:()=>dx,add:()=>K,addN:()=>EE,all:()=>tm,any:()=>cc,argMax:()=>ra,argMin:()=>hx,asin:()=>gx,asinh:()=>xx,atan:()=>yx,atan2:()=>bx,atanh:()=>wx,avgPool:()=>xu,avgPool3d:()=>Ix,basicLSTMCell:()=>$E,batchNorm:()=>oa,batchNorm2d:()=>Cx,batchNorm3d:()=>vx,batchNorm4d:()=>Sx,batchToSpaceND:()=>yu,bincount:()=>Nx,bitwiseAnd:()=>FE,booleanMaskAsync:()=>q5,broadcastArgs:()=>OE,broadcastTo:()=>sa,buffer:()=>wt,cast:()=>J,ceil:()=>kx,clipByValue:()=>vr,clone:()=>un,complex:()=>Sn,concat:()=>ie,concat1d:()=>Tx,concat2d:()=>_x,concat3d:()=>Ex,concat4d:()=>Ax,conv1d:()=>rm,conv2d:()=>Nn,conv2dTranspose:()=>om,conv3d:()=>Dx,conv3dTranspose:()=>Rx,cos:()=>bu,cosh:()=>sm,cosineWindow:()=>xh,cumprod:()=>mc,cumsum:()=>im,denseBincount:()=>mh,depthToSpace:()=>Fx,depthwiseConv2d:()=>ia,diag:()=>ME,dilation2d:()=>Ox,div:()=>ut,divNoNan:()=>Mx,dot:()=>Px,dropout:()=>dN,einsum:()=>wu,elu:()=>aa,enclosingPowerOfTwo:()=>hN,ensureShape:()=>LE,equal:()=>$r,erf:()=>am,euclideanNorm:()=>Lx,exp:()=>Ke,expandDims:()=>je,expm1:()=>zx,eye:()=>fc,fft:()=>Au,fill:()=>Co,floor:()=>la,floorDiv:()=>Qp,fused:()=>Ru,gather:()=>ua,gatherND:()=>r8,greater:()=>Re,greaterEqual:()=>cn,ifft:()=>wl,imag:()=>Iu,image:()=>fn,inTopKAsync:()=>s8,irfft:()=>bm,isFinite:()=>Bx,isInf:()=>Vx,isNaN:()=>Gx,leakyRelu:()=>Cu,less:()=>yl,lessEqual:()=>Vn,linalg:()=>xN,linspace:()=>VE,localResponseNormalization:()=>Wx,log:()=>Nr,log1p:()=>vu,logSigmoid:()=>qx,logSoftmax:()=>lm,logSumExp:()=>Su,logicalAnd:()=>Fr,logicalNot:()=>Nu,logicalOr:()=>um,logicalXor:()=>Kx,losses:()=>aY,lowerBound:()=>GE,matMul:()=>Bt,max:()=>Sr,maxPool:()=>ku,maxPool3d:()=>Xx,maxPoolWithArgmax:()=>WE,maximum:()=>kn,mean:()=>Ne,meshgrid:()=>UE,min:()=>gl,minimum:()=>lo,mirrorPad:()=>Yx,mod:()=>Zx,moments:()=>dc,movingAverage:()=>X5,mul:()=>$,multiRNNCell:()=>HE,multinomial:()=>qE,neg:()=>Ut,norm:()=>xl,notEqual:()=>ci,oneHot:()=>ca,ones:()=>ar,onesLike:()=>wr,op:()=>k,outerProduct:()=>KE,pad:()=>mn,pad1d:()=>jE,pad2d:()=>XE,pad3d:()=>YE,pad4d:()=>ZE,pool:()=>Jx,pow:()=>qr,prelu:()=>_u,print:()=>mx,prod:()=>Qx,raggedGather:()=>JE,raggedRange:()=>QE,raggedTensorToTensor:()=>tA,rand:()=>eA,randomGamma:()=>CA,randomNormal:()=>xc,randomStandardNormal:()=>vA,randomUniform:()=>Gn,randomUniformInt:()=>SA,range:()=>pa,real:()=>bl,reciprocal:()=>sy,relu:()=>Or,relu6:()=>cm,reshape:()=>R,reverse:()=>dr,reverse1d:()=>NA,reverse2d:()=>kA,reverse3d:()=>TA,reverse4d:()=>_A,rfft:()=>Du,round:()=>pm,rsqrt:()=>mm,scalar:()=>pt,scatterND:()=>Z5,searchSorted:()=>dh,selu:()=>fm,separableConv2d:()=>dm,setdiff1dAsync:()=>EA,sigmoid:()=>en,sign:()=>iy,signal:()=>iY,sin:()=>hm,sinh:()=>gm,slice:()=>Ot,slice1d:()=>xm,slice2d:()=>gh,slice3d:()=>ym,slice4d:()=>yc,softmax:()=>Eu,softplus:()=>ui,spaceToBatchND:()=>Tu,sparse:()=>lY,sparseToDense:()=>t8,spectral:()=>sY,split:()=>hr,sqrt:()=>ge,square:()=>Wt,squaredDifference:()=>wm,squeeze:()=>Wn,stack:()=>Fe,step:()=>So,stridedSlice:()=>ay,string:()=>uY,sub:()=>at,sum:()=>mt,tan:()=>ly,tanh:()=>li,tensor:()=>ir,tensor1d:()=>Oe,tensor2d:()=>pi,tensor3d:()=>uy,tensor4d:()=>AA,tensor5d:()=>DA,tensor6d:()=>$A,tensorScatterUpdate:()=>FA,tile:()=>Rr,topk:()=>cy,transpose:()=>Vt,truncatedNormal:()=>Cm,unique:()=>py,unsortedSegmentSum:()=>vm,unstack:()=>gr,upperBound:()=>OA,variable:()=>my,where:()=>Ie,whereAsync:()=>dy,zeros:()=>ke,zerosLike:()=>vt});var fF=(r,t,e,n=ae)=>{switch(r.op){case"BiasAdd":case"AddV2":case"Add":return[n.add(v("a",r,t,e),v("b",r,t,e))];case"AddN":return[n.addN(v("tensors",r,t,e))];case"FloorMod":case"Mod":return[n.mod(v("a",r,t,e),v("b",r,t,e))];case"Mul":return[n.mul(v("a",r,t,e),v("b",r,t,e))];case"RealDiv":case"Div":return[n.div(v("a",r,t,e),v("b",r,t,e))];case"DivNoNan":return[n.divNoNan(v("a",r,t,e),v("b",r,t,e))];case"FloorDiv":return[n.floorDiv(v("a",r,t,e),v("b",r,t,e))];case"Sub":return[n.sub(v("a",r,t,e),v("b",r,t,e))];case"Minimum":return[n.minimum(v("a",r,t,e),v("b",r,t,e))];case"Maximum":return[n.maximum(v("a",r,t,e),v("b",r,t,e))];case"Pow":return[n.pow(v("a",r,t,e),v("b",r,t,e))];case"SquaredDifference":return[n.squaredDifference(v("a",r,t,e),v("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var dF=(r,t,e,n=ae)=>{switch(r.op){case"Abs":case"ComplexAbs":return[n.abs(v("x",r,t,e))];case"Acos":return[n.acos(v("x",r,t,e))];case"Acosh":return[n.acosh(v("x",r,t,e))];case"Asin":return[n.asin(v("x",r,t,e))];case"Asinh":return[n.asinh(v("x",r,t,e))];case"Atan":return[n.atan(v("x",r,t,e))];case"Atan2":return[n.atan2(v("x",r,t,e),v("y",r,t,e))];case"Atanh":return[n.atanh(v("x",r,t,e))];case"Ceil":return[n.ceil(v("x",r,t,e))];case"Complex":return[n.complex(v("real",r,t,e),v("imag",r,t,e))];case"Cos":return[n.cos(v("x",r,t,e))];case"Cosh":return[n.cosh(v("x",r,t,e))];case"Elu":return[n.elu(v("x",r,t,e))];case"Erf":return[n.erf(v("x",r,t,e))];case"Exp":return[n.exp(v("x",r,t,e))];case"Expm1":return[n.expm1(v("x",r,t,e))];case"Floor":return[n.floor(v("x",r,t,e))];case"Log":return[n.log(v("x",r,t,e))];case"Log1p":return[n.log1p(v("x",r,t,e))];case"Imag":return[n.imag(v("x",r,t,e))];case"Neg":return[n.neg(v("x",r,t,e))];case"Reciprocal":return[n.reciprocal(v("x",r,t,e))];case"Real":return[n.real(v("x",r,t,e))];case"Relu":return[n.relu(v("x",r,t,e))];case"Round":return[n.round(v("x",r,t,e))];case"Selu":return[n.selu(v("x",r,t,e))];case"Sigmoid":return[n.sigmoid(v("x",r,t,e))];case"Sin":return[n.sin(v("x",r,t,e))];case"Sign":return[n.sign(v("x",r,t,e))];case"Sinh":return[n.sinh(v("x",r,t,e))];case"Softplus":return[n.softplus(v("x",r,t,e))];case"Sqrt":return[n.sqrt(v("x",r,t,e))];case"Square":return[n.square(v("x",r,t,e))];case"Tanh":return[n.tanh(v("x",r,t,e))];case"Tan":return[n.tan(v("x",r,t,e))];case"ClipByValue":return[n.clipByValue(v("x",r,t,e),v("clipValueMin",r,t,e),v("clipValueMax",r,t,e))];case"Relu6":return[n.relu6(v("x",r,t,e))];case"Rsqrt":return[n.rsqrt(pr(r.inputNames[0],t,e))];case"LeakyRelu":return[n.leakyRelu(v("x",r,t,e),v("alpha",r,t,e))];case"Prelu":return[n.prelu(v("x",r,t,e),v("alpha",r,t,e))];case"IsNan":return[n.isNaN(pr(r.inputNames[0],t,e))];case"IsInf":return[n.isInf(pr(r.inputNames[0],t,e))];case"IsFinite":return[n.isFinite(pr(r.inputNames[0],t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Hn(r,t,e=""){if(!(typeof r=="number"||typeof t=="number")){y.assert(r.length===t.length,()=>e+` Shapes ${r} and ${t} must match`);for(let n=0;n<r.length;n++){let o=r[n],s=t[n];y.assert(o<0||s<0||o===s,()=>e+` Shapes ${r} and ${t} must match`)}}}function hF(r){return!(typeof r=="number"||r.some(t=>t<0))}function ed(r,t,e){let n=tw(r,e),o=!hF(n);if(o&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${n}`);if(o&&t.forEach(s=>{n=tw(s.shape,n)}),!hF(n))throw new Error(`Non-fully-defined elementShape: ${n}`);return n}function tw(r,t){if(typeof r=="number")return t;if(typeof t=="number")return r;if(r.length!==t.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${t}`);let e=[];for(let n=0;n<r.length;++n){let o=r[n],s=t[n];if(o>=0&&s>=0&&o!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${t}`);e[n]=o>=0?o:s}return e}var ew=class{constructor(t,e,n,o,s,i,a){this.name=t,this.dtype=e,this.maxSize=n,this.elementShape=o,this.identicalElementShapes=s,this.dynamicSize=i,this.clearAfterRead=a,this.tensors=[],this.closed_=!1,this.idTensor=pt(0),De(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.tensor.id))&&e.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||t>=this.size())throw new Error(`Tried to read from index ${t}, but array size is: ${this.size()}`);let e=this.tensors[t];if(e.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${t} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(e.cleared=!0),e.read=!0,e.tensor}readMany(t){return t.map(e=>this.read(e))}write(t,e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(t<0||!this.dynamicSize&&t>=this.maxSize)throw new Error(`Tried to write to index ${t}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[t]||{};if(e.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t},
          because the value dtype is ${e.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=e.shape),Hn(this.elementShape,e.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${t}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${t}, because it has already been written.`);n.tensor=e,De(e),n.written=!0,this.tensors[t]=n}writeMany(t,e){if(t.length!==e.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${t.length} is not the same as tensors size: ${e.length}.`);t.forEach((n,o)=>this.write(n,e[o]))}gather(t,e){if(e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${e}`);if(t)t=t.slice(0,this.size());else{t=[];for(let o=0;o<this.size();o++)t.push(o)}if(t.length===0)return ir([],[0].concat(this.elementShape));let n=this.readMany(t);return Hn(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),Fe(n,0)}concat(t){if(t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${t}`);if(this.size()===0)return ir([],[0].concat(this.elementShape));let e=[];for(let o=0;o<this.size();o++)e.push(o);let n=this.readMany(e);return Hn(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),ie(n,0)}scatter(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let n=Math.max(...t);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n}  vs. ${this.maxSize})`);this.writeMany(t,gr(e,0))}split(t,e){if(e.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${e.dtype}`);let n=0,o=t.map(u=>(n+=u,n));if(n!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${n}, and tensor's shape is: ${e.shape}`);if(!this.dynamicSize&&t.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${t.length}), and the TensorArray is not marked as dynamically resizeable`);let s=n===0?0:e.size/n,i=[];B(()=>{e=R(e,[1,n,s]);for(let u=0;u<t.length;++u){let c=[0,u===0?0:o[u-1],0],p=[1,t[u],s];i[u]=R(Ot(e,c,p),this.elementShape)}return i});let a=[];for(let u=0;u<t.length;u++)a[u]=u;this.writeMany(a,i)}};var rd=class r{get id(){return this.idTensor.id}constructor(t,e,n,o=-1){this.tensors=t,this.elementShape=e,this.elementDtype=n,t!=null&&t.forEach(s=>{if(n!==s.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${s.dtype}`);Hn(e,s.shape,"TensorList shape mismatch: "),De(s)}),this.idTensor=pt(0),this.maxNumElements=o,De(this.idTensor)}copy(){return new r([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(t){this.tensors.forEach(e=>{(t==null||!t.has(e.id))&&e.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(t,e,n=-1){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);Hn(t,this.elementShape,"TensorList shape mismatch: ");let o=ed(this.elementShape,this.tensors,t);return B(()=>{let s=this.tensors.map(i=>R(i,o));return Fe(s,0)})}popBack(t,e){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=ed(this.elementShape,this.tensors,t),o=this.tensors.pop();return o.kept=!1,Hn(o.shape,t,"TensorList shape mismatch: "),R(o,n)}pushBack(t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(Hn(t.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");De(t),this.tensors.push(t)}resize(t){if(t<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${t}`);if(this.maxNumElements!==-1&&t>this.maxNumElements)throw new Error(`TensorListResize input size ${t} is greater maxNumElement ${this.maxNumElements}.`);let e=new r([],this.elementShape,this.elementDtype,this.maxNumElements);e.tensors.length=t;for(let n=0;n<Math.min(this.tensors.length,t);++n)e.tensors[n]=this.tensors[n];return e}getItem(t,e,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(t<0||t>this.tensors.length)throw new Error(`Trying to access element ${t} in a list with ${this.tensors.length} elements.`);if(this.tensors[t]==null)throw new Error(`element at index ${t} is null.`);Hn(this.tensors[t].shape,e,"TensorList shape mismatch: ");let o=ed(this.elementShape,this.tensors,e);return R(this.tensors[t],o)}setItem(t,e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(t<0||this.maxNumElements!==-1&&t>=this.maxNumElements)throw new Error(`Trying to set element ${t} in a list with max ${this.maxNumElements} elements.`);Hn(this.elementShape,e.shape,"TensorList shape mismatch: "),De(e),this.tensors[t]!=null&&(this.tensors[t].kept=!1),this.tensors[t]=e}gather(t,e,n){if(e!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e}, but list elements ${this.elementDtype}`);Hn(this.elementShape,n,"TensorList shape mismatch: "),t=t.slice(0,this.size());let o=ed(this.elementShape,this.tensors,n);return t.length===0?ir([],[0].concat(o)):B(()=>{let s=t.map(i=>R(this.tensors[i],o));return Fe(s,0)})}concat(t,e){if(t&&t!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${t}`);Hn(this.elementShape,e,"TensorList shape mismatch: ");let n=ed(this.elementShape,this.tensors,e);return this.size()===0?ir([],[0].concat(n)):B(()=>{let o=this.tensors.map(s=>R(s,n));return ie(o,0)})}};function gF(r,t,e){let n=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!==e)throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${e}`);let o=r.shape.slice(1);Hn(o,t,"TensorList shape mismatch: ");let s=gr(r);return new rd(s,t,n)}function xF(r,t,e,n){return new rd([],r,t,n)}function yF(r,t,e,n){if(t.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${r.shape[0]}`);let o=Math.max(...t);if(n!=null&&n!==-1&&o>=n)throw new Error(`Max index must be < array size (${o}  vs. ${n})`);let s=new rd([],e,r.dtype,n),i=gr(r,0);return t.forEach((a,u)=>{s.setItem(a,i[u])}),s}function bF(r,t,e){let n=0,o=t.map(c=>(n+=c,n));if(n!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${n}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),i=tw(s,e),a=n===0?0:r.size/n,u=B(()=>{let c=[];r=R(r,[1,n,a]);for(let p=0;p<t.length;++p){let f=[0,p===0?0:o[p-1],0],d=[1,t[p],a];c[p]=R(Ot(r,f,d),i)}return r.dispose(),c}),l=new rd([],e,r.dtype,t.length);for(let c=0;c<u.length;c++)l.setItem(c,u[c]);return l}var wF=async(r,t,e)=>{switch(r.op){case"If":case"StatelessIf":{let n=v("thenBranch",r,t,e),o=v("elseBranch",r,t,e),s=v("cond",r,t,e),i=v("args",r,t,e);return(await s.data())[0]?e.functionMap[n].executeFunctionAsync(i,e.tensorArrayMap,e.tensorListMap):e.functionMap[o].executeFunctionAsync(i,e.tensorArrayMap,e.tensorListMap)}case"While":case"StatelessWhile":{let n=v("body",r,t,e),o=v("cond",r,t,e),s=v("args",r,t,e),i=await e.functionMap[o].executeFunctionAsync(s,e.tensorArrayMap,e.tensorListMap),a=s.map(c=>c.id),u=await i[0].data();i.forEach(c=>{!c.kept&&a.indexOf(c.id)===-1&&c.dispose()});let l=s;for(;u[0];){let c=l;l=await e.functionMap[n].executeFunctionAsync(l,e.tensorArrayMap,e.tensorListMap);let p=l.map(f=>f.id);c.forEach(f=>{!f.kept&&a.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()});let m=await e.functionMap[o].executeFunctionAsync(l,e.tensorArrayMap,e.tensorListMap);u=await m[0].data(),m.forEach(f=>{!f.kept&&a.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()})}return l}case"LoopCond":{let n=v("pred",r,t,e);return[wi(n)]}case"Switch":{let n=v("pred",r,t,e),o=v("data",r,t,e);return o.kept||(o=wi(o)),(await n.data())[0]?[void 0,o]:[o,void 0]}case"Merge":{let n=r.inputNames.find(o=>pr(o,t,e)!==void 0);if(n){let o=pr(n,t,e);return[wi(o)]}return}case"Enter":{let n=v("frameName",r,t,e),o=v("tensor",r,t,e);return e.enterFrame(n),[wi(o)]}case"Exit":{let n=v("tensor",r,t,e);return e.exitFrame(),[wi(n)]}case"NextIteration":{let n=v("tensor",r,t,e);return e.nextIteration(),[wi(n)]}case"TensorArrayV3":{let n=v("size",r,t,e),o=v("dtype",r,t,e),s=v("elementShape",r,t,e),i=v("dynamicSize",r,t,e),a=v("clearAfterRead",r,t,e),u=v("identicalElementShapes",r,t,e),l=v("name",r,t,e),c=new ew(l,o,n,s,u,i,a);return e.addTensorArray(c),[c.idTensor,pt(1)]}case"TensorArrayWriteV3":{let n=v("tensorArrayId",r,t,e),o=v("index",r,t,e),s=v("tensor",r,t,e),i=e.getTensorArray(n.id);return i.write(o,s),[i.idTensor]}case"TensorArrayReadV3":{let n=v("tensorArrayId",r,t,e),o=v("index",r,t,e);return[e.getTensorArray(n.id).read(o)]}case"TensorArrayGatherV3":{let n=v("tensorArrayId",r,t,e),o=v("indices",r,t,e),s=v("dtype",r,t,e);return[e.getTensorArray(n.id).gather(o,s)]}case"TensorArrayScatterV3":{let n=v("tensorArrayId",r,t,e),o=v("indices",r,t,e),s=v("tensor",r,t,e),i=e.getTensorArray(n.id);return i.scatter(o,s),[i.idTensor]}case"TensorArrayConcatV3":{let n=v("tensorArrayId",r,t,e),o=e.getTensorArray(n.id),s=v("dtype",r,t,e);return[o.concat(s)]}case"TensorArraySplitV3":{let n=v("tensorArrayId",r,t,e),o=v("tensor",r,t,e),s=v("lengths",r,t,e),i=e.getTensorArray(n.id);return i.split(s,o),[i.idTensor]}case"TensorArraySizeV3":{let n=v("tensorArrayId",r,t,e),o=e.getTensorArray(n.id);return[pt(o.size(),"int32")]}case"TensorArrayCloseV3":{let n=v("tensorArrayId",r,t,e),o=e.getTensorArray(n.id);return o.clearAndClose(),[o.idTensor]}case"TensorListSetItem":{let n=v("tensorListId",r,t,e),o=v("index",r,t,e),s=v("tensor",r,t,e),i=e.getTensorList(n.id);return i.setItem(o,s),[i.idTensor]}case"TensorListGetItem":{let n=v("tensorListId",r,t,e),o=v("index",r,t,e),s=v("elementShape",r,t,e),i=v("elementDType",r,t,e);return[e.getTensorList(n.id).getItem(o,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=v("indices",r,t,e),o=v("tensor",r,t,e),s=v("elementShape",r,t,e),i=v("numElements",r,t,e),a=yF(o,n,s,i);return e.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=v("elementShape",r,t,e),o=v("elementDType",r,t,e),s;r.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=v(s,r,t,e),a=r.op==="TensorListReserve"?-1:i,u=xF(n,o,i,a);return e.addTensorList(u),[u.idTensor]}case"TensorListGather":{let n=v("tensorListId",r,t,e),o=v("indices",r,t,e),s=v("elementShape",r,t,e),i=v("elementDType",r,t,e);return[e.getTensorList(n.id).gather(o,i,s)]}case"TensorListStack":{let n=v("tensorListId",r,t,e),o=v("elementShape",r,t,e),s=v("elementDType",r,t,e),i=v("numElements",r,t,e);return[e.getTensorList(n.id).stack(o,s,i)]}case"TensorListFromTensor":{let n=v("tensor",r,t,e),o=v("elementShape",r,t,e),s=v("elementDType",r,t,e),i=gF(n,o,s);return e.addTensorList(i),[i.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{let n=v("tensorListId",r,t,e),o=e.getTensorList(n.id),s=v("dtype",r,t,e),i=v("elementShape",r,t,e);return[o.concat(s,i)]}case"TensorListPushBack":{let n=v("tensorListId",r,t,e),o=v("tensor",r,t,e),s=e.getTensorList(n.id);return s.pushBack(o),[s.idTensor]}case"TensorListPopBack":{let n=v("tensorListId",r,t,e),o=v("elementShape",r,t,e),s=v("elementDType",r,t,e);return[e.getTensorList(n.id).popBack(o,s)]}case"TensorListSplit":{let n=v("tensor",r,t,e),o=v("elementShape",r,t,e),s=v("lengths",r,t,e),i=bF(n,s,o);return e.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=v("tensorListId",r,t,e),o=e.getTensorList(n.id);return[pt(o.size(),"int32")]}case"TensorListResize":{let n=v("tensorListId",r,t,e),o=v("size",r,t,e),i=e.getTensorList(n.id).resize(o);return e.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function IF(r,t,e){let[n,o]=v("fusedOps",r,t,e),s=n==="biasadd",i=!s,a=o==="prelu",u=n==="fusedbatchnorm",l=v("numArgs",r,t,e);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&s&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=v("strides",r,t,e),p=Gh(r,t,e),m=v("dataFormat",r,t,e).toUpperCase(),f=v("dilations",r,t,e),[d,h]=v("args",r,t,e);i&&(h=d,d=void 0);let g=v("leakyreluAlpha",r,t,e);return{stride:c,pad:p,dataFormat:m,dilations:f,biasArg:d,preluArg:h,activationFunc:o,leakyreluAlpha:g}}var CF=(r,t,e,n=ae)=>{switch(r.op){case"Conv1D":{let o=v("stride",r,t,e),s=v("pad",r,t,e),i=v("dataFormat",r,t,e).toUpperCase(),a=v("dilation",r,t,e);return[n.conv1d(v("x",r,t,e),v("filter",r,t,e),o,s,i,a)]}case"Conv2D":{let o=v("strides",r,t,e),s=Gh(r,t,e),i=v("dataFormat",r,t,e).toUpperCase(),a=v("dilations",r,t,e);return[n.conv2d(v("x",r,t,e),v("filter",r,t,e),[o[1],o[2]],s,i,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=IF(r,t,e);return[n.fused.conv2d({x:v("x",r,t,e),filter:v("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:s,dataFormat:i,dilations:a,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:p}=IF(r,t,e);return[n.fused.depthwiseConv2d({x:v("x",r,t,e),filter:v("filter",r,t,e),strides:[o[1],o[2]],pad:s,dataFormat:i,dilations:[a[1],a[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:p})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let o=v("outputShape",r,t,e),s=v("strides",r,t,e),i=Gh(r,t,e);return[n.conv2dTranspose(v("x",r,t,e),v("filter",r,t,e),o,[s[1],s[2]],i)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let o=v("strides",r,t,e),s=Gh(r,t,e),i=v("dilations",r,t,e),a=v("dataFormat",r,t,e).toUpperCase();return[n.depthwiseConv2d(v("input",r,t,e),v("filter",r,t,e),[o[1],o[2]],s,a,[i[1],i[2]])]}case"Conv3D":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("dataFormat",r,t,e).toUpperCase(),a=v("dilations",r,t,e);return[n.conv3d(v("x",r,t,e),v("filter",r,t,e),[o[1],o[2],o[3]],s,i,[a[1],a[2],a[3]])]}case"AvgPool":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e);return[n.avgPool(v("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case"MaxPool":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e);return[n.maxPool(v("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s)]}case"MaxPoolWithArgmax":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e),a=v("includeBatchInIndex",r,t,e),{result:u,indexes:l}=n.maxPoolWithArgmax(v("x",r,t,e),[i[1],i[2]],[o[1],o[2]],s,a);return[u,l]}case"AvgPool3D":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e);return[n.avgPool3d(v("x",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case"MaxPool3D":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("kernelSize",r,t,e);return[n.maxPool3d(v("x",r,t,e),[i[1],i[2],i[3]],[o[1],o[2],o[3]],s)]}case"Dilation2D":{let o=v("strides",r,t,e),s=v("pad",r,t,e),i=v("dilations",r,t,e),a=o[1],u=o[2],l=i[1],c=i[2];return[n.dilation2d(v("x",r,t,e),v("filter",r,t,e),[a,u],s,[l,c],"NHWC")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var vF=(r,t,e,n=ae)=>{switch(r.op){case"Fill":{let o=v("shape",r,t,e),s=v("dtype",r,t,e),i=v("value",r,t,e);return[n.fill(o,i,s)]}case"LinSpace":{let o=v("start",r,t,e),s=v("stop",r,t,e),i=v("num",r,t,e);return[n.linspace(o,s,i)]}case"Multinomial":{let o=v("logits",r,t,e),s=v("numSamples",r,t,e),i=v("seed",r,t,e);return[n.multinomial(o,s,i)]}case"OneHot":{let o=v("indices",r,t,e),s=v("depth",r,t,e),i=v("onValue",r,t,e),a=v("offValue",r,t,e),u=v("dtype",r,t,e);return[n.oneHot(o,s,i,a,u)]}case"Ones":return[n.ones(v("shape",r,t,e),v("dtype",r,t,e))];case"OnesLike":return[n.onesLike(v("x",r,t,e))];case"RandomStandardNormal":return[n.randomStandardNormal(v("shape",r,t,e),v("dtype",r,t,e),v("seed",r,t,e))];case"RandomUniform":return[n.randomUniform(v("shape",r,t,e),v("minval",r,t,e),v("maxval",r,t,e),v("dtype",r,t,e))];case"RandomUniformInt":return[n.randomUniformInt(v("shape",r,t,e),v("minval",r,t,e),v("maxval",r,t,e),v("seed",r,t,e))];case"Range":{let o=v("start",r,t,e),s=v("stop",r,t,e),i=v("step",r,t,e);return[n.range(o,s,i,v("dtype",r,t,e))]}case"TruncatedNormal":{let o=v("shape",r,t,e),s=v("mean",r,t,e),i=v("stdDev",r,t,e),a=v("seed",r,t,e);return[n.truncatedNormal(o,s,i,v("dtype",r,t,e),a)]}case"Zeros":return[n.zeros(v("shape",r,t,e),v("dtype",r,t,e))];case"ZerosLike":return[n.zerosLike(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ek(r,t,e){let n=v("boxes",r,t,e),o=v("scores",r,t,e),s=v("maxOutputSize",r,t,e),i=v("iouThreshold",r,t,e),a=v("scoreThreshold",r,t,e),u=v("softNmsSigma",r,t,e);return{boxes:n,scores:o,maxOutputSize:s,iouThreshold:i,scoreThreshold:a,softNmsSigma:u}}var SF=async(r,t,e,n,o=ae)=>{switch(r.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=Ek(r,t,e),p=await o.image.nonMaxSuppressionWithScoreAsync(s,i,a,u,l,c);return[p.selectedIndices,p.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=Ek(r,t,e),c=v("padToMaxOutputSize",r,t,e),p=await o.image.nonMaxSuppressionPaddedAsync(s,i,a,u,l,c);return[p.selectedIndices,p.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:i,maxOutputSize:a,iouThreshold:u,scoreThreshold:l}=Ek(r,t,e);return[await o.image.nonMaxSuppressionAsync(s,i,a,u,l)]}case"Where":{let s=o.cast(v("condition",r,t,e),"bool"),i=[await o.whereAsync(s)];return s.dispose(),i}case"ListDiff":return o.setdiff1dAsync(v("x",r,t,e),v("y",r,t,e));default:throw TypeError(`Node type ${r.op} is not implemented`)}};var NF=(r,t,e,n=ae)=>{switch(r.op){case"LowerBound":{let o=v("sortedSequence",r,t,e),s=v("values",r,t,e);return[n.lowerBound(o,s)]}case"TopKV2":{let o=v("x",r,t,e),s=v("k",r,t,e),i=v("sorted",r,t,e),a=n.topk(o,s,i);return[a.values,a.indices]}case"UpperBound":{let o=v("sortedSequence",r,t,e),s=v("values",r,t,e);return[n.upperBound(o,s)]}case"Unique":{let o=v("x",r,t,e),s=n.unique(o);return[s.values,s.indices]}case"UniqueV2":{let o=v("x",r,t,e),s=v("axis",r,t,e),i=n.unique(o,s);return[i.values,i.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var kF=(r,t,e,n=ae)=>{switch(r.op){case"Const":return t[r.name];case"PlaceholderWithDefault":let o=v("default",r,t,e);return[pr(r.name,t,e)||o];case"Placeholder":return[pr(r.name,t,e)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let c=v("x",r,t,e);return[wi(c)]}case"IdentityN":return v("x",r,t,e).map(c=>wi(c));case"Snapshot":let s=v("x",r,t,e);return[wi(s)];case"Shape":return[n.tensor1d(v("x",r,t,e).shape,"int32")];case"ShapeN":return v("x",r,t,e).map(c=>n.tensor1d(c.shape));case"Size":return[n.scalar(v("x",r,t,e).size,"int32")];case"Rank":return[n.scalar(v("x",r,t,e).rank,"int32")];case"NoOp":return[n.scalar(1)];case"Print":let i=v("x",r,t,e),a=v("data",r,t,e),u=v("message",r,t,e),l=v("summarize",r,t,e);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(u);for(let c=0;c<a.length;c++)console.log(Array.prototype.slice.call(a[c].dataSync()).slice(0,l));return[i];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rw=class{get id(){return this.handle.id}constructor(t,e){this.keyDType=t,this.valueDType=e,this.handle=pt(0),this.tensorMap=new Map,De(this.handle)}clearAndClose(){this.tensorMap.forEach(t=>t.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return pt(this.size(),"int32")}async import(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return this.tensorMap.forEach(o=>o.dispose()),this.tensorMap.clear(),B(()=>{let o=gr(e),s=n.length,i=o.length;y.assert(s===i,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${i} elements.`);for(let a=0;a<s;a++){let u=n[a],l=o[a];De(l),this.tensorMap.set(u,l)}return this.handle})}async find(t,e){this.checkKeyAndValueTensor(t,e);let n=await t.data();return B(()=>{let o=[];for(let s=0;s<n.length;s++){let i=n[s],a=this.findWithDefault(i,e);o.push(a)}return Fe(o)})}findWithDefault(t,e){let n=this.tensorMap.get(t);return n!=null?n:e}checkKeyAndValueTensor(t,e){if(t.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${t.dtype}`);if(e.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${e.dtype}`)}};var TF=async(r,t,e,n)=>{switch(r.op){case"HashTable":case"HashTableV2":{let o=n.getHashTableHandleByName(r.name);if(o!=null)return[o];{let s=v("keyDType",r,t,e),i=v("valueDType",r,t,e),a=new rw(s,i);return n.addHashTable(r.name,a),[a.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{let o=v("tableHandle",r,t,e,n),s=v("keys",r,t,e),i=v("values",r,t,e);return[await n.getHashTableById(o.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let o=v("tableHandle",r,t,e,n),s=v("keys",r,t,e),i=v("defaultValue",r,t,e);return[await n.getHashTableById(o.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let o=v("tableHandle",r,t,e,n);return[n.getHashTableById(o.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var _F=(r,t,e,n=ae)=>{switch(r.op){case"ResizeBilinear":{let o=v("images",r,t,e),s=v("size",r,t,e),i=v("alignCorners",r,t,e),a=v("halfPixelCenters",r,t,e);return[n.image.resizeBilinear(o,[s[0],s[1]],i,a)]}case"ResizeNearestNeighbor":{let o=v("images",r,t,e),s=v("size",r,t,e),i=v("alignCorners",r,t,e),a=v("halfPixelCenters",r,t,e);return[n.image.resizeNearestNeighbor(o,[s[0],s[1]],i,a)]}case"CropAndResize":{let o=v("image",r,t,e),s=v("boxes",r,t,e),i=v("boxInd",r,t,e),a=v("cropSize",r,t,e),u=v("method",r,t,e),l=v("extrapolationValue",r,t,e);return[n.image.cropAndResize(o,s,i,a,u,l)]}case"ImageProjectiveTransformV3":{let o=v("images",r,t,e),s=v("transforms",r,t,e),i=v("outputShape",r,t,e),a=v("fillValue",r,t,e),u=v("interpolation",r,t,e),l=v("fillMode",r,t,e);return[n.image.transform(o,s,u.toLowerCase(),l.toLowerCase(),a,i)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var EF=(r,t,e,n=ae)=>{switch(r.op){case"Equal":return[n.equal(v("a",r,t,e),v("b",r,t,e))];case"NotEqual":return[n.notEqual(v("a",r,t,e),v("b",r,t,e))];case"Greater":return[n.greater(v("a",r,t,e),v("b",r,t,e))];case"GreaterEqual":return[n.greaterEqual(v("a",r,t,e),v("b",r,t,e))];case"Less":return[n.less(v("a",r,t,e),v("b",r,t,e))];case"LessEqual":return[n.lessEqual(v("a",r,t,e),v("b",r,t,e))];case"LogicalAnd":return[n.logicalAnd(v("a",r,t,e),v("b",r,t,e))];case"LogicalNot":return[n.logicalNot(v("a",r,t,e))];case"LogicalOr":return[n.logicalOr(v("a",r,t,e),v("b",r,t,e))];case"Select":case"SelectV2":return[n.where(v("condition",r,t,e),v("a",r,t,e),v("b",r,t,e))];case"BitwiseAnd":return[n.bitwiseAnd(v("a",r,t,e),v("b",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var AF=(r,t,e,n=ae)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[n.matMul(v("a",r,t,e),v("b",r,t,e),v("transposeA",r,t,e),v("transposeB",r,t,e))];case"Einsum":return[n.einsum(v("equation",r,t,e),...v("tensors",r,t,e))];case"Transpose":return[n.transpose(v("x",r,t,e),v("perm",r,t,e))];case"_FusedMatMul":let[o,s]=v("fusedOps",r,t,e),i=o==="biasadd",a=s==="prelu",u=v("numArgs",r,t,e),l=v("leakyreluAlpha",r,t,e);if(i){if(a&&u!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&u!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=v("args",r,t,e);return[n.fused.matMul({a:v("a",r,t,e),b:v("b",r,t,e),transposeA:v("transposeA",r,t,e),transposeB:v("transposeB",r,t,e),bias:c,activation:s,preluActivationWeights:p,leakyreluAlpha:l})];case"MatrixBandPart":return[n.linalg.bandPart(v("a",r,t,e),v("numLower",r,t,e),v("numUpper",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var DF=(r,t,e,n=ae)=>{switch(r.op){case"EuclideanNorm":return[n.euclideanNorm(v("x",r,t,e),v("axis",r,t,e),v("keepDims",r,t,e))];case"FusedBatchNorm":case"FusedBatchNormV2":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"FusedBatchNormV3":return[n.batchNorm(v("x",r,t,e),v("mean",r,t,e),v("variance",r,t,e),v("offset",r,t,e),v("scale",r,t,e),v("epsilon",r,t,e))];case"LRN":return[n.localResponseNormalization(v("x",r,t,e),v("radius",r,t,e),v("bias",r,t,e),v("alpha",r,t,e),v("beta",r,t,e))];case"Softmax":return[n.softmax(v("x",r,t,e))];case"LogSoftmax":return[n.logSoftmax(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var $F=(r,t,e,n=ae)=>{switch(r.op){case"RaggedGather":{let{outputNestedSplits:o,outputDenseValues:s}=n.raggedGather(v("paramsNestedSplits",r,t,e),v("paramsDenseValues",r,t,e),v("indices",r,t,e),v("outputRaggedRank",r,t,e));return o.concat(s)}case"RaggedRange":{let{rtNestedSplits:o,rtDenseValues:s}=n.raggedRange(v("starts",r,t,e),v("limits",r,t,e),v("splits",r,t,e));return[o,s]}case"RaggedTensorToTensor":return[n.raggedTensorToTensor(v("shape",r,t,e),v("values",r,t,e),v("defaultValue",r,t,e),v("rowPartitionTensors",r,t,e),v("rowPartitionTypes",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var RF=(r,t,e,n=ae)=>{switch(r.op){case"Max":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.max(v("x",r,t,e),a,u)]}case"Mean":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.mean(v("x",r,t,e),a,u)]}case"Min":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.min(v("x",r,t,e),a,u)]}case"Sum":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.sum(v("x",r,t,e),a,u)]}case"All":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.all(v("x",r,t,e),a,u)]}case"Any":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.any(v("x",r,t,e),a,u)]}case"ArgMax":{let a=v("axis",r,t,e);return[n.argMax(v("x",r,t,e),a)]}case"ArgMin":{let a=v("axis",r,t,e);return[n.argMin(v("x",r,t,e),a)]}case"Prod":{let a=v("axis",r,t,e),u=v("keepDims",r,t,e);return[n.prod(v("x",r,t,e),a,u)]}case"Cumprod":{let a=v("axis",r,t,e),u=v("exclusive",r,t,e),l=v("reverse",r,t,e);return[n.cumprod(v("x",r,t,e),a,u,l)]}case"Cumsum":{let a=v("axis",r,t,e),u=v("exclusive",r,t,e),l=v("reverse",r,t,e);return[n.cumsum(v("x",r,t,e),a,u,l)]}case"Bincount":let o=v("x",r,t,e),s=v("weights",r,t,e),i=v("size",r,t,e);return[n.bincount(o,s,i)];case"DenseBincount":{let a=v("x",r,t,e),u=v("weights",r,t,e),l=v("size",r,t,e),c=v("binaryOutput",r,t,e);return[n.denseBincount(a,u,l,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var FF=(r,t,e,n=ae)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=v("n",r,t,e),s=v("axis",r,t,e),i=v("tensors",r,t,e);return i=i.slice(0,o),[n.concat(i,s)]}case"Gather":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gather(o,n.cast(s,"int32"),0)]}case"GatherV2":{let o=v("axis",r,t,e),s=v("batchDims",r,t,e),i=v("x",r,t,e),a=v("indices",r,t,e);return[n.gather(i,n.cast(a,"int32"),o,s)]}case"Reverse":{let o=v("dims",r,t,e),s=[];for(let a=0;a<o.length;a++)o[a]&&s.push(a);let i=v("x",r,t,e);return[n.reverse(i,s)]}case"ReverseV2":{let o=v("axis",r,t,e),s=v("x",r,t,e);return[n.reverse(s,o)]}case"Slice":{let o=v("begin",r,t,e),s=v("size",r,t,e);return[n.slice(v("x",r,t,e),o,s)]}case"StridedSlice":{let o=v("begin",r,t,e),s=v("end",r,t,e),i=v("strides",r,t,e),a=v("beginMask",r,t,e),u=v("endMask",r,t,e),l=v("ellipsisMask",r,t,e),c=v("newAxisMask",r,t,e),p=v("shrinkAxisMask",r,t,e),m=v("x",r,t,e);return[n.stridedSlice(m,o,s,i,a,u,l,c,p)]}case"Pack":return B(()=>{let o=v("axis",r,t,e),s=v("tensors",r,t,e),i=s[0].shape,a=n.squeeze(s[0]).shape,u=s.map(l=>{let c=y.arraysEqual(l.shape,i);if(!c&&!y.arraysEqual(n.squeeze(l).shape,a))throw new Error("the input tensors shape does not match");return c?l:n.reshape(l,i)});return[n.stack(u,o)]});case"Unpack":{let o=v("axis",r,t,e),s=v("tensor",r,t,e);return n.unstack(s,o)}case"Tile":{let o=v("reps",r,t,e);return[n.tile(v("x",r,t,e),o)]}case"Split":case"SplitV":{let o=v("axis",r,t,e),s=v("numOrSizeSplits",r,t,e),i=v("x",r,t,e);return n.split(i,s,o)}case"ScatterNd":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("shape",r,t,e);return[n.scatterND(o,s,i)]}case"GatherNd":{let o=v("x",r,t,e),s=v("indices",r,t,e);return[n.gatherND(o,s)]}case"SparseToDense":{let o=v("sparseIndices",r,t,e),s=v("outputShape",r,t,e),i=v("sparseValues",r,t,e),a=v("defaultValue",r,t,e);return[n.sparseToDense(o,i,s,i.dtype===a.dtype?a:n.cast(a,i.dtype))]}case"TensorScatterUpdate":{let o=v("indices",r,t,e),s=v("values",r,t,e),i=v("tensor",r,t,e);return[n.tensorScatterUpdate(i,o,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var OF=(r,t,e,n=ae)=>{switch(r.op){case"SparseFillEmptyRows":{let{outputIndices:o,outputValues:s,emptyRowIndicator:i,reverseIndexMap:a}=n.sparse.sparseFillEmptyRows(v("indices",r,t,e),v("values",r,t,e),v("denseShape",r,t,e),v("defaultValue",r,t,e));return[o,s,i,a]}case"SparseReshape":{let{outputIndices:o,outputShape:s}=n.sparse.sparseReshape(v("inputIndices",r,t,e),v("inputShape",r,t,e),v("newShape",r,t,e));return[o,s]}case"SparseSegmentMean":return[n.sparse.sparseSegmentMean(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];case"SparseSegmentSum":return[n.sparse.sparseSegmentSum(v("data",r,t,e),v("indices",r,t,e),v("segmentIds",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MF=(r,t,e,n=ae)=>{switch(r.op){case"FFT":return[n.fft(v("x",r,t,e))];case"IFFT":return[n.ifft(v("x",r,t,e))];case"RFFT":return[n.rfft(v("x",r,t,e))];case"IRFFT":return[n.irfft(v("x",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var PF=(r,t,e,n=ae)=>{switch(r.op){case"StaticRegexReplace":return[n.string.staticRegexReplace(v("input",r,t,e),v("pattern",r,t,e),v("rewrite",r,t,e),v("replaceGlobal",r,t,e))];case"StringNGrams":{let{nGrams:o,nGramsSplits:s}=n.string.stringNGrams(v("data",r,t,e),v("dataSplits",r,t,e),v("separator",r,t,e),v("nGramWidths",r,t,e),v("leftPad",r,t,e),v("rightPad",r,t,e),v("padWidth",r,t,e),v("preserveShortSequences",r,t,e));return[o,s]}case"StringSplit":{let{indices:o,values:s,shape:i}=n.string.stringSplit(v("input",r,t,e),v("delimiter",r,t,e),v("skipEmpty",r,t,e));return[o,s,i]}case"StringToHashBucketFast":return[n.string.stringToHashBucketFast(v("input",r,t,e),v("numBuckets",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var LF=(r,t,e,n=ae)=>{switch(r.op){case"Cast":return[n.cast(v("x",r,t,e),v("dtype",r,t,e))];case"ExpandDims":{let o=v("axis",r,t,e);return[n.expandDims(v("x",r,t,e),o)]}case"Squeeze":{let o=v("axis",r,t,e);return[n.squeeze(v("x",r,t,e),o)]}case"Reshape":return[n.reshape(v("x",r,t,e),v("shape",r,t,e))];case"EnsureShape":return[n.ensureShape(v("x",r,t,e),v("shape",r,t,e))];case"MirrorPad":return[n.mirrorPad(v("x",r,t,e),v("padding",r,t,e),v("mode",r,t,e))];case"PadV2":case"Pad":return[n.pad(v("x",r,t,e),v("padding",r,t,e),v("constantValue",r,t,e))];case"SpaceToBatchND":{let o=v("blockShape",r,t,e),s=v("paddings",r,t,e);return[n.spaceToBatchND(v("x",r,t,e),o,s)]}case"BatchToSpaceND":{let o=v("blockShape",r,t,e),s=v("crops",r,t,e);return[n.batchToSpaceND(v("x",r,t,e),o,s)]}case"DepthToSpace":{let o=v("blockSize",r,t,e),s=v("dataFormat",r,t,e).toUpperCase();return[n.depthToSpace(v("x",r,t,e),o,s)]}case"BroadcastTo":return[n.broadcastTo(v("x",r,t,e),v("shape",r,t,e))];case"BroadcastArgs":return[n.broadcastArgs(v("s0",r,t,e),v("s1",r,t,e))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function Ak(r,t,e,n,o=B){let s=((i,a,u)=>{switch(i.category){case"arithmetic":return o(()=>fF(i,a,u));case"basic_math":return o(()=>dF(i,a,u));case"control":return wF(i,a,u);case"convolution":return o(()=>CF(i,a,u));case"creation":return o(()=>vF(i,a,u));case"dynamic":return SF(i,a,u);case"evaluation":return o(()=>NF(i,a,u));case"image":return o(()=>_F(i,a,u));case"graph":return o(()=>kF(i,a,u));case"logical":return o(()=>EF(i,a,u));case"matrices":return o(()=>AF(i,a,u));case"normalization":return o(()=>DF(i,a,u));case"ragged":return o(()=>$F(i,a,u));case"reduction":return o(()=>RF(i,a,u));case"slice_join":return o(()=>FF(i,a,u));case"sparse":return o(()=>OF(i,a,u));case"spectral":return o(()=>MF(i,a,u));case"string":return o(()=>PF(i,a,u));case"transformation":return o(()=>LF(i,a,u));case"hash_table":return TF(i,a,u,n);case"custom":let l=Vb(i.op);if(l&&l.customExecutor)return l.customExecutor(new Qb(i,a,u));throw TypeError(`Custom op ${i.op} is not registered.`);default:throw TypeError(`Unknown op '${i.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,t,e);return y.isPromise(s)?s.then(i=>[].concat(i)):[].concat(s)}var Uh=class{constructor(t={},e={},n={},o={},s){this.weightMap=t,this.tensorArrayMap=e,this.tensorListMap=n,this.functionMap=o,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(t,e){return{id:t,frameName:e,iterationId:0}}set currentContext(t){this.contexts!==t&&(this.contexts=t,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let t=[];for(let e=0;e<this.contexts.length-1;e++){let n=this.contexts.slice(0,this.contexts.length-e);t.push(this.contextIdforContexts(n))}t.push(""),this._currentContextIds=t}contextIdforContexts(t){return t?t.map(e=>e.id===0&&e.iterationId===0?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(t){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,t)),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 t=Object.assign({},this.contexts[this.contexts.length-1]);t.iterationId+=1,t.id=this.lastId,this.contexts.splice(-1,1,t),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(t){return this.weightMap[t]}addTensorArray(t){this.tensorArrayMap[t.id]=t}getTensorArray(t){return this.tensorArrayMap[t]}addTensorList(t){this.tensorListMap[t.id]=t}getTensorList(t){return this.tensorListMap[t]}dispose(t){for(let e in this.tensorArrayMap)this.tensorArrayMap[e].clearAndClose(t);for(let e in this.tensorListMap)this.tensorListMap[e].clearAndClose(t)}};function Dk(r,t,e,n){let o=new Set,s=[],i=null,a=null,u=new Set,l=new Set(Object.keys(r).map(m=>In(m)[0]));n=n||[];let c=new Set(n.map(m=>In(m.name)[0])),p=[...t];for(;p.length>0;){let m=p.pop();if((Bu(m)||ftt(m)||dtt(m))&&i==null&&(i=m,a=i.children.map(f=>f.name).filter(f=>o.has(f))),o.add(m.name),e[m.name]==null&&!l.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{u.has(f.name)||(u.add(f.name),p.push(f))})}}return{inputs:r,outputs:t,usedNodes:o,missingInputs:s,dynamicNode:i,syncInputs:a}}function zF(r,t){let{usedNodes:e,inputs:n}=t,o=Object.keys(n).map(g=>In(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],i=g=>e.has(typeof g=="string"?g:g.name);function a(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let u=a([...o,...r.weights,...s]).filter(i),l=a([...u,...Object.values(r.nodes)]).filter(i),c=new Map(l.map(g=>[g.name,g])),p={};for(let g of l){p[g.name]=p[g.name]||0;for(let x of g.children)i(x)||(p[x.name]=Number.POSITIVE_INFINITY),p[x.name]=(p[x.name]||0)+1}let m=Object.entries(p).filter(([,g])=>g===0).map(([g])=>g),f=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(i))--p[b.name]===0&&(f.push(b.name),m.push(b.name))}let d=f.map(g=>c.get(g)),h=ltt(d,u);return utt(h,u),h}function ltt(r,t){let e=new Map(r.map(i=>[i.name,i])),n=t.map(i=>i.name),o=new Set(n);for(;n.length>0;){let i=n.pop(),a=e.get(i);for(let u of a.children)!e.has(u.name)||o.has(u.name)||(o.add(u.name),n.push(u.name))}return r.filter(i=>o.has(i.name))}var nd=class extends Error{constructor(t){super(`NodesExecutionOrderError: ${t}`)}};function utt(r,t){let e=new Map(r.map((a,u)=>[a.name,u])),n=new Set(t.map(a=>a.name)),o=a=>n.has(typeof a=="string"?a:a.name),s=new Set(r.map(a=>a.name)),i=a=>s.has(typeof a=="string"?a:a.name);for(let a of r){for(let u of a.children.filter(i)){if(!e.has(u.name))throw new nd(`Child ${u.name} of node ${a.name} is unreachable.`);if(e.get(a.name)>e.get(u.name))throw new nd(`Node ${a.name} is scheduled to run after its child ${u.name}.`)}if(!o(a))for(let u of a.inputs){if(!e.has(u.name))throw new nd(`Input ${u.name} of node ${a.name} is unreachable.`);if(e.get(u.name)>e.get(a.name))throw new nd(`Node ${a.name} is scheduled to run before its input ${u.name}.`)}}}function BF(r){let t=new Map(r.map((a,u)=>[a.name,u])),e=Number.MAX_SAFE_INTEGER,n=r.map((a,u)=>Bu(a)?e:u),o=a=>{let u=n[t.get(a.name)];return u==null?-1:u},s=r.map((a,u)=>a.children.map(o).reduce((l,c)=>Math.max(l,c),n[u])),i=new Map;for(let a=0;a<r.length;++a){let u=s[a];if(u===e)continue;let l=r[a],c=r[u];i.has(c.name)||i.set(c.name,[]),i.get(c.name).push(l)}return i}var ctt=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),ptt=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),mtt=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function Bu(r){return ctt.has(r.op)}function ftt(r){return ptt.has(r.op)}function dtt(r){return mtt.has(r.op)}var Hh=class r{get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(t){let e=Object.keys(t).map(n=>t[n].map(o=>o.id));this._weightIds=[].concat(...e),this._weightMap=t}set resourceManager(t){this._resourceManager=t}get inputs(){return this._inputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(t=>({name:t.name,shape:t.attrParams.shape?t.attrParams.shape.value:void 0,dtype:t.attrParams.dtype?t.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(t=>t.signatureKey||t.name)}get outputNodes(){return this._outputs.map(t=>{let e=t.signatureKey||t.name;return t.defaultOutput?`${e}:${t.defaultOutput}`:e})}get functions(){return Object.keys(this._functions).reduce((t,e)=>(t[e]=this._functions[e].signature,t),{})}constructor(t,e){this.graph=t,this.parent=e,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=t.outputs,this._inputs=t.inputs,this._initNodes=t.initNodes,this._signature=t.signature,this._functions=t.functions,t.functions!=null&&Object.keys(t.functions).forEach(n=>{this._functionExecutorMap[n]=new r(t.functions[n],this)})}getCompilationKey(t,e){let n=t.map(s=>s.name).sort(),o=e.map(s=>s.name).sort();return n.join(this.SEPARATOR)+"--"+o.join(this.SEPARATOR)}compile(t,e){let n=Dk(t,e,this.weightMap,this._initNodes),{missingInputs:o,dynamicNode:s,syncInputs:i}=n;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${i}]`);if(o.length>0){let l=e.map(p=>p.name),c=Object.keys(t);throw new Error(`Cannot compute the outputs [${l}] from the provided inputs [${c}]. Missing the following inputs: [${o}]`)}let a=zF(this.graph,n),u=BF(a);return{orderedNodes:a,nodeLiveUntilMap:u}}cloneAndKeepTensor(t){if(t==null)return null;let e=t.clone();return De(e),e}cloneTensorList(t){return t?t.map(n=>this.cloneAndKeepTensor(n)):null}cloneTensorMap(t){return Object.fromEntries(Object.entries(t).map(([e,n])=>[e,this.cloneTensorList(n)]))}execute(t,e){this.disposeIntermediateTensors(),t=this.mapInputs(t);let n=Object.keys(t).sort();this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e);let o=n.map(m=>this.graph.nodes[In(m)[0]]),s=e.map(m=>In(m)[0]),i=new Set(s),a=s.map(m=>this.graph.nodes[m]);a.length===0&&(a=this._outputs);let u=this.getCompilationKey(o,a),l=this.compiledMap.get(u);l==null&&(l=this.compile(t,a),this.compiledMap.set(u,l));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},p={};return B(()=>{let m=new Uh(this.weightMap,c,p,this.functionExecutorMap,this.parseNodeNameCache),f=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(t).forEach(x=>{let[b,w]=In(x,m),I=[];I[w]=t[x],f[b]=I,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(I))});let d=this.getFrozenTensorIds(f),{orderedNodes:h,nodeLiveUntilMap:g}=l;for(let x of h){if(f[x.name])continue;let b=Ak(x,f,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);f[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,f,m,d,i,g.get(x.name))}return this.parent==null&&m.dispose(d),e.map(x=>pr(x,f,m))})}getFrozenTensorIds(t){let e=[].concat.apply([],Object.keys(t).map(n=>t[n]).map(n=>n.map(o=>o.id)));return new Set(e)}checkTensorForDisposal(t,e,n,o,s,i,a){if(!(Bu(e)||i.has(t))){for(let u of n[t])u!=null&&(a[u.id]=(a[u.id]||0)+e.children.length);for(let u of e.inputs){if(Bu(u))continue;let l=lk(u.name,n,o);if(l!=null)for(let c of l){if(!c||c.kept||s.has(c.id))continue;let p=a[c.id];p===1?(c.dispose(),delete a[c.id]):p!=null&&a[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(t,e,n,o,s,i){function a(u){return Bu(u)||s.has(u.name)}if(!(Bu(t)||i==null))for(let u of i){if(a(u))continue;let l=lk(u.name,e,n);for(let c of l)!c||c.kept||o.has(c.id)||c.dispose()}}async executeAsync(t,e){return this._executeAsync(t,e)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(t=>{for(let e of t)e&&!e.isDisposed&&e.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(t,e,n=!1,o={},s={}){this.disposeIntermediateTensors(),n||(t=this.mapInputs(t),this.checkInputs(t),this.checkInputShapeAndType(t),e=this.mapOutputs(e),this.checkOutputs(e));try{this.keepIntermediateTensors=L().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let i=new Uh(this.weightMap,o,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let a=await this.executeWithControlFlow(t,i,e,n),u=e.map(m=>pr(m,a,i)),l=u.map(m=>m.id),c=Object.keys(t).map(m=>t[m].id),p=new Set([...l,...c,...this.weightIds]);return Object.values(a).forEach(m=>{m.forEach(f=>{f&&!f.isDisposed&&!p.has(f.id)&&f.dispose()})}),this.parent==null&&i.dispose(p),u}async executeFunctionAsync(t,e,n){let o=t.reduce((s,i,a)=>(s[this.inputs[a].name]=i,s),{});return this._executeAsync(o,this.outputNodes,!0,e,n)}async executeWithControlFlow(t,e,n,o){let s=Object.keys(t),i=s.map(I=>this.graph.nodes[In(I)[0]]),a=n.map(I=>In(I)[0]),u=new Set(a),l=a.map(I=>this.graph.nodes[I]);l.length===0&&(l=this._outputs);let{usedNodes:c,missingInputs:p,dynamicNode:m,syncInputs:f}=Dk(t,l,this.weightMap,this._initNodes),d=[...i,...this.graph.weights,...this._initNodes||[]].map(I=>({node:I,contexts:e.currentContext})),h=Object.assign({},this.weightMap);Object.keys(t).forEach(I=>{let[N,E]=In(I),A=[];A[E]=t[I],h[N]=A});let g={},x=this.getFrozenTensorIds(h),b={};for(;d.length>0;){let I=this.processStack(i,d,e,h,b,x,u,g,c);await Promise.all(I)}m==null&&!o&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let w=l.filter(I=>!Bu(I)&&!pr(I.name,h,e)).map(I=>I.name);if(w.length>0){let I="";throw m!=null&&(I=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${f}]`),new Error(`Cannot compute the outputs [${w}] from the provided inputs [${s}]. Consider providing the following inputs: [${p}]. ${I}`)}return h}processStack(t,e,n,o,s,i,a,u,l){let c=[];for(;e.length>0;){let p=e.pop();n.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&v("isConstant",p.node,o,n)&&([m]=bi(p.node.name,n)),o[p.node.name]==null){let f=Ak(p.node,o,n,this._resourceManager);m||([m]=bi(p.node.name,n));let d=n.currentContext;y.isPromise(f)?c.push(f.then(h=>(o[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),n.currentContext=d,this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l),h))):(o[m]=f,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(f)),this.checkTensorForDisposal(m,p.node,o,n,i,a,u),this.processChildNodes(p.node,e,n,o,s,l))}else this.processChildNodes(p.node,e,n,o,s,l)}return c}processChildNodes(t,e,n,o,s,i){t.children.forEach(a=>{let[u]=bi(a.name,n);s[u]||!i.has(a.name)||(a.op==="Merge"?a.inputNames.some(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})):a.inputNames.every(l=>!!pr(l,o,n))&&(s[u]=!0,e.push({contexts:n.currentContext,node:a})))})}dispose(){Object.keys(this.weightMap).forEach(t=>this.weightMap[t].forEach(e=>e.dispose()))}checkInputShapeAndType(t){Object.keys(t).forEach(e=>{let n=t[e],[o]=In(e),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let i=s.attrParams.shape.value,a=i.length===n.shape.length&&n.shape.every((u,l)=>i[l]===-1||i[l]===u);y.assert(a,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${i}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(t){var e,n;let o={};for(let s in t){let i=(n=(e=this._signature)===null||e===void 0?void 0:e.inputs)===null||n===void 0?void 0:n[s];i!=null?o[i.name]=t[s]:o[s]=t[s]}return o}checkInputs(t){let e=Object.keys(t).filter(n=>{let[o]=In(n);return this.graph.nodes[o]==null});if(e.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${e}] that are not part of graph`)}mapOutputs(t){return t.map(e=>{var n,o;let s=(o=(n=this._signature)===null||n===void 0?void 0:n.outputs)===null||o===void 0?void 0:o[e];return s!=null?s.name:e},{})}checkOutputs(t){t.forEach(e=>{let[n]=In(e);if(!this.graph.nodes[n])throw new Error(`The output '${e}' is not found in the graph`)})}};var nw=class{constructor(t={},e={}){this.hashTableNameToHandle=t,this.hashTableMap=e}addHashTable(t,e){this.hashTableNameToHandle[t]=e.handle,this.hashTableMap[e.id]=e}getHashTableHandleByName(t){return this.hashTableNameToHandle[t]}getHashTableById(t){return this.hashTableMap[t]}dispose(){for(let t in this.hashTableMap)this.hashTableMap[t].clearAndClose(),delete this.hashTableMap[t];for(let t in this.hashTableNameToHandle)this.hashTableNameToHandle[t].dispose(),delete this.hashTableNameToHandle[t]}};var htt="?tfjs-format=file",gtt="model.json",qh=class{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(t,e={},n=Mr){this.modelUrl=t,this.loadOptions=e,this.version="n/a",this.io=n,e==null&&(this.loadOptions={}),this.resourceManager=new nw}findIOHandler(){let t=this.modelUrl;if(t.load!=null)this.handler=t;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(t,this.loadOptions);else{let e=this.io.getLoadHandlers(t,this.loadOptions);if(e.length===0)e.push(this.io.browserHTTPRequest(t,this.loadOptions));else if(e.length>1)throw new Error(`Found more than one (${e.length}) load handlers for URL '${[t]}'`);this.handler=e[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let t=this.handler.load();return y.isPromise(t)?t.then(e=>e.getWeightStream==null?this.loadSync(e):this.loadStreaming(e)):this.loadSync(t)}loadSync(t){let e=this.io.decodeWeights(t.weightData,t.weightSpecs);return this.loadWithWeightMap(t,e)}async loadStreaming(t){if(t.getWeightStream==null)throw new Error("Model artifacts missing streamWeights function");let e=await ax(t.getWeightStream(),t.weightSpecs);return this.loadWithWeightMap(t,e)}loadWithWeightMap(t,e){this.artifacts=t;let n=this.artifacts.modelTopology,o=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(o=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=o,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Hh(Wh.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(e),this.executor.resourceManager=this.resourceManager,t.modelInitializer!=null&&t.modelInitializer.node!=null){let s=Wh.Instance.transformGraph(t.modelInitializer);this.initializer=new Hh(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=t.initializerSignature}return!0}async save(t,e){if(typeof t=="string"){let n=this.io.getSaveHandlers(t);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${t}'`);t=n[0]}if(t.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)}addStructuredOutputNames(t){if(this.structuredOutputKeys){let e=t instanceof Lt?[t]:t,n={};return e.forEach((o,s)=>n[this.structuredOutputKeys[s]]=o),n}return t}predict(t,e){let n=this.execute(t,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(t,e){let n=await this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(t){var e;if(!(t instanceof Lt)&&!Array.isArray(t)){let s=(e=this.signature)===null||e===void 0?void 0:e.inputs;if(s!=null)for(let i in s){let a=s[i];a.resourceId!=null&&(t[i]=this.resourceIdToCapturedInput[a.resourceId])}return t}t=Array.isArray(t)?t:[t];let n=Object.keys(this.resourceIdToCapturedInput).length;if(t.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${t.length} input tensors provided.`);let o=0;return this.inputNodes.reduce((s,i)=>{var a,u,l;let c=(l=(u=(a=this.signature)===null||a===void 0?void 0:a.inputs)===null||u===void 0?void 0:u[i])===null||l===void 0?void 0:l.resourceId;return c!=null?s[i]=this.resourceIdToCapturedInput[c]:s[i]=t[o++],s},{})}normalizeOutputs(t){return t=t||this.outputNodes,Array.isArray(t)?t:[t]}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(t){if(this.resourceIdToCapturedInput={},this.initializerSignature){let e=this.initializerSignature.outputs,n=Object.keys(e);for(let o=0;o<n.length;o++){let s=n[o],i=e[s];this.resourceIdToCapturedInput[i.resourceId]=t[o]}}}execute(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=this.executor.execute(t,e);return n.length>1?n:n[0]}async executeAsync(t,e){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),e=this.normalizeOutputs(e);let n=await this.executor.executeAsync(t,e);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(t){return Object.keys(t).reduce((e,n)=>(e[n]=[t[n]],e),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&Tt(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}};async function xtt(r,t={},e=Mr){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&typeof r=="string"&&(r=btt(r));let n=new qh(r,t,e);return await n.load(),n}function ytt(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 t;if(r instanceof Array){let[n,o]=r;if(!n)throw new Error("modelJSON must be the first element of the array");if(!o||!(o instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in n))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in n))throw new Error("Model JSON is missing 'weightsManifest'");let s=Mr.getWeightSpecs(n.weightsManifest),i=Mr.getModelArtifactsForJSONSync(n,s,o);t=Mr.fromMemorySync(i)}else if("load"in r)t=r;else if("modelTopology"in r&&"weightSpecs"in r&&"weightData"in r)t=Mr.fromMemorySync(r);else throw new Error("Unknown model format");let e=new qh(t);return e.load(),e}function btt(r){return r.endsWith("/")||(r=r+"/"),`${r}${gtt}${htt}`}var VF="4.21.0";var aO={};Kt(aO,{CSVDataset:()=>id,Dataset:()=>Ii,FileDataSource:()=>ud,TextLineDataset:()=>sd,URLDataSource:()=>cd,array:()=>ZF,csv:()=>rO,func:()=>nO,generator:()=>oO,microphone:()=>iO,version_data:()=>Jk,webcam:()=>sO,zip:()=>JF});var YF=Gl(hh());var KF=Gl(hh());function GF(r,t){return ow(r,t)}function ow(r,t,e=new Map,n=new Set){if(r==null)return null;if(typeof Blob=="function"&&r instanceof Blob)return r.slice();if(n.has(r))throw new Error("Circular references are not supported.");if(e.has(r))return e.get(r);let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(o.recurse)if(Vu(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let a=r[i],u=ow(a,t,e,n);s[i]=u}return n.delete(r),r.__proto__&&(s.__proto__=r.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return e.set(r,o.value),o.value}function WF(r,t=Rk){return UF(r,t)}function UF(r,t,e=new Set){let n=r[0];if(e.has(n))throw new Error("Circular references are not supported.");let o=t(r);if(o.recurse&&o.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(o.recurse)if(Vu(n)){let s=Array.isArray(n)?[]:{};e.add(n);for(let i in n){let a=r.map(l=>l[i]),u=UF(a,t,e);s[i]=u}return e.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return o.value}function Rk(r){return r===null?null:Vu(r[0])?{value:null,recurse:!0}:{value:r,recurse:!1}}async function sw(r,t){let e=new Map;ow(r,t,e);for(let o of Array.from(e.keys())){let s=e.get(o);if(y.isPromise(s)){let i=await s;e.set(o,i)}}return ow(r,t,e)}function Vu(r){let t=!1;if(L().get("IS_BROWSER"))t=r instanceof TextDecoder;else{let{StringDecoder:e}=$k();t=r instanceof e}return r!=null&&!ArrayBuffer.isView(r)&&(Array.isArray(r)||typeof r=="object"&&!(r instanceof Lt)&&!(r instanceof Promise)&&!t)}function HF(r){return r==null||wtt(r)||Array.isArray(r)||typeof r=="object"&&r instanceof Lt||y.isTypedArray(r)}function wtt(r){return r===null||typeof r!="object"&&typeof r!="function"}function qF(r){return GF(r,Itt)}function Itt(r){return r instanceof Lt?{value:r.clone(),recurse:!1}:Vu(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var od=class{constructor(t){if(this.capacity=t,this.begin=0,this.end=0,t==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(t<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(t),this.doubledCapacity=2*t}wrap(t){for(;t<0;)t+=this.doubledCapacity;return t%this.doubledCapacity}get(t){if(t<0)throw new RangeError("Can't get item at a negative index.");return this.data[t%this.capacity]}set(t,e){if(t<0)throw new RangeError("Can't set item at a negative index.");this.data[t%this.capacity]=e}length(){let t=this.end-this.begin;return t<0&&(t=this.doubledCapacity+t),t}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,t),this.end=this.wrap(this.end+1)}pushAll(t){for(let e of t)this.push(e)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let t=this.get(this.end);return this.set(this.end,void 0),t}unshift(t){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,t)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),t}shuffleExcise(t){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.wrap(this.begin+t),n=this.get(e);return this.set(e,this.pop()),n}};var Kh=class r extends od{constructor(){super(r.INITIAL_CAPACITY)}isFull(){return!1}push(t){super.isFull()&&this.expand(),super.push(t)}unshift(t){super.isFull()&&this.expand(),super.unshift(t)}expand(){let t=this.capacity*2,e=new Array(t),n=this.length();for(let o=0;o<n;o++)e[o]=this.get(this.wrap(this.begin+o));this.data=e,this.capacity=t,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};Kh.INITIAL_CAPACITY=32;function qk(r){return new Fk(r)}function jh(r){return new Ok(r)}function jF(r,t){return new aw(r,t)}function XF(r,t=Dl.FAIL){return new Uk(r,t)}var er=class{async toArray(){let t=[],e=await this.next();for(;!e.done;)t.push(e.value),e=await this.next();return t}async toArrayForTest(){let t=this.prefetch(100),e=[],n=await t.next();for(;!n.done;)e.push(n.value),n=await t.next();return e}async resolveFully(){let t=await this.next();for(;!t.done;)t=await this.next()}async resolveWhile(t){let e=await this.next(),n=t(e.value);for(;!e.done&&n;)e=await this.next(),n=t(e.value)}handleErrors(t){return new Gk(this,t)}filter(t){return new Bk(this,t)}map(t){return new Vk(this,t)}mapAsync(t){return new iw(this,t)}serialMapAsync(t){return new iw(this,t).serial()}flatmap(t){return new Wk(this,t)}async forEachAsync(t){return this.map(t).resolveFully()}async serialForEach(t){return this.serialMapAsync(t).resolveWhile(e=>e===!0)}rowMajorBatch(t,e=!0){return new zk(this,t,e)}columnMajorBatch(t,e=!0,n=Rk){return this.rowMajorBatch(t,e).map(s=>WF(s,n))}concatenate(t,e){return new aw(qk([this,t]),e)}take(t){return t<0||t==null?this:new Lk(this,t)}skip(t){return t<0||t==null?this:new Pk(this,t)}prefetch(t){return new lw(this,t)}shuffle(t,e){return new Hk(this,t,e)}serial(){return new Mk(this)}},Fk=class extends er{constructor(t){super(),this.items=t,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let t=this.items[this.trav];return this.trav++,{value:qF(t),done:!1}}},Ok=class extends er{constructor(t){super(),this.nextFn=t}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(t){throw t.message=`Error thrown while iterating through a dataset: ${t.message}`,t}}},Mk=class extends er{constructor(t){super(),this.upstream=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},Pk=class extends er{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let t=await this.upstream.next();if(t.done)return t;Tt(t.value)}return this.upstream.next()}},Lk=class extends er{constructor(t,e){super(),this.upstream=t,this.maxCount=e,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},zk=class extends er{constructor(t,e,n=!0){super(),this.upstream=t,this.batchSize=e,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let t=[];for(;t.length<this.batchSize;){let e=await this.upstream.next();if(e.done)return this.enableSmallLastBatch&&t.length>0?{value:t,done:!1}:{value:null,done:!0};t.push(e.value)}return{value:t,done:!1}}},Bk=class extends er{constructor(t,e){super(),this.upstream=t,this.predicate=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let t=await this.upstream.next();if(t.done||this.predicate(t.value))return t;Tt(t.value)}}},Vk=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Map`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Gk=class extends er{constructor(t,e){super(),this.upstream=t,this.handler=e,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(t){if(!this.handler(t))return{value:null,done:!0}}}},iw=class extends er{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let t=await this.upstream.next();if(t.done)return{value:null,done:!0};let e=Io.getTensorsInContainer(t.value),n=await this.transform(t.value),o=Io.getTensorsInContainer(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return{value:n,done:!1}}},Zc=class extends er{constructor(){super(),this.outputQueue=new Kh,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Wk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.transform=e}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let t=await this.upstream.next();if(t.done)return!1;let e=Io.getTensorsInContainer(t.value),n=this.transform(t.value),o=Io.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let s of e)Io.isTensorInList(s,o)||s.dispose();return!0}},aw=class extends er{constructor(t,e){super(),this.baseErrorHandler=e,this.lastRead=null,this.iterator=null,this.moreIterators=t}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(t){if(await t,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let e=await this.iterator.next();return e.done?(this.iterator=null,this.readFromChain(t)):e}},Dl;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Dl||(Dl={}));var Uk=class extends er{constructor(t,e=Dl.FAIL){super(),this.iterators=t,this.mismatchMode=e,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(t){await t;let e=0,n=0;function o(i){return i instanceof er?{value:i.next().then(u=>(e++,u.done&&n++,u.value)),recurse:!1}:{value:null,recurse:!0}}let s=await sw(this.iterators,o);if(e===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Dl.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Dl.SHORTEST:return{value:null,done:!0};case Dl.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},lw=class extends er{constructor(t,e){super(),this.upstream=t,this.bufferSize=e,this.buffer=new od(e)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let t=this.upstream.next();this.buffer.push(t)}}next(){return this.refill(),this.buffer.shift()}},Hk=class extends lw{constructor(t,e,n){super(t,e),this.upstream=t,this.windowSize=e,this.upstreamExhausted=!1,this.random=KF.alea(n||y.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(t){return Math.floor(this.random()*t)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let t=this.chooseIndex(),e=await this.buffer.shuffleExcise(t);if(e.done)this.upstreamExhausted=!0;else return this.refill(),e}return{value:null,done:!0}}};var Ii=class{constructor(){this.size=null}batch(t,e=!0){let n=this;y.assert(t>0,()=>`batchSize needs to be positive, but it is
      ${t}`);let o;return this.size===1/0||this.size==null?o=this.size:e?o=Math.ceil(this.size/t):o=Math.floor(this.size/t),En(async()=>(await n.iterator()).columnMajorBatch(t,e,Ctt),o)}concatenate(t){let e=this,n;return this.size===1/0||t.size===1/0?n=1/0:this.size!=null&&t.size!=null?n=this.size+t.size:n=null,En(async()=>(await e.iterator()).concatenate(await t.iterator()),n)}filter(t){let e=this,n;return this.size===1/0?n=1/0:n=null,En(async()=>(await e.iterator()).filter(o=>B(()=>t(o))),n)}async forEachAsync(t){return(await this.iterator()).forEachAsync(t)}map(t){let e=this;return En(async()=>(await e.iterator()).map(n=>B(()=>t(n))),this.size)}mapAsync(t){let e=this;return En(async()=>(await e.iterator()).mapAsync(t),this.size)}prefetch(t){if(t==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let e=this;return En(async()=>(await e.iterator()).prefetch(t),this.size)}repeat(t){let e=this,n;return this.size!=null&&t>0?n=this.size*t:t===0?n=0:this.size!=null&&(t===void 0||t<0)?n=1/0:n=null,En(async()=>{let o=jh(async()=>({value:await e.iterator(),done:!1}));return jF(o.take(t))},n)}skip(t){let e=this,n;return this.size!=null&&t>=0&&this.size>=t?n=this.size-t:this.size!=null&&(this.size<t||t===void 0||t<0)?n=0:n=null,En(async()=>(await e.iterator()).skip(t),n)}shuffle(t,e,n=!0){if(t==null||t<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=YF.alea(e||y.now().toString());return En(async()=>{let i=s.int32();return n&&(i+=s.int32()),(await o.iterator()).shuffle(t,i.toString())},this.size)}take(t){let e=this,n;return this.size!=null&&this.size>t?n=t:this.size!=null&&this.size<=t?n=this.size:n=null,En(async()=>(await e.iterator()).take(t),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Ii.MAX_BUFFER_SIZE=1e4;function En(r,t=null){return new class extends Ii{constructor(){super(...arguments),this.size=t}async iterator(){return r()}}}function ZF(r){return En(async()=>qk(r),r.length)}function JF(r){if(!Vu(r))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(r))for(let e=0;e<r.length;e++)t=t==null?r[e].size:Math.min(t,r[e].size);else if(r instanceof Object)for(let e in r)t=t==null?r[e].size:Math.min(t,r[e].size);return En(async()=>{let e=await sw(r,n=>{if(n instanceof Ii)return{value:n.iterator(),recurse:!1};if(Vu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return XF(e,Dl.SHORTEST)},t)}function Ctt(r){if(r===null)return null;let t=r[0];return HF(t)?{value:vtt(r),recurse:!1}:{value:null,recurse:!0}}function vtt(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Lt?Fe(r):ir(r)}var sd=class extends Ii{constructor(t){super(),this.input=t}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var uw='"',Xh=Symbol("out"),QF=Symbol("field"),cw=Symbol("quote"),Kk=Symbol("quoteafterquote"),tO=Symbol("quoteinquote"),id=class extends Ii{async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let t=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!t)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&t&&y.assert(t.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+t.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=t);let e=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(e).filter(o=>e[o]>1);if(y.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let n=e.value;return this.parseRow(n,!1)}else return null}constructor(t,e){super(),this.input=t,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new sd(t),e||(e={}),this.hasHeader=e.hasHeader!==!1,this.fullColumnNames=e.columnNames,this.columnConfigs=e.columnConfigs,this.configuredColumnsOnly=e.configuredColumnsOnly,e.delimWhitespace?(y.assert(e.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=e.delimiter?e.delimiter:","}async iterator(){this.columnNamesValidated||await this.setColumnNames();let t=await this.base.iterator();return this.hasHeader&&(t=t.skip(1)),t.map(e=>this.makeDataElement(e))}makeDataElement(t){let e=this.parseRow(t),n={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let i=this.fullColumnNames[s],a=this.columnConfigs?this.columnConfigs[i]:null;if(!(this.configuredColumnsOnly&&!a)){let u=e[s],l=null;if(u==="")if(a&&a.default!==void 0)l=a.default;else{if(a&&(a.required||a.isLabel))throw new Error(`Required column ${i} is empty in this line: ${t}`);l=void 0}else{let c=Number(u);if(isNaN(c))a&&a.dtype==="bool"?l=this.getBoolean(u):l=u;else if(!a||!a.dtype)l=c;else switch(a.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(u);break;default:l=c}}a&&a.isLabel?o[i]=l:n[i]=l}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(t){return t==="1"||t.toLowerCase()==="true"?1:0}parseRow(t,e=!0){let n=[],o=0,s=t.length,i=Xh;for(let a=0;a<s;a++)switch(i){case Xh:switch(t.charAt(a)){case uw:o=a+1,i=cw;break;case this.delimiter:if(o=a+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),i=Xh;break;default:i=QF,o=a;break}break;case QF:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a)),i=Xh,o=a+1;break;default:}break;case cw:switch(t.charAt(a)){case uw:i=Kk;break;default:}break;case Kk:switch(t.charAt(a)){case this.delimiter:n.push(t.substring(o,a-1)),i=Xh,o=a+1;break;case uw:i=cw;break;default:i=tO;break}break;case tO:switch(t.charAt(a)){case uw:i=cw;break;default:}break;default:}if(i===Kk?n.push(t.substring(o,s-1)):n.push(t.substring(o)),e&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var pw=class r extends er{constructor(t){super(),this.microphoneConfig=t,this.isClosed=!1,this.fftSize=t.fftSize||1024;let e=Math.log2(this.fftSize);if(this.fftSize<0||e<4||e>14||!Number.isInteger(e))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=t.numFramesPerSpectrogram||43,this.sampleRateHz=t.sampleRateHz,this.columnTruncateLength=t.columnTruncateLength||this.fftSize,this.audioTrackConstraints=t.audioTrackConstraints,this.smoothingTimeConstant=t.smoothingTimeConstant||0,this.includeSpectrogram=t.includeSpectrogram!==!1,this.includeWaveform=t.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(t={}){if(!L().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let e=new r(t);return await e.start(),e}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let t=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new t,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let e=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,e.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let t,e,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:t,waveform:e},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let t=[],e=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&o({freqDataQueue:t,timeDataQueue:e}),t.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),e.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:t,timeDataQueue:e}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(t){let e=t[0].length,n=new Float32Array(t.length*e);return t.forEach((o,s)=>n.set(o,s*e)),n}getTensorFromAudioDataArray(t,e){let n=new Float32Array(y.sizeFromShape(e));return n.set(t,n.length-t.length),ir(n,e)}};var mw=class r extends er{constructor(t,e){if(super(),this.webcamVideoElement=t,this.webcamConfig=e,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Oe([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,i=(1-o)/2,a=s+n,u=o+i;this.cropBox=pi([i,s,u,a],[1,4])}else this.cropBox=pi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(t,e={}){if(!L().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!t){if(t=document.createElement("video"),!e.resizeWidth||!e.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");t.width=e.resizeWidth,t.height=e.resizeHeight}let n=new r(t,e);return await n.start(),n}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(t){throw t.message=`Error thrown while initializing video stream: ${t.message}`,t}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(t){console.log(t),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(t=>{this.webcamVideoElement.onloadedmetadata=()=>{t()}})}async next(){if(this.isClosed)return{value:null,done:!0};let t;try{t=_y.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(t),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{t.dispose()}else return{value:t,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(t){return B(()=>{let e=je(J(t,"float32"),0),n;n=fn.cropAndResize(e,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return R(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var ad=class{};var Yh=class extends er{split(t){return new jk(this,t)}},jk=class extends Yh{constructor(t,e){super(),this.upstream=t,this.impl=new Xk(t,e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Xk=class extends Zc{constructor(t,e){super(),this.upstream=t,this.separator=e,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let t=await this.upstream.next();if(t.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let e=t.value.split(this.separator);e[0]=this.carryover+e[0];for(let n of e.slice(0,-1))this.outputQueue.push(n);return this.carryover=e[e.length-1],!0}};var fw=class extends er{decodeUTF8(){return new Yk(this)}},Yk=class extends Yh{constructor(t){super(),this.upstream=t,this.impl=new Zk(t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Zk=class extends Zc{constructor(t){if(super(),this.upstream=t,L().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:e}=$k();this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let t=await this.upstream.next(),e;if(t.done)return!1;e=t.value;let n;return L().get("IS_BROWSER")?n=this.decoder.decode(e,{stream:!0}):n=this.decoder.write(Buffer.from(e.buffer)),this.outputQueue.push(n),!0}};var ld=class extends fw{constructor(t,e={}){super(),this.file=t,this.options=e,y.assert(t instanceof Uint8Array||(L().get("IS_BROWSER")?t instanceof File||t instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=e.offset||0,this.chunkSize=e.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=a=>{let u=s.result;if(u instanceof ArrayBuffer&&(u=new Uint8Array(u)),!(u instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));e(u)},s.onabort=a=>n(new Error("Aborted")),s.onerror=a=>n(new Error(a.type));let i=this.file.slice(this.offset,o);s.readAsArrayBuffer(i)}this.offset=o}),done:!1}}};async function eO(r,t={},e){let n,o;typeof r=="string"?n=r:(n=r.url,o=Stt(r));let s=await(e||y.fetch)(n,o);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new ld(i,t)}else throw new Error(s.statusText)}var Stt=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function dw(r){return typeof r=="string"&&r.slice(0,7)==="file://"}var ud=class extends ad{constructor(t,e={}){super(),this.input=t,this.options=e}async iterator(){if(dw(this.input)&&L().get("IS_NODE")){let t=hw();this.input=t.readFileSync(this.input.slice(7))}return new ld(this.input,this.options)}};var cd=class extends ad{constructor(t,e={}){super(),this.url=t,this.fileOptions=e}async iterator(){return dw(this.url)?new ud(this.url,this.fileOptions).iterator():eO(this.url,this.fileOptions)}};function rO(r,t={}){return new id(new cd(r),t)}function nO(r){let t=jh(r);return En(async()=>t)}function oO(r){return En(async()=>{let t=await r();return jh(()=>t.next())})}async function sO(r,t){return mw.create(r,t)}async function iO(r){return pw.create(r)}var Jk="4.21.0";function tt(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var Ntt=Xr.whereImpl,pd=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ta(this,Bn())}write(t,e,n){this.firstUse&&(this.firstUse=!1,L().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 o={id:this.nextDataId()};return this.data.set(o,{values:t,dtype:n,refCount:1}),o}makeTensorInfo(t,e,n){let o;if(e==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return{dataId:o,shape:t,dtype:e}}refCount(t){return this.data.has(t)?this.data.get(t).refCount:0}incRef(t){let e=this.data.get(t);e.refCount++}decRef(t){if(this.data.has(t)){let e=this.data.get(t);e.refCount--}}move(t,e,n,o,s){this.data.set(t,{values:e,dtype:o,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(t){return this.readSync(t)}readSync(t){let{dtype:e,complexTensorInfos:n}=this.data.get(t);if(e==="complex64"){let o=this.readSync(n.real.dataId),s=this.readSync(n.imag.dataId);return S.mergeRealAndImagArrays(o,s)}return y.convertBackendValuesAndArrayBuffer(this.data.get(t).values,e)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(e,n,t),this)}disposeData(t,e=!1){if(this.data.has(t)){if(this.data.get(t).refCount--,!e&&this.data.get(t).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(t);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(t)}return!0}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}async time(t){let e=y.now();return t(),{kernelMs:y.now()-e}}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(t){tt([t],"where");let e=this.readSync(t.dataId);return Ntt(t.shape,e)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};pd.nextDataId=0;var Ew={};Kt(Ew,{addImpl:()=>eT,bincountImpl:()=>dd,bincountReduceImpl:()=>gw,bitwiseAndImpl:()=>rT,castImpl:()=>tT,ceilImpl:()=>nT,concatImpl:()=>Jc,equalImpl:()=>oT,expImpl:()=>iT,expm1Impl:()=>lT,floorDivImpl:()=>cT,floorImpl:()=>uT,gatherNdImpl:()=>xw,gatherV2Impl:()=>yw,greaterEqualImpl:()=>mT,greaterImpl:()=>pT,lessEqualImpl:()=>dT,lessImpl:()=>fT,linSpaceImpl:()=>bw,logImpl:()=>hT,maxImpl:()=>ww,maximumImpl:()=>gT,minimumImpl:()=>xT,multiplyImpl:()=>Zh,negImpl:()=>yT,notEqualImpl:()=>bT,prodImpl:()=>wT,raggedGatherImpl:()=>Iw,raggedRangeImpl:()=>Cw,raggedTensorToTensorImpl:()=>vw,rangeImpl:()=>tp,rsqrtImpl:()=>CT,scatterImpl:()=>Ci,sigmoidImpl:()=>LO,simpleAbsImpl:()=>Qk,sliceImpl:()=>ep,sparseFillEmptyRowsImpl:()=>Sw,sparseReshapeImpl:()=>Nw,sparseSegmentReductionImpl:()=>gd,sqrtImpl:()=>VO,squaredDifferenceImpl:()=>ST,staticRegexReplaceImpl:()=>NT,stridedSliceImpl:()=>kw,stringNGramsImpl:()=>rp,stringSplitImpl:()=>np,stringToHashBucketFastImpl:()=>op,subImpl:()=>TT,tileImpl:()=>Tw,topKImpl:()=>_w,transposeImpl:()=>hd,uniqueImpl:()=>sp});function Qk(r){let t=new Float32Array(r.length);for(let e=0;e<r.length;++e)t[e]=Math.abs(r[e]);return t}var ktt=r=>{let{x:t}=r.inputs,e=r.backend;tt(t,"abs");let n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId).values;return n=Qk(o),e.makeOutput(n,t.shape,t.dtype)},lO={kernelName:Ai,backendName:"cpu",kernelFunc:ktt};function Qt(r){return(t,e,n,o,s)=>{let i=S.assertAndGetBroadcastShape(t,e),a=i.length,u=y.computeStrides(i),l=y.sizeFromShape(i),c=y.getTypedArrayFromDType(s,l),p=t.length,m=e.length,f=y.computeStrides(t),d=y.computeStrides(e),h=S.getBroadcastDims(t,i),g=S.getBroadcastDims(e,i);if(h.length+g.length===0)for(let x=0;x<c.length;++x)c[x]=r(n[x%n.length],o[x%o.length]);else for(let x=0;x<c.length;++x){let b=y.indexToLoc(x,a,u),w=b.slice(-p);h.forEach(A=>w[A]=0);let I=y.locToIndex(w,p,f),N=b.slice(-m);g.forEach(A=>N[A]=0);let E=y.locToIndex(N,m,d);c[x]=r(n[I],o[E])}return[c,i]}}function Ir(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=e.makeTensorInfo(n.shape,"complex64"),u=e.data.get(a.dataId);return u.complexTensorInfos={real:e.makeTensorInfo(n.shape,"float32",s),imag:e.makeTensorInfo(o.shape,"float32",i)},a}var uO={kernelName:Ap,backendName:"cpu",kernelFunc:Ir};function md(r,t,e="float32"){if(e==="complex64"){let o=md(r,t,"float32"),s=md(r,t,"float32");return Ir({inputs:{real:o,imag:s},backend:r})}let n=y.makeZerosTypedArray(y.sizeFromShape(t),e);return r.makeTensorInfo(t,e,n)}function Zr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var cO={kernelName:go,backendName:"cpu",kernelFunc:Zr};function Ro(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.data.get(n.dataId).complexTensorInfos.real,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}var pO={kernelName:Vp,backendName:"cpu",kernelFunc:Ro};function tT(r,t,e,n){if(n==="int32"){let o=Int32Array.from(r);return[t,"int32",o]}if(n==="bool"){let o=y.toTypedArray([0],e),[s,i]=Qt((a,u)=>a!==u?1:0)(t,[],r,o,"bool");return[i,"bool",s]}throw new Error(`Error in Cast: failed to cast ${e} to ${n}`)}function Fo(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return Zr({inputs:{x:o},backend:e});let c=md(e,o.shape,o.dtype),p=Fo({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),m=Ir({inputs:{real:p,imag:c},backend:e});return e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),m}if(o.dtype==="complex64"){let c=Ro({inputs:{input:o},backend:e}),p=Fo({inputs:{x:c},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(c),p}if(!y.hasEncodingLoss(o.dtype,s)){let c=Zr({inputs:{x:o},backend:e});return{dataId:c.dataId,shape:c.shape,dtype:s}}let i=e.data.get(o.dataId).values,[a,u,l]=tT(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}var mO={kernelName:fo,backendName:"cpu",kernelFunc:Fo};function oe(r,t,e,n){return e==null?({inputs:o,backend:s})=>{let{a:i,b:a}=o,u=s;tt([i,a],r);let l=u.data.get(i.dataId).values,c=u.data.get(a.dataId).values,p=i.dtype==="string"?S.fromUint8ToStringArray(l):l,m=i.dtype==="string"?S.fromUint8ToStringArray(c):c,f=n||i.dtype,[d,h]=t(i.shape,a.shape,p,m,f);return u.makeTensorInfo(h,f,d)}:({inputs:o,backend:s})=>{let{a:i,b:a}=o,u=s;if(i.dtype==="complex64"||a.dtype==="complex64"){let l=Fo({inputs:{x:i},backend:u,attrs:{dtype:"complex64"}}),c=u.data.get(l.dataId),p=c.complexTensorInfos.real,m=c.complexTensorInfos.imag,f=u.data.get(p.dataId).values,d=u.data.get(m.dataId).values,h=Fo({inputs:{x:a},backend:u,attrs:{dtype:"complex64"}}),g=u.data.get(h.dataId),x=g.complexTensorInfos.real,b=g.complexTensorInfos.imag,w=u.data.get(x.dataId).values,I=u.data.get(b.dataId).values,[N,E,A]=e(i.shape,a.shape,f,d,w,I),D=u.makeTensorInfo(A,"float32",N),F=u.makeTensorInfo(A,"float32",E),M=Ir({inputs:{real:D,imag:F},backend:u});return u.disposeIntermediateTensorInfo(l),u.disposeIntermediateTensorInfo(h),u.disposeIntermediateTensorInfo(D),u.disposeIntermediateTensorInfo(F),M}else{let l=u.data.get(i.dataId).values,c=u.data.get(a.dataId).values,p=n||i.dtype,[m,f]=t(i.shape,a.shape,l,c,p);return u.makeTensorInfo(f,p,m)}}}function fd(r){return(t,e,n,o,s,i)=>{let a=S.assertAndGetBroadcastShape(t,e),u=y.sizeFromShape(a),l=a.length,c=y.computeStrides(a),p=y.getTypedArrayFromDType("float32",u),m=y.getTypedArrayFromDType("float32",u),f=S.getBroadcastDims(t,a),d=S.getBroadcastDims(e,a),h=S.mergeRealAndImagArrays(n,o),g=S.mergeRealAndImagArrays(s,i),x=t.length,b=y.computeStrides(t),w=e.length,I=y.computeStrides(e);if(f.length+d.length===0)for(let N=0;N<p.length;N++){let E=N%h.length,A=N%g.length,D=r(h[E*2],h[E*2+1],g[A*2],g[A*2+1]);p[N]=D.real,m[N]=D.imag}else for(let N=0;N<p.length;N++){let E=y.indexToLoc(N,l,c),A=E.slice(-x);f.forEach(G=>A[G]=0);let D=y.locToIndex(A,x,b),F=E.slice(-w);d.forEach(G=>F[G]=0);let M=y.locToIndex(F,w,I),V=r(h[D*2],h[D*2+1],g[M*2],g[M*2+1]);p[N]=V.real,m[N]=V.imag}return[p,m,a]}}var eT=Qt((r,t)=>r+t),Ttt=fd((r,t,e,n)=>({real:r+e,imag:t+n})),ya=oe(no,eT,Ttt),fO={kernelName:no,backendName:"cpu",kernelFunc:ya};function dd(r,t,e,n,o){let s=y.sizeFromShape(n),i=y.makeZerosTypedArray(o,e);for(let a=0;a<r.length;a++){let u=r[a];if(u<0)throw new Error("Input x must be non-negative!");u>=o||(s>0?i[u]+=t[a]:i[u]+=1)}return i}function gw(r,t,e,n=!1){let o=r.shape[0],s=r.shape[1],i=wt([o,e],t.dtype);for(let a=0;a<o;a++)for(let u=0;u<s;u++){let l=r.get(a,u);if(l<0)throw new Error("Input x must be non-negative!");l>=e||(n?i.set(1,a,l):t.size>0?i.set(i.get(a,l)+t.get(a,u),a,l):i.set(i.get(a,l)+1,a,l))}return i}var rT=Qt((r,t)=>r&t),_tt=oe($a,rT),dO={kernelName:$a,backendName:"cpu",kernelFunc:_tt};function _r(r){return(t,e,n)=>{let o=y.getArrayFromDType(e,t.length);for(let s=0;s<t.length;++s)o[s]=r(t[s],n);return o}}function At(r,t,e){let n=_r(t);return An(r,n,e)}function An(r,t,e){return({inputs:n,attrs:o,backend:s})=>{let{x:i}=n;tt(i,r);let a=s,u=a.data.get(i.dataId).values,l;if(i.dtype==="string"){if(!Array.isArray(u))throw new Error("String tensor's value was not an instance of Array");l=S.fromUint8ToStringArray(u)}else l=u;let c=e||i.dtype,p=t(l,c,o);return a.makeTensorInfo(i.shape,c,p)}}var nT=_r(r=>Math.ceil(r)),Ett=An(Jo,nT),hO={kernelName:Jo,backendName:"cpu",kernelFunc:Ett};function Jc(r,t,e,n){let o=y.getArrayFromDType(e,y.sizeFromShape(t));if(n&&e!=="string"){let s=0;r.forEach(i=>{let a=y.sizeFromShape(i.shape);o.set(i.vals,s),s+=a})}else{let s=0;r.forEach(i=>{let a=e==="string"?S.fromUint8ToStringArray(i.vals):i.vals,u=0;for(let l=0;l<i.shape[0];++l){let c=l*t[1]+s;for(let p=0;p<i.shape[1];++p)o[c+p]=a[u++]}s+=i.shape[1]})}return o}var oT=Qt((r,t)=>r===t?1:0),sT=oe(za,oT,null,"bool"),gO={kernelName:za,backendName:"cpu",kernelFunc:sT};var iT=_r(r=>Math.exp(r)),aT=An(cs,iT,"float32"),xO={kernelName:cs,backendName:"cpu",kernelFunc:aT};var lT=_r(r=>Math.expm1(r)),Att=An(ps,lT),yO={kernelName:ps,backendName:"cpu",kernelFunc:Att};var uT=_r(r=>Math.floor(r)),Dtt=An(ms,uT),bO={kernelName:ms,backendName:"cpu",kernelFunc:Dtt};var cT=Qt((r,t)=>Math.floor(r/t)),$tt=oe(fs,cT,null,"int32"),wO={kernelName:fs,backendName:"cpu",kernelFunc:$tt};function xw(r,t,e,n,o,s,i,a,u){let l=wt([n,s],e);for(let c=0;c<n;c++){let p=[],m=0;for(let f=0;f<o;f++){let d=r[c*o+f];m+=d*i[f],p.push(d)}if(m<0||m>=u/s)throw new Error(`Invalid indices: ${p} does not index into ${a}`);for(let f=0;f<s;f++)l.values[c*s+f]=t.get(...t.indexToLoc(m*s+f))}return l}function yw(r,t,e){let n=wt(e,r.dtype);for(let o=0;o<n.size;++o){let i=n.indexToLoc(o).slice(),a=i[0],u=i[2],l=t.locToIndex([a,u]);i[2]=t.values[l];let c=r.locToIndex(i);0<=c&&c<r.values.length&&(n.values[o]=r.values[c])}return n}var pT=Qt((r,t)=>r>t?1:0),Rtt=oe(Ga,pT,null,"bool"),IO={kernelName:Ga,backendName:"cpu",kernelFunc:Rtt};var mT=Qt((r,t)=>r>=t?1:0),Ftt=oe(hs,mT,null,"bool"),CO={kernelName:hs,backendName:"cpu",kernelFunc:Ftt};var fT=Qt((r,t)=>r<t?1:0),Ott=oe(Wa,fT,null,"bool"),vO={kernelName:Wa,backendName:"cpu",kernelFunc:Ott};var dT=Qt((r,t)=>r<=t?1:0),Mtt=oe(Ua,dT,null,"bool"),SO={kernelName:Ua,backendName:"cpu",kernelFunc:Mtt};function bw(r,t,e){let n=(t-r)/(e-1),o=y.makeZerosTypedArray(e,"float32");o[0]=r;for(let s=1;s<o.length;s++)o[s]=o[s-1]+n;return o}var hT=_r(r=>Math.log(r)),Ptt=An(ws,hT),NO={kernelName:ws,backendName:"cpu",kernelFunc:Ptt};function ww(r,t,e,n){let o=y.getTypedArrayFromDType(n,y.sizeFromShape(e));for(let s=0;s<o.length;++s){let i=s*t,a=r[i];for(let u=0;u<t;++u){let l=r[i+u];(Number.isNaN(l)||l>a)&&(a=l)}o[s]=a}return o}var gT=Qt((r,t)=>Math.max(r,t)),Ltt=oe(Ss,gT),kO={kernelName:Ss,backendName:"cpu",kernelFunc:Ltt};var xT=Qt((r,t)=>Math.min(r,t)),ztt=oe(_s,xT),TO={kernelName:_s,backendName:"cpu",kernelFunc:ztt};var Zh=Qt((r,t)=>r*t),Btt=fd((r,t,e,n)=>({real:r*e-t*n,imag:r*n+t*e})),Qc=oe(Ds,Zh,Btt),_O={kernelName:Ds,backendName:"cpu",kernelFunc:Qc};function yT(r,t,e){let n=y.createScalarValue(-1,e);return Zh([],t,n,r,e)}function Vtt(r){let{inputs:t,backend:e}=r,{x:n}=t;tt(n,"neg");let o=e.data.get(n.dataId).values,[s,i]=yT(o,n.shape,n.dtype);return e.makeTensorInfo(i,n.dtype,s)}var EO={kernelName:zi,backendName:"cpu",kernelFunc:Vtt};var bT=Qt((r,t)=>r!==t?1:0),Gtt=oe(Za,bT,null,"bool"),AO={kernelName:Za,backendName:"cpu",kernelFunc:Gtt};function hd(r,t,e,n,o){let s=t.length,i=y.sizeFromShape(t),a=y.computeStrides(t),u=y.computeStrides(o),l=y.getTypedArrayFromDType(e,y.sizeFromShape(o));for(let c=0;c<i;++c){let p=y.indexToLoc(c,s,a),m=new Array(p.length);for(let d=0;d<m.length;d++)m[d]=p[n[d]];let f=y.locToIndex(m,s,u);l[f]=r[c]}return l}function We(r){let{inputs:t,attrs:e,backend:n}=r,{x:o}=t,{perm:s}=e;tt(o,"transpose");let i=o.shape.length,a=new Array(i);for(let p=0;p<a.length;p++)a[p]=o.shape[s[p]];let u=n.data.get(o.dataId).values,l=hd(u,o.shape,o.dtype,s,a);return{dataId:n.write(l,a,o.dtype),shape:a,dtype:o.dtype}}var DO={kernelName:so,backendName:"cpu",kernelFunc:We};function wT(r,t,e,n){let[o,s]=S.computeOutAndReduceShapes(r,n),i=ur(t,"int32"),a=y.makeZerosTypedArray(y.sizeFromShape(o),i),u=y.sizeFromShape(s);for(let l=0;l<a.length;++l){let c=l*u,p=1;for(let m=0;m<u;++m)p*=e[c+m];a[l]=p}return{outVals:a,outShape:o,outDtype:i}}function Wtt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"prod");let a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=S.getAxesPermutation(u,a),c=u,p=o,m=[];l!=null&&(p=We({inputs:{x:o},backend:e,attrs:{perm:l}}),m.push(p),c=S.getInnerMostAxes(c.length,a));let f=e.data.get(p.dataId).values,{outVals:d,outShape:h,outDtype:g}=wT(p.shape,p.dtype,f,c),x=h;return i&&(x=S.expandShapeToKeepDim(h,u)),m.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.makeTensorInfo(x,g,d)}var $O={kernelName:Ms,backendName:"cpu",kernelFunc:Wtt};function Utt(r,t,e){r.forEach((n,o)=>{if(n<0||n>=e){let s=y.indexToLoc(o,t.length,y.computeStrides(t)).join(",");throw new Error(`indices[${s}] = ${n} is not in [0, ${e})`)}})}function Htt(r,t){for(let e=0;e<r.length;++e){let n=r[e],o=e===r.length-1?t:r[e+1].length;if(n.length===0)throw new Error("Ragged splits may not be empty");if(n[0]<0)throw new Error("Ragged splits must be non-negative");if(n[n.length-1]>o)throw new Error("Ragged splits must not point past values");for(let s=1;s<n.length;++s)if(n[s-1]>n[s])throw new Error("Ragged splits must be sorted in ascending order")}}function qtt(r,t,e,n){let o=[],s=0,i=t.length-1+e.length,a=new Array(i).fill(null).map(()=>[0]);Htt(e,n);let u=1;for(let l=0;l<t.length-1;++l){u*=t[l];let c=t[l+1];for(let p=1;p<u+1;++p)a[l].push(p*c)}for(let l=0;l<r.length;++l){let c=r[l],p=r[l]+1;for(let m=0;m<e.length;++m){let f=e[m],d=m+t.length-1;if(d>=0){let h=a[d],g=h[h.length-1]-f[c];for(let x=c;x<p;++x)a[d].push(f[x+1]+g)}c=f[c],p=f[p]}p!==c&&(o.push([c,p]),s+=p-c)}return{outSplits:a,valueSlices:o,numValues:s}}function Ktt(r){let t=[];for(let e=0;e<r.length;++e){let n=r[e].length,o=y.getArrayFromDType("int32",n);t.push(o),r[e].forEach((s,i)=>o[i]=s)}return t}function RO(r,t){let e=r.slice(0,t);for(;e.length<t;)e.push(1);for(let n=t;n<r.length;n++)e[t-1]*=r[n];return e}function jtt(r,t,e,n,o,s){let i=RO(t,2)[1],a=RO(s,2)[1],u=0;for(let l of e)for(let c=l[0];c<l[1];++c){for(let p=0;p<n;++p)o[u*a+p]=r[c*i+p];++u}}function Xtt(r,t,e,n,o){let s=t.slice();s[0]=o;let i=y.getArrayFromDType(e,y.sizeFromShape(s)),a=r.length,u=a===0?0:a/t[0];return jtt(r,t,n,u,i,s),[i,s]}function Iw(r,t,e,n,o,s,i,a){if(r.length===0)throw new Error("paramsNestedSplits must be non empty");if(t[0].length===0)throw new Error("Split tensors must not be scalars");let u=t[0][0]-1;if(Utt(s,i,u),n.length===0)throw new Error("params.rank must be nonzero");let l=n[0],{outSplits:c,valueSlices:p,numValues:m}=qtt(s,i,r,l),f=Ktt(c),d=Xtt(e,n,o,p,m);return[f,d[0],d[1]]}var FO=2147483647;function Cw(r,t,e,n,o,s,i){if(t.length>1)throw new Error("starts must be a scalar or vector");if(o.length>1)throw new Error("limits must be a scalar or vector");if(i.length>1)throw new Error("deltas must be a scalar or vector");let a=t.length===0,u=o.length===0,l=i.length===0,c=[];a||c.push(t[0]),u||c.push(o[0]),l||c.push(i[0]);for(let g=1;g<c.length;++g)if(c[g]!==c[g-1])throw new Error("starts, limits, and deltas must have the same shape");let p=c.length===0?1:c[0],m=y.getArrayFromDType("int32",p+1);m[0]=0;for(let g=0;g<p;++g){let x=a?r[0]:r[g],b=u?n[0]:n[g],w=l?s[0]:s[g];if(w===0)throw new Error("Requires delta != 0");let I;if(w>0&&b<x||w<0&&b>x)I=0;else if(I=Math.ceil(Math.abs((b-x)/w)),I>FO)throw new Error(`Requires ((limit - start) / delta) <= ${FO}`);m[g+1]=m[g]+I}let f=m[p],d=y.getArrayFromDType(e,f),h=0;for(let g=0;g<p;++g){let x=m[g+1]-m[g],b=a?r[0]:r[g],w=l?s[0]:s[g];for(let I=0;I<x;++I)d[h++]=b,b+=w}return[m,d]}var Oo=S.RowPartitionType,IT=class r{constructor(t,e,n,o,s,i,a,u,l,c){this.shape=t,this.shapeShape=e,this.values=n,this.valuesShape=o,this.valuesDType=s,this.defaultValue=i,this.defaultValueShape=a,this.rowPartitionValues=u,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=S.getRowPartitionTypesHelper(c),this.raggedRank=S.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(t){return this.rowPartitionTypes[0]===Oo.FIRST_DIM_SIZE?this.rowPartitionTypes[t+1]:this.rowPartitionTypes[t]}getRowPartitionTensor(t){return this.rowPartitionTypes[0]===Oo.FIRST_DIM_SIZE?this.rowPartitionValues[t+1]:this.rowPartitionValues[t]}getMaxWidth(t){let e=this.getRowPartitionTensor(t-1);switch(this.getRowPartitionTypeByDimension(t-1)){case Oo.VALUE_ROWIDS:return r.getMaxWidthValueRowID(e);case Oo.ROW_SPLITS:return r.getMaxWidthRowSplit(e);default:throw new Error(`Cannot handle partition type ${Oo[this.getRowPartitionTypeByDimension(t-1)]}`)}}static getMaxWidthRowSplit(t){let e=t.length;if(e===0||e===1)return 0;let n=0;for(let o=0;o<e-1;++o){let s=t[o+1]-t[o];s>n&&(n=s)}return n}static getMaxWidthValueRowID(t){let e=t.length;if(e===0)return 0;let n=0,o=t[0],s=0;for(let i=1;i<e;++i){let a=t[i];a!==o&&(o=a,s=Math.max(i-n,s),n=i)}return Math.max(e-n,s)}tensorShapeFromTensor(t,e,n=!0){if(e.length===0){if(t[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return MO(t,n)}calculateOutputSize(t){let e=this.valuesShape,n=this.defaultValueShape;S.validateDefaultValueShape(n,e);let o=this.tensorShapeFromTensor(this.shape,this.shapeShape),i=S.combineRaggedTensorToTensorShapes(this.raggedRank,o,e);i[0]<0&&(i[0]=t);for(let a=1;a<=this.raggedRank;++a)i[a]<0&&(i[a]=this.getMaxWidth(a));return i}calculateFirstParentOutputIndex(t,e,n){let o=Math.min(t,n),s=[],i=0;for(let a=0;a<o;++a,i+=e)s.push(i);for(let a=o;a<t;++a)s.push(-1);return y.assert(s.length===t,()=>"Final length of result must be equal to firstDimension."),s}calculateOutputIndexRowSplit(t,e,n,o){let s=t.length,i=[];for(let a=0;a<s-1;++a){let u=t[a+1]-t[a],l=Math.min(o,u),c=e[a];c===-1&&(l=0);for(let p=0;p<l;++p)i.push(c),c+=n;for(let p=0;p<u-l;++p)i.push(-1)}if(s>0&&i.length!==t[s-1])throw new Error("Invalid row split size.");return i}calculateOutputIndexValueRowID(t,e,n,o){let s=t.length,i=[];if(s===0)return[];let a=0,u=t[0];if(u>=e.length)throw new Error(`Got currentValueRowId=${u}, which is not less than ${e.length}`);let l=e[u];i.push(l);for(let c=1;c<s;++c){let p=t[c];if(p===u)l>=0&&(++a,a<o?l+=n:l=-1);else{if(a=0,u=p,p>=e.length)throw new Error(`Got nextValueRowId=${p} which is not less than ${e.length}`);l=e[p]}i.push(l)}if(i.length!==t.length)throw new Error("Invalid row ids.");return i}calculateOutputIndex(t,e,n,o){let s=this.getRowPartitionTensor(t),i=this.getRowPartitionTypeByDimension(t);switch(i){case Oo.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(s,e,n,o);case Oo.ROW_SPLITS:if(s.length-1>e.length)throw new Error(`Row partition size is greater than output size: ${s.length-1} > ${e.length}`);return this.calculateOutputIndexRowSplit(s,e,n,o);default:throw new Error(`Unsupported partition type: ${Oo[i]}`)}}getFirstDimensionSize(){let t=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let e=this.rowPartitionTypes[0];switch(e){case Oo.FIRST_DIM_SIZE:return t[0];case Oo.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case Oo.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${Oo[e]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");let e=this.getFirstDimensionSize(),n=this.calculateOutputSize(e),o=new Array(this.raggedRank+1);o[o.length-1]=1;for(let u=o.length-2;u>=0;--u)o[u]=o[u+1]*n[u+1];let s=MO(n,!1),i=y.getArrayFromDType(this.valuesDType,y.sizeFromShape(s));if(o[0]*n[0]>0){let u=this.calculateFirstParentOutputIndex(e,o[0],n[0]);for(let l=1;l<=this.raggedRank;++l)u=this.calculateOutputIndex(l-1,u,o[l],n[l]);this.setOutput(this.raggedRank,u,i,s)}return[s,i]}setOutput(t,e,n,o){if(n.length===0)return;let s=this.values,i=n,a=o.slice();a=a.slice(t+1);let u=y.sizeFromShape(a),l=e.length,c=this.defaultValue;if(c.length!==u&&c.length!==1){let d=this.defaultValueShape;B(()=>{let h=R(c,d);c=sa(h,a).dataSync()})}let p=0,m=0,f=0;for(let d=0;d<=l;++d){let h=d<l?e[d]:-1;if(h===f){++f;continue}if(m<f){let g=s.subarray(p*u),x=i.subarray(m*u),b=(f-m)*u;OO(x,g,b)}if(d>=l){let g=n.length;h=Math.floor(g/u)}if(h>f)if(this.defaultValue.length===1)i.subarray(f*u,h*u).fill(this.defaultValue[0]),f=h;else for(;h>f;){let g=i.slice(f*u);OO(g,c,u),++f}h<0?(p=d+1,m=f):(p=d,m=f,f=m+1)}}};function OO(r,t,e){for(let n=0;n<e;n++)r[n]=t[n]}function MO(r,t){let e=[];for(let n of r){if(n<0){if(!t)throw new Error(`Dimension ${n} must be >= 0`);if(n<-1)throw new Error(`Dimension ${n} must be >= -1`);n=-1}e.push(n)}return e}function vw(r,t,e,n,o,s,i,a,u,l){return new IT(r,t,e,n,o,s,i,a,u,l).compute()}function tp(r,t,e,n){let o=r===t,s=r<t&&e<0,i=t<r&&e>1;if(o||s||i)return y.makeZerosTypedArray(0,n);let a=Math.abs(Math.ceil((t-r)/e)),u=y.makeZerosTypedArray(a,n);t<r&&e===1&&(e=-1),u[0]=r;for(let l=1;l<u.length;l++)u[l]=u[l-1]+e;return u}var CT=_r(r=>1/Math.sqrt(r)),Ytt=An(Us,CT),PO={kernelName:Us,backendName:"cpu",kernelFunc:Ytt};function Ci(r,t,e,n,o,s,i,a,u,l){let c=[n/o,o],p=r.values,m=t.values;if(n===0)return wt(e,t.dtype);let f=u instanceof le?u:wt(c,t.dtype);typeof u=="string"||typeof u=="number"?f.values.fill(u):typeof u=="boolean"&&f.values.fill(+u);for(let d=0;d<s;d++){let h=[],g=0;for(let x=0;x<i;x++){let b=p[d*i+x];h.push(b),g+=b*a[x]}if(g<0||g>=n/o)throw new Error(`Invalid indices: ${h} does not index into ${e}`);for(let x=0;x<o;x++)l?f.values[g*o+x]+=m[d*o+x]:f.values[g*o+x]=t.rank===0?m[0]:m[d*o+x]}return f}var LO=_r(r=>1/(1+Math.exp(-r))),vT=At(Xs,r=>1/(1+Math.exp(-r))),zO={kernelName:Xs,backendName:"cpu",kernelFunc:vT};function ep(r,t,e,n,o){let s=Be.isSliceContinous(n,t,e),i=y.sizeFromShape(e),a=y.computeStrides(n);if(s){let p=Be.computeFlatOffset(t,a);return o==="string"?r.slice(p,p+i):r.subarray(p,p+i)}let u=o==="string"?S.fromUint8ToStringArray(r):r,l=wt(n,o,u),c=wt(e,o);for(let p=0;p<c.size;++p){let m=c.indexToLoc(p),f=m.map((d,h)=>d+t[h]);c.set(l.get(...f),...m)}return o==="string"?S.fromStringArrayToUint8(c.values):c.values}function Mo(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n;tt(o,"slice");let[a,u]=Be.parseSliceParams(o,s,i);Be.assertParamsValid(o,a,u);let l=e.data.get(o.dataId).values,c=ep(l,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,c)}var BO={kernelName:Ui,backendName:"cpu",kernelFunc:Mo};function Sw(r,t,e,n,o,s,i){let a=t[0],u=s[0],l=new Array(u),c=new Array(a),p=t[1];if(u===0){if(a!==0)throw new Error(S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(a));let g=y.getArrayFromDType(e,0),x=y.getArrayFromDType(o,0);return[g,[0,p],x,l,c]}let m=!0,f=0,d=new Array(u).fill(0);for(let g=0;g<a;++g){let x=r[g*p];if(x<0)throw new Error(S.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,x));if(x>=u)throw new Error(S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,x,u));++d[x],m=m&&x>=f,f=x}let h=!0;for(let g=0;g<u;++g){let x=d[g]===0;l[g]=x,h=h&&!x,d[g]=Math.max(d[g],1),g>0&&(d[g]+=d[g-1])}if(h&&m){let g=r,x=n;for(let b=0;b<a;++b)c[b]=b;return[g,[a,p],x,l,c]}else{let g=d[u-1],x=y.getArrayFromDType(e,g*p),b=y.getArrayFromDType(o,g),w=new Array(u).fill(0);for(let I=0;I<a;++I){let N=r[I*p],E=w[N],A=(N===0?0:d[N-1])+E;w[N]++;for(let D=0;D<p;++D)x[A*p+D]=r[I*p+D];b[A]=n[I],c[I]=A}for(let I=0;I<u;++I)if(w[I]===0){let E=I===0?0:d[I-1];x[E*p+0]=I;for(let A=1;A<p;++A)x[E*p+A]=0;b[E]=i}return[x,[g,p],b,l,c]}}function Nw(r,t,e,n,o){let s=y.sizeFromShape(n),i=t[0],a=o.length,u=[],l=1,c=-1;for(let g=0;g<a;++g){let x=o[g];if(x===-1){if(c!==-1)throw new Error(S.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c,g));c=g,u.push(1)}else{if(x<0)throw new Error(S.getSparseReshapeNegativeOutputDimErrorMessage(g,x));l*=x,u.push(x)}}if(c!==-1){if(l<=0)throw new Error(S.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let g=Math.trunc(s/l);if(l*g!==s)throw new Error(S.getSparseReshapeInputOutputMultipleErrorMessage(n,u));u[c]=g}if(y.sizeFromShape(u)!==s)throw new Error(S.getSparseReshapeInputOutputMismatchErrorMessage(n,u));let m=n.length,f=[];if(m>0){f[m-1]=1;for(let g=m-2;g>=0;--g)f[g]=f[g+1]*n[g+1]}let d=[];if(a>0){d[a-1]=1;for(let g=a-2;g>=0;--g)d[g]=d[g+1]*u[g+1]}let h=y.getArrayFromDType(e,i*a);for(let g=0;g<i;++g){let x=0;for(let b=0;b<m;++b)x+=r[g*m+b]*f[b];for(let b=0;b<a;++b)h[g*a+b]=Math.trunc(x/d[b]),x%=d[b]}return[h,[i,a],u]}function gd(r,t,e,n,o,s=!1,i=0){let a=n.length,u=[t[0],r.length/t[0]],l=u[1],p=a>0?o[a-1]+1:0;if(p<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=t.slice();m[0]=p;let f=m.reduce((w,I)=>w*I,1),d=y.getArrayFromDType(e,f);if(a===0)return p>0&&d.fill(i),[d,m];if(p<=0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let h=0,g=1,x=0,b=o[h];for(;;){let w=0;if(g<a){if(w=o[g],b===w){++g;continue}if(b>=w)throw new Error(S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(b<0||b>=p)throw new Error(S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(b,p));b>x&&d.fill(i,x*l,b*l);for(let I=h;I<g;++I){let N=n[I];if(N<0||N>=u[0])throw new Error(S.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(I,n[I],u[0]));for(let E=0;E<l;E++)d[b*l+E]+=r[N*l+E]}if(s)for(let I=0;I<l;I++)d[b*l+I]/=g-h;if(h=g,++g,x=b+1,b=w,g>a)break}return x<p&&d.fill(i,x*l,p*l),[d,m]}var VO=_r(r=>Math.sqrt(r)),Ztt=At(Zs,r=>Math.sqrt(r)),GO={kernelName:Zs,backendName:"cpu",kernelFunc:Ztt};var ST=Qt((r,t)=>{let e=r-t;return e*e}),Jtt=oe(ti,ST),WO={kernelName:ti,backendName:"cpu",kernelFunc:Jtt};var NT=_r((r,t)=>{let{pattern:e,replaceGlobal:n,rewrite:o}=t;return r.replace(new RegExp(e,n?"g":""),o)}),Qtt=An(ec,NT),UO={kernelName:ec,backendName:"cpu",kernelFunc:Qtt};function kw(r,t,e,n){let o=wt(r,t.dtype);for(let s=0;s<o.size;s++){let i=o.indexToLoc(s),a=new Array(i.length);for(let u=0;u<a.length;u++)a[u]=i[u]*e[u]+n[u];o.set(t.get(...a),...i)}return o}var kT=class{constructor(t,e,n,o,s,i){this.separator=y.encodeString(t),this.nGramWidths=e,this.leftPad=y.encodeString(n),this.rightPad=y.encodeString(o),this.padWidth=s,this.preserveShort=i}getPadWidth(t){return Math.min(this.padWidth<0?t-1:this.padWidth,t-1)}getNumNGrams(t,e){let n=this.getPadWidth(e);return Math.max(0,t+2*n-e+1)}createNGrams(t,e,n,o,s,i){for(let a=0;a<s;++a){let u=this.getPadWidth(i),l=Math.max(0,u-a),c=Math.max(0,u-(s-(a+1))),p=i-(l+c),m=e+(l>0?0:a-u),f=0;f+=l*this.leftPad.length;for(let b=0;b<p;++b)f+=t[m+b].length;f+=c*this.rightPad.length;let d=l+c+p-1;f+=d*this.separator.length,n[o+a]=new Uint8Array(f);let h=n[o+a],g=0,x=b=>b.forEach(w=>h[g++]=w);for(let b=0;b<l;++b)x(this.leftPad),x(this.separator);for(let b=0;b<p-1;++b)x(t[m+b]),x(this.separator);if(p>0){x(t[m+p-1]);for(let b=0;b<c;++b)x(this.separator),x(this.rightPad)}else{for(let b=0;b<c-1;++b)x(this.rightPad),x(this.separator);x(this.rightPad)}}}compute(t,e){let n=t.length,o=e.length;if(o>0){let u=e[0];if(u!==0)throw new Error(`First split value must be 0, got ${u}`);for(let l=1;l<o;++l){let c=e[l]>=u;if(c=c&&e[l]<=n,!c)throw new Error(`Invalid split value ${e[l]}, must be in [${u}, ${n}]`);u=e[l]}if(u!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${u}`)}let s=o-1,i=y.getArrayFromDType("int32",o);if(n===0||o===0){let u=new Array(n);for(let l=0;l<=s;++l)i[l]=0;return[u,i]}i[0]=0;for(let u=1;u<=s;++u){let l=e[u]-e[u-1],c=0;this.nGramWidths.forEach(p=>{c+=this.getNumNGrams(l,p)}),this.preserveShort&&l>0&&c===0&&(c=1),i[u]=i[u-1]+c}let a=new Array(i[s]);for(let u=0;u<s;++u){let l=e[u],c=i[u];if(this.nGramWidths.forEach(p=>{let m=e[u+1]-e[u],f=this.getNumNGrams(m,p);this.createNGrams(t,l,a,c,f,p),c+=f}),this.preserveShort&&c===i[u]){let p=e[u+1]-e[u];if(p===0)continue;let m=p+2*this.padWidth;this.createNGrams(t,l,a,c,1,m)}}return[a,i]}};function rp(r,t,e,n,o,s,i,a){return new kT(e,n,o,s,i,a).compute(r,t)}function tet(r,t,e,n){if(!r.length)return;if(t.length===0){for(let s=0;s<r.length;++s)n.push(r.subarray(s,s+1));return}if(t.length===1){let s=t[0],i=r.indexOf(s);for(;i!==-1;){let a=r.subarray(0,i);(!e||a.length!==0)&&n.push(a),r=r.subarray(i+1),i=r.indexOf(s)}(!e||r.length!==0)&&n.push(r);return}let o=0;for(let s=0;s<r.length+1;s++)if(s===r.length||t.indexOf(r[s])!==-1){let i=r.subarray(o,s);(!e||i.length!==0)&&n.push(i),o=s+1}}function np(r,t,e){let n=r.length,o=[],s=0,i=0,a=new Array(n);for(let m=0;m<n;++m){let f=o.length;tet(r[m],t,e,o);let d=o.length-f;a[m]=d,s+=d,i=Math.max(i,d)}let u=y.getArrayFromDType("int32",s*2),l=new Array(s),c=[n,i],p=0;for(let m=0;m<n;++m)for(let f=0;f<a[m];++f)u[p*2]=m,u[p*2+1]=f,l[p]=o[p],++p;return[u,l,c]}function op(r,t){let e=y.getArrayFromDType("int32",r.length);for(let n=0;n<r.length;++n)e[n]=y.fingerPrint64(r[n]).modulo(t).getLowBitsUnsigned();return e}var TT=Qt((r,t)=>r-t),eet=fd((r,t,e,n)=>({real:r-e,imag:t-n})),Jh=oe(ei,TT,eet),HO={kernelName:ei,backendName:"cpu",kernelFunc:Jh};function Tw(r,t){let e=new Array(r.rank);for(let o=0;o<e.length;o++)e[o]=r.shape[o]*t[o];let n=wt(e,r.dtype);for(let o=0;o<n.values.length;++o){let s=n.indexToLoc(o),i=new Array(r.rank);for(let u=0;u<i.length;u++)i[u]=s[u]%r.shape[u];let a=r.locToIndex(i);n.values[o]=r.values[a]}return n}var Qh=(r,t)=>{let e=t.value-r.value;return e===0?r.index-t.index:e};function qO(r,t,e=0,n=r.length-1){for(;n>e;){if(n-e>600){let a=n-e+1,u=t-e+1,l=Math.log(a),c=.5*Math.exp(2*l/3),p=.5*Math.sqrt(l*c*(a-c)/a)*Math.sign(u-a/2),m=Math.max(e,Math.floor(t-u*c/a+p)),f=Math.min(n,Math.floor(t+(a-u)*c/a+p));qO(r,t,m,f)}let o=r[t],s=e,i=n;for(y.swap(r,e,t),Qh(r[n],o)>0&&y.swap(r,e,n);s<i;){for(y.swap(r,s,i),s++,i--;Qh(r[s],o)<0;)s=s+1;for(;Qh(r[i],o)>0;)i=i-1}Qh(r[e],o)===0?y.swap(r,e,i):(i=i+1,y.swap(r,i,n)),i<=t&&(e=i+1),t<=i&&(n=i-1)}}function _w(r,t,e,n,o){let s=t[t.length-1],[i,a]=[r.length/s,s],u=y.getTypedArrayFromDType(e,i*n),l=y.getTypedArrayFromDType("int32",i*n);for(let p=0;p<i;p++){let m=p*a,f=r.subarray(m,m+a),d=new Array(f.length);f.forEach((b,w)=>d[w]={value:b,index:w}),n<d.length&&(qO(d,n),d=d.slice(0,n)),o&&d.sort(Qh);let h=p*n,g=u.subarray(h,h+n),x=l.subarray(h,h+n);for(let b=0;b<n;b++)g[b]=d[b].value,x[b]=d[b].index}let c=t.slice();return c[c.length-1]=n,[wt(c,e,u),wt(c,"int32",l)]}function sp(r,t,e,n){let o=y.parseAxisParam(t,e)[0],s=[1,e[0],1];for(let d=0;d<o;d++)s[0]*=e[d];s[1]=e[o];for(let d=o+1;d<e.length;d++)s[2]*=e[d];let i=new Map,a=new Int32Array(e[o]),u=new le(s,n,r),l=[],c=s[0]===1&&s[2]===1;for(let d=0;d<e[o];d++){let h;if(c)h=r[d].toString();else{let x=[];for(let b=0;b<s[0];b++)for(let w=0;w<s[2];w++)x.push(u.get(b,d,w));h=x.join(",")}let g=i.get(h);if(g!=null)a[d]=g;else{let x=i.size;i.set(h,x),a[d]=x,l.push(d)}}let p=s.slice();p[1]=i.size;let m=new le(p,n);l.forEach((d,h)=>{for(let g=0;g<s[0];g++)for(let x=0;x<s[2];x++)m.set(u.get(g,d,x),g,h,x)});let f=e.slice();return f[o]=p[1],{outputValues:m.values,outputShape:f,indices:a}}var KO="4.21.0";Xp("cpu",()=>new pd,1);var _T=At(ls,r=>r>=0?r:Math.exp(r)-1),jO={kernelName:ls,backendName:"cpu",kernelFunc:_T};function ET(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n;tt([o],"leakyRelu");let i=y.sizeFromShape(o.shape),a=e.data.get(o.dataId).values,u=y.getTypedArrayFromDType("float32",i);for(let l=0;l<a.length;l++)u[l]=a[l]<0?s*a[l]:a[l];return e.makeTensorInfo(o.shape,"float32",u)}var XO={kernelName:bs,backendName:"cpu",kernelFunc:ET};var ret=Qt((r,t)=>r<0?t*r:r);function AT(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t;tt([n,o],"prelu");let s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,[a,u]=ret(n.shape,o.shape,s,i,"float32");return e.makeTensorInfo(u,"float32",a)}var YO={kernelName:Os,backendName:"cpu",kernelFunc:AT};var DT=At(Ls,r=>Math.max(0,r)),ZO={kernelName:Ls,backendName:"cpu",kernelFunc:DT};var $T=At(Vs,r=>Math.min(Math.max(0,r),6)),JO={kernelName:Vs,backendName:"cpu",kernelFunc:$T};function ip(r,t,e,n,o){if(e==="linear")return Zr({inputs:{x:t},backend:r});if(e==="relu")return DT({inputs:{x:t},backend:r});if(e==="elu")return _T({inputs:{x:t},backend:r});if(e==="relu6")return $T({inputs:{x:t},backend:r});if(e==="prelu")return AT({inputs:{x:t,alpha:n},backend:r});if(e==="leakyrelu")return ET({inputs:{x:t},backend:r,attrs:{alpha:o}});if(e==="sigmoid")return vT({inputs:{x:t},backend:r});throw new Error(`Activation ${e} has not been implemented for the CPU backend.`)}function te(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(a);y.assert(i===u,()=>`The new shape (${a}) has ${u} elements and the old shape (${o.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`),e.incRef(o.dataId);let l=e.data.get(o.dataId);if(l.complexTensorInfos!=null){let c=l.complexTensorInfos.real,p=l.complexTensorInfos.imag;c.shape=a,p.shape=a}return{dataId:o.dataId,shape:a,dtype:o.dtype}}var QO={kernelName:Gi,backendName:"cpu",kernelFunc:te};function RT(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;tt([o,s],"matMul");let u=o.shape.length,l=s.shape.length,c=i?o.shape[u-2]:o.shape[u-1],p=a?s.shape[l-1]:s.shape[l-2],m=i?o.shape[u-1]:o.shape[u-2],f=a?s.shape[l-2]:s.shape[l-1],d=o.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,f]);y.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${o.shape} and ${s.shape} and transposeA=${i} and transposeB=${a} must match.`);let I=i?[g,c,m]:[g,m,c],N=a?[x,f,p]:[x,p,f],E=te({inputs:{x:o},backend:e,attrs:{shape:I}}),A=te({inputs:{x:s},backend:e,attrs:{shape:N}}),D=i?E.shape[1]:E.shape[2],F=i?E.shape[2]:E.shape[1],M=a?A.shape[1]:A.shape[2],V=Math.max(g,x),G=e.data.get(E.dataId).values,W=e.data.get(A.dataId).values,q=y.computeStrides(E.shape),H=y.computeStrides(A.shape),[j,Y,Z]=i?[q[0],1,q[1]]:[q[0],q[1],1],[et,nt,st]=a?[1,H[1],H[0]]:[H[1],1,H[0]],lt=F*M,ot=wt([V,F,M],E.dtype),it=ot.values,ft=e.blockSize;for(let gt=0;gt<V;gt++){let Ct=gt%g,Rt=gt%x;for(let Dt=0;Dt<F;Dt+=ft){let Ht=Math.min(Dt+ft,F);for(let qt=0;qt<M;qt+=ft){let pe=Math.min(qt+ft,M);for(let ye=0;ye<D;ye+=ft){let re=Math.min(ye+ft,D);for(let be=Dt;be<Ht;be++)for(let de=qt;de<pe;de++){let Ee=0;for(let Ae=ye;Ae<re;Ae++){let On=G[Ct*j+be*Y+Ae*Z],lr=W[Ae*et+de*nt+Rt*st];Ee+=On*lr}it[gt*lt+(be*M+de)]+=Ee}}}}}return e.disposeIntermediateTensorInfo(E),e.disposeIntermediateTensorInfo(A),e.makeTensorInfo(w,ot.dtype,ot.values)}var tM={kernelName:Zo,backendName:"cpu",kernelFunc:RT};function net(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m,f,d,h=[];m=RT({inputs:{a:o,b:s},attrs:{transposeA:u,transposeB:l},backend:e}),i&&(f=ya({inputs:{a:m,b:i},backend:e}),h.push(m),m=f),c&&(d=ip(e,m,c,a,p),h.push(m),m=d);for(let x of h)e.disposeIntermediateTensorInfo(x);return m}var eM={kernelName:Xi,backendName:"cpu",kernelFunc:net};var oet=At(Go,r=>Math.acos(r)),rM={kernelName:Go,backendName:"cpu",kernelFunc:oet};var set=At(Wo,r=>Math.acosh(r)),nM={kernelName:Wo,backendName:"cpu",kernelFunc:set};function iet(r){let{inputs:t,backend:e}=r,n=t;tt(t,"addN");let o=n.map(a=>e.data.get(a.dataId).values),s=wt(n[0].shape,n[0].dtype),i=s.values;for(let a=0;a<n.length;a++){let u=o[a];for(let l=0;l<i.length;l++)i[l]+=u[l]}return e.makeTensorInfo(s.shape,s.dtype,s.values)}var oM={kernelName:Uo,backendName:"cpu",kernelFunc:iet};function aet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"all");let a=y.parseAxisParam(s,o.shape),u=a,l=S.getAxesPermutation(u,o.shape.length),c=o;l!=null&&(c=We({inputs:{x:o},backend:e,attrs:{perm:l}}),u=S.getInnerMostAxes(u.length,o.shape.length)),S.assertAxesAreInnerMostDims("all",u,c.shape.length);let[p,m]=S.computeOutAndReduceShapes(c.shape,u),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=e.data.get(c.dataId).values;for(let x=0;x<d.length;++x){let b=x*f,w=h[b];for(let I=0;I<f;++I){let N=h[b+I];w=w&&N}d[x]=w}l!=null&&e.disposeIntermediateTensorInfo(c);let g=e.makeTensorInfo(p,c.dtype,d);if(i){let x=S.expandShapeToKeepDim(p,a),b=te({inputs:{x:g},backend:e,attrs:{shape:x}});return e.disposeIntermediateTensorInfo(g),b}return g}var sM={kernelName:Ea,backendName:"cpu",kernelFunc:aet};function uet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"any");let a=y.parseAxisParam(s,o.shape),u=a,l=S.getAxesPermutation(u,o.shape.length),c=o;l!=null&&(c=We({inputs:{x:o},backend:e,attrs:{perm:l}}),u=S.getInnerMostAxes(u.length,o.shape.length)),S.assertAxesAreInnerMostDims("any",u,c.shape.length);let[p,m]=S.computeOutAndReduceShapes(c.shape,u),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=e.data.get(c.dataId).values;for(let x=0;x<d.length;++x){let b=x*f,w=h[b];for(let I=0;I<f;++I){let N=h[b+I];w=w||N}d[x]=w}l!=null&&e.disposeIntermediateTensorInfo(c);let g=e.makeTensorInfo(p,c.dtype,d);if(i){let x=S.expandShapeToKeepDim(p,a),b=te({inputs:{x:g},backend:e,attrs:{shape:x}});return e.disposeIntermediateTensorInfo(g),b}return g}var iM={kernelName:Aa,backendName:"cpu",kernelFunc:uet};function cet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n;tt(o,"argMax");let i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=We({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],S.assertAxesAreInnerMostDims("argMax",i,u.shape.length);let[c,p]=S.computeOutAndReduceShapes(u.shape,i),m=y.sizeFromShape(c),f=y.makeZerosTypedArray(m,"int32"),d=y.sizeFromShape(p),h=e.data.get(u.dataId).values;for(let g=0;g<f.length;++g){let x=g*d,b=h[x],w=0;for(let I=0;I<d;++I){let N=h[x+I];N>b&&(b=N,w=I)}f[g]=w}return l.forEach(g=>e.disposeIntermediateTensorInfo(g)),e.makeTensorInfo(c,"int32",f)}var aM={kernelName:Di,backendName:"cpu",kernelFunc:cet};function pet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n;tt(o,"argMin");let i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=We({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],S.assertAxesAreInnerMostDims("argMin",i,u.shape.length);let[c,p]=S.computeOutAndReduceShapes(u.shape,i),m=y.sizeFromShape(c),f=y.makeZerosTypedArray(m,"int32"),d=y.sizeFromShape(p),h=e.data.get(u.dataId).values;for(let g=0;g<f.length;++g){let x=g*d,b=h[x],w=0;for(let I=0;I<d;++I){let N=h[x+I];N<b&&(b=N,w=I)}f[g]=w}return l.forEach(g=>e.disposeIntermediateTensorInfo(g)),e.makeTensorInfo(c,"int32",f)}var lM={kernelName:$i,backendName:"cpu",kernelFunc:pet};var met=At(Ho,r=>Math.asin(r)),uM={kernelName:Ho,backendName:"cpu",kernelFunc:met};var fet=At(qo,r=>Math.asinh(r)),cM={kernelName:qo,backendName:"cpu",kernelFunc:fet};var det=At(Ko,r=>Math.atan(r)),pM={kernelName:Ko,backendName:"cpu",kernelFunc:det};var het=Qt((r,t)=>Math.atan2(r,t)),get=oe(Xo,het),mM={kernelName:Xo,backendName:"cpu",kernelFunc:get};var xet=At(jo,r=>Math.atanh(r)),fM={kernelName:jo,backendName:"cpu",kernelFunc:xet};function xd(r,t,e,n,o,s){let i=o.strideHeight,a=o.strideWidth,u=o.dilationHeight,l=o.dilationWidth,c=o.effectiveFilterHeight,p=o.effectiveFilterWidth,m=o.padInfo.top,f=o.padInfo.left,d=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,h=wt(o.outShape,e),g=h.values,x=o.outShape[1]*o.outShape[2]*o.outShape[3],b=o.outShape[2]*o.outShape[3],w=o.outShape[3];for(let I=0;I<o.batchSize;++I){let N=I*x,E=I*n[0];for(let A=0;A<o.inChannels;++A)for(let D=0;D<o.outHeight;++D){let F=D*i-m,M=Math.max(0,F),V=Math.min(o.inHeight,c+F),G=N+D*b;for(let W=0;W<o.outWidth;++W){let q=W*a-f,H=Math.max(0,q),j=Math.min(o.inWidth,p+q),Y=d,Z=0,et=0;for(let st=M;st<V;st+=u){let lt=E+st*n[1];for(let ot=H;ot<j;ot+=l){let it=lt+ot*n[2],ft=r[it+A];s==="max"&&ft>Y?Y=ft:s==="avg"&&(Z+=ft,et++)}if(isNaN(Y))break}let nt=G+W*w+A;g[nt]=s==="avg"?Z/et:Y}}}return h}function Aw(r,t,e,n,o=!1,s=!1){let i=wt(n.outShape,"int32"),a=n.strideHeight,u=n.strideWidth,l=n.dilationHeight,c=n.dilationWidth,p=n.effectiveFilterHeight,m=n.effectiveFilterWidth,f=n.padInfo.top,d=n.padInfo.left,h=wt(t,e,r);for(let g=0;g<n.batchSize;++g)for(let x=0;x<n.inChannels;++x)for(let b=0;b<n.outHeight;++b){let w=b*a-f,I=w;for(;I<0;)I+=l;let N=Math.min(n.inHeight,p+w);for(let E=0;E<n.outWidth;++E){let A=E*u-d,D=A;for(;D<0;)D+=c;let F=Math.min(n.inWidth,m+A),M=Number.NEGATIVE_INFINITY,V=-1;for(let G=I;G<N;G+=l){let W=G-w;for(let q=D;q<F;q+=c){let H=q-A,j=h.get(g,G,q,x);j>M&&(M=j,o?V=s?((g*n.inHeight+G)*n.inWidth+q)*n.inChannels+x:(G*n.inWidth+q)*n.inChannels+x:V=W*m+H)}}i.set(V,g,b,E,x)}}return i}function Dw(r,t,e,n,o,s){let i=o.strideDepth,a=o.strideHeight,u=o.strideWidth,l=o.dilationDepth,c=o.dilationHeight,p=o.dilationWidth,m=o.effectiveFilterDepth,f=o.effectiveFilterHeight,d=o.effectiveFilterWidth,h=o.padInfo.front,g=o.padInfo.top,x=o.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=wt(o.outShape,e),I=w.values,N=o.outShape[1]*o.outShape[2]*o.outShape[3]*o.outShape[4],E=o.outShape[2]*o.outShape[3]*o.outShape[4],A=o.outShape[3]*o.outShape[4],D=o.outShape[4];for(let F=0;F<o.batchSize;++F){let M=F*N,V=F*n[0];for(let G=0;G<o.inChannels;++G)for(let W=0;W<o.outDepth;++W){let q=W*i-h,H=q;for(;H<0;)H+=l;let j=Math.min(o.inDepth,m+q),Y=M+W*E;for(let Z=0;Z<o.outHeight;++Z){let et=Z*a-g,nt=et;for(;nt<0;)nt+=c;let st=Math.min(o.inHeight,f+et),lt=Y+Z*A;for(let ot=0;ot<o.outWidth;++ot){let it=ot*u-x,ft=it;for(;ft<0;)ft+=p;let gt=Math.min(o.inWidth,d+it),Ct=lt+ot*D,Rt=b,Dt=0,Ht=0;for(let pe=H;pe<j;pe+=l){let ye=V+pe*n[1];for(let re=nt;re<st;re+=c){let be=ye+re*n[2];for(let de=ft;de<gt;de+=p){let Ee=be+de*n[3],Ae=r[Ee+G];if(s==="max"&&Ae>Rt?Rt=Ae:s==="avg"&&(Dt+=Ae,Ht++),isNaN(Rt))break}if(isNaN(Rt))break}if(isNaN(Rt))break}let qt=Ct+G;I[qt]=s==="avg"?Dt/Math.max(Ht,1):Rt}}}}return w}function dM(r,t){let e=wt(t.outShape,"int32"),n=t.strideDepth,o=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,a=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,m=t.padInfo.front,f=t.padInfo.top,d=t.padInfo.left;for(let h=0;h<t.batchSize;++h)for(let g=0;g<t.inChannels;++g)for(let x=0;x<t.outDepth;++x){let b=x*n-m,w=b;for(;w<0;)w+=i;let I=Math.min(t.inDepth,l+b);for(let N=0;N<t.outHeight;++N){let E=N*o-f,A=E;for(;A<0;)A+=a;let D=Math.min(t.inHeight,c+E);for(let F=0;F<t.outWidth;++F){let M=F*s-d,V=M;for(;V<0;)V+=u;let G=Math.min(t.inWidth,p+M),W=Number.NEGATIVE_INFINITY,q=-1;for(let H=w;H<I;H+=i){let j=H-b;for(let Y=A;Y<D;Y+=a){let Z=Y-E;for(let et=V;et<G;et+=u){let nt=et-M,st=r.get(h,H,Y,et,g);st>=W&&(W=st,q=j*c*p+Z*c+nt)}}}e.set(q,h,x,N,F,g)}}}return e}function yet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;tt(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u),p;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))p=Zr({inputs:{x:o},backend:e});else{let m=e.data.get(o.dataId).values,f=y.computeStrides(o.shape),d=xd(m,o.shape,o.dtype,f,c,"avg");p=e.makeTensorInfo(c.outShape,o.dtype,d.values)}return p}var hM={kernelName:Yo,backendName:"cpu",kernelFunc:yet};function bet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n;tt(o,"avgPool3d");let c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.data.get(o.dataId).values,m=Dw(p,o.shape,o.dtype,y.computeStrides(o.shape),c,"avg");return e.makeTensorInfo(m.shape,"float32",m.values)}var gM={kernelName:Ri,backendName:"cpu",kernelFunc:bet};function wet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n;tt([o,s],"avgPool3DGrad");let c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,I=c.effectiveFilterDepth,N=c.effectiveFilterHeight,E=c.effectiveFilterWidth,A=I-1-c.padInfo.front,D=E-1-c.padInfo.left,F=N-1-c.padInfo.top,M=wt(s.shape,"float32"),V=1/(d*h*g),G=e.bufferSync(o);for(let W=0;W<c.batchSize;++W)for(let q=0;q<c.inChannels;++q)for(let H=0;H<c.inDepth;++H)for(let j=0;j<c.inHeight;++j)for(let Y=0;Y<c.inWidth;++Y){let Z=H-A,et=j-F,nt=Y-D,st=0;for(let lt=0;lt<I;lt+=x){let ot=(Z+lt)/p;if(!(ot<0||ot>=c.outDepth||Math.floor(ot)!==ot))for(let it=0;it<N;it+=b){let ft=(et+it)/m;if(!(ft<0||ft>=c.outHeight||Math.floor(ft)!==ft))for(let gt=0;gt<E;gt+=w){let Ct=(nt+gt)/f;if(Ct<0||Ct>=c.outWidth||Math.floor(Ct)!==Ct)continue;let Rt=G.get(W,ot,ft,Ct,q);st+=Rt}}}M.set(st*V,W,H,j,Y,q)}return e.makeTensorInfo(M.shape,M.dtype,M.values)}var xM={kernelName:Hl,backendName:"cpu",kernelFunc:wet};function Iet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;tt([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,I=x-1-c.padInfo.top,N=wt(i.shape,"float32"),E=1/(f*d),A=e.data.get(o.dataId).values,D=wt(o.shape,"float32",A);for(let F=0;F<c.batchSize;++F)for(let M=0;M<c.inChannels;++M)for(let V=0;V<c.inHeight;++V)for(let G=0;G<c.inWidth;++G){let W=V-I,q=G-w,H=0;for(let j=0;j<x;j+=h){let Y=(W+j)/p;if(!(Y<0||Y>=c.outHeight||Math.floor(Y)!==Y))for(let Z=0;Z<b;Z+=g){let et=(q+Z)/m;if(et<0||et>=c.outWidth||Math.floor(et)!==et)continue;let nt=D.get(F,Y,et,M);H+=nt}}N.set(H*E,F,V,G,M)}return e.makeTensorInfo(N.shape,N.dtype,N.values)}var yM={kernelName:Ul,backendName:"cpu",kernelFunc:Iet};function Cet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,scale:s,offset:i,mean:a,variance:u}=t;y.assert(a.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),tt([o,a,u,s,i],"batchNorm");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=e.data.get(o.dataId).values,p=e.data.get(a.dataId).values,m=e.data.get(u.dataId).values,f=s?e.data.get(s.dataId).values:new Float32Array([1]),d=i?e.data.get(i.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,I=0,N=0,E=0,A=0;for(let D=0;D<c.length;++D)h[D]=d[I++]+(c[D]-p[N++])*f[E++]/Math.sqrt(m[A++]+l),I>=g&&(I=0),N>=w&&(N=0),E>=x&&(E=0),A>=b&&(A=0);return e.makeTensorInfo(o.shape,o.dtype,h)}var bM={kernelName:ds,backendName:"cpu",kernelFunc:Cet};function vet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;tt([o],"batchToSpaceND");let a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=te({inputs:{x:o},backend:e,attrs:{shape:u}}),d=We({inputs:{x:f},backend:e,attrs:{perm:l}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Mo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var wM={kernelName:Fi,backendName:"cpu",kernelFunc:vet};function Net(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=dd(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var IM={kernelName:Da,backendName:"cpu",kernelFunc:Net};function ket(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.data.get(n.dataId).values,i=e.data.get(o.dataId).values,a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var CM={kernelName:ql,backendName:"cpu",kernelFunc:ket};var Tet=At(ho,(r,t)=>{let e=t;return r>e.clipValueMax?e.clipValueMax:r<e.clipValueMin?e.clipValueMin:r}),vM={kernelName:ho,backendName:"cpu",kernelFunc:Tet};var _et=r=>{let{x:t}=r.inputs,e=r.backend,n=new Float32Array(y.sizeFromShape(t.shape)),o=e.data.get(t.dataId),s=o.complexTensorInfos.real,i=o.complexTensorInfos.imag,a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values;for(let l=0;l<a.length;l++){let c=a[l],p=u[l];n[l]=Math.hypot(c,p)}return e.makeOutput(n,t.shape,"float32")},SM={kernelName:Kl,backendName:"cpu",kernelFunc:_et};function ba(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.data.get(n.dataId).complexTensorInfos.imag,s=e.data.get(o.dataId).values;return e.makeTensorInfo(o.shape,o.dtype,s)}var NM={kernelName:Pp,backendName:"cpu",kernelFunc:ba};function Gu(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(h=>h.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(h=>y.sizeFromShape(h.shape)>0);if(u.length===1)return Zr({inputs:{x:u[0]},backend:e});if(u[0].dtype==="complex64"){let h=u.map(I=>Ro({inputs:{input:I},backend:e})),g=u.map(I=>ba({inputs:{input:I},backend:e})),x=Gu({inputs:h,backend:e,attrs:{axis:s}}),b=Gu({inputs:g,backend:e,attrs:{axis:s}}),w=Ir({inputs:{real:x,imag:b},backend:e});return h.forEach(I=>e.disposeIntermediateTensorInfo(I)),g.forEach(I=>e.disposeIntermediateTensorInfo(I)),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),w}let l=u.map(h=>{let x=[-1,y.sizeFromShape(h.shape.slice(s))];return te({inputs:{x:h},backend:e,attrs:{shape:x}})}),c=l.map(h=>({vals:e.data.get(h.dataId).values,shape:h.shape}));a=S.computeOutShape(l.map(h=>h.shape),1);let p=l[0].shape[0]===1,m=Jc(c,a,t[0].dtype,p),f=S.computeOutShape(u.map(h=>h.shape),s),d=e.makeTensorInfo(f,t[0].dtype,m);return l.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var kM={kernelName:Oi,backendName:"cpu",kernelFunc:Gu};function FT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n;tt([o,s],"conv2d");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",I=new le(m.outShape,o.dtype),N=y.computeStrides(o.shape),E=y.computeStrides(s.shape),A=N[0],D=w?N[1]:N[2],F=w?N[2]:1,M=w?1:N[1],V=I.strides[0],G=w?I.strides[1]:I.strides[2],W=w?I.strides[2]:1,q=w?1:I.strides[1],H=e.data.get(o.dataId).values,j=e.data.get(s.dataId).values,Y=I.values;for(let Z=0;Z<m.batchSize;++Z){let et=Z*A,nt=Z*V;for(let st=0;st<m.outHeight;++st){let lt=nt+st*G,ot=st*m.strideHeight-b;for(let it=0;it<f;++it){let ft=ot+it*h;if(ft<0||ft>=m.inHeight)continue;let gt=it*E[0],Ct=et+ft*D;for(let Rt=0;Rt<m.outWidth;++Rt){let Dt=lt+Rt*W,Ht=Rt*m.strideWidth-x;for(let qt=0;qt<d;++qt){let pe=Ht+qt*g;if(pe<0||pe>=m.inWidth)continue;let ye=gt+qt*E[1],re=Ct+pe*F,be=ye;for(let de=0;de<m.inChannels;++de){let Ee=H[re+de*M];for(let Ae=0;Ae<m.outChannels;++Ae)Y[Dt+Ae*q]+=Ee*j[be+Ae];be+=m.outChannels}}}}}}return e.makeTensorInfo(I.shape,I.dtype,Y)}var TM={kernelName:Qo,backendName:"cpu",kernelFunc:FT};function Eet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n;tt([o,s],"conv2dBackpropFilter");let p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),{strideHeight:f,strideWidth:d,filterHeight:h,filterWidth:g}=m,x=m.dataFormat==="channelsLast",b=new le(m.filterShape,"float32"),w=m.padInfo.left,I=m.padInfo.top,N=e.data.get(o.dataId).values,E=e.data.get(s.dataId).values,A=new le(o.shape,o.dtype,N),D=new le(s.shape,s.dtype,E);for(let F=0;F<h;++F){let M=Math.max(0,Math.ceil((I-F)/f)),V=Math.min(m.outHeight,(m.inHeight+I-F)/f);for(let G=0;G<g;++G){let W=Math.max(0,Math.ceil((w-G)/d)),q=Math.min(m.outWidth,(m.inWidth+w-G)/d);for(let H=0;H<m.inChannels;++H)for(let j=0;j<m.outChannels;++j){let Y=0;for(let Z=0;Z<m.batchSize;++Z)for(let et=M;et<V;++et){let nt=F+et*f-I;for(let st=W;st<q;++st){let lt=G+st*d-w;x?Y+=A.get(Z,nt,lt,H)*D.get(Z,et,st,j):Y+=A.get(Z,H,nt,lt)*D.get(Z,j,et,st)}}b.set(Y,F,G,H,j)}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}var _M={kernelName:Dp,backendName:"cpu",kernelFunc:Eet};function Aet(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n;tt([o,s],"conv2dBackpropInput");let p=y.computeStrides(s.shape),m=y.computeStrides(o.shape),f=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,f),h=new le(d.inShape,"float32"),g=h.values,x=e.data.get(o.dataId).values,b=e.data.get(s.dataId).values,[w,I,N]=p,{batchSize:E,filterHeight:A,filterWidth:D,inChannels:F,inHeight:M,inWidth:V,outChannels:G,outHeight:W,outWidth:q,strideHeight:H,strideWidth:j}=d;f=d.dataFormat;let Y=A-1-d.padInfo.top,Z=D-1-d.padInfo.left,et=f==="channelsLast",nt=h.strides[0],st=et?h.strides[1]:h.strides[2],lt=et?h.strides[2]:1,ot=et?1:h.strides[1],it=m[0],ft=et?m[1]:m[2],gt=et?m[2]:1,Ct=et?1:m[1];for(let Rt=0;Rt<E;++Rt)for(let Dt=0;Dt<F;++Dt)for(let Ht=0;Ht<M;++Ht){let qt=Ht-Y,pe=Math.max(0,Math.ceil(qt/H)),ye=Math.min(W,(A+qt)/H);for(let re=0;re<V;++re){let be=re-Z,de=Math.max(0,Math.ceil(be/j)),Ee=Math.min(q,(D+be)/j),Ae=0;for(let lr=pe;lr<ye;++lr){let Zn=lr*H-qt;for(let Br=de;Br<Ee;++Br){let Xe=Br*j-be,Vr=it*Rt+ft*lr+gt*Br,Gr=w*(A-1-Zn)+I*(D-1-Xe)+N*Dt;for(let Jn=0;Jn<G;++Jn){let Qn=x[Vr+Ct*Jn],Qr=b[Gr+Jn];Ae+=Qn*Qr}}}let On=nt*Rt+st*Ht+lt*re+ot*Dt;g[On]=Ae}}return e.makeTensorInfo(h.shape,h.dtype,h.values)}var EM={kernelName:ts,backendName:"cpu",kernelFunc:Aet};function Det(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;tt([o,s],"conv3d");let l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=l,x=g.front,b=g.left,w=g.top,I=new le(l.outShape,o.dtype),N=e.data.get(o.dataId).values,E=e.data.get(s.dataId).values,A=I.values,D=y.computeStrides(o.shape),F=y.computeStrides(s.shape);for(let M=0;M<l.batchSize;++M){let V=M*D[0],G=M*I.strides[0];for(let W=0;W<l.outDepth;++W){let q=G+W*I.strides[1],H=W*l.strideDepth-x;for(let j=0;j<c;++j){let Y=H+j*f;if(Y<0||Y>=l.inDepth)continue;let Z=j*F[0],et=V+Y*D[1];for(let nt=0;nt<l.outHeight;++nt){let st=q+nt*I.strides[2],lt=nt*l.strideHeight-w;for(let ot=0;ot<p;++ot){let it=lt+ot*d;if(it<0||it>=l.inHeight)continue;let ft=Z+ot*F[1],gt=et+it*D[2];for(let Ct=0;Ct<l.outWidth;++Ct){let Rt=st+Ct*l.outChannels,Dt=Ct*l.strideWidth-b;for(let Ht=0;Ht<m;++Ht){let qt=Dt+Ht*h;if(qt<0||qt>=l.inWidth)continue;let pe=ft+Ht*F[2],ye=gt+qt*l.inChannels,re=pe;for(let be=0;be<l.inChannels;++be){let de=N[ye+be];for(let Ee=0;Ee<l.outChannels;++Ee)A[Rt+Ee]+=de*E[re+Ee];re+=l.outChannels}}}}}}}}return e.makeTensorInfo(I.shape,I.dtype,I.values)}var AM={kernelName:es,backendName:"cpu",kernelFunc:Det};function $et(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n;tt([o,s],"conv3dBackpropFilterV2");let l=y.computeStrides(o.shape),c=y.computeStrides(s.shape),p=S.computeConv3DInfo(o.shape,u,i,1,a),m=p.strideDepth,f=p.strideHeight,d=p.strideWidth,h=p.filterDepth,g=p.filterHeight,x=p.filterWidth,b=new le(p.filterShape,"float32"),w=b.values,[I,N,E,A]=b.strides,D=e.data.get(s.dataId).values,[F,M,V,G]=c,W=e.data.get(o.dataId).values,[q,H,j,Y]=l,Z=p.padInfo.front,et=p.padInfo.left,nt=p.padInfo.top;for(let st=0;st<h;++st){let lt=Math.max(0,Math.ceil((Z-st)/m)),ot=Math.min(p.outDepth,(p.inDepth+Z-st)/m),it=st*I;for(let ft=0;ft<g;++ft){let gt=Math.max(0,Math.ceil((nt-ft)/f)),Ct=Math.min(p.outHeight,(p.inHeight+nt-ft)/f),Rt=ft*N+it;for(let Dt=0;Dt<x;++Dt){let Ht=Math.max(0,Math.ceil((et-Dt)/d)),qt=Math.min(p.outWidth,(p.inWidth+et-Dt)/d),pe=Dt*E+Rt;for(let ye=0;ye<p.inChannels;++ye){let re=ye*A+pe;for(let be=0;be<p.outChannels;++be){let de=0;for(let Ee=0;Ee<p.batchSize;++Ee){let Ae=Ee*q,On=Ee*F;for(let lr=lt;lr<ot;++lr){let Br=(st+lr*m-Z)*H+Ae,Xe=lr*M+On;for(let Vr=gt;Vr<Ct;++Vr){let Jn=(ft+Vr*f-nt)*j+Br,Qn=Vr*V+Xe;for(let Qr=Ht;Qr<qt;++Qr){let zo=(Dt+Qr*d-et)*Y+Jn,Ti=Qr*G+Qn;de+=W[zo+ye]*D[Ti+be]}}}}w[re+be]=de}}}}}return e.makeTensorInfo(b.shape,b.dtype,b.values)}var DM={kernelName:Ra,backendName:"cpu",kernelFunc:$et};function Ret(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n;tt([o],"conv3dBackpropInputV2");let l=y.computeStrides(o.shape),c=y.computeStrides(s.shape),p=S.computeConv3DInfo(u,s.shape,a,1,i),m=new le(p.inShape,"float32"),f=m.values,[d,h,g,x]=m.strides,b=e.data.get(o.dataId).values,[w,I,N,E]=l,A=e.data.get(s.dataId).values,[D,F,M,V]=c,{batchSize:G,filterDepth:W,filterHeight:q,filterWidth:H,inChannels:j,inDepth:Y,inHeight:Z,inWidth:et,outChannels:nt,outDepth:st,outHeight:lt,outWidth:ot,strideDepth:it,strideHeight:ft,strideWidth:gt}=p,Ct=W-1-p.padInfo.front,Rt=q-1-p.padInfo.top,Dt=H-1-p.padInfo.left;for(let Ht=0;Ht<G;++Ht)for(let qt=0;qt<j;++qt)for(let pe=0;pe<Y;++pe){let ye=pe-Ct,re=Math.max(0,Math.ceil(ye/it)),be=Math.min(st,(W+ye)/it);for(let de=0;de<Z;++de){let Ee=de-Rt,Ae=Math.max(0,Math.ceil(Ee/ft)),On=Math.min(lt,(q+Ee)/ft);for(let lr=0;lr<et;++lr){let Zn=lr-Dt,Br=Math.max(0,Math.ceil(Zn/gt)),Xe=Math.min(ot,(H+Zn)/gt),Vr=0;for(let Gr=re;Gr<be;++Gr){let Jn=Gr*it-ye;for(let Qn=Ae;Qn<On;++Qn){let Qr=Qn*ft-Ee;for(let Ca=Br;Ca<Xe;++Ca){let zo=Ca*gt-Zn,Ti=w*Ht+I*Gr+N*Qn+E*Ca,Er=D*(W-1-Jn)+F*(q-1-Qr)+M*(H-1-zo)+V*qt;for(let va=0;va<nt;++va){let Vd=b[Ti+va],Gd=A[Er+va];Vr+=Vd*Gd}}}}f[d*Ht+h*pe+g*de+x*lr+qt]=Vr}}}return e.makeTensorInfo(m.shape,m.dtype,m.values)}var $M={kernelName:Fa,backendName:"cpu",kernelFunc:Ret};var Fet=At(rs,r=>Math.cos(r)),RM={kernelName:rs,backendName:"cpu",kernelFunc:Fet};var Oet=At(ns,r=>Math.cosh(r)),FM={kernelName:ns,backendName:"cpu",kernelFunc:Oet};function Met(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=a,x=wt([d,h,g,f],"float32"),b=e.data.get(s.dataId).values,w=e.data.get(i.dataId).values,I=e.data.get(o.dataId).values,N=y.computeStrides(o.shape),E=y.computeStrides(x.shape);for(let A=0;A<d;A++){let D=A*4,F=b[D],M=b[D+1],V=b[D+2],G=b[D+3],W=w[A];if(W>=c)continue;let q=h>1?(V-F)*(p-1)/(h-1):0,H=g>1?(G-M)*(m-1)/(g-1):0;for(let j=0;j<h;j++){let Y=h>1?F*(p-1)+j*q:.5*(F+V)*(p-1);if(Y<0||Y>p-1){for(let Z=0;Z<g;Z++)for(let et=0;et<f;et++){let nt=et+Z*E[2]+j*E[1]+A*E[0];x.values[nt]=l}continue}if(u==="bilinear"){let Z=Math.floor(Y),et=Math.ceil(Y),nt=Y-Z;for(let st=0;st<g;st++){let lt=g>1?M*(m-1)+st*H:.5*(M+G)*(m-1);if(lt<0||lt>m-1){for(let gt=0;gt<f;gt++){let Ct=gt+st*E[2]+j*E[1]+A*E[0];x.values[Ct]=l}continue}let ot=Math.floor(lt),it=Math.ceil(lt),ft=lt-ot;for(let gt=0;gt<f;gt++){let Ct=gt+ot*N[2]+Z*N[1]+W*N[0],Rt=I[Ct];Ct=gt+it*N[2]+Z*N[1]+W*N[0];let Dt=I[Ct];Ct=gt+ot*N[2]+et*N[1]+W*N[0];let Ht=I[Ct];Ct=gt+it*N[2]+et*N[1]+W*N[0];let qt=I[Ct],pe=Rt+(Dt-Rt)*ft,ye=Ht+(qt-Ht)*ft;Ct=gt+st*E[2]+j*E[1]+A*E[0],x.values[Ct]=pe+(ye-pe)*nt}}}else for(let Z=0;Z<g;++Z){let et=g>1?M*(m-1)+Z*H:.5*(M+G)*(m-1);if(et<0||et>m-1){for(let lt=0;lt<f;lt++){let ot=lt+Z*E[2]+j*E[1]+A*E[0];x.values[ot]=l}continue}let nt=Math.round(et),st=Math.round(Y);for(let lt=0;lt<f;lt++){let ot=lt+nt*N[2]+st*N[1]+W*N[0],it=lt+Z*E[2]+j*E[1]+A*E[0];x.values[it]=I[ot]}}}}return e.makeTensorInfo(x.shape,x.dtype,x.values)}var OM={kernelName:Ma,backendName:"cpu",kernelFunc:Met};function Pet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;tt(o,"cumprod");let u=S.getAxesPermutation([s],o.shape.length),l=o;u!=null&&(l=We({inputs:{x:o},backend:e,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,o.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ur(l.dtype,"int32"),m=y.makeOnesTypedArray(y.sizeFromShape(l.shape),p),f=e.data.get(l.dataId).values,d=l.shape[l.shape.length-1],h=a?(x,b)=>x+d-b-1:(x,b)=>x+b;for(let x=0;x<f.length;x+=d)for(let b=0;b<d;b++){let w=h(x,b);if(b===0)m[w]=i?1:f[w];else{let I=h(x,b-1);m[w]=i?f[I]*m[I]:f[w]*m[I]}}let g=e.makeTensorInfo(l.shape,p,m);if(u!=null){let x=S.getUndoAxesPermutation(u),b=We({inputs:{x:g},backend:e,attrs:{perm:x}});return e.disposeIntermediateTensorInfo(g),e.disposeIntermediateTensorInfo(l),b}return g}var MM={kernelName:Oa,backendName:"cpu",kernelFunc:Pet};function Let(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;tt(o,"cumsum");let u=S.getAxesPermutation([s],o.shape.length),l=o;u!=null&&(l=We({inputs:{x:o},backend:e,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,o.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=ur(l.dtype,"int32"),m=y.makeZerosTypedArray(y.sizeFromShape(l.shape),p),f=e.data.get(l.dataId).values,d=l.shape[l.shape.length-1],h=a?(x,b)=>x+d-b-1:(x,b)=>x+b;for(let x=0;x<f.length;x+=d)for(let b=0;b<d;b++){let w=h(x,b);if(b===0)m[w]=i?0:f[w];else{let I=h(x,b-1);m[w]=i?f[I]+m[I]:f[w]+m[I]}}let g=e.makeTensorInfo(l.shape,p,m);if(u!=null){let x=S.getUndoAxesPermutation(u),b=We({inputs:{x:g},backend:e,attrs:{perm:x}});return e.disposeIntermediateTensorInfo(g),e.disposeIntermediateTensorInfo(l),b}return g}var PM={kernelName:os,backendName:"cpu",kernelFunc:Let};function zet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.data.get(o.dataId).values,l=e.data.get(s.dataId).values,c=dd(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=gw(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var LM={kernelName:jl,backendName:"cpu",kernelFunc:zet};function Bet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n;y.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let a=o.shape[0],u=o.shape[1],l=o.shape[2],c=o.shape[3],p=u*s,m=l*s,f=c/(s*s),d=e.data.get(o.dataId).values,h=new Float32Array(a*p*m*f),g=0;for(let x=0;x<a;++x)for(let b=0;b<p;++b){let w=Math.floor(b/s),I=b%s;for(let N=0;N<m;++N){let E=Math.floor(N/s),A=N%s,D=(I*s+A)*f;for(let F=0;F<f;++F){let V=F+D+c*(E+l*(w+u*x));h[g++]=d[V]}}}return e.makeTensorInfo([a,p,m,f],o.dtype,h)}var zM={kernelName:Pa,backendName:"cpu",kernelFunc:Bet};function OT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n;tt([o,s],"depthwiseConv2DNative");let c=y.computeStrides(o.shape),p=y.computeStrides(s.shape),m=u;m==null&&(m=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(i,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${m}'`);let f=S.computeConv2DInfo(o.shape,s.shape,i,m,a,l,!0),{filterHeight:d,filterWidth:h,dilationHeight:g,dilationWidth:x,padInfo:b}=f,w=b.left,I=b.top,N=f.outChannels/f.inChannels,E=new le(f.outShape,o.dtype),A=e.data.get(o.dataId).values,D=e.data.get(s.dataId).values,F=E.values;for(let M=0;M<f.batchSize;++M){let V=M*c[0],G=M*E.strides[0];for(let W=0;W<f.outHeight;++W){let q=G+W*E.strides[1],H=W*f.strideHeight-I;for(let j=0;j<d;++j){let Y=H+j*g;if(Y<0||Y>=f.inHeight)continue;let Z=j*p[0],et=V+Y*c[1];for(let nt=0;nt<f.outWidth;++nt){let st=q+nt*E.strides[2],lt=nt*f.strideWidth-w;for(let ot=0;ot<h;++ot){let it=lt+ot*x;if(it<0||it>=f.inWidth)continue;let ft=Z+ot*p[1],gt=et+it*f.inChannels,Ct=st,Rt=ft;for(let Dt=0;Dt<f.inChannels;++Dt){let Ht=A[gt+Dt];for(let qt=0;qt<N;++qt)F[Ct+qt]+=Ht*D[Rt+qt];Ct+=N,Rt+=N}}}}}}return e.makeTensorInfo(E.shape,E.dtype,E.values)}var BM={kernelName:ss,backendName:"cpu",kernelFunc:OT};function Vet(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n;tt([o,s],"depthwiseConv2dNativeBackpropFilter");let p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),{strideHeight:m,strideWidth:f,filterHeight:d,filterWidth:h}=p,g=new le(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,w=p.outChannels/p.inChannels,I=e.data.get(o.dataId).values,N=new le(o.shape,o.dtype,I),E=e.data.get(s.dataId).values,A=new le(s.shape,s.dtype,E);for(let D=0;D<d;++D){let F=Math.max(0,Math.ceil((b-D)/m)),M=Math.min(p.outHeight,(p.inHeight+b-D)/m);for(let V=0;V<h;++V){let G=Math.max(0,Math.ceil((x-V)/f)),W=Math.min(p.outWidth,(p.inWidth+x-V)/f);for(let q=0;q<p.outChannels;++q){let H=Math.trunc(q/w),j=q%w,Y=0;for(let Z=0;Z<p.batchSize;++Z)for(let et=F;et<M;++et){let nt=D+et*m-b;for(let st=G;st<W;++st){let lt=V+st*f-x;Y+=N.get(Z,nt,lt,H)*A.get(Z,et,st,q)}}g.set(Y,D,V,H,j)}}}return e.makeTensorInfo(g.shape,g.dtype,g.values)}var VM={kernelName:$p,backendName:"cpu",kernelFunc:Vet};function Get(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n;tt([o,s],"depthwiseConv2DNativeBackpropInput");let p=y.computeStrides(o.shape),m=y.computeStrides(s.shape),f=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),d=new le(f.inShape,"float32"),h=d.values,[g,x,b]=d.strides,w=e.data.get(o.dataId).values,[I,N,E]=p,A=e.data.get(s.dataId).values,[D,F,M]=m,{batchSize:V,filterHeight:G,filterWidth:W,inChannels:q,inHeight:H,inWidth:j,outChannels:Y,outHeight:Z,outWidth:et,strideHeight:nt,strideWidth:st}=f,lt=G-1-f.padInfo.top,ot=W-1-f.padInfo.left,it=Y/q;for(let ft=0;ft<V;++ft)for(let gt=0;gt<q;++gt)for(let Ct=0;Ct<H;++Ct){let Rt=Ct-lt,Dt=Math.max(0,Math.ceil(Rt/nt)),Ht=Math.min(Z,(G+Rt)/nt);for(let qt=0;qt<j;++qt){let pe=qt-ot,ye=Math.max(0,Math.ceil(pe/st)),re=Math.min(et,(W+pe)/st),be=0;for(let de=Dt;de<Ht;++de){let Ee=de*nt-Rt;for(let Ae=ye;Ae<re;++Ae){let On=Ae*st-pe,lr=I*ft+N*de+E*Ae,Zn=D*(G-1-Ee)+F*(W-1-On)+M*gt;for(let Br=0;Br<it;++Br){let Xe=gt*it+Br,Vr=w[lr+Xe],Gr=A[Zn+Br];be+=Vr*Gr}}}h[g*ft+x*Ct+b*qt+gt]=be}}return e.makeTensorInfo(d.shape,d.dtype,d.values)}var GM={kernelName:Rp,backendName:"cpu",kernelFunc:Get};function Wet(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.data.get(n.dataId).values,i=wt([o,o],n.dtype),a=i.values;for(let l=0;l<s.length;l++)a[l*o+l]=s[l];let u=[...n.shape,...n.shape];return e.makeTensorInfo(u,i.dtype,i.values)}var WM={kernelName:Xl,backendName:"cpu",kernelFunc:Wet};var UM={kernelName:is,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o}=r,{strides:s,pad:i,dilations:a}=e,u=t,l=u.data.get(n.dataId).values,c=n.shape.length,p=u.data.get(o.dataId).values,m=o.shape.length,{batchSize:f,inHeight:d,inWidth:h,inChannels:g,outHeight:x,outWidth:b,padInfo:w,strideHeight:I,strideWidth:N,filterHeight:E,filterWidth:A,dilationHeight:D,dilationWidth:F,outShape:M}=S.computeDilation2DInfo(n.shape,o.shape,s,i,"NHWC",a),V=y.sizeFromShape(M),G=M.length,W=y.getArrayFromDType(n.dtype,V);for(let H=0;H<f;++H)for(let j=0;j<x;++j){let Y=j*I-w.top;for(let Z=0;Z<b;++Z){let et=Z*N-w.left;for(let nt=0;nt<g;++nt){let st=Number.MIN_SAFE_INTEGER;for(let ot=0;ot<E;++ot){let it=Y+ot*D;if(it>=0&&it<d)for(let ft=0;ft<A;++ft){let gt=et+ft*F;if(gt>=0&&gt<h){let Ct=y.locToIndex([H,it,gt,nt],c,y.computeStrides(n.shape)),Rt=y.locToIndex([ot,ft,nt],m,y.computeStrides(o.shape)),Dt=l[Ct]+p[Rt];Dt>st&&(st=Dt)}}}let lt=y.locToIndex([H,j,Z,nt],G,y.computeStrides(M));W[lt]=st}}}return{dataId:u.write(y.toTypedArray(W,n.dtype),M,n.dtype),shape:M,dtype:n.dtype}}};var HM={kernelName:Zl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Zl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(o.shape,o.dtype);for(let W=0;W<m;++W)for(let q=0;q<g;++q){let H=q*w-b.top;for(let j=0;j<x;++j){let Y=j*I-b.left;for(let Z=0;Z<h;++Z){let et=Number.MIN_SAFE_INTEGER,nt=0,st=0;for(let lt=0;lt<N;++lt){let ot=H+lt*A;if(ot>=0&&ot<f)for(let it=0;it<E;++it){let ft=Y+it*D;if(ft>=0&&ft<d){let gt=c[W][ot][ft][Z]+p[lt][it][Z];gt>et&&(et=gt,nt=lt,st=it)}}}V[nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var qM={kernelName:Yl,backendName:"cpu",kernelFunc:({inputs:r,backend:t,attrs:e})=>{let{x:n,filter:o,dy:s}=r,{strides:i,pad:a,dilations:u}=e,l=t,c=y.toNestedArray(n.shape,l.data.get(n.dataId).values),p=y.toNestedArray(o.shape,l.data.get(o.dataId).values),{batchSize:m,inHeight:f,inWidth:d,inChannels:h,outHeight:g,outWidth:x,padInfo:b,strideHeight:w,strideWidth:I,filterHeight:N,filterWidth:E,dilationHeight:A,dilationWidth:D,outShape:F}=S.computeDilation2DInfo(n.shape,o.shape,i,a,"NHWC",u);y.assert(s.rank===F.length,()=>`Error in ${Yl}, dy must have the same rank as output ${F.length}, but got ${s.rank}`);let M=y.toNestedArray(F,l.data.get(s.dataId).values),V=y.makeZerosNestedTypedArray(n.shape,n.dtype);for(let W=0;W<m;++W)for(let q=0;q<g;++q){let H=q*w-b.top;for(let j=0;j<x;++j){let Y=j*I-b.left;for(let Z=0;Z<h;++Z){let et=Number.MIN_SAFE_INTEGER,nt=H<0?0:H,st=Y<0?0:Y;for(let lt=0;lt<N;++lt){let ot=H+lt*A;if(ot>=0&&ot<f)for(let it=0;it<E;++it){let ft=Y+it*D;if(ft>=0&&ft<d){let gt=c[W][ot][ft][Z]+p[lt][it][Z];gt>et&&(et=gt,nt=ot,st=ft)}}}V[W][nt][st][Z]+=M[W][q][j][Z]}}}return{dataId:l.write(y.toTypedArray(V,n.dtype),n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function Uet(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{canvas:s,options:i}=n,{contextOptions:a,imageOptions:u}=i||{},l=(u==null?void 0:u.alpha)||1,c=(a==null?void 0:a.contextType)||"2d";if(c!=="2d")throw new Error(`Context type ${a.contextType} is not supported by the CPU backend.`);let p=s.getContext(c,(a==null?void 0:a.contextAttributes)||{});if(p==null)throw new Error(`Could not get the context with ${c} type.`);let[m,f]=o.shape.slice(0,2),d=o.shape.length===2?1:o.shape[2],h=e.data.get(o.dataId).values,g=o.dtype==="float32"?255:1,x=new Uint8ClampedArray(f*m*4);for(let w=0;w<m*f;++w){let I=[0,0,0,255*l];for(let E=0;E<d;E++){let A=h[w*d+E];if(o.dtype==="float32"){if(A<0||A>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${A}.`)}else if(o.dtype==="int32"&&(A<0||A>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${A}.`);d===1?(I[0]=A*g,I[1]=A*g,I[2]=A*g):I[E]=A*g}let N=w*4;x[N+0]=Math.round(I[0]),x[N+1]=Math.round(I[1]),x[N+2]=Math.round(I[2]),x[N+3]=Math.round(I[3])}s.width=f,s.height=m;let b=new ImageData(x,f,m);return p.putImageData(b,0,0),o}var KM={kernelName:Qd,backendName:"cpu",kernelFunc:Uet};function $l(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"sum");let a;o.dtype==="bool"?a=Fo({inputs:{x:o},backend:e,attrs:{dtype:"int32"}}):a=Zr({inputs:{x:o},backend:e});let u=a.shape.length,l=y.parseAxisParam(s,a.shape),c=S.getAxesPermutation(l,u),p=l,m=a;c!=null&&(m=We({inputs:{x:a},backend:e,attrs:{perm:c}}),p=S.getInnerMostAxes(p.length,u)),S.assertAxesAreInnerMostDims("sum",p,m.shape.length);let[f,d]=S.computeOutAndReduceShapes(m.shape,p),h=S.upcastType(m.dtype,"int32"),g=md(e,f,h),x=y.sizeFromShape(d),b=e.data.get(g.dataId).values,w=e.data.get(m.dataId).values;for(let I=0;I<b.length;++I){let N=I*x,E=0;for(let A=0;A<x;++A)E+=w[N+A];b[I]=E}if(i){let I=S.expandShapeToKeepDim(g.shape,l),N=g;g=te({inputs:{x:g},backend:e,attrs:{shape:I}}),e.disposeIntermediateTensorInfo(N)}return e.disposeIntermediateTensorInfo(a),c!=null&&e.disposeIntermediateTensorInfo(m),g}var jM={kernelName:Js,backendName:"cpu",kernelFunc:$l};function Het(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:b}=S.getEinsumPermutation(f,u[g]),w;S.isIdentityPermutation(x)?w=s[g]:(w=We({inputs:{x:s[g]},backend:e,attrs:{perm:x}}),d.push(w));let I=w.shape.slice();for(let N=0;N<b.length;++N)I.splice(b[N],0,1);y.arraysEqual(w.shape,I)||(w=te({inputs:{x:w},backend:e,attrs:{shape:I}}),d.push(w)),m===null?m=w:(m=Qc({inputs:{a:w,b:m},backend:e}),d.push(m))}h<p-1&&(l[h]>=0&&(m=$l({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var XM={kernelName:Fp,backendName:"cpu",kernelFunc:Het};function qet(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t;tt([n,o],"eluGrad");let s=new Float32Array(y.sizeFromShape(o.shape)),i=e.data.get(o.dataId).values,a=e.data.get(n.dataId).values;for(let u=0;u<i.length;++u){let l=i[u];l>=0?s[u]=a[u]:s[u]=a[u]*(l+1)}return e.makeTensorInfo(o.shape,"float32",s)}var YM={kernelName:La,backendName:"cpu",kernelFunc:qet};var Ket=S.ERF_P,jet=S.ERF_A1,Xet=S.ERF_A2,Yet=S.ERF_A3,Zet=S.ERF_A4,Jet=S.ERF_A5,Qet=At(us,r=>{let t=Math.sign(r),e=Math.abs(r),n=1/(1+Ket*e);return t*(1-((((Jet*n+Zet)*n+Yet)*n+Xet)*n+jet)*n*Math.exp(-e*e))}),ZM={kernelName:us,backendName:"cpu",kernelFunc:Qet};function yd(r){let{inputs:t,backend:e,attrs:n}=r,{input:o}=t,{dim:s}=n,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),te({inputs:{x:o},backend:e,attrs:{shape:a}})}var JM={kernelName:Mi,backendName:"cpu",kernelFunc:yd};var trt=Qt((r,t)=>r/t),tg=oe(as,trt),eg={kernelName:as,backendName:"cpu",kernelFunc:tg};function $w(r,t,e){let n=r.shape,o=n[0],s=n[1],i=e.data.get(r.dataId),a=i.complexTensorInfos.real,u=i.complexTensorInfos.imag,l=[o,s],c=y.sizeFromShape(l),p=y.getTypedArrayFromDType("float32",c),m=y.getTypedArrayFromDType("float32",c);for(let g=0;g<o;g++){let x=Mo({inputs:{x:a},backend:e,attrs:{begin:[g,0],size:[1,s]}}),b=Mo({inputs:{x:u},backend:e,attrs:{begin:[g,0],size:[1,s]}}),w=Ir({inputs:{real:x,imag:b},backend:e}),{real:I,imag:N}=ert(w,t,e),E=S.mergeRealAndImagArrays(I,N);for(let A=0;A<s;A++){let D=S.getComplexWithIndex(E,A);p[g*s+A]=D.real,m[g*s+A]=D.imag}e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(b),e.disposeIntermediateTensorInfo(w)}let f=e.makeTensorInfo(l,"float32",p),d=e.makeTensorInfo(l,"float32",m),h=Ir({inputs:{real:f,imag:d},backend:e});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),h}function ert(r,t,e){let n=y.sizeFromShape(r.shape),o=e.data.get(r.dataId),s=e.data.get(o.complexTensorInfos.real.dataId).values,i=e.data.get(o.complexTensorInfos.imag.dataId).values;if(rrt(n)){let a=MT(s,i,n,t,e),u=[r.shape[0],r.shape[1]];if(t){let l=e.makeTensorInfo(u,"float32",a.real),c=e.makeTensorInfo(u,"float32",a.imag),p=e.makeTensorInfo([],"float32",y.createScalarValue(n,"float32")),m=Zr({inputs:{x:p},backend:e}),f=eg.kernelFunc({inputs:{a:l,b:p},backend:e}),d=eg.kernelFunc({inputs:{a:c,b:m},backend:e}),h=e.data.get(f.dataId).values,g=e.data.get(d.dataId).values;return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),{real:h,imag:g}}return a}else{let a=S.mergeRealAndImagArrays(s,i),u=nrt(a,n,t);return S.splitRealAndImagArrays(u)}}function rrt(r){return(r&r-1)===0}function MT(r,t,e,n,o){if(e===1)return{real:r,imag:t};let s=S.mergeRealAndImagArrays(r,t),i=e/2,a=S.complexWithEvenIndex(s),u=a.real,l=a.imag,c=[u.length],p=o.makeTensorInfo(c,"float32",u),m=o.makeTensorInfo(c,"float32",l),f=Ir({inputs:{real:p,imag:m},backend:o}),d=S.complexWithOddIndex(s),h=d.real,g=d.imag,x=[h.length],b=o.makeTensorInfo(x,"float32",h),w=o.makeTensorInfo(x,"float32",g),I=Ir({inputs:{real:b,imag:w},backend:o}),N=MT(u,l,i,n,o),E=N.real,A=N.imag,D=[E.length],F=o.makeTensorInfo(D,"float32",E),M=o.makeTensorInfo(D,"float32",A),V=Ir({inputs:{real:F,imag:M},backend:o}),G=MT(h,g,i,n,o),W=G.real,q=G.imag,H=[W.length],j=o.makeTensorInfo(H,"float32",W),Y=o.makeTensorInfo(H,"float32",q),Z=Ir({inputs:{real:j,imag:Y},backend:o}),et=S.exponents(e,n),nt=[et.real.length],st=o.makeTensorInfo(nt,"float32",et.real),lt=o.makeTensorInfo(nt,"float32",et.imag),ot=Ir({inputs:{real:st,imag:lt},backend:o}),it=Qc({inputs:{a:ot,b:Z},backend:o}),ft=ya({inputs:{a:V,b:it},backend:o}),gt=Jh({inputs:{a:V,b:it},backend:o}),Ct=Ro({inputs:{input:ft},backend:o}),Rt=Ro({inputs:{input:gt},backend:o}),Dt=ba({inputs:{input:ft},backend:o}),Ht=ba({inputs:{input:gt},backend:o}),qt=Gu({inputs:[Ct,Rt],backend:o,attrs:{axis:0}}),pe=Gu({inputs:[Dt,Ht],backend:o,attrs:{axis:0}}),ye=o.data.get(qt.dataId).values,re=o.data.get(pe.dataId).values;return o.disposeIntermediateTensorInfo(p),o.disposeIntermediateTensorInfo(m),o.disposeIntermediateTensorInfo(f),o.disposeIntermediateTensorInfo(b),o.disposeIntermediateTensorInfo(w),o.disposeIntermediateTensorInfo(I),o.disposeIntermediateTensorInfo(F),o.disposeIntermediateTensorInfo(M),o.disposeIntermediateTensorInfo(V),o.disposeIntermediateTensorInfo(j),o.disposeIntermediateTensorInfo(Y),o.disposeIntermediateTensorInfo(Z),o.disposeIntermediateTensorInfo(st),o.disposeIntermediateTensorInfo(lt),o.disposeIntermediateTensorInfo(ot),o.disposeIntermediateTensorInfo(it),o.disposeIntermediateTensorInfo(ft),o.disposeIntermediateTensorInfo(gt),o.disposeIntermediateTensorInfo(Ct),o.disposeIntermediateTensorInfo(Dt),o.disposeIntermediateTensorInfo(Rt),o.disposeIntermediateTensorInfo(Ht),o.disposeIntermediateTensorInfo(qt),o.disposeIntermediateTensorInfo(pe),{real:ye,imag:re}}function nrt(r,t,e){let n=new Float32Array(t*2);for(let o=0;o<t;o++){let s=0,i=0;for(let a=0;a<t;a++){let u=S.exponent(o*a,t,e),l=S.getComplexWithIndex(r,a);s+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real}e&&(s/=t,i/=t),S.assignToTypedArray(n,s,i,o)}return n}function ort(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=te({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=$w(a,!1,e),l=te({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var QM={kernelName:Op,backendName:"cpu",kernelFunc:ort};function rg(r){let{backend:t,attrs:e}=r,{shape:n,value:o,dtype:s}=e,i=s||y.inferDtype(o),a=y.getArrayFromDType(i,y.sizeFromShape(n));return srt(a,o,i),t.makeTensorInfo(n,i,a)}var tP={kernelName:Jl,backendName:"cpu",kernelFunc:rg};function srt(r,t,e){r.fill(t)}var eP={kernelName:Ba,backendName:"cpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,o=e,s=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[i,a,u,l]=n.shape,c=o.data.get(n.dataId).values;for(let m=0;m<i;m++){let f=m*u*a*l;for(let d=0;d<a;d++){let h=d*(u*l);for(let g=0;g<u;g++){let x=g*l;for(let b=0;b<l;b++){let w=Math.round(u-g-1),I=f+h+x+b,N=c[I];if(w>=0&&w<u){let E=w*l,A=f+h+E+b;N=c[A]}s[I]=N}}}}return{dataId:o.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};function irt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=FT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;if(c==="NCHW"&&i.shape.length===1&&i.shape[0]!==1){let x=te({inputs:{x:i},backend:e,attrs:{shape:[i.shape[0],1,1]}});h=ya({inputs:{a:h,b:x},backend:e}),e.disposeIntermediateTensorInfo(x)}else h=ya({inputs:{a:h,b:i},backend:e});e.disposeIntermediateTensorInfo(g)}if(f){let g=h;if(c==="NCHW"&&f==="prelu"&&a.shape.length===1&&a.shape[0]!==1){let x=te({inputs:{x:a},backend:e,attrs:{shape:[a.shape[0],1,1]}});h=ip(e,h,f,x,d),e.disposeIntermediateTensorInfo(x)}else h=ip(e,h,f,a,d);e.disposeIntermediateTensorInfo(g)}return h}var rP={kernelName:Yi,backendName:"cpu",kernelFunc:irt};function art(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=OT({inputs:{x:o,filter:s},backend:e,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m}});if(i){let g=h;h=ya({inputs:{a:h,b:i},backend:e}),e.disposeIntermediateTensorInfo(g)}if(f){let g=h;h=ip(e,h,f,a,d),e.disposeIntermediateTensorInfo(g)}return h}var nP={kernelName:Zi,backendName:"cpu",kernelFunc:art};function lrt(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=y.sizeFromShape(n.shape),i=o.shape,a=i[i.length-1],[u,l,c,p]=S.prepareAndValidate(n,o);if(l===0)return e.makeTensorInfo(u,n.dtype,[]);let m=e.data.get(o.dataId).values,f=e.bufferSync(n),d=xw(m,f,n.dtype,l,a,c,p,n.shape,s);return e.makeTensorInfo(u,n.dtype,d.values)}var oP={kernelName:Va,backendName:"cpu",kernelFunc:lrt};function urt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n;tt([o,s],"gatherV2");let u=y.parseAxisParam(i,o.shape)[0],l=e.data.get(s.dataId).values,c=o.shape[u];for(let I=0;I<l.length;++I){let N=l[I];y.assert(N<=c-1&&N>=0,()=>`GatherV2: the index value ${N} is not in [0, ${c-1}]`)}let p=a;a==null&&(p=0);let m=y.sizeFromShape(s.shape),f=S.segment_util.collectGatherOpShapeInfo(o,s,u,p),d=te({inputs:{x:o},backend:e,attrs:{shape:[f.batchSize,f.outerSize,f.dimSize,f.sliceSize]}}),h=te({inputs:{x:s},backend:e,attrs:{shape:[f.batchSize,m/f.batchSize]}}),g=[f.batchSize,f.outerSize,m/f.batchSize,f.sliceSize],x=e.bufferSync(h),b=e.bufferSync(d),w=yw(b,x,g);return e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.makeTensorInfo(f.outputShape,w.dtype,w.values)}var sP={kernelName:Pi,backendName:"cpu",kernelFunc:urt};function crt(r){let{inputs:t,backend:e}=r,{input:n}=t,o=y.sizeFromShape(n.shape),s=n.shape[n.shape.length-1],i=o/s,a=te({inputs:{x:n},backend:e,attrs:{shape:[i,s]}}),u=$w(a,!0,e),l=te({inputs:{x:u},backend:e,attrs:{shape:n.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(u),l}var iP={kernelName:Mp,backendName:"cpu",kernelFunc:crt};var prt=At(gs,r=>Number.isFinite(r)?1:0,"bool"),aP={kernelName:gs,backendName:"cpu",kernelFunc:prt};var mrt=At(xs,r=>Math.abs(r)===1/0?1:0,"bool"),lP={kernelName:xs,backendName:"cpu",kernelFunc:mrt};var frt=At(ys,r=>Number.isNaN(r)?1:0,"bool"),uP={kernelName:ys,backendName:"cpu",kernelFunc:frt};function drt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=bw(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var cP={kernelName:Ha,backendName:"cpu",kernelFunc:drt};var hrt=At(Is,r=>Math.log1p(r)),pP={kernelName:Is,backendName:"cpu",kernelFunc:hrt};var grt=Qt((r,t)=>r&&t),xrt=oe(qa,grt,null,"bool"),mP={kernelName:qa,backendName:"cpu",kernelFunc:xrt};var yrt=At(Ka,r=>r?0:1,"bool"),fP={kernelName:Ka,backendName:"cpu",kernelFunc:yrt};var brt=Qt((r,t)=>r||t),wrt=oe(ja,brt,null,"bool"),dP={kernelName:ja,backendName:"cpu",kernelFunc:wrt};function Irt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;tt(o,"LRN");let l=o.shape[3],c=l-1,p=e.data.get(o.dataId).values,m=y.sizeFromShape(o.shape),f=new Float32Array(m);function d(h){let g=h%l,x=h-g+Math.max(0,g-s),b=h-g+Math.min(g+s,c),w=0;for(;x<=b;x++){let I=p[x];w+=I*I}return w}for(let h=0;h<m;h++){let g=d(h),x=p[h]*Math.pow(i+a*g,-u);f[h]=x}return e.makeTensorInfo(o.shape,o.dtype,f)}var hP={kernelName:Cs,backendName:"cpu",kernelFunc:Irt};function Crt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;tt(i,"LRNGrad");let p=y.sizeFromShape(i.shape),m=i.shape[3],f=e.data.get(i.dataId).values,d=e.data.get(o.dataId).values,h=e.data.get(s.dataId).values,g=new Float32Array(p),x=p;for(let b=0;b<x;b++){let w=b%m,I=b-w+Math.max(0,w-a),N=b-w+Math.min(m,w+a+1),E=0;for(let A=I;A<N;A++)E+=Math.pow(d[A],2);E=l*E+u;for(let A=I;A<N;A++){let D=-2*l*c*d[A]*h[b]/E;b===A&&(D+=Math.pow(E,-c)),D*=f[b],g[A]+=D}}return e.makeTensorInfo(i.shape,o.dtype,g)}var gP={kernelName:Xa,backendName:"cpu",kernelFunc:Crt};function PT(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=e,u=o.shape,l=u.length,c=y.parseAxisParam(s,u),p=c,m=S.getAxesPermutation(p,l),f=a.data.get(o.dataId).values;if(m!=null){let I=new Array(l);for(let N=0;N<I.length;N++)I[N]=u[m[N]];f=hd(f,u,o.dtype,m,I),p=S.getInnerMostAxes(p.length,l),u=I}tt(o,"max"),S.assertAxesAreInnerMostDims("max",p,l);let[d,h]=S.computeOutAndReduceShapes(u,p),g=y.sizeFromShape(h),x=ww(f,g,d,o.dtype),b=a.write(x,d,o.dtype),w=d;return i&&(w=S.expandShapeToKeepDim(d,c)),{dataId:b,shape:w,dtype:o.dtype}}var xP={kernelName:vs,backendName:"cpu",kernelFunc:PT};function vrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;tt(o,"maxPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u),p;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))p=Zr({inputs:{x:o},backend:e});else{let m=e.data.get(o.dataId).values,f=y.computeStrides(o.shape),d=xd(m,o.shape,o.dtype,f,c,"max");p=e.makeTensorInfo(c.outShape,o.dtype,d.values)}return p}var yP={kernelName:Ns,backendName:"cpu",kernelFunc:vrt};function Srt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n;tt(o,"maxPool3d");let c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.data.get(o.dataId).values,m=Dw(p,o.shape,o.dtype,y.computeStrides(o.shape),c,"max");return e.makeTensorInfo(m.shape,"float32",m.values)}var bP={kernelName:Li,backendName:"cpu",kernelFunc:Srt};function Nrt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n;tt([o,s],"maxPool3DGrad");let c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.bufferSync(s),m=dM(p,c),f=c.strideDepth,d=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,w=c.effectiveFilterDepth,I=c.effectiveFilterHeight,N=c.effectiveFilterWidth,E=w-1-c.padInfo.front,A=N-1-c.padInfo.left,D=I-1-c.padInfo.top,F=wt(s.shape,"float32"),M=e.bufferSync(o);for(let V=0;V<c.batchSize;++V)for(let G=0;G<c.inChannels;++G)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let H=0;H<c.inWidth;++H){let j=W-E,Y=q-D,Z=H-A,et=0;for(let nt=0;nt<w;nt+=g){let st=(j+nt)/f;if(!(st<0||st>=c.outDepth||Math.floor(st)!==st))for(let lt=0;lt<I;lt+=x){let ot=(Y+lt)/d;if(!(ot<0||ot>=c.outHeight||Math.floor(ot)!==ot))for(let it=0;it<N;it+=b){let ft=(Z+it)/h;if(ft<0||ft>=c.outWidth||Math.floor(ft)!==ft)continue;let gt=w*I*N-1-m.get(V,st,ot,ft,G),Ct=nt*I*N+lt*N+it,Rt=gt===Ct?1:0;if(Rt===0)continue;let Dt=M.get(V,st,ot,ft,G);et+=Dt*Rt}}}F.set(et,V,W,q,H,G)}return e.makeTensorInfo(F.shape,F.dtype,F.values)}var wP={kernelName:tu,backendName:"cpu",kernelFunc:Nrt};function krt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;tt([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=e.data.get(a.dataId).values,d=wt(m.outShape,a.dtype,Aw(f,a.shape,a.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,w=m.effectiveFilterHeight,I=m.effectiveFilterWidth,N=I-1-m.padInfo.left,E=w-1-m.padInfo.top,A=wt(a.shape,"float32"),D=e.data.get(o.dataId).values,F=wt(o.shape,"float32",D);for(let M=0;M<m.batchSize;++M)for(let V=0;V<m.inChannels;++V)for(let G=0;G<m.inHeight;++G)for(let W=0;W<m.inWidth;++W){let q=G-E,H=W-N,j=0;for(let Y=0;Y<w;Y+=x){let Z=(q+Y)/h;if(!(Z<0||Z>=m.outHeight||Math.floor(Z)!==Z))for(let et=0;et<I;et+=b){let nt=(H+et)/g;if(nt<0||nt>=m.outWidth||Math.floor(nt)!==nt)continue;let st=w*I-1-d.get(M,Z,nt,V),lt=Y*I+et,ot=st===lt?1:0;if(ot===0)continue;let it=F.get(M,Z,nt,V);j+=it*ot}}A.set(j,M,G,W,V)}return e.makeTensorInfo(A.shape,A.dtype,A.values)}var IP={kernelName:Ql,backendName:"cpu",kernelFunc:krt};function CP(r,t,e,n,o){let s=y.computeStrides(t),i=xd(r,t,e,s,o,"max"),a=Aw(r,t,e,o,!0,n);return[i.values,a.values]}var vP={kernelName:eu,backendName:"cpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;tt(n,"MaxPoolWithArgmax");let l=u.data.get(n.dataId).values,c=S.computePool2DInfo(n.shape,o,s,[1,1],i),[p,m]=CP(l,n.shape,n.dtype,a,c),f=u.write(p,c.outShape,n.dtype),d=u.write(m,c.outShape,n.dtype);return[{dataId:f,shape:c.outShape,dtype:n.dtype},{dataId:d,shape:c.outShape,dtype:"int32"}]}};function Trt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=y.parseAxisParam(s,o.shape),l=S.computeOutAndReduceShapes(o.shape,a)[1],c=y.sizeFromShape(l),p=[],m=e.makeTensorInfo([],"float32",new Float32Array([c]));p.push(m);let f=Fo({inputs:{x:o},backend:e,attrs:{dtype:"float32"}});p.push(f);let d=tg({inputs:{a:f,b:m},backend:e});p.push(d);let h=$l({inputs:{x:d},backend:e,attrs:{axis:s,keepDims:i}});return p.forEach(g=>e.disposeIntermediateTensorInfo(g)),h}var SP={kernelName:ks,backendName:"cpu",kernelFunc:Trt};function _rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;tt(o,"min");let a=y.parseAxisParam(s,o.shape),u=a,l=S.getAxesPermutation(u,o.shape.length),c=o;l!=null&&(c=We({inputs:{x:o},backend:e,attrs:{perm:l}}),u=S.getInnerMostAxes(u.length,o.shape.length)),S.assertAxesAreInnerMostDims("min",u,c.shape.length);let[p,m]=S.computeOutAndReduceShapes(c.shape,u),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=e.data.get(c.dataId).values;for(let x=0;x<d.length;++x){let b=x*f,w=h[b];for(let I=0;I<f;++I){let N=h[b+I];(Number.isNaN(N)||N<w)&&(w=N)}d[x]=w}l!=null&&e.disposeIntermediateTensorInfo(c);let g=e.makeTensorInfo(p,c.dtype,d);if(i){let x=S.expandShapeToKeepDim(p,a),b=te({inputs:{x:g},backend:e,attrs:{shape:x}});return e.disposeIntermediateTensorInfo(g),b}return g}var NP={kernelName:Ts,backendName:"cpu",kernelFunc:_rt};function Ert(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,mode:i}=n;tt(o,"mirrorPad");let a=s.map((w,I)=>w[0]+o.shape[I]+w[1]),u=s.map(w=>w[0]),l=s.map((w,I)=>w[0]+o.shape[I]),c=i==="reflect"?0:1,p=e.data.get(o.dataId).values,m=o.shape.length,f=y.computeStrides(o.shape),d=y.sizeFromShape(a),h=a.length,g=y.computeStrides(a),x=y.getTypedArrayFromDType(o.dtype,d);for(let w=0;w<d;w++){let I=y.indexToLoc(w,h,g);for(let E=0;E<h;E++)I[E]<u[E]?I[E]=u[E]*2-I[E]-c:I[E]>=l[E]&&(I[E]=(l[E]-1)*2-I[E]+c);I=I.map((E,A)=>E-u[A]);let N=y.locToIndex(I,m,f);x[w]=p[N]}return{dataId:e.write(x,a,o.dtype),shape:a,dtype:o.dtype}}var kP={kernelName:Es,backendName:"cpu",kernelFunc:Ert};var Art=Qt((r,t)=>{let e=r%t;return r<0&&t<0||r>=0&&t>=0?e:(e+t)%t}),Drt=oe(As,Art),TP={kernelName:As,backendName:"cpu",kernelFunc:Drt};var EP=Gl(hh());function LT(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=o.shape.length,a=s;if(a===-1&&(a=i-1),a!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${a}`);let u=y.parseAxisParam([a],o.shape),l=PT({inputs:{x:o},backend:e,attrs:{reductionIndices:u,keepDims:!1}}),c=S.expandShapeToKeepDim(l.shape,u),p=te({inputs:{x:l},backend:e,attrs:{shape:c}}),m=Jh({inputs:{a:o,b:p},backend:e}),f=aT({inputs:{x:m},backend:e}),d=$l({inputs:{x:f},backend:e,attrs:{axis:u,keepDims:!1}}),h=te({inputs:{x:d},backend:e,attrs:{shape:c}}),g=tg({inputs:{a:f,b:h},backend:e});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),g}var _P={kernelName:Qs,backendName:"cpu",kernelFunc:LT};function $rt(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n;tt(o,"multinomial");let u=a?o:LT({inputs:{logits:o},backend:e,attrs:{dim:-1}}),l=u.shape[0],c=u.shape[1],p=e.data.get(u.dataId).values,m=[l,s],f=y.makeZerosTypedArray(y.sizeFromShape(m),"int32");for(let d=0;d<l;++d){let h=d*c,g=new Float32Array(c-1);g[0]=p[h];for(let w=1;w<g.length;++w)g[w]=g[w-1]+p[h+w];let x=EP.alea(i.toString()),b=d*s;for(let w=0;w<s;++w){let I=x();f[b+w]=g.length;for(let N=0;N<g.length;N++)if(I<g[N]){f[b+w]=N;break}}}return a||e.disposeIntermediateTensorInfo(u),e.makeTensorInfo(m,"int32",f)}var AP={kernelName:Ya,backendName:"cpu",kernelFunc:$rt};var Rrt=Xr.nonMaxSuppressionV3Impl;function Frt(r){let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n;tt(o,"NonMaxSuppression");let l=e.data.get(o.dataId).values,c=e.data.get(s.dataId).values,{selectedIndices:p}=Rrt(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var DP={kernelName:Ja,backendName:"cpu",kernelFunc:Frt};var Ort=Xr.nonMaxSuppressionV4Impl;function Mrt(r){let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n;tt(o,"NonMaxSuppressionPadded");let c=e.data.get(o.dataId).values,p=e.data.get(s.dataId).values,{selectedIndices:m,validOutputs:f}=Ort(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var $P={kernelName:Qa,backendName:"cpu",kernelFunc:Mrt};var Prt=Xr.nonMaxSuppressionV5Impl;function Lrt(r){let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n;tt(o,"NonMaxSuppressionWithScore");let c=e.data.get(o.dataId).values,p=e.data.get(s.dataId).values,m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=Prt(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var RP={kernelName:tl,backendName:"cpu",kernelFunc:Lrt};function zrt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n;tt(o,"oneHot");let l=y.sizeFromShape(o.shape),c=new Float32Array(l*i);c.fill(u);let p=e.data.get(o.dataId).values;for(let m=0;m<l;++m)p[m]>=0&&p[m]<i&&(c[m*i+p[m]]=a);return e.makeTensorInfo([...o.shape,i],s,c)}var FP={kernelName:$s,backendName:"cpu",kernelFunc:zrt};function ng(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(n.dtype==="complex64"){let o=Ro({inputs:{input:n},backend:e}),s=ng({inputs:{x:o},backend:e}),i=ba({inputs:{input:n},backend:e}),a=ng({inputs:{x:i},backend:e}),u=Ir({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return rg({backend:e,attrs:{shape:n.shape,value:0,dtype:n.dtype}})}var OP={kernelName:ji,backendName:"cpu",kernelFunc:ng};function MP(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(n.dtype==="complex64"){let o=Ro({inputs:{input:n},backend:e}),s=MP({inputs:{x:o},backend:e}),i=ba({inputs:{input:n},backend:e}),a=ng({inputs:{x:i},backend:e}),u=Ir({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return rg({backend:e,attrs:{shape:n.shape,value:1,dtype:n.dtype}})}var PP={kernelName:Bi,backendName:"cpu",kernelFunc:MP};function zT(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return yd({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=yd({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=Gu({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var LP={kernelName:Vi,backendName:"cpu",kernelFunc:zT};function Brt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;tt(o,"pad");let a=s.map((b,w)=>b[0]+o.shape[w]+b[1]),u=s.map(b=>b[0]),l=e.data.get(o.dataId).values,c=y.sizeFromShape(o.shape),p=o.shape.length,m=y.computeStrides(o.shape),f=y.sizeFromShape(a),d=a.length,h=y.computeStrides(a),g=y.getTypedArrayFromDType(o.dtype,f);i!==0&&g.fill(i);for(let b=0;b<c;b++){let I=y.indexToLoc(b,p,m).map((E,A)=>E+u[A]),N=y.locToIndex(I,d,h);g[N]=l[b]}return{dataId:e.write(g,a,o.dtype),shape:a,dtype:o.dtype}}var Rw={kernelName:Rs,backendName:"cpu",kernelFunc:Brt};var Vrt=Qt((r,t)=>Math.pow(r,t)),Grt=oe(Fs,Vrt),zP={kernelName:Fs,backendName:"cpu",kernelFunc:Grt};function Wrt(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.data.get(x.dataId).values),l=o.map(x=>x.shape),c=e.data.get(s.dataId).values,p=e.data.get(i.dataId).values,[m,f,d]=Iw(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var BP={kernelName:Lp,backendName:"cpu",kernelFunc:Wrt};function Urt(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.data.get(n.dataId).values,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,[l,c]=Cw(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var VP={kernelName:zp,backendName:"cpu",kernelFunc:Urt};function Hrt(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.data.get(o.dataId).values,c=e.data.get(s.dataId).values,p=e.data.get(i.dataId).values,m=a.map(g=>e.data.get(g.dataId).values),f=a.map(g=>g.shape),[d,h]=vw(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var GP={kernelName:Bp,backendName:"cpu",kernelFunc:Hrt};function qrt(r){let{backend:t,attrs:e}=r,{start:n,stop:o,dtype:s,step:i}=e,a=tp(n,o,i,s);return t.makeTensorInfo([a.length],s,a)}var WP={kernelName:ru,backendName:"cpu",kernelFunc:qrt};var Krt=At(Ps,r=>1/r),UP={kernelName:Ps,backendName:"cpu",kernelFunc:Krt};function jrt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n;tt(o,"resizeBilinear");let u=y.computeStrides(o.shape),[l,c]=a,[p,m,f,d]=o.shape,h=e.data.get(o.dataId).values,g=new Float32Array(y.sizeFromShape([p,l,c,d])),x=[s&&l>1?m-1:m,s&&c>1?f-1:f],b=[s&&l>1?l-1:l,s&&c>1?c-1:c],w=0,I=x[0]/b[0],N=x[1]/b[1];for(let E=0;E<p;E++)for(let A=0;A<l;A++){let D;i?D=I*(A+.5)-.5:D=I*A;let F=Math.max(0,Math.floor(D)),M=D-F,V=Math.min(m-1,Math.ceil(D)),G=E*u[0]+F*u[1],W=E*u[0]+V*u[1];for(let q=0;q<c;q++){let H;i?H=N*(q+.5)-.5:H=N*q;let j=Math.max(0,Math.floor(H)),Y=H-j,Z=Math.min(f-1,Math.ceil(H)),et=G+j*u[2],nt=W+j*u[2],st=G+Z*u[2],lt=W+Z*u[2];for(let ot=0;ot<d;ot++){let it=h[et+ot],ft=h[nt+ot],gt=h[st+ot],Ct=h[lt+ot],Rt=it+(gt-it)*Y,Dt=ft+(Ct-ft)*Y,Ht=Rt+(Dt-Rt)*M;g[w++]=Ht}}}return e.makeTensorInfo([p,l,c,d],"float32",g)}var HP={kernelName:Bs,backendName:"cpu",kernelFunc:jrt};function Xrt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n;tt([s,o],"resizeBilinearGrad");let a=y.computeStrides(o.shape),[u,l,c,p]=o.shape,[,m,f]=s.shape,d=new Float32Array(u*l*c*p),h=[i&&m>1?l-1:l,i&&f>1?c-1:c],g=[i&&m>1?m-1:m,i&&f>1?f-1:f],x=h[0]/g[0],b=h[1]/g[1],w=e.data.get(s.dataId).values,I=0;for(let N=0;N<u;N++){let E=N*a[0];for(let A=0;A<m;A++){let D=A*x,F=Math.floor(D),M=Math.min(Math.ceil(D),l-1),V=E+F*a[1],G=E+M*a[1],W=D-F,q=1-W;for(let H=0;H<f;H++){let j=H*b,Y=Math.floor(j),Z=Math.min(Math.ceil(j),c-1),et=j-Y,nt=1-et,st=V+Y*a[2],lt=V+Z*a[2],ot=G+Y*a[2],it=G+Z*a[2],ft=q*nt,gt=q*et,Ct=W*nt,Rt=W*et;for(let Dt=0;Dt<p;Dt++){let Ht=w[I++];d[st+Dt]+=Ht*ft,d[lt+Dt]+=Ht*gt,d[ot+Dt]+=Ht*Ct,d[it+Dt]+=Ht*Rt}}}}return e.makeTensorInfo([u,c,l,p],"float32",d)}var qP={kernelName:rl,backendName:"cpu",kernelFunc:Xrt};function Yrt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n;tt(o,"resizeNearestNeighbor");let u=y.computeStrides(o.shape),[l,c]=a,[p,m,f,d]=o.shape,h=e.data.get(o.dataId).values,g=new Float32Array(p*l*c*d),x=[s&&l>1?m-1:m,s&&c>1?f-1:f],b=[s&&l>1?l-1:l,s&&c>1?c-1:c],w=x[0]/b[0],I=x[1]/b[1],N=0;for(let E=0;E<p;E++){let A=E*u[0];for(let D=0;D<l;D++){let F=i?w*(D+.5):w*D,M=Math.min(m-1,s?Math.round(F):Math.floor(F));i&&(M=Math.max(0,M));let V=A+M*u[1];for(let G=0;G<c;G++){let W=i?I*(G+.5):I*G,q=Math.min(f-1,s?Math.round(W):Math.floor(W));i&&(q=Math.max(0,q));let H=V+q*u[2];for(let j=0;j<d;j++){let Y=h[H+j];g[N++]=Y}}}}return e.makeTensorInfo([p,l,c,d],o.dtype,g)}var KP={kernelName:zs,backendName:"cpu",kernelFunc:Yrt};function Zrt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n;tt([s,o],"resizeNearestNeighborGrad");let a=y.computeStrides(o.shape),u=y.computeStrides(s.shape),[l,c,p,m]=o.shape,[,f,d]=s.shape,h=new Float32Array(l*c*p*m),g=e.data.get(s.dataId).values,x=[i&&f>1?c-1:c,i&&d>1?p-1:p],b=[i&&f>1?f-1:f,i&&d>1?d-1:d],w=x[0]/b[0],I=x[1]/b[1],N=1/w,E=1/I,A=Math.ceil(N)*2+2,D=Math.ceil(E)*2+2;for(let F=0;F<l;F++){let M=F*a[0];for(let V=0;V<c;V++){let G=M+V*a[1],W=Math.floor(V*N),q=Math.floor(W-A/2);for(let H=0;H<p;H++){let j=G+H*a[2],Y=Math.floor(H*E),Z=Math.floor(Y-D/2);for(let et=0;et<m;et++){let nt=0;for(let st=0;st<A;st++){let lt=st+q;if(lt<0||lt>=f)continue;let ot=M+lt*u[1],it=lt*w,ft=Math.min(c-1,i?Math.round(it):Math.floor(it));if(V===ft)for(let gt=0;gt<D;gt++){let Ct=gt+Z;if(Ct<0||Ct>=d)continue;let Rt=ot+Ct*u[2],Dt=Ct*I,Ht=Math.min(p-1,i?Math.round(Dt):Math.floor(Dt));H===Ht&&(nt+=g[Rt+et])}}h[j+et]=nt}}}}return e.makeTensorInfo(o.shape,o.dtype,h)}var jP={kernelName:el,backendName:"cpu",kernelFunc:Zrt};function Jrt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n;tt(o,"reverse");let i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return Zr({inputs:{x:o},backend:e});let u=new le(o.shape,o.dtype),l=e.bufferSync(o);for(let c=0;c<u.size;c++){let p=u.indexToLoc(c),m=p.slice();a.forEach(f=>m[f]=o.shape[f]-1-m[f]),u.set(l.get(...m),...p)}return e.makeTensorInfo(u.shape,u.dtype,u.values)}var XP={kernelName:Gs,backendName:"cpu",kernelFunc:Jrt};var YP={kernelName:pl,backendName:"cpu",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=y.getTypedArrayFromDType(n.dtype,y.sizeFromShape(n.shape)),[l,c,p,m]=n.shape,[f,d]=S.getImageCenter(i,c,p),h=255,g=Math.sin(o),x=Math.cos(o),b=a.data.get(n.dataId).values;for(let I=0;I<l;I++){let N=I*p*c*m;for(let E=0;E<c;E++){let A=E*(p*m);for(let D=0;D<p;D++){let F=D*m;for(let M=0;M<m;M++){let V=[l,E,D,M],G=V[2],W=V[1],q=(G-f)*x-(W-d)*g,H=(G-f)*g+(W-d)*x;q=Math.round(q+f),H=Math.round(H+d);let j=s;if(typeof s!="number"&&(M===3?j=h:j=s[M]),q>=0&&q<p&&H>=0&&H<c){let Z=H*(p*m),et=q*m,nt=N+Z+et+M;j=b[nt]}let Y=N+A+F+M;u[Y]=j}}}}return{dataId:a.write(u,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var Qrt=At(Ws,r=>{let t=Math.floor(r);return r-t<.5?Math.floor(r):r-t>.5?Math.ceil(r):t%2===0?t:t+1}),ZP={kernelName:Ws,backendName:"cpu",kernelFunc:Qrt};function tnt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=!0,f=e.bufferSync(o),d=e.bufferSync(s),h=Ci(f,d,i,p,l,u,a,c,0,m);return e.makeTensorInfo(i,h.dtype,h.values)}var JP={kernelName:nl,backendName:"cpu",kernelFunc:tnt};function ent(r,t){let e=0,n=r.length,o=0;for(;e<n;)o=Math.floor((e+n)/2),r[o]<t?e=o+1:n=o;return n}function rnt(r,t){let e=0,n=r.length,o=0;for(;e<n;)o=Math.floor((e+n)/2),r[o]<=t?e=o+1:n=o;return n}function QP(r,t,e,n,o,s){let i=y.getArrayFromDType("int32",e*o);for(let a=0;a<e;++a){let u=r.slice(a*n,(a+1)*n),l=a*o;for(let c=0;c<o;++c)i[l+c]=s==="left"?ent(u,t[c+l]):rnt(u,t[c+l])}return i}function nnt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,l=QP(a,u,o.shape[0],o.shape[1],s.shape[1],i);return e.makeTensorInfo(s.shape,"int32",l)}var tL={kernelName:sl,backendName:"cpu",kernelFunc:nnt};function ont(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t;tt([n,o,s],"select");let i=n.shape.length,a=e.data.get(n.dataId).values,u=e.data.get(o.dataId).values,l=e.data.get(s.dataId).values,c=ur(o.dtype,s.dtype),p=y.makeZerosTypedArray(y.sizeFromShape(o.shape),c),m=0,f=i===0||i>1||o.shape.length===1?1:y.sizeFromShape(o.shape.slice(1));for(let d=0;d<a.length;d++)for(let h=0;h<f;h++)a[d]===1?p[m++]=u[d]:p[m++]=l[d];return e.makeTensorInfo(o.shape,c,p)}var eL={kernelName:Wi,backendName:"cpu",kernelFunc:ont};var snt=S.SELU_SCALEALPHA,int=S.SELU_SCALE,ant=At(Hs,r=>r>=0?int*r:snt*(Math.exp(r)-1)),rL={kernelName:Hs,backendName:"cpu",kernelFunc:ant};var lnt=At(js,r=>r<0?-1:r>0?1:0),nL={kernelName:js,backendName:"cpu",kernelFunc:lnt};var unt=At(qs,r=>Math.sin(r)),oL={kernelName:qs,backendName:"cpu",kernelFunc:unt};var cnt=At(Ks,r=>Math.sinh(r)),sL={kernelName:Ks,backendName:"cpu",kernelFunc:cnt};var pnt=11920928955078125e-23,iL=Math.log(pnt)+2,mnt=At(Ys,r=>{let t=r>-iL,e=r<iL,n=Math.exp(r),o;return e?o=n:t?o=r:o=Math.log(1+n),o}),aL={kernelName:Ys,backendName:"cpu",kernelFunc:mnt};function fnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;tt([o],"spaceToBatchND");let a=y.sizeFromShape(s),u=[[0,0]];u.push(...i);for(let E=1+s.length;E<o.shape.length;++E)u.push([0,0]);let l=Rw.kernelFunc({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),c=S.getReshaped(l.shape,s,a,!1),p=S.getPermuted(c.length,s.length,!1),m=S.getReshapedPermuted(l.shape,s,a,!1),h=te({inputs:{x:l},backend:e,attrs:{shape:c}}),b=We({inputs:{x:h},backend:e,attrs:{perm:p}}),N=te({inputs:{x:b},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(b),N}var lL={kernelName:Hi,backendName:"cpu",kernelFunc:fnt};function dnt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
        ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
        ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:
        ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);let a=e.data.get(n.dataId).values,u=e.data.get(o.dataId).values,l=e.data.get(s.dataId).values,c=e.data.get(i.dataId).values[0],[p,m,f,d,h]=Sw(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var uL={kernelName:nu,backendName:"cpu",kernelFunc:dnt};function hnt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
        ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
        ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.data.get(o.dataId).values),a=e.data.get(n.dataId).values,u=Array.from(e.data.get(s.dataId).values),[l,c,p]=Nw(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var cL={kernelName:il,backendName:"cpu",kernelFunc:hnt};function gnt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
          ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
          ${s.shape}`);if(o.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=e.data.get(n.dataId).values,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,[l,c]=gd(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var pL={kernelName:ou,backendName:"cpu",kernelFunc:gnt};function xnt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
         ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
         ${s.shape}`);if(o.shape[0]!==s.shape[0])throw new Error("segmentIds and indices should have same size.");let i=e.data.get(n.dataId).values,a=e.data.get(o.dataId).values,u=e.data.get(s.dataId).values,[l,c]=gd(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var mL={kernelName:su,backendName:"cpu",kernelFunc:xnt};function ynt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1,d=e.bufferSync(o),h;switch(s.dtype){case"bool":{let g=e.bufferSync(s),x=!!e.data.get(i.dataId).values[0];h=Ci(d,g,a,m,c,l,u,p,x,f);break}case"float32":{let g=e.bufferSync(s),x=e.data.get(i.dataId).values[0];h=Ci(d,g,a,m,c,l,u,p,x,f);break}case"int32":{let g=e.bufferSync(s),x=e.data.get(i.dataId).values[0];h=Ci(d,g,a,m,c,l,u,p,x,f);break}case"string":{let g=e.bufferSync(s),x=y.decodeString(e.data.get(i.dataId).values[0]);h=Ci(d,g,a,m,c,l,u,p,x,f);break}default:throw new Error(`Unsupported type ${s.dtype}`)}return e.makeTensorInfo(a,h.dtype,h.values)}var fL={kernelName:al,backendName:"cpu",kernelFunc:ynt};function bnt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=new Array(o.shape.length).fill(0),c=o.shape.slice();return u.map(p=>{let m=[...c];m[a]=p;let f=Mo({inputs:{x:o},backend:e,attrs:{begin:l,size:m}});return l[a]+=p,f})}var dL={kernelName:qi,backendName:"cpu",kernelFunc:bnt};var hL={kernelName:iu,backendName:"cpu",kernelFunc:({inputs:r,backend:t})=>{let{x:e}=r,n=t;tt(e,"square");let o=n.data.get(e.dataId).values,s=new Float32Array(o.length);for(let a=0;a<o.length;++a){let u=o[a];s[a]=u*u}return{dataId:n.write(s,e.shape,e.dtype),shape:e.shape,dtype:e.dtype}}};var wnt=At(xo,(r,t)=>{let e=t;return isNaN(r)?NaN:r>0?1:e.alpha}),gL={kernelName:xo,backendName:"cpu",kernelFunc:wnt};function Int(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n;tt(o,"stridedSlice");let{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=Be.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=te({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let E=Be.computeOutShape(b,w,I),A=Mo({inputs:{x:o},backend:e,attrs:{begin:b,size:E}});N=te({inputs:{x:A},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(A)}else{let E=e.bufferSync(o),A=kw(f,E,I,b);N=e.makeTensorInfo(d,A.dtype,A.values)}return N}var xL={kernelName:ll,backendName:"cpu",kernelFunc:Int};function Cnt(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.data.get(c.dataId).values,f=e.data.get(p.dataId).values,[d,h]=rp(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var yL={kernelName:au,backendName:"cpu",kernelFunc:Cnt};function vnt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;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(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.data.get(s.dataId).values,u=e.data.get(i.dataId).values[0],[l,c,p]=np(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],"int32",l),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(p))]}var bL={kernelName:lu,backendName:"cpu",kernelFunc:vnt};function Snt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let i=e.data.get(s.dataId).values,a=op(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var wL={kernelName:uu,backendName:"cpu",kernelFunc:Snt};var Nnt=At(ri,r=>Math.tan(r)),IL={kernelName:ri,backendName:"cpu",kernelFunc:Nnt};var knt=At(ni,r=>Math.tanh(r)),CL={kernelName:ni,backendName:"cpu",kernelFunc:knt};function Tnt(r){let{inputs:t,backend:e}=r,{tensor:n,indices:o,updates:s}=t,{sliceRank:i,numUpdates:a,sliceSize:u,strides:l,outputSize:c}=S.calculateShapes(s,o,n.shape),p=!1,m=e.bufferSync(o),f=e.bufferSync(s),d=e.bufferSync(n),h=Ci(m,f,n.shape,c,u,a,i,l,d,p);return e.makeTensorInfo(n.shape,h.dtype,h.values)}var vL={kernelName:ol,backendName:"cpu",kernelFunc:Tnt};function _nt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reps:s}=n;tt(o,"tile");let i=Tw(e.bufferSync(o),s);return e.makeTensorInfo(i.shape,i.dtype,i.values)}var SL={kernelName:oo,backendName:"cpu",kernelFunc:_nt};function Ent(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{k:s,sorted:i}=n;tt(o,"topk");let a=e.data.get(o.dataId).values,[u,l]=_w(a,o.shape,o.dtype,s,i);return[e.makeTensorInfo(u.shape,u.dtype,u.values),e.makeTensorInfo(l.shape,l.dtype,l.values)]}var NL={kernelName:ul,backendName:"cpu",kernelFunc:Ent};function Ant(r){let{inputs:t,attrs:e,backend:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=e,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=y.computeStrides(o.shape),b=x[0],w=x[1],I=x[2],N=y.computeStrides(g),E=N[0],A=N[1],D=N[2],F=y.getTypedArrayFromDType(o.dtype,y.sizeFromShape(g));F.fill(u);let M=n.data.get(o.dataId).values,V=n.data.get(s.dataId).values;for(let W=0;W<c;++W){let q=s.shape[0]===1?V:V.subarray(W*8,W*8+8);for(let H=0;H<d;++H)for(let j=0;j<h;++j)for(let Y=0;Y<f;++Y){let Z,et=q[6]*j+q[7]*H+1;if(et===0)continue;let nt=(q[0]*j+q[1]*H+q[2])/et,st=(q[3]*j+q[4]*H+q[5])/et,lt=kL(nt,m,a),ot=kL(st,p,a);switch(i){case"nearest":Z=Ont(M,p,m,b,w,I,W,ot,lt,Y,u);break;case"bilinear":Z=Mnt(M,p,m,b,w,I,W,ot,lt,Y,u);break;default:throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`)}let it=W*E+H*A+j*D+Y;F[it]=Z}return n.makeTensorInfo(g,o.dtype,F)}return{dataId:n.write(F,g,o.dtype),shape:o.shape,dtype:o.dtype}}var TL={kernelName:cl,backendName:"cpu",kernelFunc:Ant};function kL(r,t,e){switch(e){case"reflect":return Dnt(r,t);case"wrap":return $nt(r,t);case"nearest":return Fnt(r,t);case"constant":default:return Rnt(r,t)}}function Dnt(r,t){let e=r;if(e<0)if(t<=1)e=0;else{let n=2*t;e<n&&(e=n*Math.trunc(-e/n)+e),e=e<-t?e+n:-e-1}else if(e>t-1)if(t<=1)e=0;else{let n=2*t;e-=n*Math.trunc(e/n),e>=t&&(e=n-e-1)}return y.clamp(0,e,t-1)}function $nt(r,t){let e=r;if(e<0)if(t<=1)e=0;else{let n=t-1;e+=t*(Math.trunc(-e/n)+1)}else if(e>t-1)if(t<=1)e=0;else{let n=t-1;e-=t*Math.trunc(e/n)}return y.clamp(0,e,t-1)}function Rnt(r,t){return r}function Fnt(r,t){return y.clamp(0,r,t-1)}function og(r,t,e,n,o,s,i,a,u,l,c){let p=i*n+a*o+u*s+l;return 0<=a&&a<t&&0<=u&&u<e?r[p]:c}function Ont(r,t,e,n,o,s,i,a,u,l,c){let p=Math.round(a),m=Math.round(u);return og(r,t,e,n,o,s,i,p,m,l,c)}function Mnt(r,t,e,n,o,s,i,a,u,l,c){let p=Math.floor(a),m=Math.floor(u),f=p+1,d=m+1,h=(d-u)*og(r,t,e,n,o,s,i,p,m,l,c)+(u-m)*og(r,t,e,n,o,s,i,p,d,l,c),g=(d-u)*og(r,t,e,n,o,s,i,f,m,l,c)+(u-m)*og(r,t,e,n,o,s,i,f,d,l,c);return(f-a)*h+(a-p)*g}function Pnt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;tt(s,"unique");let i=n.data.get(s.dataId).values,{outputValues:a,outputShape:u,indices:l}=sp(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var _L={kernelName:cu,backendName:"cpu",kernelFunc:Pnt};function Lnt(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o.shape.length,a=o.shape[s],u=new Array(i-1),l=0;for(let f=0;f<i;f++)f!==s&&(u[l++]=o.shape[f]);let c=new Array(i).fill(0),p=o.shape.slice();p[s]=1;let m=new Array(a);for(let f=0;f<m.length;f++){c[s]=f;let d=Mo({inputs:{x:o},backend:e,attrs:{begin:c,size:p}});m[f]=te({inputs:{x:d},backend:e,attrs:{shape:u}}),e.disposeIntermediateTensorInfo(d)}return m}var EL={kernelName:Ki,backendName:"cpu",kernelFunc:Lnt};function znt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n;tt(o,"unsortedSegmentSum");let a=o.shape.length,u=s.shape.length,l=[],c=[],p=a-u,m=s;for(let d=0;d<p;++d){let h=yd({inputs:{input:m},backend:e,attrs:{dim:d+1}});m=h,c.push(h)}for(let d=0;d<i;++d){let h=y.createScalarValue(d,"int32"),g=e.makeTensorInfo([],"int32",h),x=sT({inputs:{a:g,b:m},backend:e}),b=Fo({inputs:{x},backend:e,attrs:{dtype:"float32"}}),w=Qc({inputs:{a:b,b:o},backend:e}),I=$l({inputs:{x:w},backend:e,attrs:{axis:0,keepDims:!1}});l.push(I),c.push(g),c.push(x),c.push(b),c.push(w),c.push(I)}let f=zT({inputs:l,backend:e,attrs:{axis:0}});return c.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var AL={kernelName:pu,backendName:"cpu",kernelFunc:znt};var Bnt=[eM,lO,rM,nM,fO,oM,sM,iM,aM,lM,uM,cM,pM,mM,fM,hM,gM,xM,yM,tM,bM,wM,IM,dO,CM,mO,hO,vM,uO,SM,kM,TM,_M,EM,AM,DM,$M,RM,FM,OM,MM,PM,LM,zM,BM,VM,GM,WM,UM,HM,qM,KM,XM,jO,YM,gO,ZM,xO,JM,yO,QM,tP,eP,bO,wO,rP,nP,oP,sP,IO,CO,cO,iP,NM,aP,lP,uP,XO,vO,SO,cP,NO,pP,mP,fP,dP,hP,gP,xP,kO,yP,bP,wP,IP,vP,SP,NP,TO,kP,TP,AP,_O,EO,DP,$P,RP,AO,FP,PP,LP,Rw,zP,YO,$O,BP,VP,GP,WP,pO,eg,UP,ZO,JO,QO,HP,qP,KP,jP,XP,YP,ZP,PO,JP,tL,eL,rL,zO,nL,oL,sL,BO,_P,aL,lL,uL,cL,pL,mL,fL,dL,GO,hL,WO,UO,gL,xL,yL,bL,wL,HO,jM,IL,CL,vL,SL,NL,TL,DO,_L,EL,AL,OP];for(let r of Bnt)rc(r);var Cd={};Kt(Cd,{assertNotComplex:()=>vi,bindCanvasToFramebuffer:()=>Xnt,bindColorTextureToFramebuffer:()=>lg,bindTextureToProgramUniformSampler:()=>e1,bindTextureUnit:()=>FL,bindVertexBufferToProgramAttribute:()=>zw,callAndCheck:()=>ht,canBeRepresented:()=>WT,createFragmentShader:()=>HT,createFramebuffer:()=>JT,createProgram:()=>qT,createStaticIndexBuffer:()=>XT,createStaticVertexBuffer:()=>jT,createTexture:()=>YT,createVertexShader:()=>UT,getBatchDim:()=>Fl,getExtensionOrThrow:()=>bd,getFramebufferErrorMessage:()=>OL,getMaxTexturesInShader:()=>o1,getNumChannels:()=>Knt,getProgramUniformLocation:()=>t1,getProgramUniformLocationOrThrow:()=>QT,getRowsCols:()=>Ol,getShapeAs3D:()=>Id,getTextureShapeFromLogicalShape:()=>r1,getWebGLDisjointQueryTimerVersion:()=>s1,getWebGLErrorMessage:()=>RL,getWebGLMaxTextureSize:()=>n1,hasExtension:()=>Kn,isCapableOfRenderingToFloatTexture:()=>i1,isDownloadFloatTextureEnabled:()=>a1,isReshapeFree:()=>Uu,isWebGLFenceEnabled:()=>l1,isWebGLVersionEnabled:()=>Vw,linkProgram:()=>KT,logShaderSourceAndInfoLog:()=>Lw,resetMaxTextureSize:()=>Ynt,resetMaxTexturesInShader:()=>Znt,unbindColorTextureFromFramebuffer:()=>Bw,unbindTextureUnit:()=>jnt,validateFramebuffer:()=>wd,validateProgram:()=>ag,validateTextureSize:()=>ZT});var ap={},Fw={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function BT(r,t){ap[r]=t}function qn(r,t){if(!(r in ap)||t!=null){let n=Gnt(r,t);if(n!==null)ap[r]=n;else return console.log("Could not get context for WebGL version",r),null}let e=ap[r];return e==null||e.isContextLost()?(delete ap[r],qn(r)):(e.disable(e.DEPTH_TEST),e.disable(e.STENCIL_TEST),e.disable(e.BLEND),e.disable(e.DITHER),e.disable(e.POLYGON_OFFSET_FILL),e.disable(e.SAMPLE_COVERAGE),e.enable(e.SCISSOR_TEST),e.enable(e.CULL_FACE),e.cullFace(e.BACK),ap[r])}function Vnt(r){if(!L().getBool("IS_SAFARI")&&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 Gnt(r,t){if(r!==1&&r!==2)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");let e=t==null?Vnt(r):t;return e.addEventListener("webglcontextlost",n=>{n.preventDefault(),delete ap[r]},!1),L().getBool("SOFTWARE_WEBGL_ENABLED")&&(Fw.failIfMajorPerformanceCaveat=!1),r===1?e.getContext("webgl",Fw)||e.getContext("experimental-webgl",Fw):e.getContext("webgl2",Fw)}var Wu;(function(r){r[r.DENSE=0]="DENSE",r[r.SHARED_BATCH=1]="SHARED_BATCH"})(Wu||(Wu={}));var Jr;(function(r){r[r.RENDER=0]="RENDER",r[r.UPLOAD=1]="UPLOAD",r[r.PIXELS=2]="PIXELS",r[r.DOWNLOAD=3]="DOWNLOAD"})(Jr||(Jr={}));var Lr;(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"})(Lr||(Lr={}));function lp(r,t){return[t,r]}function DL(r,t){return r*t}function sg(r){let t=y.sizeFromShape(r),e=Math.ceil(t/4);return y.sizeToSquarishShape(e)}function wa(r,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(r/2))]}function $L(r,t){let[e,n]=wa(r,t);return e*n*4}function ig(r,t){let e=r,n,o,s,i,a,u,l,c,p,m;return L().getNumber("WEBGL_VERSION")===2?(n=e.R32F,o=e.R16F,s=e.RGBA16F,i=e.RGBA32F,a=e.RED,l=4,c=1,p=e.HALF_FLOAT,m=e.FLOAT,u=e.RGBA8):(n=r.RGBA,o=r.RGBA,s=r.RGBA,i=e.RGBA,a=r.RGBA,l=4,c=4,p=t!=null?t.HALF_FLOAT_OES:null,m=r.FLOAT,u=r.RGBA),{internalFormatFloat:n,internalFormatHalfFloat:o,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:i,textureFormatFloat:a,downloadTextureFormat:u,downloadUnpackNumChannels:l,defaultNumChannels:c,textureTypeHalfFloat:p,textureTypeFloat:m}}function ht(r,t){let e=t();return L().getBool("DEBUG")&&Wnt(r),e}function Wnt(r){let t=r.getError();if(t!==r.NO_ERROR)throw new Error("WebGL Error: "+RL(r,t))}var Unt=596e-10,Hnt=65504;function WT(r){return!!(L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||r===0||Unt<Math.abs(r)&&Math.abs(r)<Hnt)}function RL(r,t){switch(t){case r.NO_ERROR:return"NO_ERROR";case r.INVALID_ENUM:return"INVALID_ENUM";case r.INVALID_VALUE:return"INVALID_VALUE";case r.INVALID_OPERATION:return"INVALID_OPERATION";case r.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case r.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case r.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${t}`}}function bd(r,t){return Rl(r,()=>r.getExtension(t),'Extension "'+t+'" not supported on this browser.')}function UT(r,t){let e=Rl(r,()=>r.createShader(r.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(ht(r,()=>r.shaderSource(e,t)),ht(r,()=>r.compileShader(e)),r.getShaderParameter(e,r.COMPILE_STATUS)===!1)throw console.log(r.getShaderInfoLog(e)),new Error("Failed to compile vertex shader.");return e}function HT(r,t){let e=Rl(r,()=>r.createShader(r.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(ht(r,()=>r.shaderSource(e,t)),ht(r,()=>r.compileShader(e)),L().get("ENGINE_COMPILE_ONLY"))return e;if(r.getShaderParameter(e,r.COMPILE_STATUS)===!1)throw Lw(t,r.getShaderInfoLog(e)),new Error("Failed to compile fragment shader.");return e}var qnt=/ERROR: [0-9]+:([0-9]+):/g;function Lw(r,t){let e=qnt.exec(t);if(e==null){console.log(`Couldn't parse line number in error: ${t}`),console.log(r);return}let n=+e[1],o=r.split(`
`),s=o.length.toString().length+2,i=o.map((p,m)=>y.rightPad((m+1).toString(),s)+p),a=0;for(let p=0;p<i.length;p++)a=Math.max(i[p].length,a);let u=i.slice(0,n-1),l=i.slice(n-1,n),c=i.slice(n);console.log(u.join(`
`)),console.log(t.split(`
`)[0]),console.log(`%c ${y.rightPad(l[0],a)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
`))}function qT(r){return Rl(r,()=>r.createProgram(),"Unable to create WebGLProgram.")}function KT(r,t){if(ht(r,()=>r.linkProgram(t)),!L().get("ENGINE_COMPILE_ONLY")&&r.getProgramParameter(t,r.LINK_STATUS)===!1)throw console.log(r.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function ag(r,t){if(ht(r,()=>r.validateProgram(t)),r.getProgramParameter(t,r.VALIDATE_STATUS)===!1)throw console.log(r.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function jT(r,t){let e=Rl(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,e)),ht(r,()=>r.bufferData(r.ARRAY_BUFFER,t,r.STATIC_DRAW)),e}function XT(r,t){let e=Rl(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return ht(r,()=>r.bindBuffer(r.ELEMENT_ARRAY_BUFFER,e)),ht(r,()=>r.bufferData(r.ELEMENT_ARRAY_BUFFER,t,r.STATIC_DRAW)),e}function Knt(){return L().getNumber("WEBGL_VERSION")===2?1:4}function YT(r){return Rl(r,()=>r.createTexture(),"Unable to create WebGLTexture.")}function ZT(r,t){let e=L().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(r<=0||t<=0){let n=`[${r}x${t}]`;throw new Error("Requested texture size "+n+" is invalid.")}if(r>e||t>e){let n=`[${r}x${t}]`,o=`[${e}x${e}]`;throw new Error("Requested texture size "+n+" greater than WebGL maximum on this browser / GPU "+o+".")}}function JT(r){return Rl(r,()=>r.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function zw(r,t,e,n,o,s,i){let a=r.getAttribLocation(t,e);return a===-1?!1:(ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,n)),ht(r,()=>r.vertexAttribPointer(a,o,r.FLOAT,!1,s,i)),ht(r,()=>r.enableVertexAttribArray(a)),!0)}function FL(r,t,e){ML(r,e),ht(r,()=>r.activeTexture(r.TEXTURE0+e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,t))}function jnt(r,t){ML(r,t),ht(r,()=>r.activeTexture(r.TEXTURE0+t)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function QT(r,t,e){return Rl(r,()=>r.getUniformLocation(t,e),'uniform "'+e+'" not present in program.')}function t1(r,t,e){return r.getUniformLocation(t,e)}function e1(r,t,e,n){ht(r,()=>FL(r,t,n)),ht(r,()=>r.uniform1i(e,n))}function Xnt(r){ht(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,null)),ht(r,()=>r.viewport(0,0,r.canvas.width,r.canvas.height)),ht(r,()=>r.scissor(0,0,r.canvas.width,r.canvas.height))}function lg(r,t,e){ht(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,e)),ht(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,t,0))}function Bw(r,t){ht(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,t)),ht(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,null,0))}function wd(r){let t=r.checkFramebufferStatus(r.FRAMEBUFFER);if(t!==r.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+OL(r,t))}function OL(r,t){switch(t){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 ${t}`}}function Rl(r,t,e){let n=ht(r,()=>t());if(n==null)throw new Error(e);return n}function ML(r,t){let e=r.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,n=t+r.TEXTURE0;if(n<r.TEXTURE0||n>e){let o=`[gl.TEXTURE0, gl.TEXTURE${e}]`;throw new Error(`textureUnit must be in ${o}.`)}}function Fl(r,t=2){return y.sizeFromShape(r.slice(0,r.length-t))}function Ol(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 Id(r){let t=[1,1,1];return r.length===0||r.length===1&&r[0]===1||(t=[Fl(r),...Ol(r)]),t}function r1(r,t=!1){let e=L().getNumber("WEBGL_MAX_TEXTURE_SIZE"),n=L().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");n===1/0&&L().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")&&(n=e/2),t&&(e=e*2,n=n*2,r=r.map((a,u)=>u>=r.length-2?y.nearestLargerEven(r[u]):r[u]),r.length===1&&(r=[2,r[0]])),r.length!==2&&(r=y.squeezeShape(r).newShape);let o=y.sizeFromShape(r),s=null;r.length<=1&&o<=e?s=[1,o]:r.length===2&&r[0]<=e&&r[1]<=e?s=r:r.length===3&&r[0]*r[1]<=e&&r[2]<=e?s=[r[0]*r[1],r[2]]:r.length===3&&r[0]<=e&&r[1]*r[2]<=e?s=[r[0],r[1]*r[2]]:r.length===4&&r[0]*r[1]*r[2]<=e&&r[3]<=e?s=[r[0]*r[1]*r[2],r[3]]:r.length===4&&r[0]<=e&&r[1]*r[2]*r[3]<=e&&(s=[r[0],r[1]*r[2]*r[3]]);let i=s!=null&&Math.max(...s)>n&&Math.min(...s)<=(t?2:1)&&Math.min(...s)>0;if(s==null||i)if(t){let a=Fl(r),u=2,l=2;r.length&&([u,l]=Ol(r)),o=a*(u/2)*(l/2),s=y.sizeToSquarishShape(o).map(c=>c*2)}else s=y.sizeToSquarishShape(o);return s}function Ow(r){return r%2===0}function Uu(r,t){if(r=r.slice(-2),t=t.slice(-2),y.arraysEqual(r,t)||!r.length||!t.length||r[0]===0||r[1]===0||t[0]===0||t[1]===0)return!0;if(r.length!==t.length){let e=r[r.length-1],n=t[t.length-1];if(e===n||Ow(e)&&Ow(n)&&(r[0]===1||t[0]===1))return!0}return r[1]===t[1]&&Ow(r[0])&&Ow(t[0])}var Mw,Pw;function n1(r){if(Mw==null){let t=qn(r);Mw=t.getParameter(t.MAX_TEXTURE_SIZE)}return Mw}function Ynt(){Mw=null}function Znt(){Pw=null}function o1(r){if(Pw==null){let t=qn(r);Pw=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Pw)}function s1(r){if(r===0)return 0;let t,e=qn(r);return Kn(e,"EXT_disjoint_timer_query_webgl2")&&r===2?t=2:Kn(e,"EXT_disjoint_timer_query")?t=1:t=0,t}function Kn(r,t){return r.getExtension(t)!=null}function Vw(r){try{if(qn(r)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function i1(r){if(r===0)return!1;let t=qn(r);if(r===1){if(!Kn(t,"OES_texture_float"))return!1}else if(!Kn(t,"EXT_color_buffer_float"))return!1;return GT(t)}function a1(r){if(r===0)return!1;let t=qn(r);if(r===1){if(!Kn(t,"OES_texture_float")||!Kn(t,"WEBGL_color_buffer_float"))return!1}else{if(Kn(t,"EXT_color_buffer_float"))return GT(t);let n="EXT_color_buffer_half_float";if(Kn(t,n)){let o=t.getExtension(n);return Jnt(t,o)}return!1}return GT(t)}function GT(r){let t=ig(r),e=r.createTexture();r.bindTexture(r.TEXTURE_2D,e),r.texImage2D(r.TEXTURE_2D,0,t.internalFormatFloat,1,1,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,s),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,e,0);let i=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(e),r.deleteFramebuffer(s),i}function Jnt(r,t){let e=ig(r,t),n=r.createTexture();r.bindTexture(r.TEXTURE_2D,n),r.texImage2D(r.TEXTURE_2D,0,e.internalFormatHalfFloat,1,1,0,e.textureFormatFloat,e.textureTypeHalfFloat,null);let i=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,i),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,n,0);let a=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(n),r.deleteFramebuffer(i),a}function l1(r){return r!==2?!1:qn(r).fenceSync!=null}function vi(r,t){Array.isArray(r)||(r=[r]),r.forEach(e=>{e!=null&&y.assert(e.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var kt=L();kt.registerFlag("HAS_WEBGL",()=>kt.getNumber("WEBGL_VERSION")>0);kt.registerFlag("WEBGL_VERSION",()=>Vw(2)?2:Vw(1)?1:0);kt.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);kt.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>kt.get("WEBGL_VERSION")===2);kt.registerFlag("WEBGL_CPU_FORWARD",()=>!0);kt.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);kt.registerFlag("WEBGL_PACK",()=>kt.getBool("HAS_WEBGL"));kt.registerFlag("WEBGL_PACK_NORMALIZATION",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_CLIP",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_REDUCE",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_LAZILY_UNPACK",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_CONV_IM2COL",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE",()=>kt.getBool("WEBGL_PACK"));kt.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>n1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>o1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=kt.getNumber("WEBGL_VERSION");return r===0?0:s1(r)});kt.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>kt.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!du.isMobile());kt.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>i1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>kt.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:kt.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));kt.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>a1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_FENCE_API_ENABLED",()=>l1(kt.getNumber("WEBGL_VERSION")));kt.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>kt.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);kt.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(typeof r!="number")throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but got ${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}.`)});kt.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>du.isMobile()?1:-1,r=>{if(typeof r!="number")throw new Error(`WEBGL_FLUSH_THRESHOLD must be a number but got ${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}.`)});kt.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);kt.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);kt.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);kt.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);kt.registerFlag("WEBGL_EXP_CONV",()=>!1);kt.registerFlag("SOFTWARE_WEBGL_ENABLED",()=>kt.getBool("IS_TEST"));kt.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE",()=>1/0);kt.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE",()=>!1);kt.registerFlag("WEBGL2_ISNAN_CUSTOM",()=>!1);kt.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function Ue(){let r,t,e,n,o,s,i,a,u,l;return L().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",t="in",e="out",n="in",o="texture",s="outputColor",i="out vec4 outputColor;",a=L().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)
    `:"",u="",l=`
      #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="",t="attribute",e="varying",n="varying",o="texture2D",s="gl_FragColor",i="",a=`
      #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));
      }
    `,u=`
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `,l=`
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `),{version:r,attribute:t,varyingVs:e,varyingFs:n,texture2D:o,output:s,defineOutput:i,defineSpecialNaN:a,defineSpecialInf:u,defineRound:l}}function Si(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / ${o}`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${i}; ${a};`}).join("")}function up(r,t,e="index"){let n=y.computeStrides(t);return n.map((o,s)=>{let i=`int ${r[s]} = ${e} / outShapeStrides[${s}]`,a=s===n.length-1?`int ${r[s+1]} = ${e} - ${r[s]} * outShapeStrides[${s}]`:`index -= ${r[s]} * outShapeStrides[${s}]`;return`${i}; ${a};`}).join("")}function Qnt(r,t){let e=r.length,n=r.map(s=>`${t}[${s}]`),o=new Array(e-1);o[e-2]=n[e-1];for(let s=e-3;s>=0;--s)o[s]=`(${o[s+1]} * ${n[s+1]})`;return o}function PL(r,t,e="index"){let n=r.map((s,i)=>i),o=Qnt(n,t);return o.map((s,i)=>{let a=`int ${r[i]} = ${e} / ${o[i]}`,u=i===o.length-1?`int ${r[i+1]} = ${e} - ${r[i]} * ${o[i]}`:`index -= ${r[i]} * ${o[i]}`;return`${a}; ${u};`}).join("")}function vd(r){let t=y.computeStrides(r).map(e=>e.toString());return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
  }
`}function Sd(){return`
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`}var Gw=`
  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:LL}=S;function zL(r,t,e){let n=[];if(r.forEach(f=>{let d=y.sizeFromShape(f.shapeInfo.logicalShape);if(f.shapeInfo.isUniform?n.push(`uniform float ${f.name}${d>1?`[${d}]`:""};`):(n.push(`uniform sampler2D ${f.name};`),n.push(`uniform int offset${f.name};`)),e.enableShapeUniforms){let{uniformShape:h}=Ww(e.packedInputs,f.shapeInfo.logicalShape,f.shapeInfo.texShape);switch(h.length){case 1:n.push(`uniform int ${f.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${f.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${f.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${f.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${f.name}TexShape;`)}}),e.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}e.customUniforms&&e.customUniforms.forEach(f=>{n.push(`uniform ${f.type} ${f.name}${f.arrayIndex?`[${f.arrayIndex}]`:""};`)});let o=n.join(`
`),s=r.map(f=>tot(f,t,e.packedInputs,e.enableShapeUniforms)).join(`
`),i=t.texShape,a=Ue(),u=not(a),l,c,p=iot(a);return t.isPacked?(l=eot(t.logicalShape,i,e.enableShapeUniforms),c=sot(a)):(l=rot(t.logicalShape,i,e.enableShapeUniforms),c=oot(a)),e.packedInputs&&(p+=cot),[p,u,c,o,l,s,e.userCode].join(`
`)}function kd(r,t=!1){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return Cot(r,t);case 1:return Sot(r,t);case 2:return kot(r,t);case 3:return _ot(r,t);case 4:return Aot(r,t);case 5:return Dot(r);case 6:return $ot(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function BL(r,t){switch(r.shapeInfo.logicalShape.length){case 0:return Iot(r);case 1:return vot(r,t);case 2:return Not(r,t);case 3:return Tot(r,t);default:return Eot(r,t)}}function tot(r,t,e=!1,n){let o="";e?o+=BL(r,n):o+=kd(r,n);let s=r.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(e?o+=Rot(r,t):o+=Fot(r,t)),o}function eot(r,t,e){switch(r.length){case 0:return VL();case 1:return pot(r,t,e);case 2:return bot(r,t,e);case 3:return fot(r,t,e);default:return hot(r,t,e)}}function rot(r,t,e){switch(r.length){case 0:return VL();case 1:return mot(r,t,e);case 2:return wot(r,t,e);case 3:return dot(r,t,e);case 4:return got(r,t,e);case 5:return xot(r,t);case 6:return yot(r,t);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function not(r){return`
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${r.texture2D}(textureSampler, uv).r;
    }
  `}function oot(r){return`
    void setOutput(float val) {
      ${r.output} = vec4(val, 0, 0, 0);
    }
  `}function sot(r){return`
    void setOutput(vec4 val) {
      ${r.output} = val;
    }
  `}function iot(r){return`${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);
    }

    ${aot}
    ${lot}
    ${uot}
  `}var aot=`
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);
}
`,lot=`
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);
}
`,uot=`
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);
}
`,cot=`
  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 VL(){return`
    int getOutputCoords() {
      return 0;
    }
  `}function pot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?e?`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${n[1]}.0);
      }
    `:n[1]===1?e?`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `:`
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${n[0]}.0);
      }
    `:e?`
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
    }
  `}function mot(r,t,e){return t[0]===1?e?`
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.x * ${t[1]}.0);
      }
    `:t[1]===1?e?`
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    `:`
      int getOutputCoords() {
        return int(resultUV.y * ${t[0]}.0);
      }
    `:e?`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  `:`
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      return resTexRC.x * ${t[1]} + resTexRC.y;
    }
  `}function fot(r,t,e){if(e)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 n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[2]/2),s=o*Math.ceil(r[1]/2);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;

      int b = index / ${s};
      index -= b * ${s};

      int r = 2 * (index / ${o});
      int c = imod(index, ${o}) * 2;

      return ivec3(b, r, c);
    }
  `}function dot(r,t,e){if(e)return`
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${up(["r","c","d"],r)}
    return ivec3(r, c, d);
  }
`;let n=Si(["r","c","d"],r);return`
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${n}
      return ivec3(r, c, d);
    }
  `}function hot(r,t,e){if(e)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 n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],o=Math.ceil(r[r.length-1]/2),s=o*Math.ceil(r[r.length-2]/2),i=s,a="",u="b, r, c";for(let l=2;l<r.length-1;l++)i*=r[r.length-l-1],a=`
      int b${l} = index / ${i};
      index -= b${l} * ${i};
    `+a,u=`b${l}, `+u;return`
    ivec${r.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));
      int index = resTexRC.x * ${n[1]} + resTexRC.y;

      ${a}

      int b = index / ${s};
      index -= b * ${s};

      int r = 2 * (index / ${o});
      int c = imod(index, ${o}) * 2;

      return ivec${r.length}(${u});
    }
  `}function got(r,t,e){if(e)return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${up(["r","c","d","d2"],r)}
      return ivec4(r, c, d, d2);
    }
  `;let n=Si(["r","c","d","d2"],r);return`
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${n}
      return ivec4(r, c, d, d2);
    }
  `}function xot(r,t){let e=Si(["r","c","d","d2","d3"],r);return`
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${e}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `}function yot(r,t){let e=Si(["r","c","d","d2","d3","d4"],r);return`
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;

      ${e}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `}function bot(r,t,e){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(y.arraysEqual(r,t))return e?`
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
      }
    `;let o=Math.ceil(r[1]/2);return e?`
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int 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 r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${n[0]}, ${n[1]}));

      int index = resTexRC.x * ${n[1]} + resTexRC.y;
      int r = 2 * (index / ${o});
      int c = imod(index, ${o}) * 2;

      return ivec2(r, c);
    }
  `}function wot(r,t,e){return y.arraysEqual(r,t)?e?`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `:`
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
      }
    `:r[1]===1?e?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `:r[0]===1?e?`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    `:`
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${t[0]}, ${t[1]}));
        int index = resTexRC.x * ${t[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `:e?`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  `:`
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      int r = index / ${r[1]};
      int c = index - r * ${r[1]};
      return ivec2(r, c);
    }
  `}function cp(r){return`offset${r}`}function Iot(r){let t=r.name,e="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ue();return`
    vec4 ${e}() {
      return ${n.texture2D}(${t}, halfCR);
    }
  `}function Cot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${n}() {return ${e};}`;let[o,s]=r.shapeInfo.texShape;if(o===1&&s===1)return`
      float ${n}() {
        return sampleTexture(${e}, halfCR);
      }
    `;let i=cp(e);if(t)return`
    float ${n}() {
      vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], ${i});
      return sampleTexture(${e}, uv);
    }
  `;let[a,u]=r.shapeInfo.texShape;return`
    float ${n}() {
      vec2 uv = uvFromFlat(${a}, ${u}, ${i});
      return sampleTexture(${e}, uv);
    }
  `}function vot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,s=Ue();if(t)return`
    vec4 ${n}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${s.texture2D}(${e}, uv);
    }
  `;let i=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return`
    vec4 ${n}(int index) {
      vec2 uv = packedUVfrom1D(
        ${i[0]}, ${i[1]}, index);
      return ${s.texture2D}(${e}, uv);
    }
  `}function Sot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
      float ${n}(int index) {
        ${Td(r)}
      }
    `;let o=r.shapeInfo.texShape,s=o[0],i=o[1];if(i===1&&s===1)return`
      float ${n}(int index) {
        return sampleTexture(${e}, halfCR);
      }
    `;let a=cp(e);return i===1?t?`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / float(${e}TexShape[0]));
        return sampleTexture(${e}, uv);
      }
    `:`
      float ${n}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${s}.0);
        return sampleTexture(${e}, uv);
      }
    `:s===1?t?`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${a}) + 0.5) / float(${e}TexShape[1]), 0.5);
        return sampleTexture(${e}, uv);
      }
    `:`
      float ${n}(int index) {
        vec2 uv = vec2((float(index + ${a}) + 0.5) / ${i}.0, 0.5);
        return sampleTexture(${e}, uv);
      }
    `:t?`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${e}TexShape[0], ${e}TexShape[1], index + ${a});
      return sampleTexture(${e}, uv);
    }
  `:`
    float ${n}(int index) {
      vec2 uv = uvFromFlat(${s}, ${i}, index + ${a});
      return sampleTexture(${e}, uv);
    }
  `}function Not(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=s[0],a=s[1],u=Ue();if(s!=null&&y.arraysEqual(e,s))return t?`
      vec4 ${o}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);

        return ${u.texture2D}(${n}, uv);
      }
    `:`
      vec4 ${o}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${i}.0);

        return ${u.texture2D}(${n}, uv);
      }
    `;if(t)return`
    vec4 ${o}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${n}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${u.texture2D}(${n}, uv);
    }
  `;let l=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(e[1]/2);return`
    vec4 ${o}(int row, int col) {
      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
      return ${u.texture2D}(${n}, uv);
    }
  `}function kot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape;if(s!=null&&y.arraysEqual(e,s)){if(t)return`
      float ${o}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `;let m=s[0],f=s[1];return`
    float ${o}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${f}.0, ${m}.0);
      return sampleTexture(${n}, uv);
    }
  `}let{newShape:i,keptDims:a}=y.squeezeShape(e),u=i;if(u.length<e.length){let m=_d(r,u),f=["row","col"];return`
      ${kd(m,t)}
      float ${o}(int row, int col) {
        return ${o}(${Ed(f,a)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
        ${Td(r)}
      }
    `;let l=s[0],c=s[1],p=cp(n);return c===1?t?`
      float ${o}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
        return sampleTexture(${n}, uv);
      }
    `:`
    float ${o}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
      return sampleTexture(${n}, uv);
    }
  `:l===1?t?`
      float ${o}(int row, int col) {
        float index = dot(vec3(row, col, ${p}), vec3(${n}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${n}TexShape[1]), 0.5);
        return sampleTexture(${n}, uv);
      }
    `:`
    float ${o}(int row, int col) {
      float index = dot(vec3(row, col, ${p}), vec3(${e[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
      return sampleTexture(${n}, uv);
    }
  `:t?`
      float ${o}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${n}Shape[1] + col + ${p};
        vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
        return sampleTexture(${n}, uv);
      }
    `:`
  float ${o}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${e[1]} + col + ${p};
    vec2 uv = uvFromFlat(${l}, ${c}, index);
    return sampleTexture(${n}, uv);
  }
`}function Tot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(e[0]===1){let m=e.slice(1),f=[1,2],d=_d(r,m),h=["b","row","col"];return`
        ${BL(d,t)}
        vec4 ${o}(int b, int row, int col) {
          return ${o}(${Ed(h,f)});
        }
      `}let a=Ue();if(t)return`
    vec4 ${o}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${n}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${a.texture2D}(${n}, uv);
    }
  `;let u=i[0],l=i[1],c=Math.ceil(e[2]/2),p=c*Math.ceil(e[1]/2);return`
    vec4 ${o}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${u}, ${l}, ${p}, ${c}, b, row, col);
      return ${a.texture2D}(${n}, uv);
    }
  `}function _ot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[1]*e[2],i=e[2],{newShape:a,keptDims:u}=y.squeezeShape(e),l=a;if(l.length<e.length){let h=_d(r,l),g=["row","col","depth"];return`
        ${kd(h,t)}
        float ${o}(int row, int col, int depth) {
          return ${o}(${Ed(g,u)});
        }
      `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${s}, ${i}, 1)));
        ${Td(r)}
      }
    `;let c=r.shapeInfo.texShape,p=c[0],m=c[1],f=r.shapeInfo.flatOffset;if(m===s&&f==null)return t?`
      float ${o}(int row, int col, int depth) {
        int stride1 = ${n}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
        float ${o}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${i}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${m}.0, ${p}.0);
          return sampleTexture(${n}, uv);
        }
      `;if(m===i&&f==null)return t?`
      float ${o}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${n}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
    float ${o}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${m}.0, ${p}.0);
      return sampleTexture(${n}, uv);
    }
  `;let d=cp(n);return t?`
    float ${o}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${n}Shape[1] * ${n}Shape[2];
      int stride1 = ${n}Shape[2];
      int index = row * stride0 + col * stride1 + depth + ${d};
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index);
      return sampleTexture(${n}, uv);
    }
    `:`
      float ${o}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${s} + col * ${i} + depth + ${d};
        vec2 uv = uvFromFlat(${p}, ${m}, index);
        return sampleTexture(${n}, uv);
      }
  `}function Eot(r,t){let e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=Ue();if(t)return`
    vec4 ${n}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${e}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${e}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${e}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${e}TexShape[0]) / 2.0), ceil(float(${e}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${o.texture2D}(${e}, uv);
    }
  `;let s=r.shapeInfo.logicalShape,i=s.length,a=r.shapeInfo.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=Math.ceil(s[i-1]/2),m=p*Math.ceil(s[i-2]/2),f="int b, int row, int col",d=`b * ${m} + (row / 2) * ${p} + (col / 2)`;for(let h=2;h<i-1;h++)f=`int b${h}, `+f,m*=s[i-h-1],d=`b${h} * ${m} + `+d;return`
    vec4 ${n}(${f}) {
      int index = ${d};
      int texR = index / ${c};
      int texC = index - texR * ${c};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
      return ${o.texture2D}(${e}, uv);
    }
  `}function Aot(r,t){let e=r.shapeInfo.logicalShape,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e[3],i=e[2]*s,a=e[1]*i,{newShape:u,keptDims:l}=y.squeezeShape(e);if(u.length<e.length){let b=_d(r,u),w=["row","col","depth","depth2"];return`
      ${kd(b,t)}
      float ${o}(int row, int col, int depth, int depth2) {
        return ${o}(${Ed(w,l)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${o}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${a}, ${i}, ${s}, 1)));
        ${Td(r)}
      }
    `;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1],d=`int stride2 = ${n}Shape[3];`,h=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(f===a&&c==null)return t?`
      float ${o}(int row, int col, int depth, int depth2) {
        ${d}
        ${h}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${o}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${i}, ${s}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${m}.0);
        return sampleTexture(${n}, uv);
      }
    `;if(f===s&&c==null)return t?`
      float ${o}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${n}Shape[1] * ${n}Shape[2], ${n}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${n}TexShape[1], ${n}TexShape[0]);
        return sampleTexture(${n}, uv);
      }
    `:`
      float ${o}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${e[1]*e[2]}, ${e[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${m}.0);
        return sampleTexture(${n}, uv);
      }
    `;let x=cp(n);return t?`
    float ${o}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${d}
      ${h}
      ${g}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${x});
      return sampleTexture(${n}, uv);
    }
  `:`
    float ${o}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${a} + col * ${i} +
          depth * ${s} + depth2;
      vec2 uv = uvFromFlat(${m}, ${f}, index + ${x});
      return sampleTexture(${n}, uv);
    }
  `}function Dot(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),o=t[4],s=t[3]*o,i=t[2]*s,a=t[1]*i,{newShape:u,keptDims:l}=y.squeezeShape(t);if(u.length<t.length){let h=_d(r,u),g=["row","col","depth","depth2","depth3"];return`
      ${kd(h)}
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        return ${n}(${Ed(g,l)});
      }
    `}if(r.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${a}, ${i}, ${s}, ${o})) +
          depth3;
        ${Td(r)}
      }
    `;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===a&&c==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${i}, ${s}, ${o}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${f}.0, ${m}.0);
        return sampleTexture(${e}, uv);
      }
    `;if(f===o&&c==null)return`
      float ${n}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]},
               ${t[2]*t[3]}, ${t[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${f}.0, ${m}.0);
        return sampleTexture(${e}, uv);
      }
    `;let d=cp(e);return`
    float ${n}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${a} + col * ${i} + depth * ${s} +
          depth2 * ${o} + depth3 + ${d};
      vec2 uv = uvFromFlat(${m}, ${f}, index);
      return sampleTexture(${e}, uv);
    }
  `}function $ot(r){let t=r.shapeInfo.logicalShape,e=r.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),{newShape:o,keptDims:s}=y.squeezeShape(t);if(o.length<t.length){let g=_d(r,o),x=["row","col","depth","depth2","depth3","depth4"];return`
      ${kd(g)}
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${n}(${Ed(x,s)});
      }
    `}let i=t[5],a=t[4]*i,u=t[3]*a,l=t[2]*u,c=t[1]*l;if(r.shapeInfo.isUniform)return`
      float ${n}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${c}, ${l}, ${u}, ${a})) +
          dot(
            vec2(depth3, depth4),
            vec2(${i}, 1)));
        ${Td(r)}
      }
    `;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${l}, ${u}, ${a}, ${i})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${d}.0, ${f}.0);
        return sampleTexture(${e}, uv);
      }
    `;if(d===i&&p==null)return`
      float ${n}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${t[1]*t[2]*t[3]*t[4]},
               ${t[2]*t[3]*t[4]},
               ${t[3]*t[4]},
               ${t[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${d}.0, ${f}.0);
        return sampleTexture(${e}, uv);
      }
    `;let h=cp(e);return`
    float ${n}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${c} + col * ${l} + depth * ${u} +
          depth2 * ${a} + depth3 * ${i} + depth4 + ${h};
      vec2 uv = uvFromFlat(${f}, ${d}, index);
      return sampleTexture(${e}, uv);
    }
  `}function Td(r){let t=r.name,e=y.sizeFromShape(r.shapeInfo.logicalShape);return e<2?`return ${t};`:`
    for (int i = 0; i < ${e}; i++) {
      if (i == index) {
        return ${t}[i];
      }
    }
  `}function Rot(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,i=t.logicalShape.length,a=LL(r.shapeInfo.logicalShape,t.logicalShape),u=zt(i),l=i-s,c,p=["x","y","z","w","u","v"];s===0?c="":i<2&&a.length>=1?c="coords = 0;":c=a.map(b=>`coords.${p[b+l]} = 0;`).join(`
`);let m="";i<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+l]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(t.logicalShape)===1;if(s===1&&!h&&!x)f=`
      return vec4(outputValue.xy, outputValue.xy);
    `;else if(h&&!x)i===1?f=`
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `:f=`
        return vec4(outputValue.x);
      `;else if(a.length){let b=s-2,w=s-1;a.indexOf(b)>-1&&a.indexOf(w)>-1?f="return vec4(outputValue.x);":a.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":a.indexOf(w)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
    vec4 ${o}() {
      ${u} coords = getOutputCoords();
      ${c}
      vec4 outputValue = get${n}(${m});
      ${f}
    }
  `}function Fot(r,t){let e=r.name,n=e.charAt(0).toUpperCase()+e.slice(1),o="get"+n+"AtOutCoords",s=t.texShape,i=r.shapeInfo.texShape,a=r.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!r.shapeInfo.isUniform&&a===u&&r.shapeInfo.flatOffset==null&&y.arraysEqual(i,s))return`
      float ${o}() {
        return sampleTexture(${e}, resultUV);
      }
    `;let l=zt(u),c=LL(r.shapeInfo.logicalShape,t.logicalShape),p=u-a,m,f=["x","y","z","w","u","v"];a===0?m="":u<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
`);let d="";return u<2&&a>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
    float ${o}() {
      ${l} coords = getOutputCoords();
      ${m}
      return get${n}(${d});
    }
  `}function zt(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 ${r} is not yet supported`)}function Ww(r,t,e){let{newShape:n,keptDims:o}=y.squeezeShape(t),s=t.length,i=r&&s===3&&t[0]===1,a=i?t.slice(1):n,u=!r&&s>1&&!y.arraysEqual(t,e)&&n.length<s||i;return{useSqueezeShape:u,uniformShape:u?a:t,keptDims:o}}function _d(r,t){let e=JSON.parse(JSON.stringify(r));return e.shapeInfo.logicalShape=t,e}function Ed(r,t){return t.map(e=>r[e]).join(", ")}function WL(r,t,e,n){let o=e.map((c,p)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:m}}),s=o.map(c=>c.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},a=zL(o,i,t),u=HT(r.gl,a),l=r.createProgram(u);return L().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(l),Object.assign({program:t,fragmentShader:u,source:a,webGLProgram:l,inShapeInfos:s,outShapeInfo:i},u1(r,t,l)))}function u1(r,t,e){let n=[],o=[],s,i,a,u=null,l=null;l=r.getUniformLocation(e,"NAN",!1),L().getNumber("WEBGL_VERSION")===1&&(u=r.getUniformLocation(e,"INFINITY",!1));let c=!1;for(let p of t.variableNames){let m={name:p,uniform:r.getUniformLocation(e,p,c),offset:r.getUniformLocation(e,`offset${p}`,c)};t.enableShapeUniforms&&(m.shape=r.getUniformLocation(e,`${p}Shape`,c),m.texShape=r.getUniformLocation(e,`${p}TexShape`,c)),n.push(m)}if(t.enableShapeUniforms&&(s=r.getUniformLocation(e,"outShape",c),a=r.getUniformLocation(e,"outShapeStrides",c),i=r.getUniformLocation(e,"outTexShape",c)),t.customUniforms)for(let p of t.customUniforms)o.push(r.getUniformLocation(e,p.name,c));return{variablesLocations:n,customUniformLocations:o,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:a,outTexShapeLocation:i}}function GL(r,t){if(r.length!==t.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${t.length} inputs`);r.forEach((e,n)=>{let o=e.logicalShape,s=t[n],i=s.shape;if(!y.arraysEqual(o,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${i} must match`);if(e.isUniform&&s.isUniform)return;let a=e.texShape,u=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(a,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${a} and ${u} must match`)})}function UL(r,t,e,n,o){t.program.enableShapeUniforms||(GL(t.inShapeInfos,e),GL([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):r.setOutputMatrixTexture(s.texture,i[0],i[1]),r.setProgram(t.webGLProgram),r.bindVertexArray(t.webGLProgram.vao),L().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&r.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&r.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u<e.length;++u){let l=e[u],{uniform:c,offset:p,shape:m,texShape:f}=t.variablesLocations[u];if(m){let{uniformShape:d}=Ww(t.program.packedInputs,l.shape,l.texData.texShape);switch(d.length){case 1:r.gl.uniform1iv(m,new Int32Array(d));break;case 2:r.gl.uniform2iv(m,new Int32Array(d));break;case 3:r.gl.uniform3iv(m,new Int32Array(d));break;case 4:r.gl.uniform4iv(m,new Int32Array(d));break;default:break}}if(f&&r.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),c!=null){if(l.isUniform){if(y.sizeFromShape(l.shape)<2)r.gl.uniform1f(c,l.uniformValues[0]);else{let d=l.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),r.gl.uniform1fv(c,d)}continue}l.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,l.texData.slice.flatOffset),r.setInputMatrixTexture(l.texData.texture.texture,c,u)}}let a=t.outShapeLocation;if(a)switch(n.shape.length){case 1:r.gl.uniform1iv(a,new Int32Array(n.shape));break;case 2:r.gl.uniform2iv(a,new Int32Array(n.shape));break;case 3:r.gl.uniform3iv(a,new Int32Array(n.shape));break;case 4:r.gl.uniform4iv(a,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let u=y.computeStrides(n.shape);switch(n.shape.length){case 2:r.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:r.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:r.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}if(t.outTexShapeLocation&&r.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&o)for(let u=0;u<t.program.customUniforms.length;++u){let l=t.program.customUniforms[u],c=t.customUniformLocations[u],p=o[u];if(l.type==="float")r.gl.uniform1fv(c,p);else if(l.type==="vec2")r.gl.uniform2fv(c,p);else if(l.type==="vec3")r.gl.uniform3fv(c,p);else if(l.type==="vec4")r.gl.uniform4fv(c,p);else if(l.type==="int")r.gl.uniform1iv(c,p);else if(l.type==="ivec2")r.gl.uniform2iv(c,p);else if(l.type==="ivec3")r.gl.uniform3iv(c,p);else if(l.type==="ivec4")r.gl.uniform4iv(c,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}r.executeProgram()}function HL(r,t,e){let n="";t.concat(e).forEach(i=>{let a=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=Ww(r.packedInputs,i.shape,u),m="",f="",d="";if(c.length===1&&r.packedInputs){let N=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];m=`${N[0]>1}_${N[1]>1}`}else if(c.length===2&&!r.packedInputs)f=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let N=y.computeStrides(c);d=`${N[0]===u[1]}_${N[N.length-1]===u[1]}`}let h=i.shape.length,g=c.length===2&&y.arraysEqual(i.shape,u),x=y.sizeFromShape(i.shape)===1,b=S.getBroadcastDims(i.shape,e.shape),w=!r.packedInputs&&h===e.shape.length&&y.arraysEqual(u,e.texData.texShape),I=r.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;n+=`${h}_${w}_${l?p:""}_${c.length}_${x}_${b}_${g}_${m}_${f}_${d}_${I}_${a}`}else{let u=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${u}_${a}`}});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o+`${L().getNumber("WEBGL_VERSION")}`,s}function he(r){return L().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&r<=4}var Uw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Wu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?up(["r","c","d"],t):Si(["r","c","d"],t)}
        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);
        }

        ${e.output} = result;
      }
    `}};var Hw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Wu.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let e=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms?up(["r","c","d"],t):Si(["r","c","d"],t)}
        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));
        }

        ${e.output} = result;
      }
    `}};var qw=class{constructor(t){this.variableNames=["A"],this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`
      ${Gw}

      void main() {
        float x = getAAtOutCoords();
        ${e.output} = encode_float(x);
      }
    `}};var Kw=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Jr.DOWNLOAD;let e=Ue();this.outputShape=t,this.userCode=`
      ${Gw}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${e.output} = encode_float(x);
      }
    `}};var Pot={R:0,G:1,B:2,A:3},ug=class{constructor(t,e=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let o=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let s="result";e&&(s="floor(result * 255. + 0.5)");let i="";for(let a=0;a<n.length;a++){let u=n[a];i+=`
          if(offset == ${a}) {
            result = values[${Pot[u]}];
          }`}this.userCode=`
      ${this.enableShapeUniforms?Sd():vd(t)}

      void main() {
        ivec3 coords = getOutputCoords();
        int flatIndex = getFlatIndex(coords);
        float result = 0.;
        int offset = imod(flatIndex, ${n.length});

        flatIndex = idiv(flatIndex, ${n.length}, 1.);

        int r = flatIndex / texShape[1];
        if (r < texShape[0]) {
          int c = imod(flatIndex, texShape[1]);
          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
          vec4 values = ${o.texture2D}(A, uv);
          ${i}
        }
        ${o.output} = vec4(${s}, 0., 0., 0.);
      }
    `}};var jw=class{constructor(t,e=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Ue();this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let o="",s="result";e&&(s="floor(result * 255. + 0.5)");for(let i=0;i<=1;i++)for(let a=0;a<=1;a++){let u=i*2+a;o+=`
          localCoords = coords;
          if(localCoords[2] + ${a} < ${this.enableShapeUniforms?"outShape[2]":`${t[2]}`}) {
          localCoords[2] += ${a};
          if (localCoords[1] + ${i} < ${this.enableShapeUniforms?"outShape[1]":`${t[1]}`}) {
            localCoords[1] += ${i};

            flatIndex = getFlatIndex(localCoords);
            offset = imod(flatIndex, 4);

            flatIndex = idiv(flatIndex, 4, 1.);

            int r = flatIndex / texShape[1];
            int c = imod(flatIndex, texShape[1]);
            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
            values = ${n.texture2D}(A, uv);

            if (offset == 0) {
              result[${u}] = values[0];
            } else if (offset == 1) {
              result[${u}] = values[1];
            } else if (offset == 2) {
              result[${u}] = values[2];
            } else {
              result[${u}] = values[3];
            }
          }
        }
        `}this.userCode=`
        ${this.enableShapeUniforms?Sd():vd(t)}

        void main() {
          ivec3 coords = getOutputCoords();

          vec4 result = vec4(0.);
          int flatIndex, r, c, offset;
          ivec3 localCoords;
          vec2 uv;
          vec4 values;

          ${o}

          ${n.output} = ${s};
        }
    `}};var k1={};Kt(k1,{bindVertexProgramAttributeStreams:()=>y1,createBufferFromOutputTexture:()=>I1,createFloat16MatrixTexture:()=>d1,createFloat16PackedMatrixTexture:()=>x1,createFloat32MatrixTexture:()=>f1,createIndexBuffer:()=>m1,createPackedMatrixTexture:()=>g1,createUnsignedBytesMatrixTexture:()=>h1,createVertexBuffer:()=>p1,createVertexShader:()=>c1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>v1,downloadFloat32MatrixFromBuffer:()=>C1,downloadMatrixFromPackedOutputTexture:()=>N1,downloadPackedMatrixFromBuffer:()=>S1,getInternalFormatForFloat16MatrixTexture:()=>Yw,getInternalFormatForFloat16PackedMatrixTexture:()=>Qw,getInternalFormatForFloat32MatrixTexture:()=>Xw,getInternalFormatForPackedMatrixTexture:()=>Jw,getInternalFormatForUnsignedBytesMatrixTexture:()=>Zw,uploadDenseMatrixToTexture:()=>b1,uploadPixelDataToTexture:()=>w1});function c1(r){let t=Ue(),e=`${t.version}
    precision highp float;
    ${t.attribute} vec3 clipSpacePos;
    ${t.attribute} vec2 uv;
    ${t.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;return UT(r,e)}function p1(r){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return jT(r,t)}function m1(r){let t=new Uint16Array([0,1,2,2,1,3]);return XT(r,t)}function cg(r,t,e,n,o,s){ZT(t,e);let i=YT(r),a=r.TEXTURE_2D;return ht(r,()=>r.bindTexture(a,i)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MIN_FILTER,r.NEAREST)),ht(r,()=>r.texParameteri(a,r.TEXTURE_MAG_FILTER,r.NEAREST)),L().getNumber("WEBGL_VERSION")===1?ht(r,()=>r.texImage2D(a,0,n,t,e,0,o,s,null)):ht(r,()=>r.texStorage2D(a,1,n,t,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null)),{texture:i,texShape:[e,t]}}function Xw(r){return r.internalFormatFloat}function f1(r,t,e,n){let[o,s]=lp(t,e);return cg(r,o,s,Xw(n),n.textureFormatFloat,r.FLOAT)}function Yw(r){return r.internalFormatHalfFloat}function d1(r,t,e,n){let[o,s]=lp(t,e);return cg(r,o,s,Yw(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function Zw(r){return r.downloadTextureFormat}function h1(r,t,e,n){let[o,s]=lp(t,e);return cg(r,o,s,Zw(n),r.RGBA,r.UNSIGNED_BYTE)}function Jw(r){return r.internalFormatPackedFloat}function g1(r,t,e,n){let[o,s]=wa(t,e);return cg(r,o,s,Jw(n),r.RGBA,r.FLOAT)}function Qw(r){return r.internalFormatPackedHalfFloat}function x1(r,t,e,n){let[o,s]=wa(t,e);return cg(r,o,s,Qw(n),r.RGBA,n.textureTypeHalfFloat)}function y1(r,t,e){return ht(r,()=>r.bindBuffer(r.ARRAY_BUFFER,e)),zw(r,t,"clipSpacePos",e,3,20,0)&&zw(r,t,"uv",e,2,20,12)}function b1(r,t,e,n,o,s){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t));let i,a,u;o instanceof Uint8Array?(i=new Uint8Array(e*n*4),a=r.UNSIGNED_BYTE,u=r.RGBA):(i=new Float32Array(e*n*4),a=r.FLOAT,u=s.internalFormatPackedFloat),i.set(o),L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e,n,r.RGBA,a,i)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,u,e,n,0,r.RGBA,a,i)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function w1(r,t,e){ht(r,()=>r.bindTexture(r.TEXTURE_2D,t)),e.data instanceof Uint8Array?L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,e.width,e.height,r.RGBA,r.UNSIGNED_BYTE,e.data)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,e.width,e.height,0,r.RGBA,r.UNSIGNED_BYTE,e.data)):L().getNumber("WEBGL_VERSION")===2?ht(r,()=>r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,e)):ht(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,e)),ht(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function I1(r,t,e,n){let o=r.createBuffer();ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let a=4*4*t*e;return ht(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,a,r.STREAM_READ)),ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,0)),ht(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function C1(r,t,e){let n=r,o=new Float32Array(e);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function v1(r,t,e,n){let[o,s]=lp(t,e),i=4,a=new Uint8Array(DL(t*e,i));return ht(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,a)),new Float32Array(a.buffer)}function S1(r,t,e,n,o,s,i,a){let u=r,l=new Float32Array($L(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function N1(r,t,e){let n=new Float32Array(t*e*4);return ht(r,()=>r.readPixels(0,0,e,t,r.RGBA,r.FLOAT,n)),n}var pp=class{constructor(t){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let e=L().getNumber("WEBGL_VERSION");if(t!=null?(this.gl=t,BT(e,t)):this.gl=qn(e),t=this.gl,L().getNumber("WEBGL_VERSION")===2){let s=t;this.createVertexArray=()=>ht(s,()=>s.createVertexArray()),this.bindVertexArray=i=>ht(s,()=>s.bindVertexArray(i)),this.deleteVertexArray=i=>ht(s,()=>s.deleteVertexArray(i)),this.getVertexArray=()=>ht(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(t!=null){let s=t.getExtension("OES_vertex_array_object");if(s==null)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>ht(t,()=>s.createVertexArrayOES()),this.bindVertexArray=i=>ht(t,()=>s.bindVertexArrayOES(i)),this.deleteVertexArray=i=>ht(t,()=>s.deleteVertexArrayOES(i)),this.getVertexArray=()=>ht(t,()=>t.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),L().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",i="OES_texture_half_float";if(this.textureFloatExtension=bd(this.gl,s),Kn(this.gl,i))this.textureHalfFloatExtension=bd(this.gl,i);else if(L().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Kn(this.gl,o))this.colorBufferHalfFloatExtension=bd(this.gl,o);else if(L().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Kn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Kn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=p1(this.gl),this.indexBuffer=m1(this.gl),this.framebuffer=JT(this.gl),this.textureConfig=ig(this.gl,this.textureHalfFloatExtension)}get debug(){return L().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 t=this.gl;ht(t,()=>t.finish()),ht(t,()=>t.bindFramebuffer(t.FRAMEBUFFER,null)),ht(t,()=>t.deleteFramebuffer(this.framebuffer)),ht(t,()=>t.bindBuffer(t.ARRAY_BUFFER,null)),ht(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)),ht(t,()=>t.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(t,e){return this.throwIfDisposed(),f1(this.gl,t,e,this.textureConfig)}createFloat16MatrixTexture(t,e){return this.throwIfDisposed(),d1(this.gl,t,e,this.textureConfig)}createUnsignedBytesMatrixTexture(t,e){return this.throwIfDisposed(),h1(this.gl,t,e,this.textureConfig)}uploadPixelDataToTexture(t,e){this.throwIfDisposed(),w1(this.gl,t,e)}uploadDenseMatrixToTexture(t,e,n,o){this.throwIfDisposed(),b1(this.gl,t,e,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(t,e){return this.throwIfDisposed(),x1(this.gl,t,e,this.textureConfig)}createPackedMatrixTexture(t,e){return this.throwIfDisposed(),g1(this.gl,t,e,this.textureConfig)}deleteMatrixTexture(t){this.throwIfDisposed(),this.outputTexture===t&&(Bw(this.gl,this.framebuffer),this.outputTexture=null),ht(this.gl,()=>this.gl.deleteTexture(t))}downloadByteEncodedFloatMatrixFromOutputTexture(t,e,n){return this.downloadMatrixDriver(t,()=>v1(this.gl,e,n,this.textureConfig))}downloadPackedMatrixFromBuffer(t,e,n,o,s,i){return S1(this.gl,t,e,n,o,s,i,this.textureConfig)}downloadFloat32MatrixFromBuffer(t,e){return C1(this.gl,t,e)}createBufferFromTexture(t,e,n){this.bindTextureToFrameBuffer(t);let o=I1(this.gl,e,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let t=this.createFence(this.gl);return this.pollFence(t)}createFence(t){let e,n;if(L().getBool("WEBGL_FENCE_API_ENABLED")){let o=t,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);t.flush(),n=()=>{let i=o.clientWaitSync(s,0,0);return i===o.ALREADY_SIGNALED||i===o.CONDITION_SATISFIED},e=s}else L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(e=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(e,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:e,isFencePassed:n}}downloadMatrixFromPackedTexture(t,e,n){return this.downloadMatrixDriver(t,()=>N1(this.gl,e,n))}createProgram(t){this.throwIfDisposed();let e=this.gl;this.vertexShader==null&&(this.vertexShader=c1(e));let n=qT(e);ht(e,()=>e.attachShader(n,this.vertexShader)),ht(e,()=>e.attachShader(n,t)),KT(e,n);let o=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&ag(e,o),o}buildVao(t){this.setProgram(t),this.bindVertexArray(t.vao);let e=this.gl;ht(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),y1(e,t,this.vertexBuffer)}deleteProgram(t){this.throwIfDisposed(),t===this.program&&(this.program=null),t!=null&&(ht(this.gl,()=>this.gl.deleteProgram(t)),this.deleteVertexArray(t.vao))}setProgram(t){this.throwIfDisposed(),this.program=t,this.program!=null&&this.debug&&ag(this.gl,this.program),ht(this.gl,()=>this.gl.useProgram(t))}getUniformLocation(t,e,n=!0){return this.throwIfDisposed(),n?QT(this.gl,t,e):t1(this.gl,t,e)}getAttributeLocation(t,e){return this.throwIfDisposed(),ht(this.gl,()=>this.gl.getAttribLocation(t,e))}getUniformLocationNoThrow(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)}setInputMatrixTexture(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),e1(this.gl,t,e,n)}setOutputMatrixTexture(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)}setOutputPackedMatrixTexture(t,e,n){this.throwIfDisposed();let[o,s]=wa(e,n);this.setOutputMatrixTextureDriver(t,o,s)}setOutputMatrixWriteRegion(t,e,n,o){this.setOutputMatrixWriteRegionDriver(n,t,o,e)}setOutputPackedMatrixWriteRegion(t,e,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&ag(this.gl,this.program),wd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let t=this.gl;if(this.debug){let e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}ht(t,()=>t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ht(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=bd(this.gl,L().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(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let t=this.getQueryTimerExtensionWebGL1(),e=t.createQueryEXT();return t.beginQueryEXT(t.TIME_ELAPSED_EXT,e),e}endQuery(){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT);return}let t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(t){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(t,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(t,e){if(e===0)return null;if(e===2){let n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(t,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(t,e){if(e===0)return!0;if(e===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(t,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(t){return new Promise(e=>{this.addItemToPoll(()=>t.isFencePassed(),()=>e())})}pollItems(){let t=Lot(this.itemsToPoll.map(e=>e.isDoneFn));for(let e=0;e<=t;++e){let{resolveFn:n}=this.itemsToPoll[e];n()}this.itemsToPoll=this.itemsToPoll.slice(t+1)}addItemToPoll(t,e){if(this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1)return;let n;"setTimeoutCustom"in L().platform&&(n=L().platform.setTimeoutCustom.bind(L().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,n)}bindTextureToFrameBuffer(t){this.throwIfDisposed(),lg(this.gl,t,this.framebuffer),this.debug&&wd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(lg(this.gl,this.outputTexture,this.framebuffer),this.debug&&wd(this.gl)):Bw(this.gl,this.framebuffer)}downloadMatrixDriver(t,e){this.bindTextureToFrameBuffer(t);let n=e();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(t,e,n){this.throwIfDisposed();let o=this.gl;lg(o,t,this.framebuffer),this.debug&&wd(o),this.outputTexture=t,ht(o,()=>o.viewport(0,0,e,n)),ht(o,()=>o.scissor(0,0,e,n))}setOutputMatrixWriteRegionDriver(t,e,n,o){this.throwIfDisposed(),ht(this.gl,()=>this.gl.scissor(t,e,n,o))}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 Lot(r){let t=0;for(;t<r.length&&r[t]();++t);return t-1}var{addImpl:qL,bincountImpl:tI,bincountReduceImpl:KL,bitwiseAndImpl:jL,castImpl:XL,ceilImpl:YL,concatImpl:ZL,equalImpl:JL,expImpl:QL,expm1Impl:tz,floorImpl:ez,gatherNdImpl:rz,gatherV2Impl:nz,greaterImpl:oz,greaterEqualImpl:sz,lessImpl:iz,lessEqualImpl:az,linSpaceImpl:lz,logImpl:uz,maxImpl:cz,maximumImpl:pz,minimumImpl:mz,multiplyImpl:fz,negImpl:dz,notEqualImpl:hz,prodImpl:gz,raggedGatherImpl:xz,raggedRangeImpl:yz,raggedTensorToTensorImpl:bz,rangeImpl:wz,rsqrtImpl:Iz,scatterImpl:Cz,sigmoidImpl:vz,simpleAbsImpl:eI,sliceImpl:Sz,sparseFillEmptyRowsImpl:Nz,sparseReshapeImpl:kz,sparseSegmentReductionImpl:rI,sqrtImpl:Tz,staticRegexReplaceImpl:_z,stridedSliceImpl:Ez,stringNGramsImpl:Az,stringSplitImpl:Dz,stringToHashBucketFastImpl:$z,subImpl:Rz,tileImpl:Fz,topKImpl:Oz,transposeImpl:mp,uniqueImpl:Mz}=Ew;function T1(r,t){return["x","y","z","w","u","v"].slice(0,t).map(e=>`${r}.${e}`)}function rr(r,t){return t===1?[r]:T1(r,t)}function Pz(r,t){if(r===1)return"rc";let e="";for(let n=0;n<r;n++)e+=t[n],n<r-1&&(e+=",");return e}var nI=class{constructor(t){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.enableShapeUniforms=he(this.outputShape.length),this.rank===0)this.userCode=`
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;else{let e=rr("rc",this.rank),n=zt(this.rank),o=this.getOutOfBoundsCondition(e),s=this.getSetup(e),i=this.getOutput(e);this.userCode=`
        void main() {
          ${n} rc = getOutputCoords();

          if(${o}) {
            setOutput(vec4(0));
          } else {
            ${s}

            setOutput(vec4(${i}));
          }
        }
      `}}getSourceCoordsArr(t){let e=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let i=2;i<this.rank;i++)s=`${t[t.length-1-i]},`+s;e.push(s)}return e}getOutOfBoundsCondition(t){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let e="";for(let n=this.rank-2;n<this.rank;n++)e+=`${t[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(e+="||");return e}getSetup(t){if(this.rank===1)return"";let e=t.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],o=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
      int r = ${e[0]};
      int c = ${e[1]};
      int rp1 = r + 1;
      int cp1 = c + 1;

      bool cEdge = cp1 >= ${n};
      bool rEdge = rp1 >= ${o};
    `}getOutput(t){let e=this.getSourceCoordsArr(t);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${e[0]}),
            cEdge ? 0. : getA(${e[1]}),
            rEdge ? 0. : getA(${e[2]}),
            rEdge || cEdge ? 0. : getA(${e[3]})`}};var Ad=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2===1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=`
        ${s}
        ${o>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[${o}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${o>0?"}":""}
      `}this.userCode=`
      ${zot(e,this.enableShapeUniforms)}
      ${this.enableShapeUniforms?Sd():vd(t)}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${this.enableShapeUniforms?"outShape[1]":t[1]};
        int cols = ${this.enableShapeUniforms?"outShape[2]":t[2]};

        ${n}

        setOutput(result);
      }
    `}};function zot(r,t){return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t?PL(["r","c","d"],"inputShape"):Si(["r","c","d"],r)}
      return ivec3(r, c, d);
    }
  `}var oI=class{constructor(t){this.gpgpu=t,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(t,e,n){let o=zz(e,n),s=Bz(t,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let i=Lz(t,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=i,this.log();let u=this.freeTextures[s].pop();return this.usedTextures[s].push(u),u}let a;return o===Lr.PACKED_2X2_FLOAT32?a=this.gpgpu.createPackedMatrixTexture(t[0],t[1]):o===Lr.PACKED_2X2_FLOAT16?a=this.gpgpu.createFloat16PackedMatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT32?a=this.gpgpu.createFloat32MatrixTexture(t[0],t[1]):o===Lr.UNPACKED_FLOAT16?a=this.gpgpu.createFloat16MatrixTexture(t[0],t[1]):o===Lr.PACKED_4X1_UNSIGNED_BYTE&&(a=this.gpgpu.createUnsignedBytesMatrixTexture(t[0],t[1])),this.usedTextures[s].push(a),this.numUsedTextures++,this._numBytesAllocated+=i,this.log(),a}releaseTexture(t,e,n,o){if(this.freeTextures==null)return;let s=zz(n,o),i=Bz(e,s,o);i in this.freeTextures||(this.freeTextures[i]=[]);let a=Lz(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),u=L().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");u!==-1&&this._numBytesAllocated>u?(this.gpgpu.deleteMatrixTexture(t.texture),this._numBytesAllocated-=a):(this.freeTextures[i].push(t),this.numFreeTextures++,this._numBytesFree+=a),this.numUsedTextures--;let l=this.usedTextures[i],c=l&&l.indexOf(t);if(c==null||c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l[c]=l[l.length-1],l.pop(),this.log()}log(){if(!this.logEnabled)return;let t=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${t})`);let e=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*e)}%)`)}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 t in this.freeTextures)this.freeTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(let t in this.usedTextures)this.usedTextures[t].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Bot(r,t){let e=r;if(t===e.R32F)return 4;if(t===e.R16F)return 2;if(t===e.RGBA32F)return 16;if(t===r.RGBA)return 16;if(t===e.RGBA16F)return 8;if(t===e.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Lz(r,t,e,n,o){let s=Vot(t,n),i;if(o){let[u,l]=wa(r[0],r[1]);i=u*l}else{let[u,l]=lp(r[0],r[1]);i=u*l}let a=Bot(e,s);return i*a}function Vot(r,t){switch(r){case Lr.PACKED_2X2_FLOAT32:return Jw(t);case Lr.PACKED_2X2_FLOAT16:return Qw(t);case Lr.UNPACKED_FLOAT32:return Xw(t);case Lr.UNPACKED_FLOAT16:return Yw(t);case Lr.PACKED_4X1_UNSIGNED_BYTE:return Zw(t);default:throw new Error(`Unknown physical texture type ${r}`)}}function Got(r){return L().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?Lr.PACKED_2X2_FLOAT32:Lr.UNPACKED_FLOAT32:r?Lr.PACKED_2X2_FLOAT16:Lr.UNPACKED_FLOAT16}function zz(r,t){if(r===Jr.UPLOAD)return Lr.PACKED_2X2_FLOAT32;if(r===Jr.RENDER||r==null)return Got(t);if(r===Jr.DOWNLOAD||r===Jr.PIXELS)return Lr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function Bz(r,t,e){return`${r[0]}_${r[1]}_${t}_${e}`}var zr=class{constructor(t,e){this.variableNames=["A"],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`
      float unaryOperation(float x) {
        ${e}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
    `}},xr="if (isnan(x)) return x;",Vz="return x;",_1="return abs(x);";var Gz="return (x >= 0.0) ? x : (exp(x) - 1.0);",Wz=xr+`
  return (x < 0.0) ? 0.0 : x;
`,Uz=xr+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,Ia="return x;",Hz="return 1.0 / (1.0 + exp(-1.0 * x));";var Kz="return x;",jz=`
  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;
`,Xz=`
  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;
`,Yz=`
  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;
`,Zz="return 1.0 / (1.0 + exp(-1.0 * x));",Dn=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`
      vec4 unaryOperation(vec4 x) {
        ${e}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
      }
    `}};var sI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let e=t.length,n=rr("rc",e),o=zt(e),s=Pz(e,n),i=n.slice(-2),a=e<=1?"rc":`vec2(${i.join(",")})`;this.userCode=`
      void main() {
        ${o} rc = getOutputCoords();
        vec4 packedInput = getA(${s});

        setOutput(getChannel(packedInput, ${a}));
      }
    `}};var Uot=Xr.whereImpl,Hot=1e-7,qot=1e-4,iI={};function Kot(r){return r in iI||(iI[r]={}),iI[r]}var jot=L().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Xot=600;function Yot(){return L().global.screen==null?1024:L().global.screen.height*L().global.screen.width*window.devicePixelRatio*Xot/1024/1024}var Dd=class r extends Bo{nextDataId(){return r.nextDataId++}constructor(t){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,!L().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let e;if(t!=null){if(t instanceof pp)e=t;else{let n=qn(L().getNumber("WEBGL_VERSION"),t);e=new pp(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=qn(L().getNumber("WEBGL_VERSION"));e=new pp(n),this.binaryCache=Kot(L().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=e,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new oI(this.gpgpu),this.numMBBeforeWarning=Yot(),this.texData=new Ta(this,Bn())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(t,e,n,o,s,i){let a=this.makeTensorInfo(e,n),u=this.texData.get(a.dataId);u.isPacked=!1,u.texture={texture:t,texShape:[o,s]},u.texShape=[o,s];let l=Id(e),c=new ug(l,!1,i),p=this.runWebGLProgram(c,[a],n,[[o,s]]);return p.shape=e,u.texture=null,this.disposeIntermediateTensorInfo(a),p.dataId}write(t,e,n){if((L().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||L().getBool("DEBUG"))&&this.checkNumericalProblems(t),n==="complex64"&&t!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:e,dtype:n,values:t,usage:Jr.UPLOAD,refCount:1}),o}refCount(t){return this.texData.has(t)?this.texData.get(t).refCount:0}incRef(t){let e=this.texData.get(t);e.refCount++}decRef(t){if(this.texData.has(t)){let e=this.texData.get(t);e.refCount--}}move(t,e,n,o,s){if(L().getBool("DEBUG")&&this.checkNumericalProblems(e),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(t,{shape:n,dtype:o,values:e,usage:Jr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(t){this.disposeData(t.dataId)}readSync(t){let e=this.texData.get(t),{values:n,dtype:o,complexTensorInfos:s,slice:i,shape:a,isPacked:u}=e;if(i!=null){let m;u?m=new Dn(a,Ia):m=new zr(a,Ia);let f=this.runWebGLProgram(m,[{dataId:t,shape:a,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(t);if(o==="string")return n;let l=this.activeTimers!=null,c;l&&(c=y.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=S.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(t);return l&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(t,p)}async read(t){if(this.pendingRead.has(t)){let d=this.pendingRead.get(t);return new Promise(h=>d.push(h))}let e=this.texData.get(t),{values:n,shape:o,slice:s,dtype:i,complexTensorInfos:a,isPacked:u}=e;if(s!=null){let d;u?d=new Dn(o,Ia):d=new zr(o,Ia);let h=this.runWebGLProgram(d,[{dataId:t,shape:o,dtype:i}],i),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(t);if(L().getBool("DEBUG")&&!L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&L().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(i!=="complex64"&&L().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(t);let d=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(d.texture.texture,...sg(o))}this.pendingRead.set(t,[]),i!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(i==="complex64"){let d=await Promise.all([this.read(a.real.dataId),this.read(a.imag.dataId)]),h=d[0],g=d[1];p=S.mergeRealAndImagArrays(h,g)}else if(l==null)p=this.getValuesFromTexture(t);else{let d=y.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(l,d)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let d=this.gpgpu.gl;ht(d,()=>d.deleteBuffer(l))}let m=this.convertAndCacheOnCPU(t,p),f=this.pendingRead.get(t);return this.pendingRead.delete(t),f.forEach(d=>d(m)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Bn().removeDataId(t,this),this.pendingDeletes--),m}readToGPU(t,e={}){let n=this.texData.get(t),{values:o,shape:s,slice:i,dtype:a,isPacked:u,texture:l}=n;if(a==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(i!=null){let f;u?f=new Dn(s,Ia):f=new zr(s,Ia);let d=this.runWebGLProgram(f,[{dataId:t,shape:s,dtype:a}],a),h=this.readToGPU(d,e);return this.disposeIntermediateTensorInfo(d),h}if(l==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 c=this.decode(t,e.customTexShape),p=Bn().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:p},m.texture)}bufferSync(t){let e=this.readSync(t.dataId);if(t.dtype==="string")try{let n=e.map(o=>y.decodeString(o));return wt(t.shape,t.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return wt(t.shape,t.dtype,e)}checkNumericalProblems(t){if(t!=null)for(let e=0;e<t.length;e++){let n=t[e];if(!WT(n))throw L().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(t){let{shape:e,dtype:n,isPacked:o}=this.texData.get(t),s=y.sizeFromShape(e);if(L().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(t),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture.texture,...sg(e)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let i=L().getBool("WEBGL_PACK")&&o===!0,a=i?Id(e):e,u=i?new Kw(a):new qw(a),l=this.runWebGLProgram(u,[{shape:a,dtype:n,dataId:t}],"float32"),c=this.texData.get(l.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(l),p}timerAvailable(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(t){let e=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,t();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),i=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=e,o&&(this.programTimersStack=null);let a={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let u=await Promise.all(s);a.kernelMs=y.sum(u),a.getExtraProfileInfo=()=>u.map((l,c)=>({name:i[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else a.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,a})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(t){return L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),t):(t.endMs=y.now(),t)}async getQueryTime(t){if(L().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);let e=t;return e.endMs-e.startMs}disposeData(t,e=!1){if(this.pendingDisposal.has(t))return!1;if(!this.texData.has(t))return!0;if(e?this.texData.get(t).refCount=0:this.texData.get(t).refCount--,!e&&this.texData.get(t).refCount>0)return!1;if(this.pendingRead.has(t))return this.pendingDisposal.add(t),this.pendingDeletes++,!1;this.releaseGPUData(t);let{complexTensorInfos:n}=this.texData.get(t);return n!=null&&(this.disposeData(n.real.dataId,e),this.disposeData(n.imag.dataId,e)),this.texData.delete(t),!0}releaseGPUData(t){let{texture:e,dtype:n,texShape:o,usage:s,isPacked:i,slice:a}=this.texData.get(t),u=a&&a.origDataId||t,l=this.dataRefCount.get(u);l>1?this.dataRefCount.set(u,l-1):(this.dataRefCount.delete(u),e!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(e,o,s,i)));let c=this.texData.get(t);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(t){return this.uploadToGPU(t),this.texData.get(t).texture.texture}getDataInfo(t){return this.texData.get(t)}shouldExecuteOnCPU(t,e=jot){return L().getBool("WEBGL_CPU_FORWARD")&&t.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)<e)}getGPGPUContext(){return this.gpgpu}where(t){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let e=t.dataSync();return Uot(t.shape,e)}packedUnaryOp(t,e,n){let o=new Dn(t.shape,e),s=this.compileAndRun(o,[t],n);return Bn().makeTensorFromTensorInfo(s)}abs(t){if(this.shouldExecuteOnCPU([t])&&t.dtype!=="complex64"){let o=eI(this.texData.get(t.dataId).values);return this.makeOutput(t.shape,t.dtype,o)}if(L().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(t,_1,t.dtype);let e=new zr(t.shape,_1),n=this.compileAndRun(e,[t]);return Bn().makeTensorFromTensorInfo(n)}makeTensorInfo(t,e,n){let o;if(e==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(i=>y.encodeString(i));o=this.write(s,t,e)}else o=this.write(n,t,e);return this.texData.get(o).usage=null,{dataId:o,shape:t,dtype:e}}makeOutput(t,e,n){return Bn().makeTensorFromTensorInfo(this.makeTensorInfo(t,e,n),this)}unpackTensor(t){let e=new sI(t.shape);return this.runWebGLProgram(e,[t],t.dtype)}packTensor(t){let e=new nI(t.shape);return this.runWebGLProgram(e,[t],t.dtype,null,!0)}packedReshape(t,e){let n=[Fl(t.shape),...Ol(t.shape)],o={dtype:t.dtype,shape:n,dataId:t.dataId},s=[Fl(e),...Ol(e)],i=new Ad(s,n),a=!0,u=[n],l=this.runWebGLProgram(i,[o],t.dtype,u,a);return{dataId:l.dataId,shape:e,dtype:l.dtype}}decode(t,e){let n=this.texData.get(t),{isPacked:o,shape:s,dtype:i}=n;if(e!=null){let m=y.sizeFromShape(s),f=e[0]*e[1]*4;y.assert(m<=f,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let a=Id(s),u;o?u=new Hw(a):u=new Uw(a);let l=!0,c=[e!=null?e:sg(a)],p=this.runWebGLProgram(u,[{shape:a,dtype:i,dataId:t}],i,c,l,e);return{dtype:i,shape:s,dataId:p.dataId}}runWebGLProgram(t,e,n,o,s=!1,i){let a=this.makeTensorInfo(t.outputShape,n),u=this.texData.get(a.dataId);if(t.packedOutput&&(u.isPacked=!0),t.outPackingScheme===Wu.DENSE){let x=i!=null?i:sg(t.outputShape);u.texShape=x.map(b=>b*2)}if(t.outTexUsage!=null&&(u.usage=t.outTexUsage),y.sizeFromShape(a.shape)===0)return u.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],c=e.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(!t.packedInputs&&y.sizeFromShape(x.shape)<=L().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};t.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!t.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),l.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!Uu(b.shape,x.shape)){let w=x,I=x.shape;x.shape=b.shape,x=this.packedReshape(x,I),l.push(x),b=this.texData.get(x.dataId),w.shape=I}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(a.dataId);let p={shape:a.shape,texData:u,isUniform:!1},m=HL(t,c,p),f=this.getAndSaveBinary(m,()=>WL(this.gpgpu,t,c,p)),d=this.activeTimers!=null,h;d&&(h=this.startTimer()),L().get("ENGINE_COMPILE_ONLY")||UL(this.gpgpu,f,c,p,o),l.forEach(x=>this.disposeIntermediateTensorInfo(x)),d&&(h=this.endTimer(h),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(h)}));let g=L().getNumber("WEBGL_FLUSH_THRESHOLD");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!L().getBool("WEBGL_LAZILY_UNPACK")&&u.isPacked&&s===!1){let x=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),x}return a}compileAndRun(t,e,n,o,s=!1){return n=n||e[0].dtype,this.runWebGLProgram(t,e,n,o,s)}getAndSaveBinary(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(L().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=B(()=>{if(!L().get("WEBGL_RENDER_FLOAT32_ENABLED")){let t=L().getBool("DEBUG");L().set("DEBUG",!1);let e=this.abs(pt(1e-8)).dataSync()[0];if(L().set("DEBUG",t),e>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Hot:qot}uploadToGPU(t){let e=this.texData.get(t),{shape:n,dtype:o,values:s,texture:i,usage:a,isPacked:u}=e;if(i!=null)return;let l=this.activeTimers!=null,c;l&&(c=y.now());let p=e.texShape;if(p==null&&(p=r1(n,u),e.texShape=p),s!=null){let m=Id(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(u||!g)&&([d,h]=wa(p[0],p[1])),u?f=new jw(m,g):f=new ug(m,g);let x=g?[h,d]:p,b=this.makeTensorInfo(x,o),w=this.texData.get(b.dataId);g?w.usage=Jr.PIXELS:w.usage=Jr.UPLOAD,w.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),d,h,s);let I=[[h,d]],E=this.runWebGLProgram(f,[b],o,I,!0),A=this.texData.get(E.dataId);e.texShape=A.texShape,e.isPacked=A.isPacked,e.usage=A.usage,L().get("ENGINE_COMPILE_ONLY")?this.disposeData(E.dataId):(e.texture=A.texture,e.values=null,this.texData.delete(E.dataId)),this.disposeIntermediateTensorInfo(b),l&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,a,o,u);e.texture=m}}convertAndCacheOnCPU(t,e){let n=this.texData.get(t),{dtype:o}=n;return e!=null&&(n.values=Zot(e,o)),n.values}acquireTexture(t,e,n,o){if(this.numBytesInGPU+=this.computeBytes(t,n),!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(t,e,o)}computeBytes(t,e){return t[0]*t[1]*y.bytesPerElement(e)}checkCompileCompletion(){for(let[,t]of Object.entries(this.binaryCache))this.checkCompletion_(t)}async checkCompileCompletionAsync(){let t=[];if(this.gpgpu.parallelCompilationExtension){for(let[,e]of Object.entries(this.binaryCache))t.push(this.checkCompletionAsync_(e));return Promise.all(t)}else{for(let[,e]of Object.entries(this.binaryCache)){let n=new Promise(o=>{try{this.checkCompletion_(e),o(!0)}catch(s){throw s}});t.push(n)}return Promise.all(t)}}async checkCompletionAsync_(t){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(await Ch(),this.checkCompletionAsync_(t))}checkCompletion_(t){if(this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(t.webGLProgram)),this.gpgpu.gl.getShaderParameter(t.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Lw(t.source,this.gpgpu.gl.getShaderInfoLog(t.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let t of Object.values(this.binaryCache)){this.gpgpu.buildVao(t.webGLProgram);let{variablesLocations:e,customUniformLocations:n,infLoc:o,nanLoc:s,outShapeLocation:i,outShapeStridesLocation:a,outTexShapeLocation:u}=u1(this.gpgpu,t.program,t.webGLProgram);t.variablesLocations=e,t.customUniformLocations=n,t.infLoc=o,t.nanLoc=s,t.outShapeLocation=i,t.outShapeStridesLocation=a,t.outTexShapeLocation=u}}createTensorFromGPUData(t,e,n){t.channels=t.channels||"RGBA";let{texture:o,height:s,width:i,channels:a}=t,u=Bn().backend;if(!u.gpgpu.gl.isTexture(o))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 l=u.writeTexture(o,e,n,s,i,a);return Bn().makeTensorFromDataId(l,e,n,u)}};Dd.nextDataId=0;function Zot(r,t){if(t==="float32"||t==="complex64")return r;if(t==="int32"||t==="bool"){let e=t==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<e.length;++n)e[n]=Math.round(r[n]);return e}else throw new Error(`Unknown dtype ${t}`)}var Jz="4.21.0";function Qz(){L().set("WEBGL_FORCE_F16_TEXTURES",!0)}du.isBrowser()&&Xp("webgl",()=>new Dd,2);var T$e={forceHalfFloat:Qz};var $d=`
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;var $n=class{constructor(t,e,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.enableShapeUniforms=he(this.outputShape.length),this.userCode=`
      float binaryOperation(float a, float b) {
        ${t}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `}};var Xn=`
  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 jn=class{constructor(t,e,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(e,n);let s=this.outputShape.length;this.enableShapeUniforms=he(s);let i="";if(o)if(s===0||y.sizeFromShape(this.outputShape)===1)i=`
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;else if(i=`
          ${zt(s)} coords = getOutputCoords();
        `,s===1)this.enableShapeUniforms?i+=`
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `:i+=`
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;else{let u=rr("coords",s);this.enableShapeUniforms?i+=`
            bool nextRowOutOfBounds =
              (${u[s-2]} + 1) >= outShape[${s} - 2];
            bool nextColOutOfBounds =
              (${u[s-1]} + 1) >= outShape[${s} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `:i+=`
            bool nextRowOutOfBounds =
              (${u[s-2]} + 1) >= ${this.outputShape[s-2]};
            bool nextColOutOfBounds =
              (${u[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) {
        ${t}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${i}

        setOutput(result);
      }
    `}};function nr(r){let{inputs:t,backend:e}=r,{x:n}=t;return e.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var t3={kernelName:go,backendName:"webgl",kernelFunc:nr};function Rn(r){let{inputs:t,backend:e}=r,{real:n,imag:o}=t,s=e.makeTensorInfo(n.shape,"complex64"),i=e.texData.get(s.dataId),a=nr({inputs:{x:n},backend:e}),u=nr({inputs:{x:o},backend:e});return i.complexTensorInfos={real:a,imag:u},s}var e3={kernelName:Ap,backendName:"webgl",kernelFunc:Rn};var E1="return (a < 0.) ? b * a : a;",A1=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Jot(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{alpha:s}=n,i=e.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),a=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jn(A1,o.shape,i.shape):new $n(E1,o.shape,i.shape),u=e.runWebGLProgram(a,[o,i],"float32");return e.disposeIntermediateTensorInfo(i),u}var r3={kernelName:bs,backendName:"webgl",kernelFunc:Jot};var D1="return (a < 0.) ? b * a : a;",$1=`
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;function Qot(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jn($1,n.shape,o.shape):new $n(D1,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],"float32")}var n3={kernelName:Os,backendName:"webgl",kernelFunc:Qot};var Po="if (isnan(x)) return x;";function It({opSnippet:r,packedOpSnippet:t,cpuKernelImpl:e,dtype:n}){return({inputs:o,backend:s})=>{let{x:i}=o,a=s,u=n||i.dtype;if(a.shouldExecuteOnCPU([i])&&e!=null){let p=a.texData.get(i.dataId),m=e(p.values,u);return a.makeTensorInfo(i.shape,u,m)}let l=L().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Dn(i.shape,t):c=new zr(i.shape,r),a.runWebGLProgram(c,[i],u)}}function ce({opSnippet:r,packedOpSnippet:t,checkOutOfBounds:e=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:i,backend:a})=>{let{a:u,b:l}=i,c=a;if(n&&u.dtype==="complex64"){let d=c.texData.get(u.dataId),h=c.texData.get(l.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[I,N]=w,E={dataId:I.dataId,dtype:I.dtype,shape:u.shape},A={dataId:N.dataId,dtype:N.dtype,shape:l.shape},D=new $n(r,u.shape,l.shape);return c.runWebGLProgram(D,[E,A],ur(I.dtype,N.dtype))}),b=Rn({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ur(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&o!=null){let d=c.texData.get(u.dataId).values,h=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(d):d,x=u.dtype==="string"?S.fromUint8ToStringArray(h):h,[b,w]=o(u.shape,l.shape,g,x,p),I=c.makeTensorInfo(w,p),N=c.texData.get(I.dataId);return N.values=b,I}let m=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,f;return m?f=new jn(t,u.shape,l.shape,e):f=new $n(r,u.shape,l.shape),c.runWebGLProgram(f,[u,l],p)}}function Ml(r,t=!1){if(r==="linear")return t?Kz:Vz;if(r==="relu")return t?Xz:Wz;if(r==="elu")return t?jz:Gz;if(r==="relu6")return t?Yz:Uz;if(r==="prelu")return t?$1:D1;if(r==="leakyrelu")return t?A1:E1;if(r==="sigmoid")return t?Zz:Hz;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Rd=class{constructor(t,e,n,o=!1,s=!1,i=!1,a=null,u=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=he(this.outputShape.length);let c=o?t[1]:t[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";a&&(u?g=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${a}
        }`:l?g=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${a}
        }`:g=`vec4 activation(vec4 x) {
          ${a}
        }`,x="result = activation(result);");let b=i?"result += getBiasAtOutCoords();":"";i&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let w="rc.x",I="rc.x";t[0]<e[0]?w=`imod(rc.x, ${t[0]})`:e[0]<t[0]&&(I=`imod(rc.x, ${e[0]})`),this.userCode=`
      ${g}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${p}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        int batchA = ${w};
        int batchB = ${I};
        for (int i = 0; i < ${p}; i++) {
          vec4 a = getMatrixA(batchA, ${m});
          vec4 b = getMatrixB(batchB, ${f});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${d[0]} * ${h[0]});
          result += (${d[1]} * ${h[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${b}

        ${x}

        setOutput(result);
      }
    `}};var R1={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},pg=class{constructor(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(e,n),this.userCode=`
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${t}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `}};var o3="return a * b;";function mg(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=S.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),l=new pg(R1.REAL,n.shape,o.shape),c=new pg(R1.IMAG,n.shape,o.shape),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:n.shape},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:o.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:o.shape}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Rn({inputs:{real:m,imag:f},backend:e});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}if(e.shouldExecuteOnCPU([n,o])){let a=e.texData.get(n.dataId),u=e.texData.get(o.dataId),[l,c]=fz(n.shape,o.shape,a.values,u.values,s),p=e.makeTensorInfo(c,s),m=e.texData.get(p.dataId);return m.values=l,p}let i;return L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new jn(o3,n.shape,o.shape):i=new $n(o3,n.shape,o.shape),e.runWebGLProgram(i,[n,o],s)}var s3={kernelName:Ds,backendName:"webgl",kernelFunc:mg};function i3(r,t,e){let n=[Fl(r.shape),...Ol(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Fl(t),...Ol(t)],i=new Ad(s,n),a=!0,u=[n],l=e.runWebGLProgram(i,[o],r.dtype,u,a);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function rt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{shape:s}=n,i=e,a=y.sizeFromShape(o.shape),u=y.inferFromImplicitShape(s,a),l=y.sizeFromShape(u);y.assert(a===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${o.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(o.dataId);return c.isPacked&&!Uu(o.shape,u)&&!(c.texture!==null&&Uu(c.shape,u))?i3(o,u,i):(i.incRef(o.dataId),{dataId:o.dataId,shape:u,dtype:o.dtype})}var a3={kernelName:Gi,backendName:"webgl",kernelFunc:rt};var fg=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a=Math.floor(n/4)*4,u=n%4,l="sumValue += dot(values, ones);";if(e!=null){let p=1/e;l=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>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 * ${n};

        float sumValue = 0.0;

        for (int i = 0; i < ${a}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${l}
        }

        int inIdx = inOffset + ${a};
        if (${u===1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);

          ${l}
        } else if (${u===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);

          ${l}
        } else if (${u===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);

          ${l}
        }
        setOutput(sumValue);
      }
    `}};var aI=class{constructor(t,e){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:i}=t;this.outputShape=[o,i];let a="0.0",u="";e==="prod"?a="1.0":e==="min"?(a="1.0 / 1e-20",u="min"):e==="max"&&(a="-1.0 / 1e-20",u="max");let l=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="sum"?l="sumValue":e==="prod"?l="prodValue":e==="all"?l="allValue":e==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=`
      if (${e==="sum"}) {
        sumValue += dot(values, ones);
      } else if (${e==="prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${u}(values, minMaxValue);
        if (${e==="min"} || ${e==="max"}) {
          minMaxValue = ${u}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `,f="vec4";e==="all"?(a="1.0",m=`
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `,f="bvec4"):e==="any"&&(a="0.0",m=`
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `,f="bvec4");let d="";s%n>0&&(d=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `),this.userCode=`
      const float initializationValue = ${a};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${d}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${n};

        vec4 minMaxValue = vec4(${a});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;

        for (int i = 0; i < ${c}; i += 4) {
          int inIdx = inOffset + i;
          ${f} values = ${f}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${m}
        }

        int inIdx = inOffset + ${c};
        if (${p===1}) {
          ${f} values = ${f}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${m}
        } else if (${p===2}) {
          ${f} values = ${f}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${m}
        } else if (${p===3}) {
          ${f} values = ${f}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${m}
        }
        setOutput(${l});
      }
    `}};function est(r){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let e=t.length?t[t.length-1].outSize:r[1],n=S.computeOptimalWindowSize(e);t.push({inSize:e,windowSize:n,outSize:Math.ceil(e/n)})}return t}function Yn(r,t,e,n){let o=est(r.shape),s=r;for(let i=0;i<o.length;i++){let{inSize:a,windowSize:u,outSize:l}=o[i],c,p;e==="mean"?c=i===0?new fg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},a):new fg({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l}):c=new aI({windowSize:u,inSize:a,batchSize:r.shape[0],outSize:l},e),p=s,s=n.runWebGLProgram(c,[s],t),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var lI=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[e[i]];this.outputShape=n,this.rank=n.length;let o=zt(this.rank),s=rst(e);this.userCode=`
    void main() {
      ${o} resRC = getOutputCoords();
      setOutput(getA(${s}));
    }
    `}};function rst(r){let t=r.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let o=0;o<r.length;o++)n[r[o]]=e[o];return n.join()}var uI=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(t.length);for(let c=0;c<n.length;c++)n[c]=t[e[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=zt(this.rank),s=T1("rc",this.rank),i=new Array(this.rank);for(let c=0;c<e.length;c++)i[e[c]]=s[c];let a=`vec2(${i.slice(-2).join()})`,u=`++${s[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${i.join()}), ${a})`;this.userCode=`
    void main() {
      ${o} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${l};
      if(${u}) {
        result[1] = ${l};
      }
      --${s[this.rank-1]};
      if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
        result[2] = ${l};
        if(${u}) {
          result[3] = ${l};
        }
      }
      setOutput(result);
    }
    `}};function Hu(r,t,e){let n=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uI(r.shape,t):new lI(r.shape,t);return e.runWebGLProgram(n,[r],r.dtype)}function l3(r,t,e,n){let o=t,s=r.shape.length,i=y.parseAxisParam(o,r.shape),a=i,u=S.getAxesPermutation(a,s),l=u!=null,c=r;l&&(c=Hu(r,u,n),a=S.getInnerMostAxes(a.length,s)),S.assertAxesAreInnerMostDims("sum",a,s);let[p,m]=S.computeOutAndReduceShapes(c.shape,a),f=p;e&&(f=S.expandShapeToKeepDim(p,i));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=rt({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=lc(r.dtype),w=Yn(x,b,"sum",n),I=rt({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(w),l&&n.disposeIntermediateTensorInfo(c),I}function fp(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n;return l3(o,s,i,e)}var u3={kernelName:Js,backendName:"webgl",kernelFunc:fp};function Pe(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{perm:s}=n,i=e,a=o.shape.length,u=new Array(a);for(let c=0;c<u.length;c++)u[c]=o.shape[s[c]];let l;if(i.shouldExecuteOnCPU([o])){let p=i.texData.get(o.dataId).values,m=mp(p,o.shape,o.dtype,s,u);l=i.makeTensorInfo(u,o.dtype);let f=i.texData.get(l.dataId);f.values=m}else l=Hu(o,s,i);return l}var c3={kernelName:so,backendName:"webgl",kernelFunc:Pe};var F1=1e3;function dp({a:r,b:t,transposeA:e,transposeB:n,backend:o,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:a=0,activation:u=null}){let l=r.shape.length,c=t.shape.length,p=e?r.shape[l-2]:r.shape[l-1],m=n?t.shape[c-1]:t.shape[c-2],f=e?r.shape[l-1]:r.shape[l-2],d=n?t.shape[c-2]:t.shape[c-1],h=r.shape.slice(0,-2),g=t.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),I=Hr.assertAndGetBroadcastShape(r.shape.slice(0,-2),t.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${t.shape} and transposeA=${e} and transposeB=${n} must match.`);let N=e?[x,p,f]:[x,f,p],E=n?[b,d,m]:[b,m,d],A=rt({inputs:{x:r},backend:o,attrs:{shape:N}}),D=rt({inputs:{x:t},backend:o,attrs:{shape:E}}),F=[A,D],M=Math.max(x,b),V=e?A.shape[1]:A.shape[2],G=s!=null,W=i!=null,q=u==="leakyrelu",H=u!=null?Ml(u,!0):null,j=G||W||q||H!=null,Y;if((f===1||d===1)&&V>F1&&j===!1){let et=A,nt=D;e&&(et=Pe({inputs:{x:A},backend:o,attrs:{perm:[0,2,1]}}),F.push(et)),n&&(nt=Pe({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),F.push(nt));let st=d!==1,lt=d===1,ot=et;st&&(ot=rt({inputs:{x:et},backend:o,attrs:{shape:[M,V,1]}}),F.push(ot));let it=d===1?2:1,ft=nt;lt&&(ft=rt({inputs:{x:nt},backend:o,attrs:{shape:[M,1,V]}}),F.push(ft));let gt=mg({inputs:{a:ot,b:ft},backend:o});Y=fp({inputs:{x:gt},backend:o,attrs:{axis:it,keepDims:!0}}),F.push(gt)}else{let et=ur(r.dtype,t.dtype),nt=new Rd(N,E,[M,f,d],e,n,G,H,W,q),st=[A,D];if(s!=null&&st.push(s),W&&st.push(i),q){let lt=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));st.push(lt),F.push(lt)}Y=o.runWebGLProgram(nt,st,et)}let Z=rt({inputs:{x:Y},backend:o,attrs:{shape:I}});F.push(Y);for(let et of F)o.disposeIntermediateTensorInfo(et);return Z}function nst(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n;return dp({a:o,b:s,transposeA:u,transposeB:l,backend:e,bias:i,preluActivationWeights:a,leakyreluAlpha:p,activation:c})}var p3={kernelName:Xi,backendName:"webgl",kernelFunc:nst};var m3="return abs(x);";function ost(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=e.texData.get(n.dataId),i=eI(s.values);return e.makeTensorInfo(n.shape,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,m3):o=new zr(n.shape,m3),e.runWebGLProgram(o,[n],n.dtype)}var f3={kernelName:Ai,backendName:"webgl",kernelFunc:ost};var sst=xr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`,ist=It({opSnippet:sst}),d3={kernelName:Go,backendName:"webgl",kernelFunc:ist};var ast=xr+`
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`,lst=It({opSnippet:ast}),h3={kernelName:Wo,backendName:"webgl",kernelFunc:lst};var g3="return a + b;",ust=ce({opSnippet:g3,packedOpSnippet:g3,supportsComplex:!0,cpuKernelImpl:qL}),x3={kernelName:no,backendName:"webgl",kernelFunc:ust};var cI=class{constructor(t,e){this.outputShape=[],this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        float result = ${o};
        setOutput(result);
      }
    `}};var pI=class{constructor(t,e){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.variableNames=e.map((s,i)=>`T${i}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
      void main() {
        ${n.join(`
        `)}

        vec4 result = ${o};
        setOutput(result);
      }
    `}};function mI(r){let{inputs:t,backend:e}=r,n=t;if(n.length===1)return nr({inputs:{x:n[0]},backend:e});if(n.length>L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(n.length/2),l=mI({inputs:n.slice(0,u),backend:e}),c=mI({inputs:n.slice(u),backend:e});return mI({inputs:[l,c],backend:e})}let o=n.map(u=>u.dtype).reduce((u,l)=>ur(u,l)),s=n.map(u=>u.shape),a=L().getBool("WEBGL_PACK")?new pI(n[0].shape,s):new cI(n[0].shape,s);return e.runWebGLProgram(a,n,o)}var y3={kernelName:Uo,backendName:"webgl",kernelFunc:mI};function cst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("all",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,"all",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var b3={kernelName:Ea,backendName:"webgl",kernelFunc:cst};function pst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,a)),S.assertAxesAreInnerMostDims("any",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,"any",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var w3={kernelName:Aa,backendName:"webgl",kernelFunc:pst};var fI=class{constructor(t,e,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:i}=t;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,i];let a=e==="max"?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${o};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${o}; i++) {
          int inIdx = ${u};
          float candidate = getA(batch, inIdx);
          if (candidate ${a} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `}};var dI=class{constructor(t,e,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(t.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=t[t.length-1],i=Math.ceil(s/e);this.outputShape=t.slice(0,-1),i>1&&this.outputShape.push(i),o||this.variableNames.push("bestIndicesA");let a=this.outputShape,u=a.length,l=zt(u),c=rr("coords",u),p,m;if(i===1){m=u+1;let D=zt(m);p=`
        ${D} sourceLocR = ${D}(${c.join()}, 0);
        ++${c[u-1]};
        ${D} sourceLocG = ${D}(${c.join()}, 0);
        ++${c[u-2]};
        ${D} sourceLocA = ${D}(${c.join()}, 0);
        --${c[u-1]};
        ${D} sourceLocB = ${D}(${c.join()}, 0);
        --${c[u-2]};`}else m=u,p=`
        ${l} sourceLocR = coords;
        ++${c[u-1]};
        ${l} sourceLocG = coords;
        ++${c[u-2]};
        ${l} sourceLocA = coords;
        --${c[u-1]};
        ${l} sourceLocB = coords;
        --${c[u-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(D=>"int "+D),g=rr("sourceLocR",m-1).concat("inIdx.r"),x=rr("sourceLocG",m-1).concat("inIdx.g"),b=rr("sourceLocB",m-1).concat("inIdx.b"),w=rr("sourceLocA",m-1).concat("inIdx.a"),I=n==="max"?"greaterThan":"lessThan",N=o?"":`
          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
                             getBestIndicesAChannel(${x.join()}),
                             getBestIndicesAChannel(${b.join()}),
                             getBestIndicesAChannel(${w.join()})));`,E=`vec4(
            getAChannel(${g.join()}),
            hasNextCol ? getAChannel(${x.join()}) : 0.,
            hasNextRow ? getAChannel(${b.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,A=o?"":`
      float getBestIndicesAChannel(${h.join()}) {
        return getChannel(getBestIndicesA(${f.join()}),
                                          vec2(${f.slice(-2).join()}));
      }`;this.userCode=`
      float getAChannel(${h.join()}) {
        return getChannel(getA(${f.join()}),
                               vec2(${f.slice(-2).join()}));
      }
      ${A}
      void main() {
        ${l} coords = getOutputCoords();
        bool hasNextCol = ${c[u-1]} < ${a[u-1]-1};
        bool hasNextRow = ${c[u-2]} < ${a[u-2]-1};
        ${p}
        ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
          sourceLocB${d}, sourceLocA${d}) * ${e};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${E};

        for (int i = 0; i < ${e}; i++) {
          inIdx = srcIdx;
          ${N}
          vec4 candidate = ${E};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${I}(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 I3(r,t,e,n=null){let o=t.shape[0],s=t.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let i=S.computeOptimalWindowSize(s),a={windowSize:i,inSize:s,batchSize:o,outSize:Math.ceil(s/i)},u=new fI(a,e,n==null),l=[t];n!=null&&l.push(n);let c=r.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=I3(r,t,e,c);return r.disposeIntermediateTensorInfo(c),p}function C3(r,t,e,n=null){let o=n!=null?n.shape:t.shape,s=o[o.length-1],i=S.computeOptimalWindowSize(s),a=new dI(o,i,e,n==null),u=n==null?[t]:[t,n],l=r.runWebGLProgram(a,u,"int32");if(l.shape.length===t.shape.length){let c=C3(r,t,e,l);return r.disposeIntermediateTensorInfo(l),c}return l}function hI(r,t,e,n){let o=[e];if(S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,t.shape.length),!L().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=r.texData.get(t.dataId),a=i!==null&&i.isPacked,u=t;a&&(u=r.unpackTensor(t),s.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,o),p=y.sizeFromShape(c),m=rt({inputs:{x:u},backend:r,attrs:{shape:[-1,p]}});s.push(m);let f=I3(r,m,n);s.push(f);let d=rt({inputs:{x:f},backend:r,attrs:{shape:l}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}return C3(r,t,n)}function mst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=hI(e,u,i[0],"max");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var v3={kernelName:Di,backendName:"webgl",kernelFunc:mst};function fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s}=n,i=y.parseAxisParam(s,o.shape),a=S.getAxesPermutation(i,o.shape.length),u=o,l=[];a!=null&&(u=Pe({inputs:{x:o},backend:e,attrs:{perm:a}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=hI(e,u,i[0],"min");return l.forEach(p=>e.disposeIntermediateTensorInfo(p)),c}var S3={kernelName:$i,backendName:"webgl",kernelFunc:fst};var dst=xr+`
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`,hst=It({opSnippet:dst}),N3={kernelName:Ho,backendName:"webgl",kernelFunc:hst};var gst=xr+"return log(x + sqrt(x * x + 1.0));",xst=It({opSnippet:gst}),k3={kernelName:qo,backendName:"webgl",kernelFunc:xst};var yst=xr+`
  return atan(x);
`,bst=It({opSnippet:yst}),T3={kernelName:Ko,backendName:"webgl",kernelFunc:bst};var wst=$d+`
  return atan(a, b);
`,Ist=`
  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);
  `+Xn+`
  return result;
`,Cst=ce({opSnippet:wst,packedOpSnippet:Ist}),_3={kernelName:Xo,backendName:"webgl",kernelFunc:Cst};var vst=xr+`
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sst=It({opSnippet:vst}),E3={kernelName:jo,backendName:"webgl",kernelFunc:Sst};var Ni=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideHeight,u=t.strideWidth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterHeight,m=t.effectiveFilterWidth,f=t.padInfo.top,d=t.padInfo.left;this.outputShape=t.outShape;let h=e==="avg",g=`((batch  * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + d`,x=`(xR * ${t.inWidth} + xC) * ${t.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let D=">=";this.userCode=`
        const ivec2 strides = ivec2(${a}, ${u});
        const ivec2 pads = ivec2(${f}, ${d});

        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];

          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;

          // max/min x(?, ?, d) to get y(yR, yC, d).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
          float avgValue = 0.0;

          for (int wR = 0; wR < ${p};
              wR += ${l}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${m};
                wC += ${c}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${t.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 ${D} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${o?s?g:x:`wR * ${m} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let w="max",I=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(I="avgValue / max(count, 1.0)");let N=Math.floor(i/4)*4,E=i%4,A=`
      if (${h}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${w}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec2 strides = ivec2(${a}, ${u});
      const ivec2 pads = ivec2(${f}, ${d});
      const float initializationValue = ${b};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${t.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${b});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${p};
            wR += ${l}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${t.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${N}; wC += 4) {
            int xC = xCCorner + wC * ${c};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              getValue(batch, xR, xC + 2 * ${c}, d),
              getValue(batch, xR, xC + 3 * ${c}, d)
            );

            ${A}
          }

          int xC = xCCorner + ${N};
          if (${E===1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${A}
          } else if (${E===2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              initializationValue,
              initializationValue
            );

            ${A}
          } else if (${E===3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${c}, d),
              getValue(batch, xR, xC + 2 * ${c}, d),
              initializationValue
            );

            ${A}
          }
        }
        setOutput(${I});
      }
    `}},qu=class{constructor(t,e,n,o=!1,s=!1){if(this.variableNames=["x"],e==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let i=t.filterWidth,a=t.strideDepth,u=t.strideHeight,l=t.strideWidth,c=t.dilationDepth,p=t.dilationHeight,m=t.dilationWidth,f=t.effectiveFilterDepth,d=t.effectiveFilterHeight,h=t.effectiveFilterWidth,g=t.padInfo.front,x=t.padInfo.top,b=t.padInfo.left;this.outputShape=t.outShape;let w=e==="avg",I="0.0";if(w||(I="-1.0 / 1e-20"),n){let M=">=";this.userCode=`
        const ivec3 strides =
            ivec3(${a}, ${u}, ${l});
        const ivec3 pads = ivec3(${g}, ${x}, ${b});

        void main() {
          ivec5 coords = getOutputCoords();
          int batch = coords.x;
          int ch = coords.u;

          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;

          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;

          for (int wD = 0; wD < ${f};
              wD += ${c}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${t.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${d};
                wR += ${p}) {
              int xR = xRCorner + wR;

              if (xR < 0 || xR >= ${t.inHeight}) {
                continue;
              }

              for (int wC = 0; wC < ${h};
                  wC += ${m}) {
                int xC = xCCorner + wC;

                if (xC < 0 || xC >= ${t.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 ${M} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${o?s?`(((batch * ${t.inDepth} + xD) * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`((xD * ${t.inHeight} + xR) * ${t.inWidth} + xC) * ${t.inChannels} + ch`:`wD * ${d} * ${h} +
                      wR * ${h} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;return}let N="max",E=`${e}(${e}(${e}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;e==="avg"&&(E="avgValue / max(count, 1.0)");let A=Math.floor(i/4)*4,D=i%4,F=`
      if (${w}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${N}(values, minMaxValue);
      }
    `;this.userCode=`
      const ivec3 strides =
        ivec3(${a}, ${u}, ${l});
      const ivec3 pads = ivec3(${g}, ${x}, ${b});
      const float initializationValue = ${I};
      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 >= ${t.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(${I});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${f};
            wD += ${c}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${t.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${d};
            wR += ${p}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${A}; wC += 4) {
              int xC = xCCorner + wC * ${m};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${m}, ch),
                getValue(batch, xD, xR, xC + 2 * ${m}, ch),
                getValue(batch, xD, xR, xC + 3 * ${m}, ch)
              );

              ${F}
            }

            int xC = xCCorner + ${A};
            if (${D===1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${F}
            } else if (${D===2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${m}, ch),
                initializationValue,
                initializationValue
              );

              ${F}
            } else if (${D===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
              );

              ${F}
            }
          }
        }
        setOutput(${E});
      }
    `}};function Nst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;vi(o,"avgPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,"avg",!1);return e.runWebGLProgram(p,[o],"float32")}var A3={kernelName:Yo,backendName:"webgl",kernelFunc:Nst};function kst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,u,l),m=new qu(p,"avg",!1);return e.runWebGLProgram(m,[o],"float32")}var D3={kernelName:Ri,backendName:"webgl",kernelFunc:kst};var gI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=u-1-t.padInfo.top,p=l-1-t.padInfo.left,m=1/(e*n);this.userCode=`
      const ivec2 pads = ivec2(${c}, ${p});
      const float avgMultiplier = float(${m});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${u};
            wR += ${i}) {
          float dyR = float(dyRCorner + wR) / ${o}.0;

          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${l};
            wC+= ${a}) {
            float dyC = float(dyCCorner + wC) / ${s}.0;

            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `}},xI=class{constructor(t){this.variableNames=["dy"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.effectiveFilterDepth,m=t.effectiveFilterHeight,f=t.effectiveFilterWidth,d=p-1-t.padInfo.front,h=m-1-t.padInfo.top,g=f-1-t.padInfo.left,x=1/(e*n*o);this.userCode=`
      const ivec3 pads = ivec3(${d}, ${h}, ${g});
      const float avgMultiplier = float(${x});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${p};
            wD += ${u}) {
          float dyD = float(dyDCorner + wD) / ${s}.0;

          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${m};
              wR += ${l}) {
            float dyR = float(dyRCorner + wR) / ${i}.0;

            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${f};
                wC += ${c}) {
              float dyC = float(dyCCorner + wC) / ${a}.0;

              if (dyC < 0.0 || dyC >= ${t.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 Tst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new xI(m);return e.runWebGLProgram(f,[o],i.dtype)}var $3={kernelName:Hl,backendName:"webgl",kernelFunc:Tst};function _st(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s;vi([o,s],"avgPoolGrad");let{filterSize:a,strides:u,pad:l}=n,c=S.computePool2DInfo(i.shape,a,u,1,l),p=new gI(c);return e.runWebGLProgram(p,[o],i.dtype)}var R3={kernelName:Ul,backendName:"webgl",kernelFunc:_st};function Est(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;return dp({a:o,b:s,transposeA:i,transposeB:a,backend:e})}var F3={kernelName:Zo,backendName:"webgl",kernelFunc:Est};var yI=class{constructor(t,e,n,o,s,i){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="0.0";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="1.0";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${a};
        float scale = ${u};
        float inv = scale * inversesqrt(variance + float(${i}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `}};var bI=class{constructor(t,e,n,o,s,i){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(t,e),S.assertAndGetBroadcastShape(t,n);let a="vec4(0.0)";o!=null&&(S.assertAndGetBroadcastShape(t,o),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");let u="vec4(1.0)";s!=null&&(S.assertAndGetBroadcastShape(t,s),this.variableNames.push("scale"),u="getScaleAtOutCoords()"),this.outputShape=t,this.userCode=`
      void main() {
        vec4 offset = ${a};
        vec4 scale = ${u};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${i}));

        setOutput((x - mean) * inv + offset);
      }
    `}};var Ast=({inputs:r,backend:t,attrs:e})=>{let{x:n,mean:o,variance:s,offset:i,scale:a}=r;y.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=e;u==null&&(u=.001);let l=[n,o,s],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;a!=null&&(p=a.shape,l.push(a));let m=L().getBool("WEBGL_PACK_NORMALIZATION")?new bI(n.shape,o.shape,s.shape,c,p,u):new yI(n.shape,o.shape,s.shape,c,p,u);return t.runWebGLProgram(m,l,l[0].dtype)},O3={kernelName:ds,backendName:"webgl",kernelFunc:Ast};var wI=class{constructor(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;let e=zt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Dst(this.rank),o,s=t.map((i,a)=>`sourceLoc.${O1[a]} = start[${a}] + coords.${O1[a]};`);o=`
        ${e} sourceLoc;
        ${e} coords = getOutputCoords();
        ${s.join(`
`)}
      `,this.userCode=`
      void main() {
        ${o}
        setOutput(getSource(${n}));
      }
    `}},O1=["x","y","z","w","u","v"];function Dst(r){if(r===1)return"sourceLoc";if(r<=6)return O1.slice(0,r).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var II=class{constructor(t){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t,this.rank=t.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let e=zt(this.rank),n=rr("coords",this.rank),o=rr("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,i=`getChannel(getSource(${o.join()}), ${s})`,a=`
      result.x = ${i};
      if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
        ++${o[this.rank-1]};
        result.y = ${i};
        --${o[this.rank-1]};
      }
    `,u=this.rank===1?"":`
      --${n[this.rank-1]};
      if (++${n[this.rank-2]} < ${t[this.rank-2]}) {
        ++${o[this.rank-2]};
        result.z = ${i};
        if (++${n[this.rank-1]} < ${t[this.rank-1]}) {
          ++${o[this.rank-1]};
          result.w = ${i};
        }
      }
    `,l=this.rank<=4?`sourceLoc = coords +
            ${e}(${t.map((c,p)=>`start[${p}]`).join()});`:t.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
`);this.userCode=`
      void main() {
        ${e} coords = getOutputCoords();
        ${e} sourceLoc;
        ${l}
        vec4 result = vec4(0.);
        ${a}
        ${u}
        setOutput(result);
      }
    `}};function $st(r,t,e,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(e,r.dtype),i=n.texData.get(s.dataId);Object.assign(i,o),i.refCount=1,i.shape=e,i.dtype=r.dtype;let a=Be.computeFlatOffset(t,y.computeStrides(r.shape));o.slice&&(a+=o.slice.flatOffset),i.slice={flatOffset:a,origDataId:o.slice&&o.slice.origDataId||r.dataId};let u=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,u+1),s}function ki(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,size:i}=n,[a,u]=Be.parseSliceParams(o,s,i);if(Be.assertParamsValid(o,a,u),y.sizeFromShape(u)===0)return e.makeTensorInfo(u,o.dtype,[]);if(e.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=e.texData.get(o.dataId),m=Sz(p.values,a,u,o.shape,o.dtype);return e.makeTensorInfo(u,o.dtype,m)}let{isPacked:l}=e.texData.get(o.dataId),c=Be.isSliceContinous(o.shape,a,u);if(l||!c){let p=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new II(u):new wI(u),m=[a];return e.runWebGLProgram(p,[o],o.dtype,m)}return e.uploadToGPU(o.dataId),$st(o,a,u,e)}var M3={kernelName:Ui,backendName:"webgl",kernelFunc:ki};var Rst=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n;y.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((b,w)=>b*w),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=[],d=rt({inputs:{x:o},backend:e,attrs:{shape:u}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:l}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:c}}),x=ki({inputs:{x:g},backend:e,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>e.disposeIntermediateTensorInfo(b)),x},P3={kernelName:Fi,backendName:"webgl",kernelFunc:Rst};function Fst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i}=n,a=e.readSync(o.dataId),u=e.readSync(s.dataId),l=tI(a,u,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,l)}var L3={kernelName:Da,backendName:"webgl",kernelFunc:Fst};var Ost=`
  int r = int(a.r) & int(b.r);
  int g = int(a.g) & int(b.g);
  int rb = int(a.b) & int(b.b);
  int ra = int(a.a) & int(b.a);
  return vec4(r, g, rb, ra);
`,Mst=`
  return float(int(a.r) & int(b.r));
`;function Pst(r){let{inputs:t,backend:e}=r,{a:n,b:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS"),i=L().getNumber("WEBGL_VERSION");if(e.shouldExecuteOnCPU([n,o])||i===1){let u=e.texData.get(n.dataId).values,l=e.texData.get(o.dataId).values,[c,p]=jL(n.shape,o.shape,u,l,n.dtype),m=e.makeTensorInfo(p,n.dtype),f=e.texData.get(m.dataId);return f.values=c,m}let a;return s?a=new jn(Ost,n.shape,o.shape,!1):a=new $n(Mst,n.shape,o.shape),e.runWebGLProgram(a,[n,o],n.dtype)}var z3={kernelName:$a,backendName:"webgl",kernelFunc:Pst};function Lst(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.readSync(n.dataId),i=e.readSync(o.dataId),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeTensorInfo([a.length],"int32",Int32Array.from(a))}var B3={kernelName:ql,backendName:"webgl",kernelFunc:Lst};var zst="return float(a != b);",M1=ce({opSnippet:zst,cpuKernelImpl:hz,dtype:"bool"}),V3={kernelName:Za,backendName:"webgl",kernelFunc:M1};function Pl(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.real},backend:e})}var G3={kernelName:Vp,backendName:"webgl",kernelFunc:Pl};var Bst="return float(int(x));";function W3(r,t){let e=new zr(r.shape,Bst),n=t.runWebGLProgram(e,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function P1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return nr({inputs:{x:o},backend:e});let i=ke(o.shape),a=P1({inputs:{x:o},backend:e,attrs:{dtype:"float32"}}),u=Rn({inputs:{real:a,imag:i},backend:e});return i.dispose(),e.disposeIntermediateTensorInfo(a),u}if(o.dtype==="complex64"){let i=Pl({inputs:{input:o},backend:e}),a=P1({inputs:{x:i},backend:e,attrs:{dtype:s}});return e.disposeIntermediateTensorInfo(i),a}if(!y.hasEncodingLoss(o.dtype,s)){let i=nr({inputs:{x:o},backend:e});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(e.shouldExecuteOnCPU([o])){let i=e.texData.get(o.dataId).values,[a,u,l]=XL(i,o.shape,o.dtype,s);return e.makeTensorInfo(a,u,l)}if(s==="int32")return W3(o,e);if(s==="bool"){let i=e.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),u=M1({inputs:{a:o,b:i},backend:e});return e.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var U3={kernelName:fo,backendName:"webgl",kernelFunc:P1};var H3="return ceil(x);",Vst=It({opSnippet:H3,packedOpSnippet:H3,cpuKernelImpl:YL}),q3={kernelName:Jo,backendName:"webgl",kernelFunc:Vst};var CI=class{constructor(t){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`

      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, minVal, maxVal));
      }
    `}};var vI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=t,this.userCode=`
      void main() {
        vec4 value = getAAtOutCoords();

        if (any(isnan(value))) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `}};function Gst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a;L().getBool("WEBGL_PACK_CLIP")?a=new vI(o.shape):a=new CI(o.shape);let u=[[s],[i]];return e.runWebGLProgram(a,[o],o.dtype,u)}var K3={kernelName:ho,backendName:"webgl",kernelFunc:Gst};var SI=class{constructor(t){this.variableNames=["real","imag"],this.outputShape=t,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 j3(r,t){return{dataId:t.dataId,dtype:t.dtype,shape:r.shape}}function Wst(r){let{inputs:t,backend:e}=r,{x:n}=t,o=e.texData.get(n.dataId),s=new SI(n.shape),i=[j3(n,o.complexTensorInfos.real),j3(n,o.complexTensorInfos.imag)];return e.runWebGLProgram(s,i,i[0].dtype)}var X3={kernelName:Kl,backendName:"webgl",kernelFunc:Wst};var NI=class{constructor(t){this.outputShape=[],this.outputShape=S.computeOutShape(t,1),this.variableNames=t.map((i,a)=>`T${a}`);let e=new Array(t.length-1);e[0]=t[0][1];for(let i=1;i<e.length;i++)e[i]=e[i-1]+t[i][1];let n=[`if (yC < ${e[0]}) setOutput(getT0(yR, yC));`];for(let i=1;i<e.length;i++){let a=e[i-1];n.push(`else if (yC < ${e[i]}) setOutput(getT${i}(yR, yC-${a}));`)}let o=e.length,s=e[e.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${n.join(`
        `)}
      }
    `}};var TI=class{constructor(t,e){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(t,e);let n=this.outputShape,o=n.length,s=zt(o),i=rr("coords",o),a=["x","y","z","w","u","v"].slice(0,o);this.variableNames=t.map((h,g)=>`T${g}`);let u=new Array(t.length-1);u[0]=t[0][e];for(let h=1;h<u.length;h++)u[h]=u[h-1]+t[h][e];let l=a[e],c=a.slice(-2),p=a.join(),m=`if (${l} < ${u[0]}) {
        return getChannel(
            getT0(${p}), vec2(${c.join()}));
        }`;for(let h=1;h<u.length;h++){let g=u[h-1];m+=`
        if (${l} < ${u[h]}  && ${l} >= ${u[h-1]}) {
          return getChannel(
            getT${h}(${kI(a,l,g)}),
            vec2(${kI(c,l,g)}));
        }`}let f=u.length,d=u[u.length-1];m+=`
        return getChannel(
          getT${f}(${kI(a,l,d)}),
          vec2(${kI(c,l,d)}));`,this.userCode=`
      float getValue(${a.map(h=>"int "+h)}) {
        ${m}
      }

      void main() {
        ${s} coords = getOutputCoords();
        vec4 result = vec4(getValue(${i}), 0., 0., 0.);

        ${i[o-1]} = ${i[o-1]} + 1;
        if (${i[o-1]} < ${n[o-1]}) {
          result.g = getValue(${i});
        }

        ${i[o-2]} = ${i[o-2]} + 1;
        if (${i[o-2]} < ${n[o-2]}) {
          result.a = getValue(${i});
        }

        ${i[o-1]} = ${i[o-1]} - 1;
        if (${i[o-2]} < ${n[o-2]} &&
            ${i[o-1]} < ${n[o-1]}) {
          result.b = getValue(${i});
        }
        setOutput(result);
      }
    `}};function kI(r,t,e){let n=r.indexOf(t);return r.map((s,i)=>i===n?`${s} - ${e}`:s).join()}function hp(r){let{inputs:t,backend:e}=r,{input:n}=t,o=e.texData.get(n.dataId);return nr({inputs:{x:o.complexTensorInfos.imag},backend:e})}var Y3={kernelName:Pp,backendName:"webgl",kernelFunc:hp};function Fd(r,t,e){let n=r[0].dtype;if(n==="complex64"){let f=r.map(b=>Pl({inputs:{input:b},backend:e})),d=r.map(b=>hp({inputs:{input:b},backend:e})),h=Fd(f,t,e),g=Fd(d,t,e),x=Rn({inputs:{real:h,imag:g},backend:e});return f.forEach(b=>e.disposeIntermediateTensorInfo(b)),d.forEach(b=>e.disposeIntermediateTensorInfo(b)),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),x}let o=e.shouldExecuteOnCPU(r);if(n==="string"&&(o=!0),o){let f=r.map(I=>{let E=[-1,y.sizeFromShape(I.shape.slice(t))];return rt({inputs:{x:I},backend:e,attrs:{shape:E}})}),d=f.map(I=>({vals:e.readSync(I.dataId),shape:I.shape})),h=S.computeOutShape(f.map(I=>I.shape),1),g=f[0].shape[0]===1,x=ZL(d,h,n,g),b=S.computeOutShape(r.map(I=>I.shape),t),w=e.makeTensorInfo(b,n,x);return f.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}let s=r.filter(f=>y.sizeFromShape(f.shape)>0),i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(s.length===1){let f=i?new zr(r[0].shape,Ia):new Dn(r[0].shape,Ia);return e.runWebGLProgram(f,r,n)}let a=L().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>a){let f=[];for(let h=0;h<s.length;h+=a){let g=s.slice(h,h+a);f.push(Fd(g,t,e))}let d=Fd(f,t,e);for(let h of f)e.disposeIntermediateTensorInfo(h);return d}if(i){let f=new TI(s.map(d=>d.shape),t);return e.runWebGLProgram(f,s,n)}let{tensors2D:u,outShape:l}=Ust(s,t,e),c=new NI(u.map(f=>f.shape)),p=e.runWebGLProgram(c,u,n);u.forEach(f=>e.disposeIntermediateTensorInfo(f));let m=rt({inputs:{x:p},attrs:{shape:l},backend:e});return e.disposeIntermediateTensorInfo(p),m}function Ust(r,t,e){let n=S.computeOutShape(r.map(s=>s.shape),t);return{tensors2D:r.map(s=>rt({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(t))]},backend:e})),outShape:n}}function L1(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n,s=y.parseAxisParam(o,t[0].shape)[0],i=t.map(l=>l.shape);S.assertParamsConsistent(i,s);let a=S.computeOutShape(t.map(l=>l.shape),s);if(y.sizeFromShape(a)===0)return e.makeTensorInfo(a,t[0].dtype,[]);let u=t.filter(l=>y.sizeFromShape(l.shape)>0);return u.length===1?nr({inputs:{x:u[0]},backend:e}):Fd(u,s,e)}var Z3={kernelName:Oi,backendName:"webgl",kernelFunc:L1};var Od=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=t.outShape;let i=t.padInfo.top,a=t.padInfo.left,u=t.strideHeight,l=t.strideWidth,c=t.dilationHeight,p=t.dilationWidth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4,g=t.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,I="",N="";n&&(o?I=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?I=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:I=`
          float activation(float x) {
            ${n}
          }
        `,N="result = activation(result);");let E=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${I}

      const ivec2 strides = ivec2(${u}, ${l});
      const ivec2 pads = ivec2(${i}, ${a});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${w}];

        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 >= ${t.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${f}; wC++) {
            int xC = xCCorner + wC * ${p};

            if (xC < 0 || xC >= ${t.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${d}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${g}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }

            if (${h===1}) {

              if (${g}) {
                dotProd +=
                    getX(batch, xR, xC, ${d}) *
                    getW(wR, wC, ${d}, d2);
              } else {
                dotProd +=
                    getX(batch, ${d}, xR, xC) *
                    getW(wR, wC, ${d}, d2);
              }

            } else if (${h===2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${d}, d2),
                getW(wR, wC, ${d} + 1, d2)
              );

              if (${g}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${d}),
                  getX(batch, xR, xC, ${d} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${d}, xR, xC),
                  getX(batch, ${d} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${h===3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${d}, d2),
                getW(wR, wC, ${d} + 1, d2),
                getW(wR, wC, ${d} + 2, d2)
              );

              if (${g}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${d}),
                  getX(batch, xR, xC, ${d} + 1),
                  getX(batch, xR, xC, ${d} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${d}, xR, xC),
                  getX(batch, ${d} + 1, xR, xC),
                  getX(batch, ${d} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${E}
        ${N}
        setOutput(result);
      }
    `}},_I=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let e=t.padInfo.front,n=t.padInfo.top,o=t.padInfo.left,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=t.dilationDepth,l=t.dilationHeight,c=t.dilationWidth,p=t.filterDepth,m=t.filterHeight,f=t.filterWidth,d=Math.floor(t.inChannels/4)*4,h=t.inChannels%4;this.userCode=`
      const ivec3 strides = ivec3(${s}, ${i}, ${a});
      const ivec3 pads = ivec3(${e}, ${n}, ${o});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d2 = coords.u;

        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xFCorner = xFRCCorner.x;
        int xRCorner = xFRCCorner.y;
        int xCCorner = xFRCCorner.z;

        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
        // y(yF, yR, yC, d2). ? = to be determined. : = across all
        // values in that axis.
        float dotProd = 0.0;
        for (int wF = 0; wF < ${p}; wF++) {
          int xF = xFCorner + wF * ${u};

          if (xF < 0 || xF >= ${t.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${m}; wR++) {
            int xR = xRCorner + wR * ${l};

            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${f}; wC++) {
              int xC = xCCorner + wC * ${c};

              if (xC < 0 || xC >= ${t.inWidth}) {
                continue;
              }

              for (int d1 = 0; d1 < ${d}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${h===1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${d}) *
                  getW(wF, wR, wC, ${d}, d2);
              } else if (${h===2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${d}),
                  getX(batch, xF, xR, xC, ${d} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${d}, d2),
                  getW(wF, wR, wC, ${d} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${h===3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${d}),
                  getX(batch, xF, xR, xC, ${d} + 1),
                  getX(batch, xF, xR, xC, ${d} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${d}, d2),
                  getW(wF, wR, wC, ${d} + 1, d2),
                  getW(wF, wR, wC, ${d} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};var Md=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.padInfo.left,a=t.strideWidth,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,p=c,m=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)m+=`
           vec4 xTexelC${g*2};
           int xTexelC${g*2}Ready;
           vec4 xTexelC${g*2+1};
           int xTexelC${g*2+1}Ready;
           vec4 xC${g};`;m+=`
     for (int r = 0; r < ${l}; r++) {
      for (int d1 = 0; d1 < ${t.inChannels}; d1 += 2) {
       `;for(let g=0;g<c;g++)m+=`
           xTexelC${g*2} = vec4(0.0);
           xTexelC${g*2}Ready = 0;
           xTexelC${g*2+1} = vec4(0.0);
           xTexelC${g*2+1}Ready = 0;
           xC${g} = vec4(0.0);`;m+=`
         xR = xRCorner + r * dilations[0];
         if (xR >=0 && xR < inDims[0]) {
       `;for(let g=0;g<(p+1)/2;g++){let x=g*2;if(m+=`
           xC = xCCorner + ${x*u};
           `,a===1){if(x<c&&(i%2===1?(m+=`
                 xCOffset = xC + 1;
                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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;
                 }
               `,u===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<c)){let b=i%2===0?y.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(m+=`
                   xCOffset = xC + imod(pads[1], 2) + ${b};

                   if (xCOffset >= 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;
                   }
                   `,u>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<c&&(i%2===1?(m+=`
                 xCOffset = xC + 1 - strides[1];
                 if(xCOffset >= 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${x+1}.zw);
               `,x+1<c&&(m+=`
                   final = vec4(0.0);
                   xCOffset = xC + 1 + strides[1];
                   if(xCOffset >= 0 && xCOffset < inDims[1]) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
                   xC${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${x+1}.xy);
               `,x+1<c&&(m+=`
                   xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);
                 `)));x<c&&(m+=`
             wTexel = getW(r, ${x}, d1, d2);
             dotProd += xC${x}.xxzz * vec4(wTexel.xy, wTexel.xy);
             if(d1 + 1 < ${t.inChannels}) {
               dotProd += xC${x}.yyww * vec4(wTexel.zw, wTexel.zw);
             }
           `,x+1<c&&(m+=`
               wTexel = getW(r, ${x+1}, d1, d2);
               dotProd += xC${x+1}.xxzz * vec4(wTexel.xy, wTexel.xy);
               if(d1 + 1 < ${t.inChannels}) {
                 dotProd += xC${x+1}.yyww * vec4(wTexel.zw, wTexel.zw);
               }
             `))}m+=`
     }
   `,m+=`
     }
   `,m+=`
     }
   `;let f="",d="";n&&(o?f=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:s?f=`vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
           ${n}
         }`:f=`vec4 activation(vec4 x) {
           ${n}
         }`,d="result = activation(result);");let h=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${f}

       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;

         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
         vec4 dotProd = vec4(0.000000000000001);

         ${m}

         vec4 result = dotProd - vec4(0.000000000000001);
         ${h}
         ${d}
         setOutput(result);
       }
     `}};var EI=class{constructor(t,e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=t,this.enableShapeUniforms=he(this.outputShape.length);let{dataFormat:n}=e,o=Ue(),s=n==="channelsLast",i=s?1:2,a=s?2:3,u=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${t[2]} && pos < ${t[1]}) {`,l="";for(let c=0;c<=1;c++)for(let p=0;p<=1;p++)l+=`
          blockIndex = rc.z + ${p};
          pos = rc.y + ${c};

          ${u}
            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);

            if(d0 < inputShape[${i}] && d0 >= 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[${a}] && d1 >= 0) {

                ch = imod(pos, inChannels);

                if (${s}) {
                  innerDims = vec2(d1, ch);
                  result[${c*2+p}] = getChannel(
                    getA(rc.x, d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${c*2+p}] = 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;

        ${l}

        ${o.output} = result;
      }
    `}};function AI(r,t){let e=r.length;return e>=3?t?[...r.slice(0,-3),r[e-3]*r[e-2],r[e-1]]:[...r.slice(0,-3),r[e-3],r[e-2]*r[e-1]]:!t&&e===1&&r[0]>1?[r[0],1]:null}function DI({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let u=r.shape,l=n.texData.get(r.dataId),c=e.inChannels,p=u[0]*u[1]*u[2],m=e.outChannels,f=e.dataFormat==="channelsLast",d=!1,h=!1,g,x=[];if(s!=null){let I=AI(s.shape,f);I!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:I}}),x.push(s))}if(o!=null){let I=AI(o.shape,f);I!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:I}}),x.push(o))}if(!((p===1||m===1)&&c>F1)&&l.isPacked&&f&&l.texture!=null&&u[2]%2!==0&&y.arraysEqual(l.shape.slice(-3),u.slice(-3))){let I=u[0]*u[1]*(u[2]+1),N={dataId:r.dataId,shape:[1,I,e.inChannels],dtype:r.dtype},E=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,y.assert(Uu(l.shape,N.shape),()=>`packed reshape ${l.shape} to ${N.shape} isn't free`);let A=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}});x.push(A);let D=dp({a:N,b:A,backend:n,transposeA:d,transposeB:h,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i}),F=n.texData.get(D.dataId);y.assert(F.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=E,F.shape=e.outShape,g=nr({inputs:{x:D},backend:n}),g.shape=e.outShape,x.push(D)}else{let I=e.outHeight*e.outWidth,N=rt({inputs:{x:r},backend:n,attrs:{shape:f?[e.batchSize,I,e.inChannels]:[e.batchSize,e.inChannels,I]}}),E=rt({inputs:{x:t},backend:n,attrs:{shape:[1,e.inChannels,e.outChannels]}}),A=dp({a:f?N:E,b:f?E:N,transposeA:!f,transposeB:h,backend:n,bias:o,activation:a,preluActivationWeights:s,leakyreluAlpha:i});g=rt({inputs:{x:A},backend:n,attrs:{shape:e.outShape}}),x.push(N),x.push(E),x.push(A)}for(let I of x)n.disposeIntermediateTensorInfo(I);return g}function $I({x:r,filter:t,convInfo:e,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:a=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=e,d=f==="channelsLast",h=u*l*c,g=m*p,x=[e.batchSize,h,g],b=!0,w=!1,I=[];if(s!=null){let Z=AI(s.shape,d);Z!=null&&(s=rt({inputs:{x:s},backend:n,attrs:{shape:Z}}),I.push(s))}if(o!=null){let Z=AI(o.shape,d);Z!=null&&(o=rt({inputs:{x:o},backend:n,attrs:{shape:Z}}),I.push(o))}let N=rt({inputs:{x:t},backend:n,attrs:{shape:[1,h,y.sizeFromShape(t.shape)/h]}});I.push(N);let E=new EI(x,e),A=[r.shape,[e.padInfo.top,e.padInfo.left],[e.strideHeight,e.strideWidth],[e.dilationHeight,e.dilationWidth],[e.inChannels],[e.filterWidth*e.inChannels],[e.outWidth]],D=n.runWebGLProgram(E,[r],"float32",A),F=rt({inputs:{x:D},backend:n,attrs:{shape:x}});I.push(D),I.push(F);let M=o!=null,V=s!=null,G=a==="leakyrelu",W=a?Ml(a,!0):null,q=new Rd(d?F.shape:N.shape,d?N.shape:F.shape,d?[e.batchSize,g,e.outChannels]:[e.batchSize,e.outChannels,g],b,w,M,W,V,G),H=d?[F,N]:[N,F];if(o&&H.push(o),V&&H.push(s),G){let Z=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));H.push(Z),I.push(Z)}let j=n.runWebGLProgram(q,H,"float32"),Y=rt({inputs:{x:j},backend:n,attrs:{shape:e.outShape}});I.push(j);for(let Z of I)n.disposeIntermediateTensorInfo(Z);return Y}function Hst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,s.shape,i,l,a,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=DI({x:o,filter:s,convInfo:m,backend:e});else if(m.strideWidth<=2&&p==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let h=new Md(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];f=e.runWebGLProgram(h,[o,s],"float32",g)}else if(L().getBool("WEBGL_CONV_IM2COL"))f=$I({x:o,filter:s,convInfo:m,backend:e});else{let h=new Od(m);f=e.runWebGLProgram(h,[o,s],"float32")}let d=rt({inputs:{x:f},backend:e,attrs:{shape:m.outShape}});return e.disposeIntermediateTensorInfo(f),d}var J3={kernelName:Qo,backendName:"webgl",kernelFunc:Hst};var RI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.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 < ${t.batchSize}; b++) {
          for (int yR = 0; yR < ${t.outHeight}; yR++) {
            int xR = wR + yR * ${e} - ${o};

            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${t.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${s};

              if (xC < 0 || xC >= ${t.inWidth}) {
                continue;
              }

              ${i?`float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);
              float xValue = getX(b, d1, xR, xC);
              dotProd += (xValue * dyValue);`}
            }
          }
        }
        setOutput(dotProd);
      }
    `}},FI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=t.dataFormat==="channelsLast",a=e-1-t.padInfo.top,u=n-1-t.padInfo.left,l=i?1:2,c=i?2:3,p=i?3:1;this.userCode=`
      const ivec2 pads = ivec2(${a}, ${u});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${p}];

        ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${e}; wR++) {
          float dyR = float(dyRCorner + wR) / ${o}.0;

          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${e} - 1 - wR;

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${s}.0;

            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${n} - 1 - wC;

            for (int d2 = 0; d2 < ${t.outChannels}; d2++) {

              if (${i}) {
                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);
      }
    `}},OI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.padInfo.front,i=t.padInfo.top,a=t.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 < ${t.batchSize}; b++) {
          for (int yF = 0; yF < ${t.outDepth}; yF++) {
            int xF = wF + yF * ${e} - ${s};

            if (xF < 0 || xF >= ${t.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${t.outHeight}; yR++) {
              int xR = wR + yR * ${n} - ${i};

              if (xR < 0 || xR >= ${t.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${t.outWidth}; yC++) {
                int xC = wC + yC * ${o} - ${a};

                if (xC < 0 || xC >= ${t.inWidth}) {
                  continue;
                }

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}},MI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterDepth,n=t.filterHeight,o=t.filterWidth,s=t.strideDepth,i=t.strideHeight,a=t.strideWidth,u=e-1-t.padInfo.front,l=n-1-t.padInfo.top,c=o-1-t.padInfo.left;this.userCode=`
      const ivec3 pads = ivec3(${u}, ${l}, ${c});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyFCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        float dotProd = 0.0;
        for (int wF = 0; wF < ${e}; wF++) {
          float dyF = float(dyFCorner + wF) / ${s}.0;

          if (dyF < 0.0 || dyF >= ${t.outDepth}.0 || fract(dyF) > 0.0) {
            continue;
          }
          int idyF = int(dyF);

          int wFPerm = ${e} - 1 - wF;

          for (int wR = 0; wR < ${n}; wR++) {
            float dyR = float(dyRCorner + wR) / ${i}.0;

            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
              fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            int wRPerm = ${n} - 1 - wR;

            for (int wC = 0; wC < ${o}; wC++) {
              float dyC = float(dyCCorner + wC) / ${a}.0;

              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              int wCPerm = ${o} - 1 - wC;

              for (int d2 = 0; d2 < ${t.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function qst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,filterShape:c}=n,p=S.convertConv2DDataFormat(u),m=S.computeConv2DInfo(o.shape,c,i,1,a,l,!1,p),f=new RI(m);return e.runWebGLProgram(f,[o,s],"float32")}var Q3={kernelName:Dp,backendName:"webgl",kernelFunc:qst};var PI=class{constructor(t){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=t.inShape,this.enableShapeUniforms=he(this.outputShape.length);let e=t.filterHeight,n=t.filterWidth,o=e-1-t.padInfo.top,s=n-1-t.padInfo.left;this.userCode=`
      const ivec2 pads = ivec2(${o}, ${s});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];

        ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        vec4 result = vec4(0.);
        for (int wR = 0; wR < ${e}; wR++) {
          float dyR = float(dyRCorner + wR) / strides[0];
          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);
          int wRPerm = ${e} - 1 - wR;

          for (int wC = 0; wC < ${n}; wC++) {
            int wCPerm = ${n} - 1 - wC;

            float dyC = float(dyCCorner + wC) / strides[1];
            bool idyCVal = (dyC >= 0.0) && (dyC < ${t.outWidth}.0)
              && (fract(dyC) == 0.0);
            int idyC = int(dyC);

            float dyC2 = float(dyCCorner + wC + 1) / strides[1];
            bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${t.outWidth}.0)
              && (fract(dyC2) == 0.0);
            int idyC2 = int(dyC2);

            if (idyCVal && idyCVal2) {
              for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC, d2);
                vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?
                  dySample : getDy(batch, idyR, idyC2, d2);

                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.xy += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));

                dyValue = mod(float(idyC2), 2.) == 0. ?
                  dySample2.xy : dySample2.zw;
                result.zw += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            } else if (idyCVal) {
              for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC, d2);
                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.xy += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            } else if (idyCVal2) {
              for (int d2 = 0; d2 < ${t.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC2, d2);
                vec2 dyValue = mod(float(idyC2), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.zw += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            }
          }
        }
        setOutput(result);
      }
    `}};function Kst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{inputShape:i,strides:a,pad:u,dataFormat:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(l),m=S.computeConv2DInfo(i,s.shape,a,1,u,c,!1,p);if(L().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&p==="channelsLast"){let f=[[m.strideHeight,m.strideWidth]],d=new PI(m);return e.runWebGLProgram(d,[o,s],"float32",f)}else{let f=new FI(m);return e.runWebGLProgram(f,[o,s],"float32")}}var tB={kernelName:ts,backendName:"webgl",kernelFunc:Kst};function jst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=new _I(l);return e.runWebGLProgram(c,[o,s],"float32")}var eB={kernelName:es,backendName:"webgl",kernelFunc:jst};function Xst(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n,l=S.computeConv3DInfo(o.shape,u,i,1,a),c=new OI(l);return e.runWebGLProgram(c,[o,s],"float32")}var rB={kernelName:Ra,backendName:"webgl",kernelFunc:Xst};function Yst(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n,l=S.computeConv3DInfo(u,s.shape,a,1,i),c=new MI(l);return e.runWebGLProgram(c,[o,s],"float32")}var nB={kernelName:Fa,backendName:"webgl",kernelFunc:Yst};var Zst=Po+`
  return cos(x);
`,Jst=`
  vec4 result = cos(x);
  bvec4 isNaN = isnan(x);
  ${Xn}
  return result;
`,Qst=It({opSnippet:Zst,packedOpSnippet:Jst}),oB={kernelName:rs,backendName:"webgl",kernelFunc:Qst};var tit=`
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`,eit=It({opSnippet:tit}),sB={kernelName:ns,backendName:"webgl",kernelFunc:eit};var LI=class{constructor(t,e,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[i,a,u,l]=t,[c]=e,[p,m]=n;this.outputShape=[c,p,m,l];let f=o==="bilinear"?1:0,[d,h]=[`${a-1}.0`,`${u-1}.0`],[g,x,b]=p>1?[`${(a-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,I,N]=m>1?[`${(u-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(${w});
      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 >= ${i}) {
          return;
        }

        float height_scale = ${x};
        float width_scale = ${I};

        float in_y = ${b};
        if( in_y < 0.0 || in_y > ${d} ) {
          setOutput(float(${s}));
          return;
        }
        float in_x = ${N};
        if( in_x < 0.0 || in_x > ${h} ) {
          setOutput(float(${s}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${f} == 1) {
          // Compute the four integer indices.
          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));

          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `}};var rit=r=>{let{inputs:t,backend:e,attrs:n}=r,{image:o,boxes:s,boxInd:i}=t,{cropSize:a,method:u,extrapolationValue:l}=n,c=new LI(o.shape,s.shape,a,u,l);return e.runWebGLProgram(c,[o,s,i],"float32")},iB={kernelName:Ma,backendName:"webgl",kernelFunc:rit};var gp;(function(r){r.Prod="*",r.Sum="+"})(gp||(gp={}));var dg=class{constructor(t,e,n,o){this.op=t,this.outputShape=e,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let s=this.outputShape.length,i=this.op===gp.Prod?"1.0":"0.0",a=n?i:`getX(${aB(s,"coords",this.op)})`,u=this.outputShape[this.outputShape.length-1],l="",c="";n?(l=o?`end != ${u-1}`:"end != 0",c=o?"end + 1":"end - 1"):(l=o?`end + pow2 < ${u}`:"end >= pow2",c=o?"end + pow2":"end - pow2"),this.userCode=`
      void main() {
        ${zt(s)} coords = getOutputCoords();
        int end = ${lB(s,"coords",this.op)};
        float val = ${a};
        int pow2 = int(pow(2.0, index));
        if (${l}) {
          int idx = ${c};
          ${lB(s,"coords",this.op)} = idx;
          val ${this.op}= getX(${aB(s,"coords",this.op)});
        }
        setOutput(val);
      }
    `}};function aB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.x, ${t}.y`;if(r===3)return`${t}.x, ${t}.y, ${t}.z`;if(r===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function lB(r,t,e){if(r===1)return`${t}`;if(r===2)return`${t}.y`;if(r===3)return`${t}.z`;if(r===4)return`${t}.w`;throw new Error(`Cumulative ${e} for rank ${r} is not yet supported`)}function zI(r,t,e,n,o,s){let i=t.shape.length,a=S.getAxesPermutation([n],i),u=t;a!=null&&(u=Pe({inputs:{x:t},backend:e,attrs:{perm:a}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let c=u.shape[l],p=nr({inputs:{x:u},backend:e});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new dg(r,u.shape,!1,s),d=[[m]],h=p;p=e.runWebGLProgram(f,[p],p.dtype,d),e.disposeIntermediateTensorInfo(h)}if(o){let m=new dg(r,u.shape,o,s),f=p;p=e.runWebGLProgram(m,[p],p.dtype),e.disposeIntermediateTensorInfo(f)}if(a!=null){let m=S.getUndoAxesPermutation(a),f=Pe({inputs:{x:p},backend:e,attrs:{perm:m}});return e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(u),f}return p}function nit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Prod,o,e,s,i,a)}var uB={kernelName:Oa,backendName:"webgl",kernelFunc:nit};function oit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n;return zI(gp.Sum,o,e,s,i,a)}var cB={kernelName:os,backendName:"webgl",kernelFunc:oit};function sit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,weights:s}=t,{size:i,binaryOutput:a}=n;if(o.shape.length===1){let u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=tI(u,l,s.dtype,s.shape,i);return e.makeTensorInfo([i],s.dtype,c)}else if(o.shape.length===2){let u=e.bufferSync(o),l=e.bufferSync(s),c=KL(u,l,i,a);return e.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var pB={kernelName:jl,backendName:"webgl",kernelFunc:sit};var BI=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode=`
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

      int in_h = h / ${e};
      int offset_h = imod(h, ${e});
      int in_w = w / ${e};
      int offset_w = imod(w, ${e});
      int offset_d = (offset_h * ${e} + 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 iit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=new BI(d,s,i);return e.runWebGLProgram(h,[o],o.dtype)}var mB={kernelName:Pa,backendName:"webgl",kernelFunc:iit};var Pd=class{constructor(t,e=!1,n=null,o=!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=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.filterHeight,a=t.filterWidth,u=t.outChannels/t.inChannels,l="",c="";n&&(o?l=`float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?l=`float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:l=`
          float activation(float x) {
            ${n}
          }
        `,c="result = activation(result);");let p=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${l}

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${u};
        int q = d2 - d1 * ${u};

        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 < ${i}; wR++) {
          int xR = xRCorner + wR * dilations[0];

          if (xR < 0 || xR >= inDims[0]) {
            continue;
          }

          for (int wC = 0; wC < ${a}; 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;
        ${p}
        ${c}
        setOutput(result);
      }
    `}};var Ld=class{constructor(t,e=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=t.outShape,this.enableShapeUniforms=he(this.outputShape.length);let i=t.outChannels/t.inChannels,a=t.padInfo.left,u=t.strideWidth,l=t.dilationWidth,c=t.filterHeight,p=t.filterWidth,m=p,f=`
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x<p;x++)f+=`
          vec4 xTexelC${x*2};
          int xTexelC${x*2}Ready;
          vec4 xTexelC${x*2+1};
          int xTexelC${x*2+1}Ready;
          vec4 xC${x};`;f+=`
    for (int r = 0; r < ${c}; r++) {
      `;for(let x=0;x<p;x++)f+=`
          xTexelC${x*2} = vec4(0.0);
          xTexelC${x*2}Ready = 0;
          xTexelC${x*2+1} = vec4(0.0);
          xTexelC${x*2+1}Ready = 0;
          xC${x} = vec4(0.0);`;f+=`
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(f+=`
          xC = xCCorner + ${b*l};
          `,u===1){if(b<p&&(a%2===1?(f+=`
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${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;
                }
              `,l===1&&b>0?f+=`
                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);
                `:f+=`
                  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);
                  }
                  `):f+=`
                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<p)){let w=a%2===0?y.nearestLargerEven(l):l;l%2===0&&a%2===1||l%2!==0&&a%2!==1?(f+=`
                  xCOffset = xC + imod(pads[1], 2) + ${w};

                  if (xCOffset >= 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;
                  }
                  `,l>1?f+=`
                    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);
                    }
                    `:f+=`
                    xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);
                    `):w===1?f+=`
                    xC${b+1} = xTexelC${b};
                    `:f+=`
                    xCOffset = xC + ${w};

                    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<p&&(a%2===1?(f+=`
                xCOffset = xC + 1 - strides[1];
                if(xCOffset >= 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${b+1}.zw);
              `,b+1<p&&(f+=`
                  final = vec4(0.0);
                  xCOffset = xC + 1 + strides[1];
                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);
                `)):(f+=`
                if(xC >= 0 && xC < inDims[1] && xTexelC${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${b+1}.xy);
              `,b+1<p&&(f+=`
                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);
                `)));b<p&&(f+=`
            wTexel = getW(r, ${b}, d1, q);
            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);
          `,b+1<p&&(f+=`
              wTexel = getW(r, ${b+1}, d1, q);
              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);
            `))}f+=`
    }
  `,f+=`
      }
    `;let d="",h="";n&&(o?d=`vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${n}
        }`:s?d=`vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${n}
        }`:d=`vec4 activation(vec4 x) {
          ${n}
        }`,h="result = activation(result);");let g=e?"result += getBiasAtOutCoords();":"";e&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
      ${d}

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${i};
        int q = d2 - d1 * ${i};
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);

        ${f}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${g}
        ${h}
        setOutput(result);
      }
    `}};function ait(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u,dimRoundingMode:l}=n,c=u;c==null&&(c=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(o.shape,s.shape,i,c,a,l,!0),m;L().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?m=new Ld(p):m=new Pd(p);let f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return e.runWebGLProgram(m,[o,s],"float32",f)}var fB={kernelName:ss,backendName:"webgl",kernelFunc:ait};var VI=class{constructor(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;let e=t.strideHeight,n=t.strideWidth,o=t.padInfo.top,s=t.padInfo.left,i=t.outChannels/t.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 * ${i} + dm;

        float dotProd = 0.0;

        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${t.batchSize}; b++) {
          for (int yR = 0; yR < ${t.outHeight}; yR++) {
            int xR = wR + yR * ${e} - ${o};

            if (xR < 0 || xR >= ${t.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${t.outWidth}; yC++) {
              int xC = wC + yC * ${n} - ${s};

              if (xC < 0 || xC >= ${t.inWidth}) {
                continue;
              }

              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `}},GI=class{constructor(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;let e=t.filterHeight,n=t.filterWidth,o=t.strideHeight,s=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,u=t.outChannels/t.inChannels;this.userCode=`
      const ivec2 pads = ivec2(${i}, ${a});

      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 < ${e}; wR++) {
          float dyR = float(dyRCorner + wR) / ${o}.0;

          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${e} - 1 - wR;

          for (int wC = 0; wC < ${n}; wC++) {
            float dyC = float(dyCCorner + wC) / ${s}.0;

            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${n} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${u}; dm++) {
              int d2 = d1 * ${u} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function lit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,filterShape:c}=n,p=S.computeConv2DInfo(o.shape,c,i,a,u,l,!0),m=new VI(p);return e.runWebGLProgram(m,[o,s],"float32")}var dB={kernelName:$p,backendName:"webgl",kernelFunc:lit};function uit(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{strides:i,dilations:a,pad:u,dimRoundingMode:l,inputShape:c}=n,p=S.computeConv2DInfo(c,s.shape,i,a,u,l,!0),m=new GI(p);return e.runWebGLProgram(m,[o,s],"float32")}var hB={kernelName:Rp,backendName:"webgl",kernelFunc:uit};var WI=class{constructor(t){this.variableNames=["X"],this.outputShape=[t,t],this.userCode=`
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `}};function cit(r){let{inputs:t,backend:e}=r,{x:n}=t,o=[...n.shape,...n.shape],s=y.sizeFromShape(n.shape),i=rt({inputs:{x:n},backend:e,attrs:{shape:[s]}}),a=new WI(s),u=e.runWebGLProgram(a,[i],i.dtype),l=rt({inputs:{x:u},backend:e,attrs:{shape:o}});return e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(u),l}var gB={kernelName:Xl,backendName:"webgl",kernelFunc:cit};var UI=class{constructor(t){this.variableNames=["x","W"],this.outputShape=t.outShape;let{inHeight:e,inWidth:n,padInfo:o,strideHeight:s,strideWidth:i,filterHeight:a,filterWidth:u,dilationHeight:l,dilationWidth:c}=t,{top:p,left:m}=o;this.userCode=`
      const ivec2 strides = ivec2(${s}, ${i});
      const ivec2 pads = ivec2(${p}, ${m});
      const float neg_infinity = -3.4e38;

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.w;
        ivec2 outTopLeftCorner =
            coords.yz * strides - pads;
        int hBeg = outTopLeftCorner.x;
        int wBeg = outTopLeftCorner.y;

        float curVal = neg_infinity;
        for (int h = 0; h < ${a}; h++) {
          int hIn = hBeg + h * ${l};

          if (hIn >= 0 && hIn < ${e}) {
            for (int w = 0; w < ${u}; w++) {
              int wIn = wBeg + w * ${c};

              if (wIn >= 0 && wIn < ${n}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);

                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }

        float result = curVal;
        setOutput(result);
      }
    `}};function pit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n,l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c,p=new UI(l);c=e.runWebGLProgram(p,[o,s],"float32");let m=rt({inputs:{x:c},backend:e,attrs:{shape:l.outShape}});return e.disposeIntermediateTensorInfo(c),m}var xB={kernelName:is,backendName:"webgl",kernelFunc:pit};function mit(r){let{inputs:t,backend:e,attrs:n}=r,{equation:o}=n,s=t,{allDims:i,summedDims:a,idDims:u}=S.decodeEinsumEquation(o,s.length);S.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:c}=S.getEinsumComputePath(a,u),p=c.length,m=null,f=i.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:x,expandDims:b}=S.getEinsumPermutation(f,u[g]),w;S.isIdentityPermutation(x)?w=s[g]:(w=Pe({inputs:{x:s[g]},backend:e,attrs:{perm:x}}),d.push(w));let I=w.shape.slice();for(let N=0;N<b.length;++N)I.splice(b[N],0,1);y.arraysEqual(w.shape,I)||(w=rt({inputs:{x:w},backend:e,attrs:{shape:I}}),d.push(w)),m===null?m=w:(m=mg({inputs:{a:w,b:m},backend:e}),d.push(m))}h<p-1&&(l[h]>=0&&(m=fp({inputs:{x:m},backend:e,attrs:{axis:l[h]-(i.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&e.disposeIntermediateTensorInfo(h);return m}var yB={kernelName:Fp,backendName:"webgl",kernelFunc:mit};var fit="return (x >= 0.0) ? x : (exp(x) - 1.0);",dit=`
  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;
`,hit=It({opSnippet:fit,packedOpSnippet:dit}),bB={kernelName:ls,backendName:"webgl",kernelFunc:hit};var git="return (b >= 0.0) ? a : a * (b + 1.0);",xit=`
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`,yit=r=>{let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=L().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jn(xit,n.shape,o.shape):new $n(git,n.shape,o.shape);return e.runWebGLProgram(s,[n,o],n.dtype)},wB={kernelName:La,backendName:"webgl",kernelFunc:yit};var bit=`
  return vec4(equal(a, b));
`,wit="return float(a == b);",Iit=ce({opSnippet:wit,packedOpSnippet:bit,dtype:"bool",cpuKernelImpl:JL}),IB={kernelName:za,backendName:"webgl",kernelFunc:Iit};var Cit=`
  // 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));
`,vit=It({opSnippet:Cit}),CB={kernelName:us,backendName:"webgl",kernelFunc:vit};var Sit=Po+`
  return exp(x);
`,Nit=`
  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;
`,z1=It({opSnippet:Sit,packedOpSnippet:Nit,cpuKernelImpl:QL,dtype:"float32"}),vB={kernelName:cs,backendName:"webgl",kernelFunc:z1};function HI(r){let{inputs:t,attrs:e,backend:n}=r,{dim:o}=e,{input:s}=t,i=s.shape.length,a=s.shape.slice(),u=o;return o<0&&(y.assert(-(i+1)<=o,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+o+1),a.splice(u,0,1),rt({inputs:{x:s},backend:n,attrs:{shape:a}})}var SB={kernelName:Mi,backendName:"webgl",kernelFunc:HI};var NB="return exp(x) - 1.0;",kit=It({opSnippet:NB,packedOpSnippet:NB,cpuKernelImpl:tz}),kB={kernelName:ps,backendName:"webgl",kernelFunc:kit};var hg=class{constructor(t,e,n){this.variableNames=["real","imag"];let o=e[1];this.outputShape=e;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,i=n?`${o}.0`:"1.0",a;if(t==="real")a="return real * expR - imag * expI;";else if(t==="imag")a="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${t}.`);this.userCode=`
      const float exponentMultiplier = ${s};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${a}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${o});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${o}; 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) / ${i};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `}};function qI(r,t,e){let n=e.texData.get(r.dataId),o=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],i=o/s,a=rt({inputs:{x:r},backend:e,attrs:{shape:[i,s]}}),u=a.shape,l=new hg("real",u,t),c=new hg("imag",u,t),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:u},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:u}],m=e.runWebGLProgram(l,p,"float32"),f=e.runWebGLProgram(c,p,"float32"),d=Rn({inputs:{real:m,imag:f},backend:e});e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f);let h=rt({inputs:{x:d},backend:e,attrs:{shape:r.shape}});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(d),h}function Tit(r){let{inputs:t,backend:e}=r,{input:n}=t;return qI(n,!1,e)}var TB={kernelName:Op,backendName:"webgl",kernelFunc:Tit};var KI=class{constructor(t,e){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=t,this.userCode=`
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `}};function Ll(r){let{backend:t,attrs:e}=r,{shape:n,value:o}=e,{dtype:s}=e;if(s=s||y.inferDtype(o),s==="string"){let i=y.getArrayFromDType(s,y.sizeFromShape(n));return i.fill(o),t.makeTensorInfo(n,s,i)}else{let i=new KI(n,o),a=[[o]];return t.runWebGLProgram(i,[],s,a)}}var _B={kernelName:Jl,backendName:"webgl",kernelFunc:Ll};var jI=class{constructor(t){this.variableNames=["Image"],this.outputShape=[];let e=t[2];this.outputShape=t,this.userCode=`
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${e} - x - 1;
          float outputValue;
          if(coordX >= 0 && coordX < ${e}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `}};var EB={kernelName:Ba,backendName:"webgl",kernelFunc:({inputs:r,backend:t})=>{let{image:e}=r,n=t,o=new jI(e.shape);return n.runWebGLProgram(o,[e],e.dtype)}};var AB="return floor(x);",_it=It({opSnippet:AB,packedOpSnippet:AB,cpuKernelImpl:ez}),DB={kernelName:ms,backendName:"webgl",kernelFunc:_it};var Eit=`
  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;
  }
`,Ait=`
  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);
`,Dit=ce({opSnippet:Eit,packedOpSnippet:Ait,dtype:"int32"}),$B={kernelName:fs,backendName:"webgl",kernelFunc:Dit};var XI=class{constructor(t){this.variableNames=["A"];let e=Ue(),[n,o]=t;this.outputShape=t,this.userCode=`
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);

        vec4 values = ${e.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 YI=class{constructor(t){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let e=Ue(),[n,o]=t;this.outputShape=t,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(${o}.0, ${n}.0);
            vec4 values = ${e.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);
          }
        }

        ${e.output} = result;
      }
    `}};var RB={kernelName:th,backendName:"webgl",kernelFunc:$it},zd,B1=L().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function $it(r){let{inputs:t,backend:e,attrs:n}=r,{pixels:o}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,a=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[u,l]=i?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[l,u],p=[l,u,s];if(a||i){let h=L().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(zd==null||h!==B1)&&(B1=h,zd=document.createElement("canvas").getContext("2d",{willReadFrequently:B1})),zd.canvas.width=u,zd.canvas.height=l,zd.drawImage(o,0,0,u,l),o=zd.canvas}let m=e.makeTensorInfo(c,"int32");e.texData.get(m.dataId).usage=Jr.PIXELS,e.gpgpu.uploadPixelDataToTexture(e.getTexture(m.dataId),o);let f=L().getBool("WEBGL_PACK")?new YI(p):new XI(p),d=e.runWebGLProgram(f,[m],"int32");return e.disposeData(m.dataId),d}function Rit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(o.shape,s.shape,u,p,l,m,!1,h),x,b=[],w=i!=null,I=a!=null,N=f==="leakyrelu",E=()=>{let D=[o,s],F=(M,V)=>{if(V==="NCHW"&&M.shape.length===1&&M.shape[0]!==1){let G=rt({inputs:{x:M},backend:e,attrs:{shape:[M.shape[0],1,1]}});return b.push(G),G}return M};if(w&&D.push(F(i,c)),I&&D.push(F(a,c)),N){let M=e.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));D.push(M),b.push(M)}return D};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=DI({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else if(g.strideWidth<=2&&h==="channelsLast"&&L().getBool("WEBGL_EXP_CONV")){let D=f?Ml(f,!0):null,F=new Md(g,w,D,I,N),M=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],V=E();x=e.runWebGLProgram(F,V,"float32",M)}else if(L().getBool("WEBGL_CONV_IM2COL"))x=$I({x:o,filter:s,convInfo:g,backend:e,bias:i,activation:f,preluActivationWeights:a,leakyreluAlpha:d});else{let D=f?Ml(f,!1):null,F=new Od(g,w,D,I,N),M=E();x=e.runWebGLProgram(F,M,"float32")}let A=rt({inputs:{x},backend:e,attrs:{shape:g.outShape}});return b.push(x),b.forEach(D=>e.disposeIntermediateTensorInfo(D)),A}var FB={kernelName:Yi,backendName:"webgl",kernelFunc:Rit};function Fit(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),y.assert(S.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);let g=S.computeConv2DInfo(o.shape,s.shape,u,h,l,p,!0),x=L().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,b=m?Ml(m,x):null,w=[o,s],I=i!=null,N=a!=null,E=m==="leakyrelu";if(I&&w.push(i),N&&w.push(a),E){let M=e.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));w.push(M),d.push(M)}let A;x?A=new Ld(g,I,b,N,E):A=new Pd(g,I,b,N,E);let D=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],F=e.runWebGLProgram(A,w,"float32",D);return d.forEach(M=>e.disposeIntermediateTensorInfo(M)),F}var OB={kernelName:Zi,backendName:"webgl",kernelFunc:Fit};var ZI=class{constructor(t,e,n,o){this.sliceDim=t,this.strides=e,this.paramsShape=o,this.variableNames=["x","indices"],this.outputShape=n;let s=zt(n.length),i=`
    int index;`;for(let a=0;a<this.sliceDim;a++)i+=`
          index = round(getIndices(coords[0], ${a}));
          out_of_bounds = out_of_bounds || index < 0;
          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[a]};
          flattenIndex += index * ${this.strides[a]};`;this.userCode=`
         void main() {
          ${s} coords = getOutputCoords();
          int flattenIndex = 0;
          bool out_of_bounds = false;

          ${i}

          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
        }
      `}};function Oit(r){let{inputs:t,backend:e}=r,{params:n,indices:o}=t,s=o.shape,i=s[s.length-1],a=y.sizeFromShape(n.shape),[u,l,c,p]=S.prepareAndValidate(n,o),m=rt({inputs:{x:o},backend:e,attrs:{shape:[l,i]}}),f=rt({inputs:{x:n},backend:e,attrs:{shape:[y.sizeFromShape(n.shape)/c,c]}});if(e.shouldExecuteOnCPU([n,o])||n.dtype==="string"){let x=e.readSync(o.dataId),b=e.bufferSync(n),w=rz(x,b,n.dtype,l,i,c,p,n.shape,a);return e.makeTensorInfo(u,n.dtype,w.values)}let d=new ZI(i,p,[l,c],n.shape),h=e.runWebGLProgram(d,[f,m],f.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:u}});return e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(h),g}var MB={kernelName:Va,backendName:"webgl",kernelFunc:Oit};var JI=class{constructor(t,e){this.variableNames=["A","indices"],this.outputShape=e,this.rank=e.length;let n=zt(this.rank),o=Mit(t,2);this.userCode=`
      void main() {
        ${n} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${t[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${o}));
      }
    `}};function Mit(r,t){let e=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("index"):n.push(`${e[o]}`);return n.join()}function V1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,indices:s}=t,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0];if(L().get("DEBUG")){let b=e.readSync(s.dataId),w=o.shape[u];for(let I=0;I<b.length;++I){let N=b[I];y.assert(N<=w-1&&N>=0,()=>`GatherV2: the index value ${N} is not in [0, ${w-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),c=y.sizeFromShape(s.shape),p=[],m=rt({inputs:{x:o},backend:e,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),f=rt({inputs:{x:s},backend:e,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(m),p.push(f);let d=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(e.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=e.bufferSync(f),w=e.bufferSync(m),I=nz(w,b,d);return p.forEach(N=>e.disposeIntermediateTensorInfo(N)),e.makeTensorInfo(l.outputShape,I.dtype,I.values)}let h=new JI(m.shape,d),g=e.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=rt({inputs:{x:g},backend:e,attrs:{shape:l.outputShape}});return p.forEach(b=>e.disposeIntermediateTensorInfo(b)),x}var PB={kernelName:Pi,backendName:"webgl",kernelFunc:V1};var Pit="return float(a > b);",Lit=`
  return vec4(greaterThan(a, b));
`,zit=ce({opSnippet:Pit,packedOpSnippet:Lit,cpuKernelImpl:oz,dtype:"bool"}),LB={kernelName:Ga,backendName:"webgl",kernelFunc:zit};var Bit="return float(a >= b);",Vit=`
  return vec4(greaterThanEqual(a, b));
`,Git=ce({opSnippet:Bit,packedOpSnippet:Vit,dtype:"bool",cpuKernelImpl:sz}),zB={kernelName:hs,backendName:"webgl",kernelFunc:Git};function Wit(r){let{inputs:t,backend:e}=r,{input:n}=t;return qI(n,!0,e)}var BB={kernelName:Mp,backendName:"webgl",kernelFunc:Wit};var Uit="return float(!isnan(x) && !isinf(x));",Hit=It({opSnippet:Uit,dtype:"bool"}),VB={kernelName:gs,backendName:"webgl",kernelFunc:Hit};var qit="return float(isinf(x));",Kit=It({opSnippet:qit,dtype:"bool"}),GB={kernelName:xs,backendName:"webgl",kernelFunc:Kit};var jit="return float(isnan(x));",Xit=It({opSnippet:jit,dtype:"bool"}),WB={kernelName:ys,backendName:"webgl",kernelFunc:Xit};var Yit="return float(a < b);",Zit=`
  return vec4(lessThan(a, b));
`,Jit=ce({opSnippet:Yit,packedOpSnippet:Zit,cpuKernelImpl:iz,dtype:"bool"}),UB={kernelName:Wa,backendName:"webgl",kernelFunc:Jit};var Qit="return float(a <= b);",tat=`
  return vec4(lessThanEqual(a, b));
`,eat=ce({opSnippet:Qit,packedOpSnippet:tat,cpuKernelImpl:az,dtype:"bool"}),HB={kernelName:Ua,backendName:"webgl",kernelFunc:eat};function rat(r){let{backend:t,attrs:e}=r,{start:n,stop:o,num:s}=e,i=lz(n,o,s);return t.makeTensorInfo([i.length],"float32",i)}var qB={kernelName:Ha,backendName:"webgl",kernelFunc:rat};var nat=Po+`
  return x < 0.0 ? 0./0. : log(x);
`,oat=`
  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;
`,sat=It({opSnippet:nat,packedOpSnippet:oat,cpuKernelImpl:uz}),KB={kernelName:ws,backendName:"webgl",kernelFunc:sat};var iat=Po+`
  return log(1.0 + x);
`,aat=It({opSnippet:iat}),jB={kernelName:Is,backendName:"webgl",kernelFunc:aat};var lat="return float(a >= 1.0 && b >= 1.0);",uat=`
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`,cat=ce({opSnippet:lat,packedOpSnippet:uat,dtype:"bool"}),XB={kernelName:qa,backendName:"webgl",kernelFunc:cat};var pat="return float(!(x >= 1.0));",mat=It({opSnippet:pat}),YB={kernelName:Ka,backendName:"webgl",kernelFunc:mat};var fat="return float(a >= 1.0 || b >= 1.0);",dat=`
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`,hat=ce({opSnippet:fat,packedOpSnippet:dat,dtype:"bool"}),ZB={kernelName:ja,backendName:"webgl",kernelFunc:hat};var QI=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[];let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * float(-${s}));`,this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${i}; j <= ${i}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${a}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${u};
        setOutput(val);
      }
    `}};var tC=class{constructor(t,e,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let i=e,a=t[3]-1;this.outputShape=t;let u,l=`float(${n}) + float(${o}) * sum`;s===.5?u=`inversesqrt(${l})`:s===1?u=`1.0/(${l})`:u=`exp(log(${l}) * 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 - ${i};
        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 = - ${i}; j <= ${i}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${a}));

          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 * ${u};
        setOutput(result);
      }
    `}};var gat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n,l=L().getBool("WEBGL_PACK_NORMALIZATION")?new tC(o.shape,s,i,a,u):new QI(o.shape,s,i,a,u);return e.runWebGLProgram(l,[o],o.dtype)},JB={kernelName:Cs,backendName:"webgl",kernelFunc:gat};var eC=class{constructor(t,e,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=o,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 - ${e})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${e} + 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(${o}) * norm + float(${n});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${o})
                * float(${s})
                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${s});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `}};var xat=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n,p=new eC(o.shape,a,u,l,c);return e.runWebGLProgram(p,[o,s,i],o.dtype)},QB={kernelName:Xa,backendName:"webgl",kernelFunc:xat};function tV(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Yn(a,r.dtype,"max",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}function G1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reductionIndices:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=e.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=e.texData.get(f.dataId).values,I=new Array(a);for(let A=0;A<I.length;A++)I[A]=o.shape[c[A]];let N=mp(w,o.shape,o.dtype,c,I);f=e.makeTensorInfo(I,o.dtype);let E=e.texData.get(f.dataId);E.values=N}else f=Hu(o,c,e);l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("max",l,a);let[d,h]=S.computeOutAndReduceShapes(f.shape,l),g=d;i&&(g=S.expandShapeToKeepDim(d,u));let x;if(m){let w=e.texData.get(f.dataId).values,I=cz(w,y.sizeFromShape(h),g,o.dtype);x=e.makeTensorInfo(g,o.dtype);let N=e.texData.get(x.dataId);N.values=I}else x=tV(f,h,g,e);return p&&e.disposeIntermediateTensorInfo(f),x}var eV={kernelName:vs,backendName:"webgl",kernelFunc:G1};var yat=$d+`
  return max(a, b);
`,bat=`
  vec4 result = vec4(max(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);
  `+Xn+`
  return result;
`,wat=ce({opSnippet:yat,packedOpSnippet:bat,cpuKernelImpl:pz}),rV={kernelName:Ss,backendName:"webgl",kernelFunc:wat};function Iat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;vi(o,"maxPool");let{filterSize:s,strides:i,pad:a,dimRoundingMode:u}=n,l=1;y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,l,a,u);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return nr({inputs:{x:o},backend:e});let p=new Ni(c,"max",!1);return e.runWebGLProgram(p,[o],o.dtype)}var nV={kernelName:Ns,backendName:"webgl",kernelFunc:Iat};function Cat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dataFormat:u,dimRoundingMode:l}=n,c=[1,1,1],p=S.computePool3DInfo(o.shape,s,i,c,a,l,u),m=new qu(p,"max",!1);return e.runWebGLProgram(m,[o],o.dtype)}var oV={kernelName:Li,backendName:"webgl",kernelFunc:Cat};var rC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideHeight,n=t.strideWidth,o=t.dilationHeight,s=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=s-1-t.padInfo.top,u=i-1-t.padInfo.left,l=s*i-1;this.userCode=`
      const ivec2 pads = ivec2(${a}, ${u});

      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 += ${o}) {
          float dyR = float(dyRCorner + wR) / ${e}.0;

          if (dyR < 0.0 || dyR >= ${t.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${i}; wC++) {
            float dyC = float(dyCCorner + wC) / ${n}.0;

            if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));

            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${i} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `}},nC=class{constructor(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;let e=t.strideDepth,n=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,i=t.dilationHeight,a=t.dilationWidth,u=t.effectiveFilterDepth,l=t.effectiveFilterHeight,c=t.effectiveFilterWidth,p=u-1-t.padInfo.front,m=l-1-t.padInfo.top,f=c-1-t.padInfo.left,d=u*l*c-1;this.userCode=`
      const ivec3 pads = ivec3(${p}, ${m}, ${f});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${u};
           wD += ${s}) {
          float dyD = float(dyDCorner + wD) / ${e}.0;

          if (dyD < 0.0 || dyD >= ${t.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${l};
              wR += ${i}) {
            float dyR = float(dyRCorner + wR) / ${n}.0;

            if (dyR < 0.0 || dyR >= ${t.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${c};
                wC += ${a}) {
              float dyC = float(dyCCorner + wC) / ${o}.0;

              if (dyC < 0.0 || dyC >= ${t.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${d} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${l} * ${c} +
                  wR * ${c} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `}};function vat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,i=s,{filterSize:a,strides:u,pad:l,dimRoundingMode:c}=n,p=[1,1,1],m=S.computePool3DInfo(i.shape,a,u,p,l,c),f=new qu(m,"max",!0),d=e.runWebGLProgram(f,[i],i.dtype),h=new nC(m),g=e.runWebGLProgram(h,[o,d],i.dtype);return e.disposeIntermediateTensorInfo(d),g}var sV={kernelName:tu,backendName:"webgl",kernelFunc:vat};function Sat(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s,output:i}=t,a=s;vi([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=n,m=S.computePool2DInfo(a.shape,u,l,1,c,p),f=!0,d=new Ni(m,"max",f),h=e.runWebGLProgram(d,[a],a.dtype),g=new rC(m),x=e.runWebGLProgram(g,[o,h],a.dtype);return e.disposeIntermediateTensorInfo(h),x}var iV={kernelName:Ql,backendName:"webgl",kernelFunc:Sat};function aV(r,t,e,n){let o=new Ni(e,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new Ni(e,"max",!0,!0,t);let i=n.runWebGLProgram(o,[r],"float32");return[s,i]}var lV={kernelName:eu,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{filterSize:o,strides:s,pad:i,includeBatchInIndex:a}=t,u=e;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let c=S.computePool2DInfo(n.shape,o,s,l,i),[p,m]=aV(n,a,c,u);return[p,m]}};function uV(r,t,e,n){let o=y.sizeFromShape(t),i=y.sizeFromShape(r.shape)/o,a=rt({inputs:{x:r},attrs:{shape:[i,o]},backend:n}),u=Yn(a,"float32","mean",n),l=rt({inputs:{x:u},attrs:{shape:e},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(u),l}var cV={kernelName:ks,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{x:n}=r,{keepDims:o,axis:s}=t,i=e,a=n.shape.length,u=y.parseAxisParam(s,n.shape),l=u,c=S.getAxesPermutation(l,a),p=c!=null,m=i.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let I=i.texData.get(d.dataId).values,N=new Array(a);for(let D=0;D<N.length;D++)N[D]=n.shape[c[D]];let E=mp(I,n.shape,n.dtype,c,N);d=i.makeTensorInfo(N,n.dtype);let A=i.texData.get(d.dataId);A.values=E}else d=Hu(n,c,i);f.push(d),l=S.getInnerMostAxes(l.length,a)}S.assertAxesAreInnerMostDims("sum",l,a);let[h,g]=S.computeOutAndReduceShapes(d.shape,l),x=h;o&&(x=S.expandShapeToKeepDim(h,u));let b=uV(d,g,x,i);for(let w of f)i.disposeIntermediateTensorInfo(w);return b}};function Nat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=y.parseAxisParam(s,o.shape),l=u,c=S.getAxesPermutation(l,a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o.shape.length)),S.assertAxesAreInnerMostDims("min",l,a);let[m,f]=S.computeOutAndReduceShapes(p.shape,l),d=y.sizeFromShape(f),h=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,d]}}),g=Yn(h,h.dtype,"min",e),x;if(i){let b=S.expandShapeToKeepDim(m,u);x=rt({inputs:{x:g},backend:e,attrs:{shape:b}})}else x=rt({inputs:{x:g},backend:e,attrs:{shape:m}});return e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(g),c!=null&&e.disposeIntermediateTensorInfo(p),x}var pV={kernelName:Ts,backendName:"webgl",kernelFunc:Nat};var kat=$d+`
  return min(a, b);
`,Tat=`
  vec4 result = vec4(min(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);
  `+Xn+`
  return result;
`,_at=ce({opSnippet:kat,packedOpSnippet:Tat,cpuKernelImpl:mz}),mV={kernelName:_s,backendName:"webgl",kernelFunc:_at};var oC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=e.map((c,p)=>c[0]+t[p]+c[1]);let o=t.length,s=zt(o),i=e.map(c=>c[0]).join(","),a=e.map((c,p)=>c[0]+t[p]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),l=n==="reflect"?0:1;if(o===1){this.userCode=`
        int start = ${i};
        int end = ${a};

        void main() {
          int outC = getOutputCoords();
          if (outC < start) {
            outC = start * 2 - outC - ${l};
          } else if(outC >= end) {
            outC = (end - 1) * 2 - outC + ${l};
          }
          setOutput(getX(outC - start));
        }
      `;return}this.userCode=`
      ${s} start = ${s}(${i});
      ${s} end = ${s}(${a});

      void main() {
        ${s} outC = getOutputCoords();
        for (int i = 0; i < ${o}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${l};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
          }
        }
        ${s} coords = outC - start;
        setOutput(getX(${u}));
      }
    `}};var sC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.map((d,h)=>d[0]+t[h]+d[1]);let o=t.length,s=zt(o),i=e.map(d=>d[0]).join(","),a=e.map((d,h)=>d[0]+t[h]).join(","),u=rr("rc",o),l=rr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=`
        ${s} source = rc;
        if (source < start) {
          source = start * 2 - source - ${m};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${m};
        }
        source -= start;
      `;f=`
        ${s} rc = outputLoc;
        ${d}
        result[0] = getChannel(getX(${l.join()}), ${p});
        ${u[o-1]} += 1;
        if(${c}) {
          ${d}
          result[1] = getChannel(getX(${l.join()}), ${p});
        }
      `}else{let d=`
        ${s} source = rc;
        ${s} lt = ${s}(lessThan(source, start));
        ${s} gte = ${s}(greaterThanEqual(source, end));
        ${s} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${m}) +
                gte * ((end - 1) * 2 - source + ${m});
        source -= start;
      `;f=`
        ${s} rc = outputLoc;
        ${d}
        result[0] = getChannel(getX(${l.join()}), ${p});
        ${u[o-1]} += 1;
        if(${c}) {
          ${d}
          result[1] = getChannel(getX(${l.join()}), ${p});
        }
        rc = outputLoc;
        ${u[o-2]} += 1;
        if(${u[o-2]} < ${this.outputShape[o-2]}) {
          ${d}
          result[2] = getChannel(getX(${l.join()}), ${p});
          ${u[o-1]} += 1;
          if(${c}) {
            ${d}
            result[3] = getChannel(getX(${l.join()}), ${p});
          }
        }
      `}this.userCode=`
      const ${s} start = ${s}(${i});
      const ${s} end = ${s}(${a});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${f}
        setOutput(result);
      }
    `}};var Eat=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{paddings:o,mode:s}=e,i=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new sC(n.shape,o,s):new oC(n.shape,o,s);return t.runWebGLProgram(i,[n],n.dtype)},fV={kernelName:Es,backendName:"webgl",kernelFunc:Eat};var Aat=`if (b == 0.0) return NAN;
  return mod(a, b);`,Dat=`
  vec4 result = mod(a, b);
  bvec4 isNaN = equal(b, vec4(0.0));
  `+Xn+`
  return result;
`,$at=ce({opSnippet:Aat,packedOpSnippet:Dat}),dV={kernelName:As,backendName:"webgl",kernelFunc:$at};var iC=class{constructor(t,e,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[t,n],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

        float r = random(seed);
        float cdf = 0.0;

        for (int i = 0; i < ${e-1}; i++) {
          cdf += getProbs(batch, i);

          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutput(float(${e-1}));
      }
    `}};var Rat=`
if (a == b) {
  return 1.0;
};
return a / b;`,Fat=`
  // 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;
`,W1=ce({opSnippet:Rat,packedOpSnippet:Fat,checkOutOfBounds:!0}),hV={kernelName:as,backendName:"webgl",kernelFunc:W1};var gV="return a - b;",U1=ce({opSnippet:gV,packedOpSnippet:gV,supportsComplex:!0,cpuKernelImpl:Rz}),xV={kernelName:ei,backendName:"webgl",kernelFunc:U1};function H1(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{dim:s}=n,i=y.parseAxisParam([s],o.shape),a=G1({inputs:{x:o},backend:e,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(a.shape,i),l=rt({inputs:{x:a},backend:e,attrs:{shape:u}}),c=U1({inputs:{a:o,b:l},backend:e}),p=z1({inputs:{x:c},backend:e}),m=fp({inputs:{x:p},backend:e,attrs:{axis:i,keepDims:!1}}),f=rt({inputs:{x:m},backend:e,attrs:{shape:u}}),d=W1({inputs:{a:p,b:f},backend:e});return e.disposeIntermediateTensorInfo(a),e.disposeIntermediateTensorInfo(l),e.disposeIntermediateTensorInfo(c),e.disposeIntermediateTensorInfo(p),e.disposeIntermediateTensorInfo(m),e.disposeIntermediateTensorInfo(f),d}var yV={kernelName:Qs,backendName:"webgl",kernelFunc:H1};function Oat(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n,u=a?o:H1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new iC(l,c,s),m=[[i]],f=e.runWebGLProgram(p,[u],"int32",m);return a||e.disposeIntermediateTensorInfo(u),f}var bV={kernelName:Ya,backendName:"webgl",kernelFunc:Oat};var Mat=xr+`
  return -x;
`,Pat=`
  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 Lat(r){let{inputs:t,backend:e}=r,{x:n}=t;if(e.shouldExecuteOnCPU([n])){let s=e.texData.get(n.dataId),[i,a]=dz(s.values,n.shape,n.dtype);return e.makeTensorInfo(a,n.dtype,i)}let o;return L().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Dn(n.shape,Pat):o=new zr(n.shape,Mat),e.runWebGLProgram(o,[n],n.dtype)}var wV={kernelName:zi,backendName:"webgl",kernelFunc:Lat};var zat=Xr.nonMaxSuppressionV3Impl;function Bat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),{selectedIndices:p}=zat(l,c,i,a,u);return e.makeTensorInfo([p.length],"int32",new Int32Array(p))}var IV={kernelName:Ja,backendName:"webgl",kernelFunc:Bat};var Vat=Xr.nonMaxSuppressionV4Impl;function Gat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,padToMaxOutputSize:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=Vat(c,p,i,a,u,l);return[e.makeTensorInfo([m.length],"int32",new Int32Array(m)),e.makeTensorInfo([],"int32",new Int32Array([f]))]}var CV={kernelName:Qa,backendName:"webgl",kernelFunc:Gat};var Wat=Xr.nonMaxSuppressionV5Impl;function Uat(r){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:e,attrs:n}=r,{boxes:o,scores:s}=t,{maxOutputSize:i,iouThreshold:a,scoreThreshold:u,softNmsSigma:l}=n,c=e.readSync(o.dataId),p=e.readSync(s.dataId),m=i,f=a,d=u,h=l,{selectedIndices:g,selectedScores:x}=Wat(c,p,m,f,d,h);return[e.makeTensorInfo([g.length],"int32",new Int32Array(g)),e.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var vV={kernelName:tl,backendName:"webgl",kernelFunc:Uat};var aC=class{constructor(t,e,n,o){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode=`
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${o}), float(${n}),
                      float(index == coords.y)));
      }
    `}};var Hat=r=>{let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=y.sizeFromShape(o.shape),c=new aC(l,i,a,u),p=rt({inputs:{x:o},backend:e,attrs:{shape:[l]}}),m=e.runWebGLProgram(c,[p],s);e.disposeIntermediateTensorInfo(p);let f=[...o.shape,i],d=rt({inputs:{x:m},backend:e,attrs:{shape:f}});return e.disposeIntermediateTensorInfo(m),d},SV={kernelName:$s,backendName:"webgl",kernelFunc:Hat};function gg(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="complex64"){let o=Pl({inputs:{input:n},backend:e}),s=gg({inputs:{x:o},backend:e}),i=hp({inputs:{input:n},backend:e}),a=gg({inputs:{x:i},backend:e}),u=Rn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Ll({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:e})}var NV={kernelName:ji,backendName:"webgl",kernelFunc:gg};function kV(r){let{inputs:t,backend:e}=r,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Pl({inputs:{input:n},backend:e}),s=kV({inputs:{x:o},backend:e}),i=hp({inputs:{input:n},backend:e}),a=gg({inputs:{x:i},backend:e}),u=Rn({inputs:{real:s,imag:a},backend:e});return e.disposeIntermediateTensorInfo(o),e.disposeIntermediateTensorInfo(s),e.disposeIntermediateTensorInfo(i),e.disposeIntermediateTensorInfo(a),u}else return Ll({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:e})}var TV={kernelName:Bi,backendName:"webgl",kernelFunc:kV};function qat(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return HI({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=HI({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=L1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeIntermediateTensorInfo(c)),l}var _V={kernelName:Vi,backendName:"webgl",kernelFunc:qat};var lC=class{constructor(t,e,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((l,c)=>l[0]+t[c]+l[1]);let o=t.length,s=zt(o),i=e.map(l=>l[0]).join(","),a=e.map((l,c)=>l[0]+t[c]).join(","),u=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
        int start = ${i};
        int end = ${a};

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;return}this.userCode=`
      ${s} start = ${s}(${i});
      ${s} end = ${s}(${a});

      void main() {
        ${s} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${s} coords = outC - start;
          setOutput(getX(${u}));
        }
      }
    `}};var uC=class{constructor(t,e,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=e.map((h,g)=>h[0]+t[g]+h[1]);let o=t.length,s=zt(o),i=e.map(h=>h[0]).join(","),a=e.map((h,g)=>h[0]+t[g]).join(","),u=rr("rc",o),l=rr("source",o),c=`${u[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${l.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${u[o-1]} += 1;
       if(${c}) {
      `,o===1?"":`}
       rc = outputLoc;
       ${u[o-2]} += 1;
       if(${u[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":`  ${u[o-1]} += 1;
         if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h<g;h++)d+=`
        ${m[h]}
        if (${f}) {
          result[${h}] = float(value);
        } else {
          ${s} source = rc - start;
          result[${h}] = getChannel(getX(${l.join()}), ${p});
        }
      `;d+=o===1?"} ":"}}",this.userCode=`
      const ${s} start = ${s}(${i});
      const ${s} end = ${s}(${a});

      void main() {
        ${s} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${d}
        setOutput(result);
      }
    `}};var q1=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{paddings:s,constantValue:i}=n;if(y.sizeFromShape(o.shape)===0){let l=s.map((c,p)=>c[0]+o.shape[p]+c[1]);return Ll({backend:e,attrs:{shape:l,value:i,dtype:o.dtype}})}let a=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new uC(o.shape,s,i):new lC(o.shape,s,i),u=[[i]];return e.runWebGLProgram(a,[o],o.dtype,u)},EV={kernelName:Rs,backendName:"webgl",kernelFunc:q1};var Kat=`
  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);
`,jat=`
  // 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);
  `+Xn+`
  return result;
`,Xat=ce({opSnippet:Kat,packedOpSnippet:jat}),AV={kernelName:Fs,backendName:"webgl",kernelFunc:Xat};function Yat(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,keepDims:i}=n,a=o.shape.length,u=[],l=y.parseAxisParam(s,o.shape),c=l,p=S.getAxesPermutation(c,a),m=o;p!=null&&(m=Pe({inputs:{x:o},backend:e,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,a),u.push(m)),S.assertAxesAreInnerMostDims("prod",c,a);let f;if(e.shouldExecuteOnCPU([m])){let d=e.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=gz(m.shape,m.dtype,d,c);f=e.makeTensorInfo(g,x,h)}else{let[d,h]=S.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=rt({inputs:{x:m},backend:e,attrs:{shape:[-1,g]}}),b=lc(o.dtype),w=Yn(x,b,"prod",e);f=rt({inputs:{x:w},backend:e,attrs:{shape:d}}),u.push(x),u.push(w)}if(i){u.push(f);let d=S.expandShapeToKeepDim(f.shape,l);f=rt({inputs:{x:f},backend:e,attrs:{shape:d}})}return u.forEach(d=>e.disposeIntermediateTensorInfo(d)),f}var DV={kernelName:Ms,backendName:"webgl",kernelFunc:Yat};function Zat(r){let{inputs:t,backend:e,attrs:n}=r,{paramsNestedSplits:o,paramsDenseValues:s,indices:i}=t,{outputRaggedRank:a}=n,u=o.map(x=>e.readSync(x.dataId)),l=o.map(x=>x.shape),c=e.readSync(s.dataId),p=e.readSync(i.dataId),[m,f,d]=xz(u,l,c,s.shape,s.dtype,p,i.shape,a),h=m.map(x=>e.makeTensorInfo([x.length],"int32",x)),g=e.makeTensorInfo(d,s.dtype,f);return h.concat([g])}var $V={kernelName:Lp,backendName:"webgl",kernelFunc:Zat};function Jat(r){let{inputs:t,backend:e}=r,{starts:n,limits:o,deltas:s}=t,i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=yz(i,n.shape,n.dtype,a,o.shape,u,s.shape),p=e.makeTensorInfo([l.length],"int32",l),m=e.makeTensorInfo([c.length],n.dtype,c);return[p,m]}var RV={kernelName:zp,backendName:"webgl",kernelFunc:Jat};function Qat(r){let{inputs:t,backend:e,attrs:n}=r,{shape:o,values:s,defaultValue:i,rowPartitionTensors:a}=t,{rowPartitionTypes:u}=n,l=e.readSync(o.dataId),c=e.readSync(s.dataId),p=e.readSync(i.dataId),m=a.map(g=>e.readSync(g.dataId)),f=a.map(g=>g.shape),[d,h]=bz(l,o.shape,c,s.shape,s.dtype,p,i.shape,m,f,u);return e.makeTensorInfo(d,s.dtype,h)}var FV={kernelName:Bp,backendName:"webgl",kernelFunc:Qat};var K1=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=wz(n,o,s,i);return t.makeTensorInfo([a.length],i,a)},OV={kernelName:ru,backendName:"webgl",kernelFunc:K1};var tlt="return 1.0 / x;",elt=It({opSnippet:tlt}),MV={kernelName:Ps,backendName:"webgl",kernelFunc:elt};var rlt=xr+`
  return (x < 0.0) ? 0.0 : x;
`,nlt=`
  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;
`,olt=It({opSnippet:rlt,packedOpSnippet:nlt}),PV={kernelName:Ls,backendName:"webgl",kernelFunc:olt};var slt=xr+`
  return (x < 0.0) ? 0.0 : min(6.0, x);
`,ilt=`
  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;
`,alt=It({opSnippet:slt,packedOpSnippet:ilt}),LV={kernelName:Vs,backendName:"webgl",kernelFunc:alt};var cC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0]/p[0]},
          ${c[1]/p[1]});
      const vec2 inputShapeRC = vec2(${a}.0, ${u}.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 pC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${c[0]/p[0]},
          ${c[1]/p[1]},
          ${c[1]/p[1]});
      const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
                                     ${u}.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 < ${l-1};
        bool hasNextRow = coords.z < ${n-1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `}};function llt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new pC(o.shape,u,l,s,i):new cC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],"float32")}var zV={kernelName:Bs,backendName:"webgl",kernelFunc:llt};var mC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${c});
        const float widthScale = float(${p});

        const float invHeightScale = float(${m});
        const float invWidthScale = float(${f});

        const int winHeight = int(${d});
        const int winWidth = int(${h});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${i}) {
            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 >= ${a}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${o-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 ult(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new mC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var BV={kernelName:rl,backendName:"webgl",kernelFunc:ult};var fC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${c[0]/p[0]},
          ${c[1]/p[1]});
      const vec2 inputShapeRC = vec2(${a}.0, ${u}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${f};

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `}};var dC=class{constructor(t,e,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[i,a,u,l]=t;this.outputShape=[i,e,n,l];let c=[o&&e>1?a-1:a,o&&n>1?u-1:u],p=[o&&e>1?e-1:e,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":f="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${c[0]/p[0]},
          ${c[1]/p[1]},
          ${c[1]/p[1]});
      const vec3 inputShapeRC = vec3(${a}.0, ${u}.0,
                                     ${u}.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 = ${f};

        // 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 < ${l-1};
        bool hasNextRow = coords.z < ${n-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 clt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,c=L().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new dC(o.shape,u,l,s,i):new fC(o.shape,u,l,s,i);return e.runWebGLProgram(c,[o],o.dtype)}var VV={kernelName:zs,backendName:"webgl",kernelFunc:clt};var hC=class{constructor(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e;let[,o,s]=e,[,i,a]=t,u=[n&&i>1?o-1:o,n&&a>1?s-1:s],l=[n&&i>1?i-1:i,n&&a>1?a-1:a],c=u[0]/l[0],p=u[1]/l[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${c});
        const float widthScale = float(${p});

        const float invHeightScale = float(${m});
        const float invWidthScale = float(${f});

        const int winHeight = int(${d});
        const int winWidth = int(${h});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${i}) {
            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 >= ${a}) {
              continue;
            }

            float sourceFracRow =
              float(${u[0]}) *
                (float(dyR) / float(${l[0]}));

            float sourceFracCol =
                float(${u[1]}) *
                  (float(dyC) / float(${l[1]}));

            int sourceNearestRow = int(min(
                float(int(${o}) - 1),
                ${n} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${s}) - 1),
                ${n} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));

            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `}};function plt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=new hC(s.shape,o.shape,i);return e.runWebGLProgram(a,[s],s.dtype)}var GV={kernelName:el,backendName:"webgl",kernelFunc:plt};var gC=class{constructor(t,e){this.variableNames=["x"];let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=t,n===1){this.userCode=`
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${t[0]} - coord - 1));
        }
      `;return}let o=a=>e.indexOf(a)!==-1&&t[a]!==1?`${t[a]} - coords[${a}] - 1`:`coords[${a}]`,s=t.map((a,u)=>o(u)).join(","),i=zt(n);this.userCode=`
      void main() {
        ${i} coords = getOutputCoords();
        setOutput(getX(${s}));
      }
    `}};var xC=class{constructor(t,e){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=t.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=t;let o=rr("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,i=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,a=zt(n);n===1?this.userCode=`
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${t[0]} - rc - 1),
            ${t[0]} - rc - 1);
          if(${s}){
              result.g = getChannel(getX(${t[0]} - (rc  + 1) - 1),
                ${t[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `:this.userCode=`
        void main() {
          ${a} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${u(o.slice())};
          if(${s}){
            result.g = ${l(o.slice())};
          }
          if(${i}) {
            result.b = ${c(o.slice())};
            if(${s}) {
              result.a = ${p(o.slice())};
            }
          }
          setOutput(result);
        }
    `;function u(d){return m(d)}function l(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=t.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return e.indexOf(d)!==-1&&t[d]!==1?`${t[d]} - ${h[d]} - 1`:`${h[d]}`}}};function mlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=o.shape.length,a=y.parseAxisParam(s,o.shape);if(i===0)return nr({inputs:{x:o},backend:e});let u=L().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xC(o.shape,a):new gC(o.shape,a);return e.runWebGLProgram(u,[o],o.dtype)}var WV={kernelName:Gs,backendName:"webgl",kernelFunc:mlt};var yC=class{constructor(t,e){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=t[1],o=t[2];this.outputShape=t;let s="";typeof e=="number"?s=`float outputValue = ${e.toFixed(2)};`:s=`
        vec3 fill = vec3(${e.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 < ${o} && coordY >= 0 && coordY < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `}};var UV={kernelName:pl,backendName:"webgl",kernelFunc:({inputs:r,attrs:t,backend:e})=>{let{image:n}=r,{radians:o,fillValue:s,center:i}=t,a=e,u=new yC(n.shape,s),[l,c]=S.getImageCenter(i,n.shape[1],n.shape[2]),p=[[l,c,Math.sin(o),Math.cos(o)]];return a.runWebGLProgram(u,[n],n.dtype,p)}};var flt=`
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`,dlt=It({opSnippet:flt}),HV={kernelName:Ws,backendName:"webgl",kernelFunc:dlt};var hlt="return inversesqrt(x);",glt=It({opSnippet:hlt,cpuKernelImpl:Iz}),qV={kernelName:Us,backendName:"webgl",kernelFunc:glt};var Ku=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides";this.userCode=`
        ${l} strides = ${l}(${s});

        void main() {
          ${c} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${t}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${e}; j++) {
              int index = round(${m});
              flattenedIndex += index * ${x};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${d};
              found = true;
            }
          }
          setOutput(mix(${g}, sum, float(found)));
        }
      `}};var bC=class{constructor(t,e,n,o,s,i,a=!0,u=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=i;let l=zt(s.length),c=zt(i.length),p="";n===1?p="i":n===2&&(p="i, j");let m=`getIndices(${p})`,f="";o===1?f="i":o===2&&(f="i, coords[1]");let d=`getUpdates(${f})`,h="";u&&(h="coords[0], coords[1]");let g=`getDefaultValue(${h})`,x=e>1?"strides[j]":"strides",b=e>1?"strides[j + 1]":"strides";this.userCode=`
        ${l} strides = ${l}(${s});

        void main() {
          ${c} coords = getOutputCoords();
          vec4 sum = vec4(0.);
          vec4 found = vec4(0.);
          for (int i = 0; i < ${t}; i+=2) {
            ivec2 flattenedIndex = ivec2(0);
            for (int j = 0; j < ${e}; j+=2) {
              ivec4 index = round(${m});
              flattenedIndex += index.xz * ${x};
              if (j + 1 < ${e}) {
                flattenedIndex += index.yw * ${b};
              }
            }
            if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||
                flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {
              vec4 updVals = ${d};
              if (flattenedIndex[0] == coords[0]) {
                sum.xy += updVals.xy;
                found.xy = vec2(1.);
              } else if (flattenedIndex[0] == coords[0] + 1) {
                sum.zw += updVals.xy;
                found.zw = vec2(1.);
              }
              if (flattenedIndex[1] == coords[0]) {
                sum.xy += updVals.zw;
                found.xy = vec2(1.);
              } else if (flattenedIndex[1] == coords[0] + 1) {
                sum.zw += updVals.zw;
                found.zw = vec2(1.);
              }
            }
          }
          setOutput(mix(${g}, sum, found));
        }
      `}};function xlt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o,updates:s}=t,{shape:i}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(s,o,i),m=[p/l,l];if(p===0)return e.makeTensorInfo(i,o.dtype);let f=rt({inputs:{x:o},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:s},backend:e,attrs:{shape:[u,l]}}),h=e.makeTensorInfo([],"float32",new Float32Array([0])),g;L().getBool("WEBGL_PACK")?g=new bC(u,a,f.shape.length,d.shape.length,c,m):g=new Ku(u,a,f.shape.length,d.shape.length,c,m);let x=e.runWebGLProgram(g,[d,f,h],d.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:i}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(x),e.disposeIntermediateTensorInfo(h),b}var KV={kernelName:nl,backendName:"webgl",kernelFunc:xlt};var wC=class{constructor(t,e,n,o){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[t,n];let s="while (left < right) {",i=`for (int i = 0; i < ${Math.ceil(Math.log2(e+1))}; ++i) { if (left >= right) break;`,a=L().getNumber("WEBGL_VERSION")===2?s:i,u=o==="left"?"<":"<=";this.userCode=`
       int findBound(int batch, float value) {
         int left = 0;
         int right = numInputs;
         int mid;
         ${a}
           mid = (left + right) / 2;
           if (getSortedSequence(batch, mid) ${u} 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 ylt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n,a=new wC(o.shape[0],o.shape[1],s.shape[1],i),u=[[o.shape[1]]];return e.runWebGLProgram(a,[o,s],"int32",u)}var jV={kernelName:sl,backendName:"webgl",kernelFunc:ylt};var IC=class{constructor(t,e,n){this.variableNames=["c","a","b"],this.outputShape=e;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let a=["resRC.x","resRC.y","resRC.z","resRC.w"],u=[],l=[];for(let c=0;c<e.length;c++)l.push(`${a[c]}`),c<t&&u.push(`${a[c]}`);o=u.join(),s=l.join()}let i=zt(n);this.userCode=`
      void main() {
        ${i} resRC = getOutputCoords();
        float cVal = getC(${o});
        if (cVal >= 1.0) {
          setOutput(getA(${s}));
        } else {
          setOutput(getB(${s}));
        }
      }
    `}};function blt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=new IC(n.shape.length,o.shape,o.shape.length);return e.runWebGLProgram(i,[n,o,s],ur(o.dtype,s.dtype))}var XV={kernelName:Wi,backendName:"webgl",kernelFunc:blt};var wlt=`
  // 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);
`,Ilt=It({opSnippet:wlt}),YV={kernelName:Hs,backendName:"webgl",kernelFunc:Ilt};var Clt=Po+`
  return 1.0 / (1.0 + exp(-1.0 * x));
`,vlt=`
  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;
`,Slt=It({opSnippet:Clt,packedOpSnippet:vlt,cpuKernelImpl:vz}),ZV={kernelName:Xs,backendName:"webgl",kernelFunc:Slt};var Nlt=`
  if (isnan(x)) { return 0.0; }
  return sign(x);
`,klt=It({opSnippet:Nlt}),JV={kernelName:js,backendName:"webgl",kernelFunc:klt};var Tlt=Po+`
  return sin(x);
`,_lt=`
  vec4 result = sin(x);
  bvec4 isNaN = isnan(x);
  ${Xn}
  return result;
`,Elt=It({opSnippet:Tlt,packedOpSnippet:_lt}),QV={kernelName:qs,backendName:"webgl",kernelFunc:Elt};var Alt=`
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`,Dlt=It({opSnippet:Alt}),tG={kernelName:Ks,backendName:"webgl",kernelFunc:Dlt};var $lt=`
  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;
`,Rlt=It({opSnippet:$lt}),eG={kernelName:Ys,backendName:"webgl",kernelFunc:Rlt};var Flt=r=>{let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n;y.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let a=s.reduce((x,b)=>x*b),u=[[0,0]];u.push(...i);for(let x=1+s.length;x<o.shape.length;++x)u.push([0,0]);let l=[],c=q1({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,s,a,!1),m=S.getPermuted(p.length,s.length,!1),f=S.getReshapedPermuted(c.shape,s,a,!1),d=rt({inputs:{x:c},backend:e,attrs:{shape:p}}),h=Pe({inputs:{x:d},backend:e,attrs:{perm:m}}),g=rt({inputs:{x:h},backend:e,attrs:{shape:f}});return l.push(c),l.push(d),l.push(h),l.forEach(x=>e.disposeIntermediateTensorInfo(x)),g},rG={kernelName:Hi,backendName:"webgl",kernelFunc:Flt};function Olt(r){let{inputs:t,backend:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
         ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
         ${n.shape}`);if(o.shape.length!==1)throw new Error(`Values must be a vector, saw:
         ${o.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
        ${i.shape}`);let a=e.readSync(n.dataId),u=e.readSync(o.dataId),l=e.readSync(s.dataId),c=e.readSync(i.dataId)[0],[p,m,f,d,h]=Nz(a,n.shape,n.dtype,u,o.dtype,l,c);return[e.makeTensorInfo(m,n.dtype,p),e.makeTensorInfo([m[0]],o.dtype,f),e.makeTensorInfo([d.length],"bool",new Uint8Array(d.map(g=>Number(g)))),e.makeTensorInfo([h.length],n.dtype,new Int32Array(h))]}var nG={kernelName:nu,backendName:"webgl",kernelFunc:Olt};function Mlt(r){let{inputs:t,backend:e}=r,{inputIndices:n,inputShape:o,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(e.readSync(o.dataId)),a=e.readSync(n.dataId),u=Array.from(e.readSync(s.dataId)),[l,c,p]=kz(a,n.shape,n.dtype,i,u);return[e.makeTensorInfo(c,n.dtype,l),e.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var oG={kernelName:il,backendName:"webgl",kernelFunc:Mlt};function Plt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
              ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u,!0);return e.makeTensorInfo(c,n.dtype,l)}var sG={kernelName:ou,backendName:"webgl",kernelFunc:Plt};function Llt(r){let{inputs:t,backend:e}=r,{data:n,indices:o,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indices should be a vector but received shape
             ${o.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
             ${s.shape}`);let i=e.readSync(n.dataId),a=e.readSync(o.dataId),u=e.readSync(s.dataId),[l,c]=rI(i,n.shape,n.dtype,a,u);return e.makeTensorInfo(c,n.dtype,l)}var iG={kernelName:su,backendName:"webgl",kernelFunc:Llt};function zlt(r){let{inputs:t,backend:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=t,{outputShape:a}=n,{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=S.calculateShapes(s,o,a),f=!1;if(s.dtype==="string"){let x=e.bufferSync(o),b=e.bufferSync(s),w=y.decodeString(e.readSync(i.dataId)[0]),I=Cz(x,b,a,m,c,l,u,p,w,f);return e.makeTensorInfo(a,I.dtype,I.values)}let d=new Ku(l,u,o.shape.length,s.shape.length,p,[m,1],f),h=e.runWebGLProgram(d,[s,o,i],s.dtype),g=rt({inputs:{x:h},backend:e,attrs:{shape:a}});return e.disposeIntermediateTensorInfo(h),g}var aG={kernelName:al,backendName:"webgl",kernelFunc:zlt};function Blt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=n,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=o.shape.length,c=new Array(l).fill(0),p=o.shape.slice();return u.map(m=>{let f=[...p];f[a]=m;let d=ki({inputs:{x:o},backend:e,attrs:{begin:c,size:f}});return c[a]+=m,d})}var lG={kernelName:qi,backendName:"webgl",kernelFunc:Blt};var uG="return sqrt(x);",Vlt=It({opSnippet:uG,packedOpSnippet:uG,cpuKernelImpl:Tz}),cG={kernelName:Zs,backendName:"webgl",kernelFunc:Vlt};var Glt="return x * x;",Wlt=It({opSnippet:Glt}),pG={kernelName:iu,backendName:"webgl",kernelFunc:Wlt};var mG="return (a - b) * (a - b);",Ult=ce({opSnippet:mG,packedOpSnippet:mG}),fG={kernelName:ti,backendName:"webgl",kernelFunc:Ult};function Hlt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t;if(o.dtype!=="string")throw new Error("Input must be of datatype string");let s=e.readSync(o.dataId),i=S.fromUint8ToStringArray(s),a=_z(i,"string",n);return e.makeTensorInfo(o.shape,"string",a)}var dG={kernelName:ec,backendName:"webgl",kernelFunc:Hlt};function qlt({inputs:r,attrs:t,backend:e}){let{x:n}=r,o=xr+`
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `,s=new zr(n.shape,o);return e.runWebGLProgram(s,[n],n.dtype)}var hG={kernelName:xo,backendName:"webgl",kernelFunc:qlt};var CC=class{constructor(t,e,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=zt(n.length),i=zt(n.length),a="";if(o===1)a="coords * strides + begin";else{let u=0;a=n.map((l,c)=>(u++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${u-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
      ${s} begin = ${s}(${t});
      ${s} strides = ${s}(${e});

      void main() {
        ${i} coords = getOutputCoords();
        setOutput(getX(${a}));
      }
    `}};function Klt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=Be.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=rt({inputs:{x:o},backend:e,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let A=Be.computeOutShape(b,w,I),D=ki({inputs:{x:o},backend:e,attrs:{begin:b,size:A}});N=rt({inputs:{x:D},backend:e,attrs:{shape:d}}),e.disposeIntermediateTensorInfo(D)}else if(e.shouldExecuteOnCPU([o])){let D=e.readSync(o.dataId),F=wt(o.shape,o.dtype,D),M=Ez(f,F,I,b);N=e.makeTensorInfo(d,o.dtype,M.values)}else{let D=new CC(b,I,f);N=e.runWebGLProgram(D,[o],o.dtype)}let E=rt({inputs:{x:N},backend:e,attrs:{shape:d}});return e.disposeIntermediateTensorInfo(N),E}var gG={kernelName:ll,backendName:"webgl",kernelFunc:Klt};function jlt(r){let{inputs:t,backend:e,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:i,rightPad:a,padWidth:u,preserveShortSequences:l}=n,{data:c,dataSplits:p}=t,m=e.readSync(c.dataId),f=e.readSync(p.dataId),[d,h]=Az(m,f,o,s,i,a,u,l);return[e.makeTensorInfo([d.length],"string",d),e.makeTensorInfo(p.shape,"int32",h)]}var xG={kernelName:au,backendName:"webgl",kernelFunc:jlt};function Xlt(r){let{inputs:t,backend:e,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:i}=t;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(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let a=e.readSync(s.dataId),u=e.readSync(i.dataId)[0],[l,c,p]=Dz(a,u,o),m=c.length;return[e.makeTensorInfo([m,2],"int32",l),e.makeTensorInfo([m],"string",c),e.makeTensorInfo([2],"int32",new Int32Array(p))]}var yG={kernelName:lu,backendName:"webgl",kernelFunc:Xlt};function Ylt(r){let{inputs:t,backend:e,attrs:n}=r,{numBuckets:o}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let i=e.readSync(s.dataId),a=$z(i,o);return e.makeTensorInfo(s.shape,"int32",a)}var bG={kernelName:uu,backendName:"webgl",kernelFunc:Ylt};var Zlt="return tan(x);",Jlt=It({opSnippet:Zlt}),wG={kernelName:ri,backendName:"webgl",kernelFunc:Jlt};var Qlt=`
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`,tut=It({opSnippet:Qlt}),IG={kernelName:ni,backendName:"webgl",kernelFunc:tut};function eut(r){let{inputs:t,backend:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=t,{}=n,{sliceRank:a,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(i,s,o.shape),m=[p/l,l];if(p===0)return e.makeTensorInfo(o.shape,s.dtype);let f=rt({inputs:{x:s},backend:e,attrs:{shape:[u,a]}}),d=rt({inputs:{x:i},backend:e,attrs:{shape:[u,l]}}),h=rt({inputs:{x:o},backend:e,attrs:{shape:m}}),g=new Ku(u,a,f.shape.length,d.shape.length,c,m,!1,!0),x=e.runWebGLProgram(g,[d,f,h],h.dtype),b=rt({inputs:{x},backend:e,attrs:{shape:o.shape}});return e.disposeIntermediateTensorInfo(f),e.disposeIntermediateTensorInfo(d),e.disposeIntermediateTensorInfo(h),e.disposeIntermediateTensorInfo(x),b}var CG={kernelName:ol,backendName:"webgl",kernelFunc:eut};var vC=class{constructor(t,e){this.variableNames=["A"];let n=new Array(t.length);for(let i=0;i<n.length;i++)n[i]=t[i]*e[i];this.outputShape=n,this.rank=n.length;let o=zt(this.rank),s=rut(t);this.userCode=`
      void main() {
        ${o} resRC = getOutputCoords();
        setOutput(getA(${s}));
      }
    `}};function rut(r){let t=r.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${r[0]})`;let e=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${e[o]}, ${r[o]})`);return n.join()}function j1(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{reps:s}=n;if(o.dtype==="string"||o.shape.length>5){let u=e.readSync(o.dataId),l=o.dtype==="string"?u.map(m=>y.decodeString(m)):u,c=wt(o.shape,o.dtype,l),p=Fz(c,s);return e.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new vC(o.shape,s);return e.runWebGLProgram(i,[o],o.dtype)}var vG={kernelName:oo,backendName:"webgl",kernelFunc:j1};var SC=class{constructor(t){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=t,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));
         }
       }
     `}},NC=class{constructor(t){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=t,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 xp(r,t){t!==null&&r.disposeIntermediateTensorInfo(t)}function SG(r){let t=1;for(;t<r;)t*=2;return t}function nut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{k:s,sorted:i}=n,a=L().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=L().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=o.shape,c=l[l.length-1];if(e.shouldExecuteOnCPU([o])||c<a||s>u){let M=e.readSync(o.dataId),[V,G]=Oz(M,l,o.dtype,s,i);return[e.makeTensorInfo(V.shape,V.dtype,V.values),e.makeTensorInfo(G.shape,G.dtype,G.values)]}if(s===0)return l[l.length-1]=0,[e.makeTensorInfo(l,o.dtype,[]),e.makeTensorInfo(l,"int32",[])];if(c===1)return[o,Ll({attrs:{shape:l,dtype:"int32",value:0},backend:e})];let p=e.texData.get(o.dataId),m=p!==null&&p.isPacked,f=m?e.unpackTensor(o):o,h=y.sizeFromShape(l)/c,g=rt({inputs:{x:f},attrs:{shape:[h,c]},backend:e});m&&xp(e,f);let x=SG(s),b=SG(c),w=null,I=()=>w===null?[g,g]:[g,w],N=(M,V,G)=>{let W=I(),q=new SC(G),j=[[c],[w===null?1:0],[Number.NEGATIVE_INFINITY],[M],[V]],Y=w;w=e.runWebGLProgram(q,W,"int32",j),xp(e,Y)};for(let M=1;M<x;M*=2){let V=M*2;for(let G=M;G>=1;G/=2)N(V,G,[h,b])}for(let M=b;M>x;M/=2){let V=I(),G=new NC([h,M/2]),q=[[c],[w===null?1:0],[x]],H=w;w=e.runWebGLProgram(G,V,"int32",q),xp(e,H);let j=x/2,Y=j*2;for(let Z=j;Z>=1;Z/=2)N(Y,Z,w.shape)}let E=w;w=ki({inputs:{x:w},backend:e,attrs:{begin:0,size:[h,s]}}),xp(e,E);let A=V1({inputs:{x:g,indices:w},backend:e,attrs:{axis:1,batchDims:1}});xp(e,g);let D=l.slice(0,-1);D.push(s),E=w,w=rt({inputs:{x:w},attrs:{shape:D},backend:e}),xp(e,E);let F=A;return A=rt({inputs:{x:A},attrs:{shape:D},backend:e}),xp(e,F),[A,w]}var NG={kernelName:ul,backendName:"webgl",kernelFunc:nut};var kC=class{constructor(t,e,n,o,s,i){this.variableNames=["Image","Transforms"],this.outputShape=i;let a=n==="nearest"?1:2,u;switch(o){case"constant":u=1;break;case"reflect":u=2;break;case"wrap":u=3;break;case"nearest":u=4;break;default:u=1;break}this.userCode=`
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${u} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${u} == 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 (${u} == 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 < ${t} && 0 <= coordX && coordX < ${e}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${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(${s});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${e}));
                float mapY = mapCoord(inY, float(${t}));

                if (${a} == 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 out(r){let{inputs:t,backend:e,attrs:n}=r,{image:o,transforms:s}=t,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new kC(p,m,i,a,u,g);return e.runWebGLProgram(x,[o,s],"float32")}var kG={kernelName:cl,backendName:"webgl",kernelFunc:out};function sut(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t;vi(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:a,outputShape:u,indices:l}=Mz(i,o,s.shape,s.dtype);return[n.makeTensorInfo(u,s.dtype,a),n.makeTensorInfo([l.length],"int32",l)]}var TG={kernelName:cu,backendName:"webgl",kernelFunc:sut};function iut(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o,a=i.shape.length,u=o.shape[s],l=new Array(a-1),c=0;for(let h=0;h<a;h++)h!==s&&(l[c++]=i.shape[h]);let p=[],m=new Array(a).fill(0),f=i.shape.slice();f[s]=1;let d=new Array(u);for(let h=0;h<d.length;h++){m[s]=h;let g=ki({inputs:{x:i},backend:e,attrs:{begin:m,size:f}}),x=rt({inputs:{x:g},backend:e,attrs:{shape:l}});d[h]=x,p.push(g)}return p.forEach(h=>e.disposeIntermediateTensorInfo(h)),d}var _G={kernelName:Ki,backendName:"webgl",kernelFunc:iut};var TC=class{constructor(t,e){this.variableNames=["x","segmentIds"];let n=t.windowSize,o=t.batchSize,s=t.inSize,i=t.numSegments,a=i*Math.ceil(s/n);this.outputShape=[o,a];let u="0.0",l="sumValue",c=Math.floor(n/4)*4,p=n%4,m=`
        sumValue += dot(values, segFilter);
    `,f="";s%n>0&&(f=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return initializationValue;
        }
      `);let d="";s%n>0&&(d=`
        if (inIdx < 0 || inIdx >= ${s}) {
          return -1.0;
        }
      `),this.userCode=`
      const float initializationValue = ${u};

      float getValue(int batch, int inIdx) {
        ${f}
        return getX(batch, inIdx);
      }

      float getSegmentIdAtIndex(int inIdx) {
        ${d}
        return getSegmentIds(inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${i})) * float(${n}));
        int currentSeg = int(mod(float(outIdx), float(${i})));

        float sumValue = 0.0;

        for (int i = 0; i < ${c}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
          );

          ${m}
        }

        int inIdx = inOffset + ${c};
        if (${p===1}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            0,
            0,
            0
          );

          ${m}
        } else if (${p===2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
              0,
              0
          );

          ${m}
        } else if (${p===3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            0
          );

          ${m}
        }
        setOutput(${l});
      }
    `}};function aut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,segmentIds:s}=t,{numSegments:i}=n,a=o.shape.length,u=[],l=0,c=S.getAxesPermutation([l],a),p=o;c!=null&&(p=Pe({inputs:{x:o},backend:e,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,a)[0]);let m=S.segment_util.computeOutShape(p.shape,l,i),f=y.sizeFromShape([p.shape[l]]),d=rt({inputs:{x:p},backend:e,attrs:{shape:[-1,f]}});u.push(d);let h=lc(o.dtype),g=(I,N,E,A,D)=>{let F=I.shape[0],M=I.shape[1],V=S.segment_util.segOpComputeOptimalWindowSize(M,D),G={windowSize:V,inSize:M,batchSize:F,numSegments:D},W=new TC(G,N),q=e.compileAndRun(W,[I,E],A);if(u.push(q),q.shape[1]===D)return q;let H=K1({backend:e,attrs:{start:0,stop:D,step:1,dtype:"float32"}}),j=j1({inputs:{x:H},backend:e,attrs:{reps:[M/V]}});return u.push(H),u.push(j),g(q,N,j,A,D)},x=g(d,"unsortedSegmentSum",s,h,i),b=rt({inputs:{x},backend:e,attrs:{shape:m}}),w=b;if(c!=null){u.push(b);let I=S.getUndoAxesPermutation(c);w=Pe({inputs:{x:w},backend:e,attrs:{perm:I}})}return u.forEach(I=>e.disposeIntermediateTensorInfo(I)),w}var EG={kernelName:pu,backendName:"webgl",kernelFunc:aut};var lut=[p3,f3,d3,h3,x3,y3,b3,w3,v3,S3,N3,k3,T3,_3,E3,A3,D3,$3,R3,F3,O3,P3,L3,z3,B3,U3,q3,K3,e3,X3,Z3,J3,Q3,tB,eB,rB,nB,oB,sB,iB,uB,cB,pB,mB,fB,dB,hB,gB,xB,yB,bB,wB,IB,CB,vB,SB,kB,TB,_B,EB,DB,$B,RB,FB,OB,MB,PB,LB,zB,t3,BB,Y3,VB,GB,WB,r3,UB,HB,qB,KB,jB,XB,YB,ZB,JB,QB,eV,rV,nV,oV,sV,iV,lV,cV,pV,mV,fV,dV,bV,s3,wV,IV,CV,vV,V3,SV,TV,_V,EV,AV,n3,DV,$V,RV,FV,OV,G3,hV,MV,PV,LV,a3,zV,BV,VV,GV,WV,UV,HV,qV,KV,jV,XV,YV,ZV,JV,QV,tG,M3,yV,eG,rG,nG,oG,sG,iG,aG,lG,cG,pG,fG,dG,hG,gG,xG,yG,bG,xV,u3,wG,IG,CG,vG,NG,kG,c3,TG,_G,EG,NV];for(let r of lut)rc(r);var Nt;(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"})(Nt||(Nt={}));var ju;(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"})(ju||(ju={}));var AG;function uut(r){AG=r.wasm.cwrap(Xi,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function cut(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s,bias:i,preluActivationWeights:a}=t;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=n,m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=0;if(i!=null){let D=e.dataIdMap.get(i.dataId);if(D.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${D.shape.length}.`);d=D.id}let h=a==null?0:e.dataIdMap.get(a.dataId).id,g=ju[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=u?o.shape[2]:o.shape[1],b=l?s.shape[1]:s.shape[2],w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)),I=e.makeOutput([...w,x,b],o.dtype),N=e.dataIdMap.get(I.dataId).id,E=new Uint8Array(new Int32Array(o.shape).buffer),A=new Uint8Array(new Int32Array(s.shape).buffer);return AG(m,E,o.shape.length,f,A,s.shape.length,u,l,g,d,h,p||0,N),I}var DG={kernelName:Xi,backendName:"wasm",setupFunc:uut,kernelFunc:cut};function yt(r,t){let e;function n(s){e=s.wasm.cwrap(r,null,["number","number","number"])}function o(s){let{backend:i,inputs:{x:a}}=s,u=i.dataIdMap.get(a.dataId).id,l=i.makeOutput(a.shape,t||a.dtype),c=i.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(u,Nt[a.dtype],c),l}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:o}}var $G=yt(Ai);var RG=yt(Go);var FG=yt(Wo);function ee(r,t,e){let n;function o(i){n=i.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:a,inputs:u}=i,{a:l,b:c}=u,p=a.dataIdMap.get(l.dataId).id,m=a.dataIdMap.get(c.dataId).id,f=e!=null?e:l.dtype,d=S.assertAndGetBroadcastShape(l.shape,c.shape),h=a.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(l.shape).buffer),x=new Uint8Array(new Int32Array(c.shape).buffer),b=a.dataIdMap.get(h.dataId).id;return n(p,g,l.shape.length,m,x,c.shape.length,Nt[l.dtype],b),h}return{kernelName:r,backendName:"wasm",setupFunc:o,kernelFunc:s}}var put=!0,OG=ee(no,put);var MG;function mut(r){MG=r.wasm.cwrap(Uo,null,["array","number","number","number"])}function fut(r){let{inputs:t,backend:e}=r,n=e.makeOutput(t[0].shape,t[0].dtype);if(y.sizeFromShape(n.shape)===0)return n;let o=t.map(a=>e.dataIdMap.get(a.dataId).id),s=new Uint8Array(new Int32Array(o).buffer),i=e.dataIdMap.get(n.dataId).id;return MG(s,o.length,Nt[n.dtype],i),n}var PG={kernelName:Uo,backendName:"wasm",setupFunc:mut,kernelFunc:fut};function yp(r){let{inputs:{x:t},backend:e}=r;if(t.dtype==="string")return ir(e.readSync(t.dataId),t.shape,t.dtype);let n=e.makeOutput(t.shape,t.dtype),o=e.typedArrayFromHeap(t);return e.typedArrayFromHeap(n).set(o),n}var LG={kernelName:go,backendName:"wasm",kernelFunc:yp};var zG;function dut(r){zG=r.wasm.cwrap(so,null,["number","array","number","number","number","array","number"])}function mo(r){let{inputs:t,backend:e,attrs:n}=r,[o,s]=gut(t.x.shape,n.perm),i=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(i=!1);let a=hut(t.x.shape,n.perm),u={dataId:t.x.dataId,shape:o,dtype:t.x.dtype};if(i){let d=yp({inputs:t,backend:e});return d.shape=a,d}let l=e.makeOutput(a,u.dtype),c=e.dataIdMap.get(u.dataId).id,p=e.dataIdMap.get(l.dataId).id,m=new Uint8Array(new Int32Array(s).buffer),f=new Uint8Array(new Int32Array(u.shape).buffer);return zG(c,f,u.shape.length,Nt[u.dtype],p,m,s.length),l}function hut(r,t){let e=new Array(r.length);for(let n=0;n<e.length;n++)e[n]=r[t[n]];return e}function gut(r,t){let e=[],n=[];for(let o=0;o<r.length;++o)r[o]!==1&&e.push(r[o]),r[t[o]]!==1&&n.push(t[o]);for(let o=0;o<n.length;++o){let s=-1;for(let i=0;i<n.length;++i)n[i]>=o&&(s===-1||n[s]>n[i])&&(s=i);n[s]=o}return[e,n]}var BG={kernelName:so,backendName:"wasm",kernelFunc:mo,setupFunc:dut};function Cn(r,t,e){let n=r.shape,o=r.shape.length,s=y.parseAxisParam(t,n),i=s,a=S.getAxesPermutation(i,o),u=null,l=!1;if(a!=null){let c=new Array(o);for(let f=0;f<c.length;f++)c[f]=n[a[f]];i=S.getInnerMostAxes(i.length,o),u=mo({inputs:{x:r},attrs:{perm:a},backend:e});let p=e.dataIdMap.get(r.dataId).id;e.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:s,axes:i,inputWasTransposed:l}}var VG;function xut(r){VG=r.wasm.cwrap(Ea,null,["number, number, number"])}function yut(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("all",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;VG(u,x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var GG={kernelName:Ea,backendName:"wasm",setupFunc:xut,kernelFunc:yut};var WG;function but(r){WG=r.wasm.cwrap(Aa,null,["number, number, number"])}function wut(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("any",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;WG(u,x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var UG={kernelName:Aa,backendName:"wasm",setupFunc:but,kernelFunc:wut};function _C(r){let t;function e(o){t=o.wasm.cwrap(r,null,["number","number","number","number","number"])}function n(o){let{backend:s,inputs:i,attrs:a}=o,{axis:u}=a,{x:l}=i,c=s.dataIdMap.get(l.dataId).id,p=c,m=l,{transposed:f,axes:d,inputWasTransposed:h}=Cn(l,u,s);if(h){let N=s.dataIdMap.get(f.dataId).id;N!==c&&(m=f,p=N)}let g=m.shape.slice(0,-1),x=s.makeOutput(g,"int32"),b=s.dataIdMap.get(x.dataId).id,w=y.sizeFromShape(x.shape),I=m.shape[d[0]];return t(p,Nt[m.dtype],w,I,b),h&&s.disposeData(f.dataId),x}return{kernelName:r,backendName:"wasm",setupFunc:e,kernelFunc:n}}var HG=_C(Di);var qG=_C($i);var KG=yt(Ho);var jG=yt(qo);var XG=yt(Ko);var YG=ee(Xo,!1);var ZG=yt(jo);var JG;function Iut(r){JG=r.wasm.cwrap(Yo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Cut(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.strideHeight,b=c.strideWidth,w=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let I=n.makeOutput(c.outShape,"float32"),N=n.dataIdMap.get(I.dataId).id;return JG(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,N),I}var QG={kernelName:Yo,backendName:"wasm",setupFunc:Iut,kernelFunc:Cut};var tW;function vut(r){tW=r.wasm.cwrap("AvgPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return tW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var eW={kernelName:Ri,backendName:"wasm",setupFunc:vut,kernelFunc:Sut};var rW;function Nut(r){rW=r.wasm.cwrap("AvgPool3DGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kut(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return rW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left,c.filterDepth,c.filterHeight,c.filterWidth),p}var nW={kernelName:Hl,backendName:"wasm",setupFunc:Nut,kernelFunc:kut};var oW;function Tut(r){oW=r.wasm.cwrap("AvgPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _ut(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u}=n,l=S.computePool2DInfo(s.shape,i,a,1,u),c=e.makeOutput(s.shape,s.dtype);return oW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.effectiveFilterHeight,l.effectiveFilterWidth,l.padInfo.top,l.padInfo.left,l.filterHeight,l.filterWidth),c}var sW={kernelName:Ul,backendName:"wasm",setupFunc:Tut,kernelFunc:_ut};function mr(r){let{inputs:t,attrs:e}=r,{x:n}=t,{shape:o}=e,s=y.sizeFromShape(n.shape),i=y.inferFromImplicitShape(o,s);return y.assert(s===y.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),r.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var iW={kernelName:Gi,backendName:"wasm",kernelFunc:mr};var aW;function Eut(r){aW=r.wasm.cwrap(Zo,null,["number","array","number","number","array","number","number","number","number"])}function Aut(r){let{inputs:t,backend:e,attrs:n}=r,{a:o,b:s}=t,{transposeA:i,transposeB:a}=n;if(o.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=o.shape.length,l=s.shape.length,c=i?o.shape[u-2]:o.shape[u-1],p=a?s.shape[l-1]:s.shape[l-2],m=i?o.shape[u-1]:o.shape[u-2],f=a?s.shape[l-2]:s.shape[l-1],d=o.shape.slice(0,-2),h=s.shape.slice(0,-2),g=y.sizeFromShape(d),x=y.sizeFromShape(h),w=Hr.assertAndGetBroadcastShape(o.shape.slice(0,-2),s.shape.slice(0,-2)).concat([m,f]);y.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${o.shape} and ${s.shape} and transposeA=${i} and transposeB=${a} must match.`);let I=i?[g,c,m]:[g,m,c],N=a?[x,f,p]:[x,p,f],E=mr({inputs:{x:o},backend:e,attrs:{shape:I}}),A=mr({inputs:{x:s},backend:e,attrs:{shape:N}}),D=e.dataIdMap.get(E.dataId).id,F=e.dataIdMap.get(A.dataId).id,M=i?E.shape[2]:E.shape[1],V=a?A.shape[1]:A.shape[2],G=Math.max(g,x),W=e.makeOutput([G,M,V],E.dtype),q=e.dataIdMap.get(W.dataId).id,H=new Uint8Array(new Int32Array(E.shape).buffer),j=new Uint8Array(new Int32Array(A.shape).buffer);return aW(D,H,E.shape.length,F,j,A.shape.length,i,a,q),e.disposeData(E.dataId),e.disposeData(A.dataId),W.shape=w,W}var lW={kernelName:Zo,backendName:"wasm",setupFunc:Eut,kernelFunc:Aut};function Lo(r){let{inputs:{x:t},attrs:{begin:e,size:n},backend:o}=r,[s,i]=Be.parseSliceParams(t,e,n),a=Be.isSliceContinous(t.shape,s,i),u=o.readSync(t.dataId),l=o.makeOutput(i,t.dtype),c=y.computeStrides(t.shape),p=o.dataIdMap.get(l.dataId);if(a){let d=Be.computeFlatOffset(s,c);return t.dtype==="string"?p.stringBytes=u.slice(d,d+y.sizeFromShape(i)):o.typedArrayFromHeap(l).set(u.subarray(d,d+y.sizeFromShape(i))),l}if(t.dtype==="string"){let d=ep(u,s,i,t.shape,t.dtype);return p.stringBytes=d,l}let m=o.typedArrayFromHeap(l),f=t.shape.length;if(f===2)Dut(u,c[0],m,s,i);else if(f===3)$ut(u,c[0],c[1],m,s,i);else if(f===4)Rut(u,c[0],c[1],c[2],m,s,i);else{let d=ep(u,s,i,t.shape,t.dtype);m.set(d)}return l}function Dut(r,t,e,n,o){let s=0,i=n[0],a=n[1],u=i+o[0];for(let l=i;l<u;l++){let c=l*t+a;e.set(r.subarray(c,c+o[1]),s),s+=o[1]}}function $ut(r,t,e,n,o,s){let i=0,a=o[0],u=o[1],l=o[2],c=a+s[0],p=u+s[1];for(let m=a;m<c;m++)for(let f=u;f<p;f++){let d=m*t+f*e+l;n.set(r.subarray(d,d+s[2]),i),i+=s[2]}}function Rut(r,t,e,n,o,s,i){let a=0,u=s[0],l=s[1],c=s[2],p=u+i[0],m=l+i[1],f=c+i[2],d=s[3];for(let h=u;h<p;h++)for(let g=l;g<m;g++)for(let x=c;x<f;x++){let b=h*t+g*e+x*n+d;o.set(r.subarray(b,b+i[3]),a),a+=i[3]}}var uW={kernelName:Ui,backendName:"wasm",kernelFunc:Lo};function Fut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,crops:i}=n,a=s.reduce((x,b)=>x*b),u=S.getReshaped(o.shape,s,a),l=S.getPermuted(u.length,s.length),c=S.getReshapedPermuted(o.shape,s,a),p=S.getSliceBeginCoords(i,s.length),m=S.getSliceSize(c,i,s.length),f=mr({inputs:{x:o},backend:e,attrs:{shape:u}}),d=mo({inputs:{x:f},backend:e,attrs:{perm:l}}),h=mr({inputs:{x:d},backend:e,attrs:{shape:c}}),g=Lo({inputs:{x:h},backend:e,attrs:{begin:p,size:m}});return e.disposeData(f.dataId),e.disposeData(d.dataId),e.disposeData(h.dataId),g}var cW={kernelName:Fi,backendName:"wasm",kernelFunc:Fut};var pW;function Out(r){pW=r.wasm.cwrap(Da,null,["number","number","boolean","number","number","number"])}function Mut(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i}=n,a=s.shape.reduce((p,m)=>p*m,1)!==0,u=o.shape.length===1?[i]:[o.shape[0],i],l=t.makeOutput(u,s.dtype);function c(p){return t.dataIdMap.get(p.dataId).id}return pW(c(o),i,a,c(s),Nt[s.dtype],c(l)),l}var mW={kernelName:Da,backendName:"wasm",setupFunc:Out,kernelFunc:Mut};var Put=!0,fW=ee($a,Put);function Lut(r){let{inputs:t,backend:e}=r,{s0:n,s1:o}=t,s=e.typedArrayFromHeap(n),i=e.typedArrayFromHeap(o),a=S.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return e.makeOutput([a.length],"int32",void 0,new Int32Array(a))}var dW={kernelName:ql,backendName:"wasm",kernelFunc:Lut};function Fn(r){let{inputs:{x:t},attrs:{dtype:e},backend:n}=r,o=n.makeOutput(t.shape,e),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(o).set(s),o}var hW={kernelName:fo,backendName:"wasm",kernelFunc:Fn};var gW=yt(Jo);var xW;function zut(r){xW=r.wasm.cwrap(ho,null,["number","number","number","number"])}function But(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{clipValueMin:s,clipValueMax:i}=n,a=e.dataIdMap.get(o.dataId).id,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(u.dataId).id;return xW(a,s,i,l),u}var yW={kernelName:ho,backendName:"wasm",setupFunc:zut,kernelFunc:But};function X1(r){let{inputs:t,backend:e}=r,n=y.parseAxisParam(r.attrs.axis,t[0].shape)[0],o=t.map(f=>f.shape);S.assertParamsConsistent(o,n);let s=S.computeOutShape(t.map(f=>f.shape),n),i=t.filter(f=>y.sizeFromShape(f.shape)>0);if(i.length===1)return yp({inputs:{x:i[0]},backend:e});let a=e.makeOutput(s,t[0].dtype);if(y.sizeFromShape(s)===0)return a;if(i[0].dtype==="string"){let f=i.map(w=>{let N=[-1,y.sizeFromShape(w.shape.slice(n))];return mr({inputs:{x:w},backend:e,attrs:{shape:N}})}),d=f.map(w=>({vals:e.readSync(w.dataId),shape:w.shape}));s=S.computeOutShape(f.map(w=>w.shape),1);let h=f[0].shape[0]===1,g=Jc(d,s,t[0].dtype,h),x=S.computeOutShape(i.map(w=>w.shape),n);a.shape=x;let b=e.dataIdMap.get(a.dataId);return b.stringBytes=S.fromStringArrayToUint8(g),f.forEach(w=>e.disposeData(w.dataId)),a}let u=y.sizeFromShape(i[0].shape.slice(0,n)),l=0,c=i.map(f=>{let d=y.sizeFromShape(f.shape.slice(n));return l+=d,d}),p=i.map(f=>e.typedArrayFromHeap(f)),m=e.typedArrayFromHeap(a);for(let f=0;f<u;f++){let d=f*l;for(let h=0;h<p.length;h++){let g=c[h],x=f*g,b=p[h].subarray(x,x+g);m.set(b,d),d+=g}}return a}var bW={kernelName:Oi,backendName:"wasm",kernelFunc:X1};var wW;function Vut(r){wW=r.wasm.cwrap(Qo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Gut(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:m}=e,f=S.convertConv2DDataFormat(m),d=S.computeConv2DInfo(o.shape,s.shape,u,l,c,p,!1,f),h=d.filterHeight,g=d.filterWidth,x=d.padInfo.top,b=d.padInfo.right,w=d.padInfo.bottom,I=d.padInfo.left,N=d.dilationHeight,E=d.dilationWidth,A=d.strideHeight,D=d.strideWidth,F=d.inChannels,M=d.outChannels,V=d.padInfo.type==="SAME"?1:0;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let G=n.makeOutput(d.outShape,"float32"),W=n.dataIdMap.get(G.dataId).id;return wW(i,o.shape[0],o.shape[1],o.shape[2],a,h,g,x,b,w,I,V,N,E,A,D,F,M,W),G}var IW={kernelName:Qo,backendName:"wasm",setupFunc:Vut,kernelFunc:Gut};var CW;function Wut(r){CW=r.wasm.cwrap(ts,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Uut(r){let{backend:t,inputs:e,attrs:n}=r,{dy:o,filter:s}=e,{strides:i,pad:a,dataFormat:u,dimRoundingMode:l,inputShape:c}=n,p=1,m=S.convertConv2DDataFormat(u),f=S.computeConv2DInfo(c,s.shape,i,p,a,l,!1,m),{batchSize:d,filterHeight:h,filterWidth:g,inChannels:x,inHeight:b,inWidth:w,outChannels:I,outHeight:N,outWidth:E,strideHeight:A,strideWidth:D}=f,F=h-1-f.padInfo.top,M=g-1-f.padInfo.left,V=f.dataFormat==="channelsLast",G=y.computeStrides(f.inShape),W=y.computeStrides(o.shape),[q,H,j]=y.computeStrides(s.shape),Y=G[0],Z=V?G[1]:G[2],et=V?G[2]:1,nt=V?1:G[1],st=W[0],lt=V?W[1]:W[2],ot=V?W[2]:1,it=V?1:W[1],ft=t.makeOutput(f.inShape,"float32"),gt=t.dataIdMap.get(ft.dataId).id,Ct=t.dataIdMap.get(o.dataId).id,Rt=t.dataIdMap.get(s.dataId).id;return CW(Ct,Rt,d,h,g,b,w,x,N,E,I,A,D,F,M,q,H,j,Y,Z,et,nt,st,lt,ot,it,gt),ft}var vW={kernelName:ts,backendName:"wasm",setupFunc:Wut,kernelFunc:Uut};var SW;function Hut(r){SW=r.wasm.cwrap(es,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function qut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!=="float32")throw new Error(`Tensor x must have dtype float32, got ${o.dtype}`);if(s.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let l=S.computeConv3DInfo(o.shape,s.shape,i,u,a),c=e.makeOutput(l.outShape,o.dtype);return SW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var NW={kernelName:es,backendName:"wasm",setupFunc:Hut,kernelFunc:qut};var kW;function Kut(r){kW=r.wasm.cwrap(Ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function jut(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,dy:s}=t,{strides:i,pad:a,filterShape:u}=n;if(o.dtype!=="float32")throw new Error(`Tensor dy must have dtype float32, got ${o.dtype}`);if(s.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let l=S.computeConv3DInfo(o.shape,u,i,1,a),c=e.makeOutput(l.filterShape,s.dtype);return kW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var TW={kernelName:Ra,backendName:"wasm",setupFunc:Kut,kernelFunc:jut};var _W;function Xut(r){_W=r.wasm.cwrap(Fa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Yut(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,filter:s}=t,{pad:i,strides:a,inputShape:u}=n;if(o.dtype!=="float32")throw new Error(`Tensor dy must have dtype float32, got ${o.dtype}`);if(s.dtype!=="float32")throw new Error(`Tensor filter must have dtype float32, got ${s.dtype}`);let l=S.computeConv3DInfo(u,s.shape,a,1,i),c=e.makeOutput(l.inShape,o.dtype);return _W(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(c.dataId).id,l.batchSize,l.inDepth,l.inHeight,l.inWidth,l.inChannels,l.outDepth,l.outHeight,l.outWidth,l.outChannels,l.strideDepth,l.strideHeight,l.strideWidth,l.dilationDepth,l.dilationHeight,l.dilationWidth,l.filterDepth,l.filterHeight,l.filterWidth,l.padInfo.front,l.padInfo.top,l.padInfo.left),c}var EW={kernelName:Fa,backendName:"wasm",setupFunc:Xut,kernelFunc:Yut};var AW=yt(rs);var DW=yt(ns);var Y1;(function(r){r[r.bilinear=0]="bilinear",r[r.nearest=1]="nearest"})(Y1||(Y1={}));var $W;function Zut(r){$W=r.wasm.cwrap(Ma,null,["number","number","number","number","array","number","number","number","number","number"])}function Jut(r){let{backend:t,inputs:e,attrs:n}=r,{method:o,extrapolationValue:s,cropSize:i}=n,{image:a,boxes:u,boxInd:l}=e,c=u.shape[0],[p,m]=i,f=[c,p,m,a.shape[3]],d=t.dataIdMap.get(a.dataId),h;a.dtype!=="float32"&&(h=Fn({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),d=t.dataIdMap.get(h.dataId));let g=d.id,x=t.dataIdMap.get(u.dataId).id,b=t.dataIdMap.get(l.dataId).id,w=t.makeOutput(f,"float32"),I=t.dataIdMap.get(w.dataId).id,N=new Uint8Array(new Int32Array(a.shape).buffer);return $W(g,x,b,c,N,p,m,Y1[o],s,I),h!=null&&t.disposeData(h.dataId),w}var RW={kernelName:Ma,backendName:"wasm",setupFunc:Zut,kernelFunc:Jut};var FW;function Qut(r){FW=r.wasm.cwrap(Oa,null,["number","number","number","number","number","number"])}function tct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumprod does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumprod",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;FW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var OW={kernelName:Oa,backendName:"wasm",setupFunc:Qut,kernelFunc:tct};var MW;function ect(r){MW=r.wasm.cwrap(os,null,["number","number","number","number","number","number"])}function rct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{axis:s,exclusive:i,reverse:a}=n,u=o.shape.length;y.assert(o.dtype==="float32"||o.dtype==="int32",()=>`cumsum does not support ${o.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([s],u),c=o;l!==null&&(c=mo({inputs:{x:o},attrs:{perm:l},backend:e}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let m=e.makeOutput(c.shape,c.dtype),f=c.shape[p],d=e.dataIdMap.get(c.dataId).id,h=e.dataIdMap.get(m.dataId).id;MW(d,i?1:0,a?1:0,f,h,Nt[o.dtype]);let g=m;if(l!==null){let x=S.getUndoAxesPermutation(l);g=mo({inputs:{x:m},attrs:{perm:x},backend:e}),e.disposeData(c.dataId),e.disposeData(m.dataId)}return g}var PW={kernelName:os,backendName:"wasm",setupFunc:ect,kernelFunc:rct};var LW;function nct(r){LW=r.wasm.cwrap("DenseBincount",null,["number","array","number","number","boolean","number","number","boolean","number"])}function oct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,weights:s}=e,{size:i,binaryOutput:a}=n,u=s.shape.reduce((m,f)=>m*f,1)!==0,l=o.shape.length===1?[i]:[o.shape[0],i],c=t.makeOutput(l,s.dtype);function p(m){return t.dataIdMap.get(m.dataId).id}return LW(p(o),new Uint8Array(new Int32Array(o.shape).buffer),o.shape.length,i,u,p(s),Nt[s.dtype],a,p(c)),c}var zW={kernelName:jl,backendName:"wasm",setupFunc:nct,kernelFunc:oct};var BW;function sct(r){BW=r.wasm.cwrap(Pa,null,["number","number","number","array","number","array","array","number","number"])}function ict(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:i}=n,a=o.shape[0],u=i==="NHWC"?o.shape[1]:o.shape[2],l=i==="NHWC"?o.shape[2]:o.shape[3],c=i==="NHWC"?o.shape[3]:o.shape[1],p=u*s,m=l*s,f=c/(s*s),d=i==="NHWC"?[a,p,m,f]:[a,f,p,m],h=t.makeOutput(d,"float32"),x=t.dataIdMap.get(o.dataId).id,b=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),w=new Uint8Array(new Int32Array(d).buffer),I=new Uint8Array(new Int32Array(y.computeStrides(d)).buffer),N=t.dataIdMap.get(h.dataId).id;return BW(x,s,i==="NHWC"?1:0,b,o.shape.length-1,w,I,d.length,N),h}var VW={kernelName:Pa,backendName:"wasm",setupFunc:sct,kernelFunc:ict};var GW;function act(r){GW=r.wasm.cwrap(ss,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s}=t,i=n.dataIdMap.get(o.dataId).id,a=n.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=e,m=l==null?[1,1]:l,f=S.computeConv2DInfo(o.shape,s.shape,u,m,c,p,!0),d=f.filterHeight,h=f.filterWidth,g=f.padInfo.top,x=f.padInfo.right,b=f.padInfo.bottom,w=f.padInfo.left,I=f.dilationHeight,N=f.dilationWidth,E=f.strideHeight,A=f.strideWidth,D=f.inChannels,F=f.outChannels,M=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let V=n.makeOutput(f.outShape,"float32"),G=n.dataIdMap.get(V.dataId).id;return GW(i,o.shape[0],o.shape[1],o.shape[2],a,d,h,g,x,b,w,M,I,N,E,A,D,F,G),V}var WW={kernelName:ss,backendName:"wasm",setupFunc:act,kernelFunc:lct};var UW;function uct(r){UW=r.wasm.cwrap("Diag",null,["number","number","number","number"])}function cct(r){let{inputs:t,backend:e}=r,{x:n}=t,o=y.sizeFromShape(n.shape),s=e.makeOutput([...n.shape,...n.shape],n.dtype);return UW(e.dataIdMap.get(n.dataId).id,Nt[n.dtype],o,e.dataIdMap.get(s.dataId).id),s}var HW={kernelName:Xl,backendName:"wasm",setupFunc:uct,kernelFunc:cct};var qW;function pct(r){qW=r.wasm.cwrap(is,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function mct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s}=t,{strides:i,pad:a,dilations:u}=n;if(o.dtype!==s.dtype)throw new Error(`Dilation2D error: x must have the same dtype as filter. Got ${o.dtype} and ${s.dtype}`);let l=S.computeDilation2DInfo(o.shape,s.shape,i,a,"NHWC",u),c=e.makeOutput(l.outShape,o.dtype);return qW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(c.dataId).id,Nt[o.dtype],l.batchSize,l.inChannels,l.inHeight,l.inWidth,l.outHeight,l.outWidth,l.strideHeight,l.strideWidth,l.dilationHeight,l.dilationWidth,l.filterHeight,l.filterWidth,l.padInfo.top,l.padInfo.left),c}var KW={kernelName:is,backendName:"wasm",setupFunc:pct,kernelFunc:mct};var jW;function fct(r){jW=r.wasm.cwrap(Zl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function dct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropFilter error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(s.shape,s.dtype);return jW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var XW={kernelName:Zl,backendName:"wasm",setupFunc:fct,kernelFunc:dct};var YW;function hct(r){YW=r.wasm.cwrap(Yl,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,filter:s,dy:i}=t,{strides:a,pad:u,dilations:l}=n;if(o.dtype!==s.dtype||o.dtype!==i.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${o.dtype}, ${s.dtype}, and ${i.dtype}`);let c=S.computeDilation2DInfo(o.shape,s.shape,a,u,"NHWC",l),p=e.makeOutput(o.shape,o.dtype);return YW(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,Nt[o.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),p}var ZW={kernelName:Yl,backendName:"wasm",setupFunc:hct,kernelFunc:gct};var JW=yt(ls);var QW;function xct(r){QW=r.wasm.cwrap(La,null,["number","number","number"])}function yct(r){let{inputs:t,backend:e}=r,{dy:n,y:o}=t,s=e.makeOutput(o.shape,"float32"),i=a=>e.dataIdMap.get(a.dataId).id;return QW(i(o),i(n),i(s)),s}var tU={kernelName:La,backendName:"wasm",setupFunc:xct,kernelFunc:yct};var bct=!1,eU=ee(za,bct,"bool");var rU=yt(us);var nU=yt(cs,"float32");function EC(r){let{inputs:t,attrs:e,backend:n}=r,{input:o}=t,{dim:s}=e,i=o.shape.length,a=o.shape.slice(),u=s;return s<0&&(y.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+s+1),a.splice(u,0,1),mr({inputs:{x:o},backend:n,attrs:{shape:a}})}var oU={kernelName:Mi,backendName:"wasm",kernelFunc:EC};var sU=yt(ps,"float32");function Z1(r){let{attrs:{shape:t,value:e},backend:n}=r,{attrs:{dtype:o}}=r;o=o||y.inferDtype(e);let s=n.makeOutput(t,o);return n.typedArrayFromHeap(s).fill(e),s}var iU={kernelName:Jl,backendName:"wasm",kernelFunc:Z1};var aU;function wct(r){aU=r.wasm.cwrap(Ba,null,["number","number","number","number","number","number"])}function Ict(r){let{inputs:t,backend:e}=r,{image:n}=t,o=e.makeOutput(n.shape,n.dtype),s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,[a,u,l,c]=n.shape;return aU(s,a,u,l,c,i),o}var lU={kernelName:Ba,backendName:"wasm",kernelFunc:Ict,setupFunc:wct};var uU=yt(ms);var Cct=!1,cU=ee(fs,Cct);var pU;function vct(r){pU=r.wasm.cwrap(ds,null,["number","number","number","number","number","number","number"])}function Sct(r){let{backend:t,inputs:e,attrs:n}=r,{varianceEpsilon:o}=n,{x:s,mean:i,variance:a,offset:u,scale:l}=e,c=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(i.dataId).id,m=t.dataIdMap.get(a.dataId).id,f=u!=null?t.dataIdMap.get(u.dataId).id:0,d=l!=null?t.dataIdMap.get(l.dataId).id:0,h=t.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=t.dataIdMap.get(h.dataId).id;return pU(c,p,m,f,d,o,g),h}var mU={kernelName:ds,backendName:"wasm",setupFunc:vct,kernelFunc:Sct};var fU;function Nct(r){fU=r.wasm.cwrap(Yi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function kct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type==="SAME"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return fU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var dU={kernelName:Yi,backendName:"wasm",setupFunc:Nct,kernelFunc:kct};var hU;function Tct(r){hU=r.wasm.cwrap(Zi,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 _ct(r){let{inputs:t,attrs:e,backend:n}=r,{x:o,filter:s,bias:i,preluActivationWeights:a}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=e,h=S.computeConv2DInfo(o.shape,s.shape,u,c,l,m,!0),g=ju[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=n.dataIdMap.get(o.dataId).id,b=n.dataIdMap.get(s.dataId).id,w=h.outChannels,I=0;if(i!=null){let ot=n.dataIdMap.get(i.dataId);if(ot.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ot.shape.length}.`);if(ot.shape[0]!==w)throw new Error(`FusedDepthwiseConv2D bias shape (${ot.shape}) does not match the number of output channels (${w})`);I=ot.id}let N=h.filterHeight,E=h.filterWidth,A=h.padInfo.top,D=h.padInfo.right,F=h.padInfo.bottom,M=h.padInfo.left,V=h.dilationHeight,G=h.dilationWidth,W=h.strideHeight,q=h.strideWidth,H=h.inChannels,j=h.padInfo.type==="SAME"?1:0,Y=h.batchSize,Z=h.inHeight,et=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let nt=n.makeOutput(h.outShape,"float32"),st=n.dataIdMap.get(nt.dataId).id,lt=a==null?0:n.dataIdMap.get(a.dataId).id;return hU(x,Y,Z,et,b,N,E,I,A,D,F,M,j,V,G,W,q,H,w,g,lt,d||0,st),nt}var gU={kernelName:Zi,backendName:"wasm",setupFunc:Tct,kernelFunc:_ct};var xU;function Ect(r){xU=r.wasm.cwrap(Va,null,["number","number","number","number","number","number","array","number"])}function Act(r){let{backend:t,inputs:e}=r,{params:n,indices:o}=e,[s,i,a,u]=Ey.prepareAndValidate(n,o),l=t.makeOutput(s,n.dtype);if(i===0)return l;let c=o.shape,p=c[c.length-1],f=t.dataIdMap.get(n.dataId).id,h=t.dataIdMap.get(o.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),x=t.dataIdMap.get(l.dataId).id;return xU(f,Nt[n.dtype],h,i,p,a,g,x),l}var yU={kernelName:Va,backendName:"wasm",setupFunc:Ect,kernelFunc:Act};var bU;function Dct(r){bU=r.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function $ct(r){let{backend:t,inputs:e,attrs:n}=r,{x:o,indices:s}=e,{axis:i,batchDims:a}=n,u=y.parseAxisParam(i,o.shape)[0],l=t.readSync(s.dataId),c=o.shape[u];for(let F=0;F<l.length;++F){let M=l[F];y.assert(M<=c-1&&M>=0,()=>`GatherV2: the index value ${M} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(o,s,u,a),m=mr({inputs:{x:o},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),f=y.sizeFromShape(s.shape),d=mr({inputs:{x:s},attrs:{shape:[p.batchSize,f/p.batchSize]},backend:t}),h=[p.batchSize,p.outerSize,f/p.batchSize,p.sliceSize],g=t.makeOutput(h,o.dtype);if(y.sizeFromShape(o.shape)===0)return g;let x=m.shape.length-1,w=t.dataIdMap.get(m.dataId).id,N=t.dataIdMap.get(d.dataId).id,E=t.dataIdMap.get(g.dataId).id,A=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),D=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return bU(w,Nt[o.dtype],A,x,N,p.batchSize,D,E),t.disposeData(m.dataId),t.disposeData(d.dataId),g.shape=p.outputShape,g}var wU={kernelName:Pi,backendName:"wasm",setupFunc:Dct,kernelFunc:$ct};var Rct=!1,IU=ee(Ga,Rct,"bool");var Fct=!1,CU=ee(hs,Fct,"bool");var vU=yt(gs,"bool");var SU=yt(xs,"bool");var NU=yt(ys,"bool");var kU;function Oct(r){kU=r.wasm.cwrap(bs,null,["number","number","number","number"])}function Mct(r){let{inputs:{x:t},attrs:{alpha:e},backend:n}=r,o=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(y.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;kU(o,Nt[t.dtype],e,i)}return s}var TU={kernelName:bs,backendName:"wasm",setupFunc:Oct,kernelFunc:Mct};var Pct=!1,_U=ee(Wa,Pct,"bool");var Lct=!1,EU=ee(Ua,Lct,"bool");var AU;function zct(r){AU=r.wasm.cwrap(Ha,null,["number","number","number","number"])}function Bct(r){let{attrs:t,backend:e}=r,{start:n,stop:o,num:s}=t,i=Math.floor(s),a=e.makeOutput([i],"float32");return AU(e.dataIdMap.get(a.dataId).id,n,o,i),a}var DU={kernelName:Ha,backendName:"wasm",setupFunc:zct,kernelFunc:Bct};var $U=yt(ws);var RU=yt(Is);var Vct=!1,FU=ee(qa,Vct,"bool");var OU=yt(Ka);var Gct=!1,MU=ee(ja,Gct,"bool");var Wct=!1,PU=ee(E_,Wct,"bool");var LU;function Uct(r){LU=r.wasm.cwrap(Cs,null,["number","number","number","number","number","number","number"])}function Hct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{depthRadius:s,bias:i,alpha:a,beta:u}=n;if(o.dtype!=="float32")throw new Error("LRN error: x must have dtype float32");let l=e.makeOutput(o.shape,o.dtype);return LU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(l.dataId).id,o.shape[3],s,i,a,u),l}var zU={kernelName:Cs,backendName:"wasm",setupFunc:Uct,kernelFunc:Hct};var BU;function qct(r){BU=r.wasm.cwrap(Xa,null,["number","number","number","number","number","number","number","number","number"])}function Kct(r){let{inputs:t,backend:e,attrs:n}=r,{x:o,y:s,dy:i}=t,{depthRadius:a,bias:u,alpha:l,beta:c}=n;if(o.dtype!=="float32"||s.dtype!=="float32"||i.dtype!=="float32")throw new Error("LRNGrad error: x, y, and dy must have dtype float32");let p=e.makeOutput(o.shape,o.dtype);return BU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(i.dataId).id,e.dataIdMap.get(p.dataId).id,i.shape[3],a,u,l,c),p}var VU={kernelName:Xa,backendName:"wasm",setupFunc:qct,kernelFunc:Kct};var GU;function jct(r){GU=r.wasm.cwrap(vs,null,["number","number","number","number"])}function Xct(r){let{backend:t,inputs:e,attrs:n}=r,{reductionIndices:o,keepDims:s}=n,{x:i}=e,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;l=c,u=w}let d=l.shape.length;S.assertAxesAreInnerMostDims("max",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,i.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;GU(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var WU={kernelName:vs,backendName:"wasm",setupFunc:jct,kernelFunc:Xct};var Yct=!1,UU=ee(Ss,Yct);var HU;function Zct(r){HU=r.wasm.cwrap(Ns,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Jct(r){let{inputs:t,attrs:e,backend:n}=r,o=t.x,s=n.dataIdMap.get(o.dataId).id;y.assert(o.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${o.dtype}.`);let{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=e,c=S.computePool2DInfo(o.shape,i,a,1,u,l),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,w=c.strideHeight,I=c.strideWidth,N=c.inChannels,E=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let A=n.makeOutput(c.outShape,"float32"),D=n.dataIdMap.get(A.dataId).id;return HU(s,o.shape[0],o.shape[1],o.shape[2],p,m,f,d,h,g,x,b,w,I,N,E,D),A}var qU={kernelName:Ns,backendName:"wasm",setupFunc:Zct,kernelFunc:Jct};var KU;function Qct(r){KU=r.wasm.cwrap("MaxPool3D",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function tpt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,dimRoundingMode:u,dataFormat:l}=n,c=S.computePool3DInfo(o.shape,s,i,1,a,u,l),p=e.makeOutput(c.outShape,o.dtype);return KU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var jU={kernelName:Li,backendName:"wasm",setupFunc:Qct,kernelFunc:tpt};var XU;function ept(r){XU=r.wasm.cwrap("MaxPool3DGrad",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 rpt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool3DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return XU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),p}var YU={kernelName:tu,backendName:"wasm",setupFunc:ept,kernelFunc:rpt};var ZU;function npt(r){ZU=r.wasm.cwrap("MaxPoolGrad",null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function opt(r){let{inputs:t,backend:e,attrs:n}=r,{dy:o,input:s}=t,{filterSize:i,strides:a,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(s.shape,i,a,1,u,l),p=e.makeOutput(s.shape,s.dtype);return ZU(e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),p}var JU={kernelName:Ql,backendName:"wasm",setupFunc:npt,kernelFunc:opt};var QU;function spt(r){QU=r.wasm.cwrap("MaxPoolWithArgmax",null,["number","number","number","number","boolean","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ipt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{filterSize:s,strides:i,pad:a,includeBatchInIndex:u}=n;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let l=[1,1];y.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(o.shape,s,i,[1,1],a),p=e.makeOutput(c.outShape,o.dtype),m=e.makeOutput(c.outShape,"int32");return QU(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(p.dataId).id,e.dataIdMap.get(m.dataId).id,Nt[o.dtype],u,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[p,m]}var t4={kernelName:eu,backendName:"wasm",setupFunc:spt,kernelFunc:ipt};var e4;function apt(r){e4=r.wasm.cwrap(ks,null,["number, number, number"])}function lpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t),d=p;if(f){let I=t.dataIdMap.get(c.dataId).id;I!==a&&(l=c,u=I,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims("mean",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=l;l.dtype!=="float32"&&(b=Fn({backend:t,inputs:{x:l},attrs:{dtype:"float32"}}),u=t.dataIdMap.get(b.dataId).id);let w=t.makeOutput(h,"float32");if(y.sizeFromShape(l.shape)!==0){let I=t.dataIdMap.get(w.dataId).id;e4(u,x,I)}if(f&&t.disposeData(c.dataId),s){let I=S.expandShapeToKeepDim(w.shape,m);w.shape=I}return l.dtype!=="float32"&&t.disposeData(b.dataId),w}var r4={kernelName:ks,backendName:"wasm",setupFunc:apt,kernelFunc:lpt};var n4;function upt(r){n4=r.wasm.cwrap(Ts,null,["number","number","number","number"])}function cpt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t);if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w)}let d=l.shape.length;S.assertAxesAreInnerMostDims("min",p,d);let[h,g]=S.computeOutAndReduceShapes(l.shape,p),x=y.sizeFromShape(g),b=t.makeOutput(h,l.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;n4(u,Nt[i.dtype],x,w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var o4={kernelName:Ts,backendName:"wasm",setupFunc:upt,kernelFunc:cpt};var ppt=!1,s4=ee(_s,ppt);var J1;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(J1||(J1={}));var i4;function mpt(r){i4=r.wasm.cwrap(Es,null,["number","array","number","number","array","array","number","number"])}function fpt(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,mode:o}}=r,s=n.map((d,h)=>d[0]+t.shape[h]+d[1]),i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),u=e.dataIdMap.get(a.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new Int32Array(p).buffer);return i4(i,l,t.shape.length,Nt[t.dtype],m,f,J1[o],u),a}var a4={kernelName:Es,backendName:"wasm",kernelFunc:fpt,setupFunc:mpt};var l4;function dpt(r){l4=r.wasm.cwrap(Qs,null,["number","number","number","number"])}function Q1(r){let{backend:t,inputs:{logits:e},attrs:{dim:n}}=r,o=t.dataIdMap.get(e.dataId).id,s=t.makeOutput(e.shape,e.dtype),i=t.dataIdMap.get(s.dataId).id,a=e.shape[n],u=y.sizeFromShape(e.shape)/a;return y.sizeFromShape(s.shape)===0||l4(o,i,a,u),s}var u4={kernelName:Qs,backendName:"wasm",setupFunc:dpt,kernelFunc:Q1};var c4;function hpt(r){c4=r.wasm.cwrap(Ya,null,["number","number","number","number","number","number"])}function gpt(r){let{inputs:t,backend:e,attrs:n}=r,{logits:o}=t,{numSamples:s,seed:i,normalized:a}=n;if(o.dtype!=="float32")throw new Error(`Tensor logits must have dtype float32, got ${o.dtype}`);let u=a?o:Q1({inputs:{logits:o},backend:e,attrs:{dim:o.shape.length-1}}),[l,c]=u.shape,p=e.makeOutput([l,s],"int32");return c4(e.dataIdMap.get(u.dataId).id,l,c,s,i,e.dataIdMap.get(p.dataId).id),a||e.disposeData(u.dataId),p}var p4={kernelName:Ya,backendName:"wasm",setupFunc:hpt,kernelFunc:gpt};var m4=ee(As,!0);var xpt=!0,f4=ee(Ds,xpt);var d4=yt(zi);function Bd(r,t){let e=new Int32Array(r.wasm.HEAPU8.buffer,t,4),n=e[0],o=e[1],s=e[2],i=e[3];return r.wasm._free(t),{pSelectedIndices:n,selectedSize:o,pSelectedScores:s,pValidOutputs:i}}var h4;function ypt(r){h4=r.wasm.cwrap(Ja,"number",["number","number","number","number","number"])}function bpt(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i}=n,{boxes:a,scores:u}=e,l=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=h4(l,c,s,o,i),{pSelectedIndices:m,selectedSize:f,pSelectedScores:d,pValidOutputs:h}=Bd(t,p);return t.wasm._free(d),t.wasm._free(h),t.makeOutput([f],"int32",m)}var g4={kernelName:Ja,backendName:"wasm",setupFunc:ypt,kernelFunc:bpt};var x4;function wpt(r){x4=r.wasm.cwrap(Qa,"number",["number","number","number","number","number","bool"])}function Ipt(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:a}=n,{boxes:u,scores:l}=e,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,m=x4(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Bd(t,m);t.wasm._free(h);let x=t.makeOutput([d],"int32",f),b=t.makeOutput([],"int32",g);return[x,b]}var y4={kernelName:Qa,backendName:"wasm",setupFunc:wpt,kernelFunc:Ipt};var b4;function Cpt(r){b4=r.wasm.cwrap(tl,"number",["number","number","number","number","number","number"])}function vpt(r){let{backend:t,inputs:e,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:i,softNmsSigma:a}=n,{boxes:u,scores:l}=e,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,m=b4(c,p,s,o,i,a),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Bd(t,m);t.wasm._free(g);let x=t.makeOutput([d],"int32",f),b=t.makeOutput([d],"float32",h);return[x,b]}var w4={kernelName:tl,backendName:"wasm",setupFunc:Cpt,kernelFunc:vpt};var Spt=!1,I4=ee(Za,Spt,"bool");var C4;function Npt(r){C4=r.wasm.cwrap($s,null,["number","number","number","number","number"])}function kpt(r){let{inputs:t,backend:e,attrs:n}=r,{indices:o}=t,{dtype:s,depth:i,onValue:a,offValue:u}=n,l=e.makeOutput([...o.shape,i],s),c=e.dataIdMap.get(l.dataId).id,m=e.dataIdMap.get(o.dataId).id;return C4(m,i,a,u,c),l}var v4={kernelName:$s,backendName:"wasm",setupFunc:Npt,kernelFunc:kpt};function Tpt(r){let{inputs:{x:t},backend:e}=r,n=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(n).fill(1),n}var S4={kernelName:Bi,backendName:"wasm",kernelFunc:Tpt};function _pt(r){let{inputs:t,backend:e,attrs:n}=r,{axis:o}=n;if(t.length===1)return EC({inputs:{input:t[0]},backend:e,attrs:{dim:o}});let s=t[0].shape,i=t[0].dtype;t.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let a=[],u=t.map(c=>{let p=EC({inputs:{input:c},backend:e,attrs:{dim:o}});return a.push(p),p}),l=X1({inputs:u,backend:e,attrs:{axis:o}});return a.forEach(c=>e.disposeData(c.dataId)),l}var N4={kernelName:Vi,backendName:"wasm",kernelFunc:_pt};var k4;function Ept(r){k4=r.wasm.cwrap(Rs,null,["number","array","number","number","array","array","number","number"])}function Apt(r){let{inputs:{x:t},backend:e,attrs:{paddings:n,constantValue:o}}=r,s=n.map((h,g)=>h[0]+t.shape[g]+h[1]);if(y.sizeFromShape(t.shape)===0)return Z1({backend:e,attrs:{shape:s,value:o,dtype:t.dtype}});let i=e.dataIdMap.get(t.dataId).id,a=e.makeOutput(s,t.dtype),l=e.dataIdMap.get(a.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=n.map(h=>h[0]),m=n.map(h=>h[1]),f=new Uint8Array(new Int32Array(p).buffer),d=new Uint8Array(new Int32Array(m).buffer);return k4(i,c,t.shape.length,Nt[t.dtype],f,d,o,l),a}var AC={kernelName:Rs,backendName:"wasm",kernelFunc:Apt,setupFunc:Ept};var Dpt=!1,T4=ee(Fs,Dpt);var _4;function $pt(r){_4=r.wasm.cwrap(Os,null,["number","number","number"])}function Rpt(r){let{inputs:t,backend:e}=r,{x:n,alpha:o}=t,s=e.dataIdMap.get(n.dataId).id,i=e.dataIdMap.get(o.dataId).id,a=s,u=n,l=u;u.dtype!=="float32"&&(l=Fn({backend:e,inputs:{x:n},attrs:{dtype:"float32"}}),a=e.dataIdMap.get(l.dataId).id);let c=e.makeOutput(n.shape,"float32"),p=e.dataIdMap.get(c.dataId).id;return _4(a,i,p),u.dtype!=="float32"&&e.disposeData(l.dataId),c}var E4={kernelName:Os,backendName:"wasm",setupFunc:$pt,kernelFunc:Rpt};var A4;function Fpt(r){A4=r.wasm.cwrap(Ms,null,["number","number","number","number"])}function Opt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t),d=p;if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims("prod",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=t.makeOutput(h,l.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;A4(u,x,Nt[b.dtype],w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var D4={kernelName:Ms,backendName:"wasm",setupFunc:Fpt,kernelFunc:Opt};var Mpt=r=>{let{backend:t,attrs:e}=r,{start:n,stop:o,step:s,dtype:i}=e,a=tp(n,o,s,i),u=t.makeOutput([a.length],i);return t.typedArrayFromHeap(u).set(a),u},$4={kernelName:ru,backendName:"wasm",kernelFunc:Mpt};var Ppt=!0,R4=ee(as,Ppt);var F4=yt(Ps);var O4=yt(Ls);var M4=yt(Vs);var P4;function Lpt(r){P4=r.wasm.cwrap(Bs,null,["number","number","number","number","number","number","number","number","number","number"])}function zpt(r){let{backend:t,inputs:e,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,[c,p,m,f]=o.shape,d=[c,u,l,f],h=t.dataIdMap.get(o.dataId),g;h.dtype!=="float32"&&(g=Fn({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),h=t.dataIdMap.get(g.dataId));let x=h.id,b=t.makeOutput(d,"float32");if(y.sizeFromShape(o.shape)===0)return b;let w=t.dataIdMap.get(b.dataId).id;return P4(x,c,p,m,f,u,l,s?1:0,i?1:0,w),g!=null&&t.disposeData(g.dataId),b}var L4={kernelName:Bs,backendName:"wasm",setupFunc:Lpt,kernelFunc:zpt};var z4;function Bpt(r){z4=r.wasm.cwrap(rl,null,["number","number","number","array","array","boolean"])}function Vpt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=e.makeOutput(o.shape,"float32"),u=e.dataIdMap.get(o.dataId),l;return u.dtype!=="float32"&&(l=Fn({backend:e,inputs:{x:o},attrs:{dtype:"float32"}}),u=e.dataIdMap.get(l.dataId)),z4(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,new Uint8Array(new Int32Array(o.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),l!=null&&e.disposeData(l.dataId),a}var B4={kernelName:rl,backendName:"wasm",setupFunc:Bpt,kernelFunc:Vpt};var V4;function Gpt(r){V4=r.wasm.cwrap(zs,null,["number","number","number","number","number","number","number","number","number","number"])}function Wpt(r){let{backend:t,inputs:e,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:i,size:a}=n,[u,l]=a,[c,p,m,f]=o.shape,d=[c,u,l,f],h=t.makeOutput(d,"float32");if(y.sizeFromShape(o.shape)===0)return h;let g=t.dataIdMap.get(o.dataId),x;g.dtype!=="float32"&&(x=Fn({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),g=t.dataIdMap.get(x.dataId));let b=g.id,w=t.dataIdMap.get(h.dataId).id;return V4(b,c,p,m,f,u,l,s?1:0,i?1:0,w),x!=null&&t.disposeData(x.dataId),h}var G4={kernelName:zs,backendName:"wasm",setupFunc:Gpt,kernelFunc:Wpt};var W4;function Upt(r){W4=r.wasm.cwrap(el,null,["number","number","number","array","array","boolean"])}function Hpt(r){let{inputs:t,backend:e,attrs:n}=r,{images:o,dy:s}=t,{alignCorners:i}=n,a=e.makeOutput(o.shape,"float32"),u=e.dataIdMap.get(o.dataId),l;return u.dtype!=="float32"&&(l=Fn({backend:e,inputs:{x:o},attrs:{dtype:"float32"}}),u=e.dataIdMap.get(l.dataId)),W4(e.dataIdMap.get(o.dataId).id,e.dataIdMap.get(s.dataId).id,e.dataIdMap.get(a.dataId).id,new Uint8Array(new Int32Array(o.shape).buffer),new Uint8Array(new Int32Array(s.shape).buffer),i),l!=null&&e.disposeData(l.dataId),a}var U4={kernelName:el,backendName:"wasm",setupFunc:Upt,kernelFunc:Hpt};var H4;function qpt(r){H4=r.wasm.cwrap(Gs,null,["number","array","number","array","number","number"])}function Kpt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{dims:s}=n,i=y.parseAxisParam(s,o.shape);if(o.shape.length===0)return yp({inputs:{x:o},backend:e});let a=e.makeOutput(o.shape,o.dtype),u=e.dataIdMap.get(o.dataId).id,l=e.dataIdMap.get(a.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(o.shape).buffer);H4(u,c,i.length,p,o.shape.length,l);let m=mr({inputs:{x:a},attrs:{shape:o.shape},backend:e});return e.disposeData(a.dataId),m}var q4={kernelName:Gs,backendName:"wasm",kernelFunc:Kpt,setupFunc:qpt};var K4;function jpt(r){K4=r.wasm.cwrap(pl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Xpt(r){let{inputs:t,backend:e,attrs:n}=r,{image:o}=t,{radians:s,fillValue:i,center:a}=n,u=e.makeOutput(o.shape,o.dtype),l=e.dataIdMap.get(o.dataId).id,c=e.dataIdMap.get(u.dataId).id,[p,m,f,d]=o.shape,[h,g]=S.getImageCenter(a,m,f),x=i===0,b=255,w=typeof i=="number"?[i,i,i,x?0:b]:[...i,b],I=new Uint8Array(new Int32Array(w).buffer);return K4(l,p,m,f,d,s,h,g,I,w.length,c),u}var j4={kernelName:pl,backendName:"wasm",kernelFunc:Xpt,setupFunc:jpt};var X4=yt(Ws);var Y4=yt(Us);var Z4;function Ypt(r){Z4=r.wasm.cwrap(nl,null,["number","number","number","number","number","number","array","number","number"])}function Zpt(r){let{backend:t,inputs:e,attrs:n}=r,{indices:o,updates:s}=e,{shape:i}=n,a=t.makeOutput(i,s.dtype);if(y.sizeFromShape(i)===0)return a;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=$u.calculateShapes(s,o,i),d=t.dataIdMap.get(o.dataId).id,g=t.dataIdMap.get(s.dataId).id,x=new Uint8Array(new Int32Array(p).buffer),b=t.dataIdMap.get(a.dataId).id;return Z4(d,g,Nt[s.dtype],u,l,c,x,m,b),a}var J4={kernelName:nl,backendName:"wasm",setupFunc:Ypt,kernelFunc:Zpt};var Q4;function Jpt(r){Q4=r.wasm.cwrap(sl,null,["number","number","number","number","number","number","bool","number"])}function Qpt(r){let{inputs:t,backend:e,attrs:n}=r,{sortedSequence:o,values:s}=t,{side:i}=n;if(o.dtype!==s.dtype)throw new Error(`SearchSorted error: sorted_sequence must have the same dtype as values. Got ${o.dtype} and ${s.dtype}`);let a=e.makeOutput(s.shape,"int32");function u(l){return e.dataIdMap.get(l.dataId).id}return Q4(u(o),u(s),o.shape[0],o.shape[1],s.shape[1],Nt[o.dtype],i==="left",u(a)),a}var tH={kernelName:sl,backendName:"wasm",setupFunc:Jpt,kernelFunc:Qpt};var eH;function tmt(r){eH=r.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function emt(r){let{inputs:t,backend:e}=r,{condition:n,t:o,e:s}=t,i=e.dataIdMap.get(n.dataId).id,a=e.dataIdMap.get(o.dataId).id,u=e.dataIdMap.get(s.dataId).id,l=e.makeOutput(o.shape,o.dtype),c=e.dataIdMap.get(l.dataId).id,p=n.shape.length,m=o.shape.length,f=p===0||p>1||m===1?1:y.sizeFromShape(o.shape.slice(1));return eH(i,a,u,f,c),l}var rH={kernelName:Wi,backendName:"wasm",kernelFunc:emt,setupFunc:tmt};var nH=yt(Hs);var oH;function rmt(r){oH=r.wasm.cwrap(Xs,null,["number","number"])}function nmt(r){let{backend:t,inputs:{x:e}}=r,n=t.dataIdMap.get(e.dataId).id,o=t.makeOutput(e.shape,e.dtype),s=t.dataIdMap.get(o.dataId).id;return y.sizeFromShape(o.shape)===0||oH(n,s),o}var sH={kernelName:"Sigmoid",backendName:"wasm",setupFunc:rmt,kernelFunc:nmt};var iH=yt(js);var aH=yt(qs);var lH=yt(Ks);var uH=yt(Ys);function omt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,{blockShape:s,paddings:i}=n,a=y.sizeFromShape(s),u=[[0,0]];u.push(...i);for(let E=1+s.length;E<o.shape.length;++E)u.push([0,0]);let l=AC.kernelFunc({inputs:{x:o},backend:e,attrs:{paddings:u,constantValue:0}}),c=S.getReshaped(l.shape,s,a,!1),p=S.getPermuted(c.length,s.length,!1),m=S.getReshapedPermuted(l.shape,s,a,!1),h=mr({inputs:{x:l},backend:e,attrs:{shape:c}}),b=mo({inputs:{x:h},backend:e,attrs:{perm:p}}),N=mr({inputs:{x:b},backend:e,attrs:{shape:m}});return e.disposeData(l.dataId),e.disposeData(h.dataId),e.disposeData(b.dataId),N}var cH={kernelName:Hi,backendName:"wasm",kernelFunc:omt};var pH;function smt(r){pH=r.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function imt(r){let{backend:t,inputs:e}=r,{indices:n,values:o,denseShape:s,defaultValue:i}=e,a=n.shape[0],u=n.shape[1],l=t.readSync(s.dataId)[0],c=[a+l,u],p=t.dataIdMap.get(n.dataId).id,m=t.dataIdMap.get(o.dataId).id,f=t.dataIdMap.get(i.dataId).id,d=t.makeOutput(c,n.dtype),h=t.dataIdMap.get(d.dataId).id,g=t.makeOutput(c.slice(0,1),o.dtype),x=t.dataIdMap.get(g.dataId).id,b=t.makeOutput([l],"bool"),w=t.dataIdMap.get(b.dataId).id,I=t.makeOutput([a],n.dtype),N=t.dataIdMap.get(I.dataId).id,E=t.makeOutput([4],"int32"),A=t.dataIdMap.get(E.dataId).id,D=pH(p,m,Nt[o.dtype],a,l,u,f,h,x,w,N,A),F=t.readSync(E.dataId),M;switch(F[0]){case 1:{M=S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(F[1]);break}case 2:{M=S.getSparseFillEmptyRowsNegativeIndexErrorMessage(F[1],F[2]);break}case 3:M=S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(F[1],F[2],F[3]);break;default:M=""}if(t.disposeData(E.dataId),M)throw t.disposeData(d.dataId),t.disposeData(g.dataId),t.disposeData(b.dataId),t.disposeData(I.dataId),new Error(M);let V=d,G=g;return D!==c[0]&&(V=Lo({inputs:{x:d},attrs:{begin:0,size:[D,u]},backend:t}),G=Lo({inputs:{x:g},attrs:{begin:0,size:D},backend:t}),t.disposeData(d.dataId),t.disposeData(g.dataId)),[V,G,b,I]}var mH={kernelName:nu,backendName:"wasm",setupFunc:smt,kernelFunc:imt};var fH;function amt(r){fH=r.wasm.cwrap(il,null,["number","number","number","number","number","number","number"])}function lmt(r){let{backend:t,inputs:e}=r,{inputIndices:n,inputShape:o,newShape:s}=e;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
        ${n.shape}`);if(o.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
        ${o.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=t.dataIdMap.get(n.dataId).id,a=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(s.dataId).id,l=n.shape[0],c=y.sizeFromShape(s.shape),p=t.makeOutput([l,c],n.dtype),m=t.dataIdMap.get(p.dataId).id,f=t.makeOutput([c],s.dtype),d=t.dataIdMap.get(f.dataId).id,h=t.makeOutput([3],"int32"),g=t.dataIdMap.get(h.dataId).id;fH(i,a,u,l,m,d,g);let x=t.readSync(h.dataId),b;switch(x[0]){case 0:{b=S.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(x[1],x[2]);break}case 1:{b=S.getSparseReshapeNegativeOutputDimErrorMessage(x[1],x[2]);break}case 2:b=S.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let w=Array.from(t.readSync(o.dataId)),I=Array.from(t.readSync(f.dataId));b=S.getSparseReshapeInputOutputMultipleErrorMessage(w,I);break}case 4:{let w=Array.from(t.readSync(o.dataId)),I=Array.from(t.readSync(f.dataId));b=S.getSparseReshapeInputOutputMismatchErrorMessage(w,I);break}default:b=""}if(t.disposeData(h.dataId),b)throw t.disposeData(p.dataId),t.disposeData(f.dataId),new Error(b);return[p,f]}var dH={kernelName:il,backendName:"wasm",setupFunc:amt,kernelFunc:lmt};var hH;function DC(r){hH=r.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function $C(r,t){let{backend:e,inputs:n}=r,{data:o,indices:s,segmentIds:i}=n,a=s.shape[0],u=e.readSync(i.dataId,a-1,a)[0],c=a>0?u+1:0;if(c<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=o.shape.slice();p[0]=c;let m=e.dataIdMap.get(o.dataId).id,f=e.dataIdMap.get(s.dataId).id,d=e.dataIdMap.get(i.dataId).id,h=e.makeOutput(p,o.dtype),g=e.dataIdMap.get(h.dataId).id,x=e.makeOutput([4],"int32"),b=e.dataIdMap.get(x.dataId).id;hH(m,Nt[o.dtype],o.shape[0],f,d,g,b,t,0);let w=e.readSync(x.dataId),I;switch(w[0]){case 0:{I=S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{I=S.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:I=S.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(w[1],w[2]);break;case 3:I=S.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(w[1],w[2],w[3]);break;default:I=""}if(e.disposeData(x.dataId),I)throw e.disposeData(h.dataId),new Error(I);return h}function umt(r){return $C(r,!0)}var gH={kernelName:ou,backendName:"wasm",setupFunc:DC,kernelFunc:umt};function cmt(r){return $C(r,!1)}var xH={kernelName:su,backendName:"wasm",setupFunc:DC,kernelFunc:cmt};var yH;function pmt(r){yH=r.wasm.cwrap(al,null,["number","number","number","number","number","number","number","number","array","number","number"])}function mmt(r){let{backend:t,inputs:e,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:i}=e,{outputShape:a}=n,u=t.makeOutput(a,i.dtype);if(y.sizeFromShape(a)===0)return u;let{sliceRank:l,numUpdates:c,sliceSize:p,strides:m,outputSize:f}=S.calculateShapes(s,o,a),d=t.dataIdMap.get(o.dataId).id,h=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(i.dataId).id,x=new Uint8Array(new Int32Array(m).buffer),b=t.dataIdMap.get(u.dataId).id;return yH(d,h,s.shape.length,g,Nt[i.dtype],l,c,p,x,f,b),u}var bH={kernelName:al,backendName:"wasm",setupFunc:pmt,kernelFunc:mmt};function fmt(r){let{inputs:t,attrs:e,backend:n}=r,{x:o}=t,{numOrSizeSplits:s,axis:i}=e,a=y.parseAxisParam(i,o.shape)[0],u=S.prepareSplitSize(o,s,a),l=new Array(o.shape.length).fill(0),c=o.shape.slice();return u.map(p=>{let m=[...c];m[a]=p;let f=Lo({inputs:{x:o},attrs:{begin:l,size:m},backend:n});return l[a]+=p,f})}var wH={kernelName:qi,backendName:"wasm",kernelFunc:fmt};var IH=yt(Zs);var CH=yt(iu);var dmt=!0,vH=ee(ti,dmt);var SH;function hmt(r){SH=r.wasm.cwrap(xo,null,["number","number","number","number"])}function gmt(r){let{backend:t,inputs:e,attrs:n}=r,{alpha:o}=n,{x:s}=e,i=t.dataIdMap.get(s.dataId).id,a=t.makeOutput(s.shape,s.dtype),u=t.dataIdMap.get(a.dataId).id;return SH(i,o,Nt[s.dtype],u),a}var NH={kernelName:xo,backendName:"wasm",setupFunc:hmt,kernelFunc:gmt};var kH;function xmt(r){kH=r.wasm.cwrap(ll,null,["number","array","number","array","array","array","array","array","number","number"])}function ymt(r){let{backend:t,inputs:e,attrs:n}=r,{x:o}=e,{begin:s,end:i,strides:a,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{finalShapeSparse:f,finalShape:d,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:w,strides:I}=Be.sliceInfo(o.shape,s,i,a,u,l,c,p,m),N;if(h)N=mr({inputs:{x:o},backend:t,attrs:{shape:d}});else if(g||x){y.assert(o.shape.length>=1,()=>`Input must have rank at least 1, got: ${o.shape.length}`);let E=Be.computeOutShape(b,w,I),A=Lo({inputs:{x:o},backend:t,attrs:{begin:b,size:E}});N=mr({inputs:{x:A},backend:t,attrs:{shape:d}}),t.disposeData(A.dataId)}else{let E=t.makeOutput(f,"float32"),A=t.dataIdMap.get(o.dataId).id,D=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),F=new Uint8Array(new Int32Array(b).buffer),M=new Uint8Array(new Int32Array(w).buffer),V=new Uint8Array(new Int32Array(I).buffer),G=new Uint8Array(new Int32Array(f).buffer),W=new Uint8Array(new Int32Array(y.computeStrides(f)).buffer),q=t.dataIdMap.get(E.dataId).id;kH(A,D,o.shape.length,F,M,V,G,W,f.length,q),N=mr({inputs:{x:E},backend:t,attrs:{shape:d}}),t.disposeData(E.dataId)}return N}var TH={kernelName:ll,backendName:"wasm",setupFunc:xmt,kernelFunc:ymt};function bmt(r){let{backend:t,inputs:e,attrs:n}=r,{data:o,dataSplits:s}=e,{separator:i,nGramWidths:a,leftPad:u,rightPad:l,padWidth:c,preserveShortSequences:p}=n,m=t.readSync(o.dataId),f=t.readSync(s.dataId),[d,h]=rp(m,f,i,a,u,l,c,p),g=t.makeOutput([d.length],"string"),x=t.dataIdMap.get(g.dataId);x.stringBytes=d;let b=t.makeOutput(s.shape,"int32");return t.typedArrayFromHeap(b).set(h),[g,b]}var _H={kernelName:au,backendName:"wasm",kernelFunc:bmt};function wmt(r){let{backend:t,inputs:e,attrs:n}=r,{input:o,delimiter:s}=e,{skipEmpty:i}=n,a=t.readSync(o.dataId),u=t.readSync(s.dataId),[l,c,p]=np(a,u[0],i),m=c.length,f=t.makeOutput([m,2],"int32");t.typedArrayFromHeap(f).set(l);let h=t.makeOutput([m],"string"),g=t.dataIdMap.get(h.dataId);g.stringBytes=c;let x=t.makeOutput([2],"int32");return t.typedArrayFromHeap(x).set(p),[f,h,x]}var EH={kernelName:lu,backendName:"wasm",kernelFunc:wmt};function Imt(r){let{backend:t,inputs:e,attrs:n}=r,{input:o}=e,{numBuckets:s}=n,i=t.readSync(o.dataId),a=op(i,s),u=t.makeOutput(o.shape,"int32");return t.typedArrayFromHeap(u).set(a),u}var AH={kernelName:uu,backendName:"wasm",kernelFunc:Imt};var Cmt=!0,DH=ee(ei,Cmt);var $H;function vmt(r){$H=r.wasm.cwrap(Js,null,["number","number","number","number"])}function Smt(r){let{backend:t,inputs:e,attrs:n}=r,{axis:o,keepDims:s}=n,{x:i}=e,a=t.dataIdMap.get(i.dataId).id,u=a,l=i,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=Cn(i,o,t),d=p;if(f){let w=t.dataIdMap.get(c.dataId).id;w!==a&&(l=c,u=w,d=S.getInnerMostAxes(d.length,l.shape.length))}S.assertAxesAreInnerMostDims("sum",d,l.shape.length);let[h,g]=S.computeOutAndReduceShapes(l.shape,d),x=y.sizeFromShape(g),b=t.makeOutput(h,l.dtype);if(y.sizeFromShape(l.shape)!==0){let w=t.dataIdMap.get(b.dataId).id;$H(u,x,Nt[b.dtype],w)}if(f&&t.disposeData(c.dataId),s){let w=S.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var RH={kernelName:Js,backendName:"wasm",setupFunc:vmt,kernelFunc:Smt};var FH=yt(ri);var OH=yt(ni);var MH;function Nmt(r){MH=r.wasm.cwrap(ol,null,["number","number","number","number","number","number","array","number","number","number"])}function kmt(r){let{backend:t,inputs:e,attrs:n}=r,{tensor:o,indices:s,updates:i}=e,{}=n,a=t.makeOutput(o.shape,o.dtype);if(y.sizeFromShape(o.shape)===0)return a;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:m}=$u.calculateShapes(i,s,o.shape),d=t.dataIdMap.get(s.dataId).id,g=t.dataIdMap.get(i.dataId).id,b=t.dataIdMap.get(o.dataId).id,w=new Uint8Array(new Int32Array(p).buffer),I=t.dataIdMap.get(a.dataId).id;return MH(d,g,Nt[i.dtype],u,l,c,w,m,I,b),a}var PH={kernelName:ol,backendName:"wasm",setupFunc:Nmt,kernelFunc:kmt};var LH;function Tmt(r){LH=r.wasm.cwrap(oo,null,["number","array","number","array","number","number"])}function _mt(r){let{inputs:t,backend:e,attrs:n}=r,{x:o}=t,s=e.dataIdMap.get(o.dataId).id,{reps:i}=n,a=new Array(o.shape.length);for(let m=0;m<a.length;m++)a[m]=o.shape[m]*i[m];let u=new Uint8Array(new Int32Array(o.shape).buffer),l=new Uint8Array(new Int32Array(a).buffer),c=e.makeOutput(a,o.dtype),p=e.dataIdMap.get(c.dataId).id;return LH(s,u,o.shape.length,l,a.length,Nt[c.dtype],p),c}var zH={kernelName:oo,backendName:"wasm",setupFunc:Tmt,kernelFunc:_mt};var BH;function Emt(r){BH=r.wasm.cwrap(ul,null,["number","array","number","number","number","bool","number","number"])}var Amt=({inputs:r,backend:t,attrs:e})=>{let{x:n}=r,{k:o,sorted:s}=e,i=t.dataIdMap.get(n.dataId).id,a=new Uint8Array(new Int32Array(n.shape).buffer),u=n.shape.slice();u[u.length-1]=o;let l=t.makeOutput(u,n.dtype),c=t.dataIdMap.get(l.dataId).id,p=t.makeOutput(u,"int32"),m=t.dataIdMap.get(p.dataId).id;return BH(i,a,n.shape.length,Nt[n.dtype],o,s,c,m),[l,p]},VH={kernelName:ul,backendName:"wasm",setupFunc:Emt,kernelFunc:Amt};var GH;function Dmt(r){GH=r.wasm.cwrap(cl,null,["number","number","bool","number","number","number","number","number","number","array","number","array","number","number","number","number","number"])}function $mt(r){let{backend:t,inputs:e,attrs:n}=r,{image:o,transforms:s}=e,{interpolation:i,fillMode:a,fillValue:u,outputShape:l}=n,[c,p,m,f]=o.shape,[d,h]=l!=null?l:[p,m],g=[c,d,h,f],x=new Uint8Array(new Int32Array(y.computeStrides(o.shape)).buffer),b=new Uint8Array(new Int32Array(y.computeStrides(g)).buffer),w=t.makeOutput(g,o.dtype),I=t.dataIdMap.get(w.dataId).id,E=t.dataIdMap.get(o.dataId).id,D=t.dataIdMap.get(s.dataId).id,F=i==="nearest"?1:2,M;switch(a){case"constant":M=1;break;case"reflect":M=2;break;case"wrap":M=3;break;case"nearest":M=4;break;default:M=1;break}return GH(E,D,s.shape[0]>1,c,d,h,f,m,p,x,o.shape.length-1,b,g.length-1,F,M,u,I),w}var WH={kernelName:cl,backendName:"wasm",setupFunc:Dmt,kernelFunc:$mt};function Rmt(r){let{inputs:t,attrs:e,backend:n}=r,{axis:o}=e,{x:s}=t,{outputValues:i,outputShape:a,indices:u}=sp(n.readSync(s.dataId),o,s.shape,s.dtype);return[n.makeOutput(a,s.dtype,void 0,i),n.makeOutput([u.length],"int32",void 0,u)]}var UH={kernelName:cu,backendName:"wasm",kernelFunc:Rmt};function Fmt(r){let{inputs:t,backend:e,attrs:n}=r,{value:o}=t,{axis:s}=n;s<0&&(s+=o.shape.length);let i=o.shape[s],a=o.shape.length,u=new Array(a-1),l=0;for(let f=0;f<a;f++)f!==s&&(u[l++]=o.shape[f]);let c=new Array(i),p=new Array(a).fill(0),m=o.shape.slice();m[s]=1;for(let f=0;f<c.length;f++)p[s]=f,c[f]=Lo({inputs:{x:o},attrs:{begin:p,size:m},backend:e});return c.map(({dataId:f,dtype:d})=>({dataId:f,dtype:d,shape:u}))}var HH={kernelName:Ki,backendName:"wasm",kernelFunc:Fmt};function Omt(r){let{inputs:{x:t},backend:e}=r,n=e.makeOutput(t.shape,t.dtype);return e.typedArrayFromHeap(n).fill(0),n}var qH={kernelName:ji,backendName:"wasm",kernelFunc:Omt};var Mmt=[DG,$G,RG,FG,OG,PG,GG,UG,HG,qG,KG,jG,XG,YG,ZG,QG,sW,eW,nW,lW,cW,mW,fW,dW,hW,gW,yW,bW,IW,vW,NW,TW,EW,AW,DW,RW,OW,PW,zW,VW,WW,HW,KW,XW,ZW,JW,tU,eU,rU,nU,oU,sU,iU,lU,uU,cU,mU,dU,gU,yU,wU,IU,CU,LG,vU,SU,NU,TU,_U,EU,DU,RU,$U,FU,OU,MU,PU,zU,VU,WU,UU,qU,jU,YU,JU,t4,r4,o4,s4,a4,p4,m4,f4,d4,g4,y4,w4,I4,v4,S4,N4,AC,T4,E4,D4,$4,R4,F4,O4,M4,iW,L4,B4,G4,U4,q4,j4,X4,Y4,J4,tH,rH,nH,sH,iH,aH,lH,uW,u4,uH,cH,mH,dH,gH,xH,bH,wH,IH,CH,vH,NH,TH,_H,EH,AH,DH,RH,FH,OH,PH,zH,VH,WH,BG,UH,HH,qH];for(let r of Mmt)rc(r);var t_=L();t_.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}});t_.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(t_.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 a_=Gl(YH()),rq=Gl(JH()),l_=Gl(QH());var tq=a_.default||a_,Pmt=l_.default||l_,wg=class extends Bo{constructor(t){super(),this.wasm=t,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(oq),i_=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Ta(this,Bn())}write(t,e,n){let o={id:this.dataIdNextNumber++};return this.move(o,t,e,n,1),o}numDataIds(){return this.dataIdMap.numDataIds()}async time(t){let e=y.now();return t(),{kernelMs:y.now()-e}}move(t,e,n,o,s){let i=this.dataIdNextNumber++;if(o==="string"){let c=e;this.dataIdMap.set(t,{id:i,stringBytes:c,shape:n,dtype:o,memoryOffset:null,refCount:s});return}let a=y.sizeFromShape(n),u=a*y.bytesPerElement(o),l=this.wasm._malloc(u)>>>0;this.dataIdMap.set(t,{id:i,memoryOffset:l,shape:n,dtype:o,refCount:s}),this.wasm.tfjs.registerTensor(i,a,l),e!=null&&this.wasm.HEAPU8.set(new Uint8Array(e.buffer,e.byteOffset,u),l)}async read(t){return this.readSync(t)}readSync(t,e,n){let{memoryOffset:o,dtype:s,shape:i,stringBytes:a}=this.dataIdMap.get(t);if(s==="string")return(e==null||e===0)&&(n==null||n>=a.length)?a:a.slice(e,n);e=e||0,n=n||y.sizeFromShape(i);let u=y.bytesPerElement(s),l=this.wasm.HEAPU8.slice(o+e*u,o+n*u);return zmt(l.buffer,s)}disposeData(t,e=!1){if(this.dataIdMap.has(t)){let n=this.dataIdMap.get(t);if(n.refCount--,!e&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(t)}return!0}refCount(t){return this.dataIdMap.has(t)?this.dataIdMap.get(t).refCount:0}incRef(t){let e=this.dataIdMap.get(t);e!=null&&e.refCount++}floatPrecision(){return 32}getMemoryOffset(t){return this.dataIdMap.get(t).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(t,e,n,o){let s;if(n==null)s=this.write(o!=null?o:null,t,e);else{let i=this.dataIdNextNumber++;s={id:i},this.dataIdMap.set(s,{id:i,memoryOffset:n,shape:t,dtype:e,refCount:1});let a=y.sizeFromShape(t);this.wasm.tfjs.registerTensor(i,a,n)}return{dataId:s,shape:t,dtype:e}}typedArrayFromHeap({shape:t,dtype:e,dataId:n}){let o=this.wasm.HEAPU8.buffer,{memoryOffset:s}=this.dataIdMap.get(n),i=y.sizeFromShape(t);switch(e){case"float32":return new Float32Array(o,s,i);case"int32":return new Int32Array(o,s,i);case"bool":return new Uint8Array(o,s,i);default:throw new Error(`Unknown dtype ${e}`)}}};function Lmt(r){return(t,e)=>(y.fetch(r,{credentials:"same-origin"}).then(n=>{n.ok||t.env.a(`failed to load wasm binary file at '${r}'`),n.arrayBuffer().then(o=>{WebAssembly.instantiate(o,t).then(s=>{e(s.instance,s.module)})})}),{})}function eq(r,t,e){if(OC!=null)return OC;let n="tfjs-backend-wasm.wasm";return r&&t?n="tfjs-backend-wasm-threaded-simd.wasm":r&&(n="tfjs-backend-wasm-simd.wasm"),yg!=null&&yg[n]!=null?yg[n]:e+n}async function nq(){let[r,t]=await Promise.all([L().getAsync("WASM_HAS_SIMD_SUPPORT"),L().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((e,n)=>{let o={};o.locateFile=(a,u)=>{if(a.endsWith(".worker.js")){let l=rq.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([l],{type:"application/javascript"});return URL.createObjectURL(c)}return a.endsWith(".wasm")?eq(r,t,xg!=null?xg:u):u+a},u_&&(o.instantiateWasm=Lmt(eq(r,t,xg!=null?xg:"")));let s=!1;o.onAbort=()=>{if(s||bg)return;bg=!0,n({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 i;t&&r&&OC==null?(o.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+tq.toString()],{type:"text/javascript"}),i=tq(o)):i=Pmt(o),i.then(a=>{s=!0,bg=!1;let u=null;a.tfjs={init:a.cwrap("init",null,[]),initWithThreadsCount:a.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:a.cwrap("get_threads_count","number",[]),registerTensor:a.cwrap("register_tensor",null,["number","number","number"]),disposeData:a.cwrap("dispose_data",u,["number"]),dispose:a.cwrap("dispose",u,[])},e({wasm:a})}).catch(n)})}function zmt(r,t){switch(t){case"float32":return new Float32Array(r);case"int32":return new Int32Array(r);case"bool":return new Uint8Array(r);default:throw new Error(`Unknown dtype ${t}`)}}var Bmt=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],OC=null,xg=null,yg={},bg=!1,u_=!1;function Vmt(r,t=!1){if(G0("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),bg)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");OC=r,u_=t}function Gmt(r,t=!1){if(bg)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")xg=r;else{yg=r;let e=Bmt.filter(n=>yg[n]==null);if(e.length>0)throw new Error(`There were no entries found for the following binaries: ${e.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}u_=t}var oq=-1,i_=-1;function Wmt(r){oq=r}function Umt(){if(i_===-1)throw new Error("WASM backend not initialized.");return i_}var Hmt="4.21.0";var qmt=2;Xp("wasm",async()=>{let{wasm:r}=await nq();return new wg(r)},qmt);var sq="4.21.0",Kmt="4.21.0",jmt="4.21.0",Xmt="4.21.0",Ymt="4.21.0",Zmt={tfjs:sq,"tfjs-core":sq,"tfjs-converter":Kmt,"tfjs-backend-cpu":jmt,"tfjs-backend-webgl":Xmt,"tfjs-backend-wasm":Ymt};export{Ai as Abs,Go as Acos,Wo as Acosh,vc as AdadeltaOptimizer,Sc as AdagradOptimizer,Nc as AdamOptimizer,kc as AdamaxOptimizer,no as Add,Uo as AddN,Ea as All,Aa as Any,Di as ArgMax,$i as ArgMin,Ho as Asin,qo as Asinh,Ko as Atan,Xo as Atan2,jo as Atanh,Yo as AvgPool,Ri as AvgPool3D,Hl as AvgPool3DGrad,Ul as AvgPoolGrad,wg as BackendWasm,Zo as BatchMatMul,Fi as BatchToSpaceND,Da as Bincount,$a as BitwiseAnd,ql as BroadcastArgs,__ as BroadcastTo,zb as Callback,Xy as CallbackList,fo as Cast,Jo as Ceil,ho as ClipByValue,Ap as Complex,Kl as ComplexAbs,Oi as Concat,Qo as Conv2D,Dp as Conv2DBackpropFilter,ts as Conv2DBackpropInput,es as Conv3D,Ra as Conv3DBackpropFilterV2,Fa as Conv3DBackpropInputV2,rs as Cos,ns as Cosh,Ma as CropAndResize,Oa as Cumprod,os as Cumsum,Zy as CustomCallback,Ta as DataStorage,jl as DenseBincount,Pa as DepthToSpace,ss as DepthwiseConv2dNative,$p as DepthwiseConv2dNativeBackpropFilter,Rp as DepthwiseConv2dNativeBackpropInput,Xl as Diag,is as Dilation2D,Zl as Dilation2DBackpropFilter,Yl as Dilation2DBackpropInput,Qd as Draw,w0 as ENV,Bb as EarlyStopping,Fp as Einsum,ls as Elu,La as EluGrad,Zd as Environment,za as Equal,us as Erf,cs as Exp,Mi as ExpandDims,ps as Expm1,Op as FFT,Jl as Fill,Ba as FlipLeftRight,ms as Floor,fs as FloorDiv,th as FromPixels,ds as FusedBatchNorm,Yi as FusedConv2D,Zi as FusedDepthwiseConv2D,pp as GPGPUContext,Va as GatherNd,Pi as GatherV2,qh as GraphModel,Ga as Greater,hs as GreaterEqual,Yy as History,Mp as IFFT,go as Identity,Pp as Imag,Ce as InputSpec,gs as IsFinite,xs as IsInf,ys as IsNan,Bo as KernelBackend,Cs as LRN,Xa as LRNGrad,Th as LayerVariable,Un as LayersModel,bs as LeakyRelu,Wa as Less,Ua as LessEqual,Ha as LinSpace,ws as Log,Is as Log1p,A_ as LogSoftmax,qa as LogicalAnd,Ka as LogicalNot,ja as LogicalOr,E_ as LogicalXor,nft as LowerBound,pd as MathBackendCPU,Dd as MathBackendWebGL,oft as MatrixBandPart,vs as Max,Ns as MaxPool,Li as MaxPool3D,tu as MaxPool3DGrad,Ql as MaxPoolGrad,eu as MaxPoolWithArgmax,Ss as Maximum,ks as Mean,Ts as Min,_s as Minimum,Es as MirrorPad,As as Mod,Tc as MomentumOptimizer,Ya as Multinomial,Ds as Multiply,zi as Neg,Ja as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,tl as NonMaxSuppressionV5,Za as NotEqual,V0 as OP_SCOPE_SUFFIX,$s as OneHot,Bi as OnesLike,jr as Optimizer,Ih as OptimizerConstructors,Vi as Pack,Rs as PadV2,sft as Pool,Fs as Pow,Os as Prelu,Ms as Prod,_c as RMSPropOptimizer,po as RNN,Lp as RaggedGather,zp as RaggedRange,Bp as RaggedTensorToTensor,ru as Range,D0 as Rank,Vp as Real,as as RealDiv,Ps as Reciprocal,Je as Reduction,Ls as Relu,Vs as Relu6,Gi as Reshape,Bs as ResizeBilinear,rl as ResizeBilinearGrad,zs as ResizeNearestNeighbor,el as ResizeNearestNeighborGrad,Gs as Reverse,pl as RotateWithOffset,Ws as Round,Us as Rsqrt,Il as SGDOptimizer,nl as ScatterNd,sl as SearchSorted,Wi as Select,Hs as Selu,Wc as Sequential,Xs as Sigmoid,js as Sign,qs as Sin,Ks as Sinh,Ui as Slice,Qs as Softmax,Ys as Softplus,Hi as SpaceToBatchND,nu as SparseFillEmptyRows,il as SparseReshape,ou as SparseSegmentMean,su as SparseSegmentSum,al as SparseToDense,qi as SplitV,Zs as Sqrt,iu as Square,ti as SquaredDifference,ec as StaticRegexReplace,xo as Step,ll as StridedSlice,au as StringNGrams,lu as StringSplit,uu as StringToHashBucketFast,ei as Sub,Js as Sum,nn as SymbolicTensor,ri as Tan,ni as Tanh,Lt as Tensor,le as TensorBuffer,ol as TensorScatterUpdate,oo as Tile,ul as TopK,cl as Transform,so as Transpose,cu as Unique,Ki as Unpack,pu as UnsortedSegmentSum,ift as UpperBound,ml as Variable,ji as ZerosLike,Xi as _FusedMatMul,_e as abs,fx as acos,dx as acosh,K as add,EE as addN,tm as all,cc as any,ra as argMax,hx as argMin,gx as asin,xx as asinh,yx as atan,bx as atan2,wx as atanh,xu as avgPool,Ix as avgPool3d,sx as backend,S as backend_util,$E as basicLSTMCell,oa as batchNorm,Cx as batchNorm2d,vx as batchNorm3d,Sx as batchNorm4d,yu as batchToSpaceND,Nx as bincount,FE as bitwiseAnd,q5 as booleanMaskAsync,OE as broadcastArgs,sa as broadcastTo,Hr as broadcast_util,_y as browser,wt as buffer,xQ as callbacks,J as cast,kx as ceil,vr as clipByValue,un as clone,Sn as complex,ie as concat,Tx as concat1d,_x as concat2d,Ex as concat3d,Ax as concat4d,CR as constraints,rm as conv1d,Nn as conv2d,om as conv2dTranspose,Dx as conv3d,Rx as conv3dTranspose,fft as copyRegisteredKernels,bu as cos,sm as cosh,xh as cosineWindow,mc as cumprod,im as cumsum,pn as customGrad,aO as data,mh as denseBincount,G0 as deprecationWarn,Fx as depthToSpace,ia as depthwiseConv2d,IQ as deregisterOp,du as device_util,ME as diag,Ox as dilation2d,Sdt as disableDeprecationWarnings,Tt as dispose,Ndt as disposeVariables,ut as div,Mx as divNoNan,Px as dot,dN as dropout,wu as einsum,aa as elu,vdt as enableDebugMode,Cdt as enableProdMode,hN as enclosingPowerOfTwo,Bn as engine,LE as ensureShape,L as env,$r as equal,am as erf,Lx as euclideanNorm,Ke as exp,je as expandDims,zx as expm1,fc as eye,Au as fft,Co as fill,Adt as findBackend,Ddt as findBackendFactory,la as floor,Qp as floorDiv,Qz as forceHalfFloat,Ru as fused,ua as gather,r8 as gatherND,Ey as gather_util,oE as getBackend,v0 as getGradient,Wp as getKernel,Yg as getKernelsForBackend,Umt as getThreadsCount,k1 as gpgpu_util,X6 as grad,Y6 as grads,Re as greater,cn as greaterEqual,wl as ifft,Iu as imag,fn as image,s8 as inTopKAsync,vR as initializers,JN as input,Mr as io,bm as irfft,Bx as isFinite,Vx as isInf,Gx as isNaN,De as keep,Xr as kernel_impls,oF as layers,Cu as leakyRelu,yl as less,Vn as lessEqual,xN as linalg,VE as linspace,xtt as loadGraphModel,ytt as loadGraphModelSync,UR as loadLayersModel,Wx as localResponseNormalization,Nr as log,vu as log1p,qx as logSigmoid,lm as logSoftmax,Su as logSumExp,Fr as logicalAnd,Nu as logicalNot,um as logicalOr,Kx as logicalXor,aY as losses,GE as lowerBound,Bt as matMul,O2 as math,Sr as max,ku as maxPool,Xx as maxPool3d,WE as maxPoolWithArgmax,kn as maximum,Ne as mean,lh as memory,UE as meshgrid,sF as metrics,gl as min,lo as minimum,Yx as mirrorPad,Zx as mod,gJ as model,iF as models,dc as moments,X5 as movingAverage,$ as mul,HE as multiRNNCell,qE as multinomial,Ut as neg,Ch as nextFrame,xl as norm,ci as notEqual,ca as oneHot,ar as ones,wr as onesLike,k as op,KE as outerProduct,mn as pad,jE as pad1d,XE as pad2d,YE as pad3d,ZE as pad4d,Jx as pool,qr as pow,_u as prelu,mx as print,Qx as prod,kdt as profile,JE as raggedGather,QE as raggedRange,tA as raggedTensorToTensor,eA as rand,CA as randomGamma,xc as randomNormal,vA as randomStandardNormal,Gn as randomUniform,SA as randomUniformInt,pa as range,_dt as ready,bl as real,sy as reciprocal,Xp as registerBackend,yJ as registerCallbackConstructor,$_ as registerGradient,rc as registerKernel,wQ as registerOp,aF as regularizers,Or as relu,cm as relu6,Edt as removeBackend,R as reshape,dr as reverse,NA as reverse1d,kA as reverse2d,TA as reverse3d,_A as reverse4d,Du as rfft,pm as round,mm as rsqrt,pt as scalar,Z5 as scatterND,$u as scatter_util,dh as searchSorted,fm as selu,dm as separableConv2d,xJ as sequential,Q as serialization,OK as setBackend,$dt as setPlatform,Wmt as setThreadsCount,Vmt as setWasmPath,Gmt as setWasmPaths,BT as setWebGLContext,EA as setdiff1dAsync,Ew as shared,en as sigmoid,iy as sign,iY as signal,hm as sin,gm as sinh,Ot as slice,xm as slice1d,gh as slice2d,ym as slice3d,yc as slice4d,Be as slice_util,Eu as softmax,ui as softplus,Tu as spaceToBatchND,lY as sparse,t8 as sparseToDense,sY as spectral,hr as split,ge as sqrt,Wt as square,wm as squaredDifference,Wn as squeeze,Fe as stack,So as step,ay as stridedSlice,uY as string,at as sub,mt as sum,lc as sumOutType,ly as tan,li as tanh,ir as tensor,Oe as tensor1d,pi as tensor2d,uy as tensor3d,AA as tensor4d,DA as tensor5d,$A as tensor6d,FA as tensorScatterUpdate,Io as tensor_util,IA as test_util,B as tidy,Rr as tile,Tdt as time,cy as topk,Ac as train,Vt as transpose,Cm as truncatedNormal,py as unique,mft as unregisterGradient,pft as unregisterKernel,vm as unsortedSegmentSum,gr as unstack,ur as upcastType,OA as upperBound,y as util,Z6 as valueAndGrad,J6 as valueAndGrads,my as variable,Ux as variableGrads,Zmt as version,VF as version_converter,X2 as version_core,KO as version_cpu,jm as version_layers,Hmt as version_wasm,Jz as version_webgl,T$e as webgl,Cd as webgl_util,Ie as where,dy as whereAsync,ke as zeros,vt as zerosLike};