diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js
index b0df5756..b7ae9287 100644
--- a/dist/human.esm-nobundle.js
+++ b/dist/human.esm-nobundle.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
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   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +14,7 @@ var It=Object.defineProperty;var ir=Object.getOwnPropertyDescriptor;var lr=Objec
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var vo=`
+`;var wo=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +26,7 @@ var It=Object.defineProperty;var ir=Object.getOwnPropertyDescriptor;var lr=Objec
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,To=`
+`,Ro=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +38,7 @@ var It=Object.defineProperty;var ir=Object.getOwnPropertyDescriptor;var lr=Objec
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,wo=`
+`,ko=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +51,7 @@ var It=Object.defineProperty;var ir=Object.getOwnPropertyDescriptor;var lr=Objec
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,Ro=`
+`,Eo=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +74,7 @@ var It=Object.defineProperty;var ir=Object.getOwnPropertyDescriptor;var lr=Objec
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,ko=`
+`,zo=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,7 +96,7 @@ var It=Object.defineProperty;var ir=Object.getOwnPropertyDescriptor;var lr=Objec
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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A=t.shape[1],n=t.shape[2],s=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return r.image.cropAndResize(t,s,[0],o)}function QA(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function yt(e,t=1.5){let o=Le(e),A=lt(e),n=[t*A[0]/2,t*A[1]/2],s=[o[0]-n[0],o[1]-n[1]],a=[o[0]+n[0],o[1]+n[1]];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function xt(e){let t=Le(e),o=lt(e),n=Math.max(...o)/2,s=[t[0]-n,t[1]-n],a=[t[0]+n,t[1]+n];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function $r(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function _A(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return $r(o)}var JA=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Z0(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function e2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function YA(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let s=0;s<A;s++)o[n].push(Z0(e[n],e2(t,s)))}return o}function R5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],s=JA(t[0],t[1]),a=YA(s,n),i=JA(-t[0],-t[1]);return YA(a,i)}function $A(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-Z0(t[0],o),-Z0(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function k5(e,t){return[Z0(e,t[0]),Z0(e,t[1])]}var 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Object.keys(o).forEach(n=>r.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=r.reshape(t,[-1,7,2]),A.div=r.div(A.reshape,this.inputSizeTensor),A.landmarks=r.add(A.div,this.anchors[o]);let n=r.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(s=>r.dispose(A[s])),n}async predict(t,o){let A={};A.resize=r.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=r.div(A.resize,L.tf127),A.image=r.sub(A.div,L.tf1),A.batched=this.model.execute(A.image),A.predictions=r.squeeze(A.batched),A.slice=r.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=r.sigmoid(A.slice),A.scores=r.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=r.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await r.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let s=await A.nms.array(),a=[];for(let i of s){let x={};x.box=r.slice(A.norm,[i,0],[1,-1]),x.slice=r.slice(A.predictions,[i,5],[1,14]),x.norm=this.normalizeLandmarks(x.slice,i),x.palmLandmarks=r.reshape(x.norm,[-1,2]);let d=await x.box.data(),l=d.slice(0,2),y=d.slice(2,4),c=await x.palmLandmarks.array(),f={startPoint:l,endPoint:y,palmLandmarks:c,confidence:n[i]},h=QA(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);a.push(h),Object.keys(x).forEach(m=>r.dispose(x[m]))}return Object.keys(A).forEach(i=>r.dispose(A[i])),a}};var A2=5,on=1.65,An=[0,5,9,13,17,1,2],n2=0,r2=2,nn=0,z5=class{constructor(t,o){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(a=>a[0]),A=t.map(a=>a[1]),n=[Math.min(...o),Math.min(...A)],s=[Math.max(...o),Math.max(...A)];return{startPoint:n,endPoint:s}}getBoxForPalmLandmarks(t,o){let A=t.map(s=>k5([...s,1],o)),n=this.calculateLandmarksBoundingBox(A);return yt(xt(n),A2)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),A=yt(xt(o),on);A.palmLandmarks=[];for(let n=0;n<An.length;n++)A.palmLandmarks.push(t[An[n]].slice(0,2));return A}transformRawCoords(t,o,A,n){let s=lt(o),a=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],i=t.map(f=>[a[0]*(f[0]-this.inputSize/2),a[1]*(f[1]-this.inputSize/2),a[2]*f[2]]),x=R5(A,[0,0]),d=i.map(f=>[...k5(f,x),f[2]]),l=$A(n),y=[...Le(o),1],c=[Z0(y,l[0]),Z0(y,l[1])];return d.map(f=>[Math.trunc(f[0]+c[0]),Math.trunc(f[1]+c[1]),Math.trunc(f[2])])}async estimateHands(t,o){let A=!1,n,s=(o.hand.skipTime||0)>M()-nn,a=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&s&&a&&(n=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,n&&n.length>0&&(n.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...n],this.storedBoxes.length>0&&(A=!0));let i=[];for(let x=0;x<this.storedBoxes.length;x++){let d=this.storedBoxes[x];if(!!d)if(o.hand.landmarks){let l=o.hand.rotation?_A(d.palmLandmarks[n2],d.palmLandmarks[r2]):0,y=Le(d),c=[y[0]/t.shape[2],y[1]/t.shape[1]],f=o.hand.rotation&&w.kernels.includes("rotatewithoffset")?r.image.rotateWithOffset(t,l,0,c):t.clone(),h=R5(-l,y),m=A?this.getBoxForPalmLandmarks(d.palmLandmarks,h):d,v=KA(m,f,[this.inputSize,this.inputSize]),b=r.div(v,L.tf255);r.dispose(v),r.dispose(f);let[P,p]=this.handPoseModel.execute(b);nn=M(),r.dispose(b);let u=(await P.data())[0];if(r.dispose(P),u>=o.hand.minConfidence/4){let E=r.reshape(p,[-1,3]),W=await E.array();r.dispose(p),r.dispose(E);let C=this.transformRawCoords(W,m,l,h),S=this.getBoxForHandLandmarks(C);this.storedBoxes[x]={...S,confidence:u};let O={landmarks:C,confidence:u,boxConfidence:d.confidence,fingerConfidence:u,box:{topLeft:S.startPoint,bottomRight:S.endPoint}};i.push(O)}else this.storedBoxes[x]=null;r.dispose(p)}else{let l=yt(xt(d),on),y={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:l.startPoint,bottomRight:l.endPoint},landmarks:[]};i.push(y)}}return this.storedBoxes=this.storedBoxes.filter(x=>x!==null),this.detectedHands=i.length,i.length>o.hand.maxDetected&&(i.length=o.hand.maxDetected),i}};var 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i=o[n].box?[Math.trunc(Math.max(0,o[n].box.topLeft[0])),Math.trunc(Math.max(0,o[n].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[n].box.bottomRight[0])-Math.max(0,o[n].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[n].box.bottomRight[1])-Math.max(0,o[n].box.topLeft[1]))]:[0,0,0,0],x=[o[n].box.topLeft[0]/(e.shape[2]||0),o[n].box.topLeft[1]/(e.shape[1]||0),(o[n].box.bottomRight[0]-o[n].box.topLeft[0])/(e.shape[2]||0),(o[n].box.bottomRight[1]-o[n].box.topLeft[1])/(e.shape[1]||0)];let d=ct(a);A.push({id:n,score:Math.round(100*o[n].confidence)/100,boxScore:Math.round(100*o[n].boxConfidence)/100,fingerScore:Math.round(100*o[n].fingerConfidence)/100,label:"hand",box:i,boxRaw:x,keypoints:a,annotations:s,landmarks:d})}return A}async function C5(e){var o,A;w.initial&&(be=null,ge=null),!be||!ge?[be,ge]=await Promise.all([e.hand.enabled?N((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?N((A=e.hand.skeleton)==null?void 0:A.modelPath):null]):(e.debug&&g("cached model:",be.modelUrl),e.debug&&g("cached model:",ge.modelUrl));let t=new E5(be);return mn=new z5(t,ge),[be,ge]}var $=[null,null],f2=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],J0=[[0,0],[0,0]],m2=["hand","fist","pinch","point","face","tip","pinchtip"],un=4,hn=1.6,p2=512,u2=1.4,dt=Number.MAX_SAFE_INTEGER,I5=0,G0=[0,0],J={boxes:[],hands:[]},bn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function gn(e){var t;if(w.initial&&($[0]=null),$[0])e.debug&&g("cached model:",$[0].modelUrl);else{ft(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),$[0]=await N((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values($[0].modelSignature.inputs);J0[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,J0[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return $[0]}async function Mn(e){var t;if(w.initial&&($[1]=null),$[1])e.debug&&g("cached model:",$[1].modelUrl);else{$[1]=await N((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values($[1].modelSignature.inputs);J0[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,J0[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return $[1]}async function h2(e,t){let o=[];if(!e||!$[0])return o;let A={},n=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,p2),a=Math.round(s*n/8)*8;A.resize=r.image.resizeBilinear(e,[s,a]),A.cast=r.cast(A.resize,"int32"),[A.rawScores,A.rawBoxes]=await $[0].executeAsync(A.cast,f2),A.boxes=r.squeeze(A.rawBoxes,[0,2]),A.scores=r.squeeze(A.rawScores,[0]);let i=r.unstack(A.scores,1);r.dispose(i[un]),i.splice(un,1),A.filtered=r.stack(i,1),r.dispose(i),A.max=r.max(A.filtered,1),A.argmax=r.argMax(A.filtered,1);let x=0;A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await A.nms.data(),l=await A.max.data(),y=await A.argmax.data();for(let c of Array.from(d)){let f=r.slice(A.boxes,c,1),h=await f.data();r.dispose(f);let m=[h[1],h[0],h[3]-h[1],h[2]-h[0]],v=ot(m,u2),b=[Math.trunc(m[0]*G0[0]),Math.trunc(m[1]*G0[1]),Math.trunc(m[2]*G0[0]),Math.trunc(m[3]*G0[1])],P=l[c],p=m2[y[c]],u={id:x++,score:P,box:b,boxRaw:v,label:p};o.push(u)}return Object.keys(A).forEach(c=>r.dispose(A[c])),o.sort((c,f)=>f.score-c.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function O5(e,t,o){let A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&$[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=r.image.cropAndResize(e,[s],[0],[J0[1][0],J0[1][1]],"bilinear"),n.div=r.div(n.crop,L.tf255),[n.score,n.keypoints]=$[1].execute(n.div,["Identity_1","Identity"]);let a=(await n.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(o.hand.minConfidence||0)){A.fingerScore=i,n.reshaped=r.reshape(n.keypoints,[-1,3]);let l=(await n.reshaped.array()).map(y=>[y[0]/J0[1][1],y[1]/J0[1][0],y[2]||0]).map(y=>[y[0]*t.boxRaw[2],y[1]*t.boxRaw[3],y[2]||0]);A.keypoints=l.map(y=>[G0[0]*(y[0]+t.boxRaw[0]),G0[1]*(y[1]+t.boxRaw[1]),y[2]||0]),A.landmarks=ct(A.keypoints);for(let y of Object.keys(bn))A.annotations[y]=bn[y].map(c=>A.landmarks&&A.keypoints[c]?A.keypoints[c]:null)}Object.keys(n).forEach(x=>r.dispose(n[x]))}return A}async function N5(e,t){var n,s;if(!$[0]||!$[1]||!((n=$[0])!=null&&n.inputs[0].shape)||!((s=$[1])!=null&&s.inputs[0].shape))return[];G0=[e.shape[2]||0,e.shape[1]||0],dt++;let o=(t.hand.skipTime||0)>M()-I5,A=dt<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?J.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>M()-I5,x=dt<3*(t.hand.skipFrames||0);t.skipAllowed&&J.hands.length===t.hand.maxDetected?J.hands=await Promise.all(J.boxes.map(l=>O5(e,l,t))):t.skipAllowed&&i&&x&&J.hands.length>0?J.hands=await Promise.all(J.boxes.map(l=>O5(e,l,t))):(J.boxes=await h2(e,t),I5=M(),J.hands=await Promise.all(J.boxes.map(l=>O5(e,l,t))),dt=0);let d=[...J.boxes];if(J.boxes.length=0,t.cacheSensitivity>0)for(let l=0;l<J.hands.length;l++){let y=dA(J.hands[l].keypoints,G0);if(y.box[2]/(e.shape[2]||1)>.05&&y.box[3]/(e.shape[1]||1)>.05&&J.hands[l].fingerScore&&J.hands[l].fingerScore>(t.hand.minConfidence||0)){let c=ot(y.box,hn),f=ot(y.boxRaw,hn);J.boxes.push({...d[l],box:c,boxRaw:f})}}for(let l=0;l<J.hands.length;l++){let y=W0(J.hands[l].keypoints,G0);J.hands[l].box=y.box,J.hands[l].boxRaw=y.boxRaw}a(J.hands)})}var r0,mt=[],L5=Number.MAX_SAFE_INTEGER,vn=0,Tn=0;async function wn(e){var t;return w.initial&&(r0=null),r0?e.debug&&g("cached model:",r0.modelUrl):r0=await N((t=e.face.liveness)==null?void 0:t.modelPath),r0}async function G5(e,t,o,A){var a,i;if(!r0)return 0;let n=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>M()-Tn,s=L5<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&n&&s&&vn===A&&mt[o]?(L5++,mt[o]):(L5=0,new Promise(async x=>{let d=r.image.resizeBilinear(e,[r0!=null&&r0.inputs[0].shape?r0.inputs[0].shape[2]:0,r0!=null&&r0.inputs[0].shape?r0.inputs[0].shape[1]:0],!1),l=r0==null?void 0:r0.execute(d),y=(await l.data())[0];mt[o]=Math.round(100*y)/100,vn=A,Tn=M(),r.dispose([d,l]),x(mt[o])}))}var Ge={};Y0(Ge,{connected:()=>ut,horizontal:()=>B5,kpt:()=>pt,relative:()=>F5,vertical:()=>H5});var pt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],B5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],H5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],F5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],ut={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var kn=.005,d0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function V5(e){for(let t of B5){let o=e.keypoints.findIndex(n=>n.part===t[0]),A=e.keypoints.findIndex(n=>n.part===t[1]);if(e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[0]<e.keypoints[A].position[0]){let n=e.keypoints[o];e.keypoints[o]=e.keypoints[A],e.keypoints[A]=n}}for(let t of H5){let o=e.keypoints.findIndex(n=>n&&n.part===t[0]),A=e.keypoints.findIndex(n=>n&&n.part===t[1]);e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[1]<e.keypoints[A].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of F5){let A=e.keypoints.findIndex(d=>d&&d.part===t[0]),n=e.keypoints.findIndex(d=>d&&d.part===t[1]),s=e.keypoints.findIndex(d=>d&&d.part===o[0]),a=e.keypoints.findIndex(d=>d&&d.part===o[1]);if(!e.keypoints[s]||!e.keypoints[a])continue;let i=e.keypoints[A]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[A].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[A].position[0])]:[0,0],x=e.keypoints[n]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0];if(i[0]>i[1]||x[0]>x[1]){let d=e.keypoints[A];e.keypoints[A]=e.keypoints[n],e.keypoints[n]=d}}}function En(e){for(let t=0;t<e.length;t++)if(e[t]&&d0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-d0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-d0.keypoints[t].positionRaw[1])];o[0]<kn&&o[1]<kn?e[t]=d0.keypoints[t]:d0.keypoints[t]=e[t]}else d0.keypoints[t]=e[t];return e}function zn(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;d0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=r.pad(e,d0.padding),o.resize=r.image.resizeBilinear(o.pad,[t,t]);let A=r.cast(o.resize,"int32");return Object.keys(o).forEach(n=>r.dispose(o[n])),A}function jn(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+d0.padding[2][0]+d0.padding[2][1])/t[0]-d0.padding[2][0],A.position[1]*(t[1]+d0.padding[1][0]+d0.padding[1][1])/t[1]-d0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=W0(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var f0,ht=0,Z5=Number.MAX_SAFE_INTEGER,ne={boxes:[],bodies:[],last:0};async function Sn(e){return w.initial&&(f0=null),f0?e.debug&&g("cached model:",f0.modelUrl):(ft(["size"],e),f0=await N(e.body.modelPath)),ht=f0.inputs[0].shape?f0.inputs[0].shape[2]:0,ht<64&&(ht=256),f0}async function g2(e,t,o){let A=e[0][0],n=[],s=0;for(let l=0;l<A.length;l++)if(s=A[l][2],s>t.body.minConfidence){let y=[A[l][1],A[l][0]];n.push({score:Math.round(100*s)/100,part:pt[l],positionRaw:y,position:[Math.round((o.shape[2]||0)*y[0]),Math.round((o.shape[1]||0)*y[1])]})}s=n.reduce((l,y)=>y.score>l?y.score:l,0);let a=[],i=W0(n.map(l=>l.position),[o.shape[2],o.shape[1]]),x={};for(let[l,y]of Object.entries(ut)){let c=[];for(let f=0;f<y.length-1;f++){let h=n.find(v=>v.part===y[f]),m=n.find(v=>v.part===y[f+1]);h&&m&&h.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&c.push([h.position,m.position])}x[l]=c}let d={id:0,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:n,annotations:x};return V5(d),a.push(d),a}async function M2(e,t,o){let A=[];for(let n=0;n<e[0].length;n++){let s=e[0][n],a=Math.round(100*s[51+4])/100;if(a>t.body.minConfidence){let i=[];for(let y=0;y<17;y++){let c=s[3*y+2];if(c>t.body.minConfidence){let f=[s[3*y+1],s[3*y+0]];i.push({part:pt[y],score:Math.round(100*c)/100,positionRaw:f,position:[Math.round((o.shape[2]||0)*f[0]),Math.round((o.shape[1]||0)*f[1])]})}}let x=W0(i.map(y=>y.position),[o.shape[2],o.shape[1]]),d={};for(let[y,c]of Object.entries(ut)){let f=[];for(let h=0;h<c.length-1;h++){let m=i.find(b=>b.part===c[h]),v=i.find(b=>b.part===c[h+1]);m&&v&&m.score>(t.body.minConfidence||0)&&v.score>(t.body.minConfidence||0)&&f.push([m.position,v.position])}d[y]=f}let l={id:n,score:a,box:x.box,boxRaw:x.boxRaw,keypoints:[...i],annotations:d};V5(l),A.push(l)}}return A.sort((n,s)=>s.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function X5(e,t){if(!f0||!(f0!=null&&f0.inputs[0].shape))return[];t.skipAllowed||(ne.boxes.length=0),Z5++;let o=(t.body.skipTime||0)>M()-ne.last,A=Z5<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?ne.bodies:new Promise(async n=>{let s={};Z5=0,s.input=zn(e,ht),s.res=f0==null?void 0:f0.execute(s.input),ne.last=M();let a=await s.res.array();ne.bodies=s.res.shape[2]===17?await g2(a,t,e):await M2(a,t,e);for(let i of ne.bodies)jn(i,[e.shape[2]||1,e.shape[1]||1]),En(i.keypoints);Object.keys(s).forEach(i=>r.dispose(s[i])),n(ne.bodies)})}var Me,bt=[],Cn=0,D5=Number.MAX_SAFE_INTEGER,Mt=0,gt=2.5;async function In(e){if(!Me||w.initial){Me=await N(e.object.modelPath);let t=Object.values(Me.modelSignature.inputs);Mt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&g("cached model:",Me.modelUrl);return Me}async function P2(e,t,o){let A=0,n=[];for(let x of[1,2,4])r.tidy(async()=>{let d=x*13,l=r.squeeze(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)===ce.length)),y=r.squeeze(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)<ce.length)),f=await y.reshape([-1,4,y.shape[1]/4]).argMax(2).array(),h=await l.array();for(let m=0;m<l.shape[0];m++)for(let v=0;v<l.shape[1];v++){let b=h[m][v];if(b>(o.object.minConfidence||0)&&v!==61){let P=(.5+Math.trunc(m%d))/d,p=(.5+Math.trunc(m/d))/d,u=f[m].map(H=>H*(d/x/Mt)),[E,W]=[P-gt/x*u[0],p-gt/x*u[1]],[C,S]=[P+gt/x*u[2]-E,p+gt/x*u[3]-W],O=[E,W,C,S];O=O.map(H=>Math.max(0,Math.min(H,1)));let V=[O[0]*t[0],O[1]*t[1],O[2]*t[0],O[3]*t[1]],G={id:A++,score:Math.round(100*b)/100,class:v+1,label:ce[v].label,box:V.map(H=>Math.trunc(H)),boxRaw:O};n.push(G)}}});e.forEach(x=>r.dispose(x));let s=n.map(x=>[x.boxRaw[1],x.boxRaw[0],x.boxRaw[3],x.boxRaw[2]]),a=n.map(x=>x.score),i=[];if(s&&s.length>0){let x=await r.image.nonMaxSuppressionAsync(s,a,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);i=await x.data(),r.dispose(x)}return n=n.filter((x,d)=>i.includes(d)).sort((x,d)=>d.score-x.score),n}async function q5(e,t){let o=(t.object.skipTime||0)>M()-Cn,A=D5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&bt.length>0?(D5++,bt):(D5=0,!w.kernels.includes("mod")||!w.kernels.includes("sparsetodense")?bt:new Promise(async n=>{let s=[e.shape[2]||0,e.shape[1]||0],a=r.image.resizeBilinear(e,[Mt,Mt],!1),i=r.div(a,L.tf255),x=i.transpose([0,3,1,2]);r.dispose(i),r.dispose(a);let d;t.object.enabled&&(d=Me.execute(x)),Cn=M(),r.dispose(x);let l=await P2(d,s,t);bt=l,n(l)}))}var He=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],v2=He.length,Be=He.reduce((e,t,o)=>(e[t]=o,e),{}),T2=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Va=T2.map(([e,t])=>[Be[e],Be[t]]),Nn=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Ln(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:s},{position:{x:a,y:i}})=>({maxX:Math.max(o,a),maxY:Math.max(A,i),minX:Math.min(n,a),minY:Math.min(s,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function Gn(e,[t,o],[A,n]){let s=t/A,a=o/n,i=(d,l)=>({id:l,score:d.score,boxRaw:[d.box[0]/n,d.box[1]/A,d.box[2]/n,d.box[3]/A],box:[Math.trunc(d.box[0]*a),Math.trunc(d.box[1]*s),Math.trunc(d.box[2]*a),Math.trunc(d.box[3]*s)],keypoints:d.keypoints.map(({score:y,part:c,position:f})=>({score:y,part:c,position:[Math.trunc(f.x*a),Math.trunc(f.y*s)],positionRaw:[f.x/A,f.y/A]})),annotations:{}});return e.map((d,l)=>i(d,l))}var U5=class{constructor(t,o){R(this,"priorityQueue");R(this,"numberOfElements");R(this,"getElementValue");this.priorityQueue=new 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J5(e,t,o,A){return{y:A.get(e,t,o),x:A.get(e,t,o+v2)}}function Y5(e,t,o){let{heatmapY:A,heatmapX:n,id:s}=e,{y:a,x:i}=J5(A,n,s,o);return{x:e.heatmapX*t+i,y:e.heatmapY*t+a}}function K5(e,t,o){return e<t?t:e>o?o:e}function Bn(e,t,o,A){let n=o-e,s=A-t;return n*n+s*s}function Q5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var v0,R2=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],Pt=1,Pe=16,k2=50**2;function Hn(e,t,o,A,n,s,a=2){let i=b=>({y:s.get(b.y,b.x,e),x:s.get(b.y,b.x,s.shape[2]/2+e)}),x=(b,P,p)=>({y:K5(Math.round(b.y/Pe),0,P-1),x:K5(Math.round(b.x/Pe),0,p-1)}),[d,l]=A.shape,y=x(t.position,d,l),c=i(y),h=Q5(t.position,c);for(let b=0;b<a;b++){let P=x(h,d,l),p=J5(P.y,P.x,o,n);h=Q5({x:P.x*Pe,y:P.y*Pe},{x:p.x,y:p.y})}let m=x(h,d,l),v=A.get(m.y,m.x,o);return{position:h,part:He[o],score:v}}function E2(e,t,o,A,n){let 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A=t.shape[1],n=t.shape[2],s=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return r.image.cropAndResize(t,s,[0],o)}function QA(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function xt(e,t=1.5){let o=Le(e),A=yt(e),n=[t*A[0]/2,t*A[1]/2],s=[o[0]-n[0],o[1]-n[1]],a=[o[0]+n[0],o[1]+n[1]];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function ct(e){let t=Le(e),o=yt(e),n=Math.max(...o)/2,s=[t[0]-n,t[1]-n],a=[t[0]+n,t[1]+n];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function e2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function _A(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return e2(o)}var JA=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Z0(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function t2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function YA(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let s=0;s<A;s++)o[n].push(Z0(e[n],t2(t,s)))}return o}function S5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],s=JA(t[0],t[1]),a=YA(s,n),i=JA(-t[0],-t[1]);return YA(a,i)}function $A(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-Z0(t[0],o),-Z0(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function W5(e,t){return[Z0(e,t[0]),Z0(e,t[1])]}var 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Object.keys(o).forEach(n=>r.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=r.reshape(t,[-1,7,2]),A.div=r.div(A.reshape,this.inputSizeTensor),A.landmarks=r.add(A.div,this.anchors[o]);let n=r.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(s=>r.dispose(A[s])),n}async predict(t,o){let A={};A.resize=r.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=r.div(A.resize,L.tf127),A.image=r.sub(A.div,L.tf1),A.batched=this.model.execute(A.image),A.predictions=r.squeeze(A.batched),A.slice=r.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=r.sigmoid(A.slice),A.scores=r.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=r.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await r.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let s=await A.nms.array(),a=[];for(let i of s){let x={};x.box=r.slice(A.norm,[i,0],[1,-1]),x.slice=r.slice(A.predictions,[i,5],[1,14]),x.norm=this.normalizeLandmarks(x.slice,i),x.palmLandmarks=r.reshape(x.norm,[-1,2]);let d=await x.box.data(),l=d.slice(0,2),y=d.slice(2,4),c=await x.palmLandmarks.array(),f={startPoint:l,endPoint:y,palmLandmarks:c,confidence:n[i]},h=QA(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);a.push(h),Object.keys(x).forEach(m=>r.dispose(x[m]))}return Object.keys(A).forEach(i=>r.dispose(A[i])),a}};var n2=5,on=1.65,An=[0,5,9,13,17,1,2],r2=0,s2=2,nn=0,ft=class{constructor(t,o){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(a=>a[0]),A=t.map(a=>a[1]),n=[Math.min(...o),Math.min(...A)],s=[Math.max(...o),Math.max(...A)];return{startPoint:n,endPoint:s}}getBoxForPalmLandmarks(t,o){let A=t.map(s=>W5([...s,1],o)),n=this.calculateLandmarksBoundingBox(A);return xt(ct(n),n2)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),A=xt(ct(o),on);A.palmLandmarks=[];for(let n=0;n<An.length;n++)A.palmLandmarks.push(t[An[n]].slice(0,2));return A}transformRawCoords(t,o,A,n){let s=yt(o),a=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],i=t.map(f=>[a[0]*(f[0]-this.inputSize/2),a[1]*(f[1]-this.inputSize/2),a[2]*f[2]]),x=S5(A,[0,0]),d=i.map(f=>[...W5(f,x),f[2]]),l=$A(n),y=[...Le(o),1],c=[Z0(y,l[0]),Z0(y,l[1])];return d.map(f=>[Math.trunc(f[0]+c[0]),Math.trunc(f[1]+c[1]),Math.trunc(f[2])])}async estimateHands(t,o){let A=!1,n,s=(o.hand.skipTime||0)>M()-nn,a=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&s&&a&&(n=await 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C=this.transformRawCoords(W,m,l,h),S=this.getBoxForHandLandmarks(C);this.storedBoxes[x]={...S,confidence:u};let O={landmarks:C,confidence:u,boxConfidence:d.confidence,fingerConfidence:u,box:{topLeft:S.startPoint,bottomRight:S.endPoint}};i.push(O)}else this.storedBoxes[x]=null;r.dispose(p)}else{let l=xt(ct(d),on),y={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:l.startPoint,bottomRight:l.endPoint},landmarks:[]};i.push(y)}}return this.storedBoxes=this.storedBoxes.filter(x=>x!==null),this.detectedHands=i.length,i.length>o.hand.maxDetected&&(i.length=o.hand.maxDetected),i}};var 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i0.all)o[i0.getName(A)]={curl:D0.getName(t.curls[A]),direction:U.getName(t.directions[A])};return o}function dn(e){let t=[];if(!e||e.length===0)return t;let o=cn(e);for(let A of rn){let n=A.matchAgainst(o.curls,o.directions);n>=x2&&t.push({name:A.name,confidence:n})}return t}var fn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},be,ge,mn;async function O5(e,t){let o=await mn.estimateHands(e,t);if(!o)return[];let A=[];for(let n=0;n<o.length;n++){let s={};if(o[n].landmarks)for(let l of Object.keys(fn))s[l]=fn[l].map(y=>o[n].landmarks[y]);let a=o[n].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],x=[0,0,0,0];if(a&&a.length>0){for(let l of a)l[0]<i[0]&&(i[0]=l[0]),l[1]<i[1]&&(i[1]=l[1]),l[0]>i[2]&&(i[2]=l[0]),l[1]>i[3]&&(i[3]=l[1]);i[2]-=i[0],i[3]-=i[1],x=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=o[n].box?[Math.trunc(Math.max(0,o[n].box.topLeft[0])),Math.trunc(Math.max(0,o[n].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[n].box.bottomRight[0])-Math.max(0,o[n].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[n].box.bottomRight[1])-Math.max(0,o[n].box.topLeft[1]))]:[0,0,0,0],x=[o[n].box.topLeft[0]/(e.shape[2]||0),o[n].box.topLeft[1]/(e.shape[1]||0),(o[n].box.bottomRight[0]-o[n].box.topLeft[0])/(e.shape[2]||0),(o[n].box.bottomRight[1]-o[n].box.topLeft[1])/(e.shape[1]||0)];let d=mt(a);A.push({id:n,score:Math.round(100*o[n].confidence)/100,boxScore:Math.round(100*o[n].boxConfidence)/100,fingerScore:Math.round(100*o[n].fingerConfidence)/100,label:"hand",box:i,boxRaw:x,keypoints:a,annotations:s,landmarks:d})}return A}async function N5(e){var o,A;w.initial&&(be=null,ge=null),!be||!ge?[be,ge]=await Promise.all([e.hand.enabled?N((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?N((A=e.hand.skeleton)==null?void 0:A.modelPath):null]):(e.debug&&g("cached model:",be.modelUrl),e.debug&&g("cached model:",ge.modelUrl));let t=new dt(be);return mn=new ft(t,ge),[be,ge]}var $=[null,null],m2=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Y0=[[0,0],[0,0]],p2=["hand","fist","pinch","point","face","tip","pinchtip"],un=4,hn=1.6,u2=512,h2=1.4,pt=Number.MAX_SAFE_INTEGER,L5=0,G0=[0,0],J={boxes:[],hands:[]},bn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function gn(e){var t;if(w.initial&&($[0]=null),$[0])e.debug&&g("cached model:",$[0].modelUrl);else{ut(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),$[0]=await N((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values($[0].modelSignature.inputs);Y0[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Y0[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return $[0]}async function Mn(e){var t;if(w.initial&&($[1]=null),$[1])e.debug&&g("cached model:",$[1].modelUrl);else{$[1]=await N((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values($[1].modelSignature.inputs);Y0[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Y0[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return $[1]}async function b2(e,t){let o=[];if(!e||!$[0])return o;let A={},n=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,u2),a=Math.round(s*n/8)*8;A.resize=r.image.resizeBilinear(e,[s,a]),A.cast=r.cast(A.resize,"int32"),[A.rawScores,A.rawBoxes]=await $[0].executeAsync(A.cast,m2),A.boxes=r.squeeze(A.rawBoxes,[0,2]),A.scores=r.squeeze(A.rawScores,[0]);let i=r.unstack(A.scores,1);r.dispose(i[un]),i.splice(un,1),A.filtered=r.stack(i,1),r.dispose(i),A.max=r.max(A.filtered,1),A.argmax=r.argMax(A.filtered,1);let x=0;A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await A.nms.data(),l=await A.max.data(),y=await A.argmax.data();for(let c of Array.from(d)){let f=r.slice(A.boxes,c,1),h=await f.data();r.dispose(f);let m=[h[1],h[0],h[3]-h[1],h[2]-h[0]],v=At(m,h2),b=[Math.trunc(m[0]*G0[0]),Math.trunc(m[1]*G0[1]),Math.trunc(m[2]*G0[0]),Math.trunc(m[3]*G0[1])],P=l[c],p=p2[y[c]],u={id:x++,score:P,box:b,boxRaw:v,label:p};o.push(u)}return Object.keys(A).forEach(c=>r.dispose(A[c])),o.sort((c,f)=>f.score-c.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function G5(e,t,o){let A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&$[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=r.image.cropAndResize(e,[s],[0],[Y0[1][0],Y0[1][1]],"bilinear"),n.div=r.div(n.crop,L.tf255),[n.score,n.keypoints]=$[1].execute(n.div,["Identity_1","Identity"]);let a=(await n.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(o.hand.minConfidence||0)){A.fingerScore=i,n.reshaped=r.reshape(n.keypoints,[-1,3]);let l=(await n.reshaped.array()).map(y=>[y[0]/Y0[1][1],y[1]/Y0[1][0],y[2]||0]).map(y=>[y[0]*t.boxRaw[2],y[1]*t.boxRaw[3],y[2]||0]);A.keypoints=l.map(y=>[G0[0]*(y[0]+t.boxRaw[0]),G0[1]*(y[1]+t.boxRaw[1]),y[2]||0]),A.landmarks=mt(A.keypoints);for(let y of Object.keys(bn))A.annotations[y]=bn[y].map(c=>A.landmarks&&A.keypoints[c]?A.keypoints[c]:null)}Object.keys(n).forEach(x=>r.dispose(n[x]))}return A}async function B5(e,t){var n,s;if(!$[0]||!$[1]||!((n=$[0])!=null&&n.inputs[0].shape)||!((s=$[1])!=null&&s.inputs[0].shape))return[];G0=[e.shape[2]||0,e.shape[1]||0],pt++;let o=(t.hand.skipTime||0)>M()-L5,A=pt<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?J.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>M()-L5,x=pt<3*(t.hand.skipFrames||0);t.skipAllowed&&J.hands.length===t.hand.maxDetected?J.hands=await Promise.all(J.boxes.map(l=>G5(e,l,t))):t.skipAllowed&&i&&x&&J.hands.length>0?J.hands=await Promise.all(J.boxes.map(l=>G5(e,l,t))):(J.boxes=await b2(e,t),L5=M(),J.hands=await Promise.all(J.boxes.map(l=>G5(e,l,t))),pt=0);let d=[...J.boxes];if(J.boxes.length=0,t.cacheSensitivity>0)for(let l=0;l<J.hands.length;l++){let y=dA(J.hands[l].keypoints,G0);if(y.box[2]/(e.shape[2]||1)>.05&&y.box[3]/(e.shape[1]||1)>.05&&J.hands[l].fingerScore&&J.hands[l].fingerScore>(t.hand.minConfidence||0)){let c=At(y.box,hn),f=At(y.boxRaw,hn);J.boxes.push({...d[l],box:c,boxRaw:f})}}for(let l=0;l<J.hands.length;l++){let y=W0(J.hands[l].keypoints,G0);J.hands[l].box=y.box,J.hands[l].boxRaw=y.boxRaw}a(J.hands)})}var r0,ht=[],H5=Number.MAX_SAFE_INTEGER,vn=0,Tn=0;async function wn(e){var t;return w.initial&&(r0=null),r0?e.debug&&g("cached model:",r0.modelUrl):r0=await N((t=e.face.liveness)==null?void 0:t.modelPath),r0}async function F5(e,t,o,A){var a,i;if(!r0)return 0;let n=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>M()-Tn,s=H5<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&n&&s&&vn===A&&ht[o]?(H5++,ht[o]):(H5=0,new Promise(async x=>{let d=r.image.resizeBilinear(e,[r0!=null&&r0.inputs[0].shape?r0.inputs[0].shape[2]:0,r0!=null&&r0.inputs[0].shape?r0.inputs[0].shape[1]:0],!1),l=r0==null?void 0:r0.execute(d),y=(await l.data())[0];ht[o]=Math.round(100*y)/100,vn=A,Tn=M(),r.dispose([d,l]),x(ht[o])}))}var Ge={};K0(Ge,{connected:()=>gt,horizontal:()=>V5,kpt:()=>bt,relative:()=>X5,vertical:()=>Z5});var bt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],V5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Z5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],X5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],gt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var kn=.005,d0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function D5(e){for(let t of V5){let o=e.keypoints.findIndex(n=>n.part===t[0]),A=e.keypoints.findIndex(n=>n.part===t[1]);if(e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[0]<e.keypoints[A].position[0]){let n=e.keypoints[o];e.keypoints[o]=e.keypoints[A],e.keypoints[A]=n}}for(let t of Z5){let o=e.keypoints.findIndex(n=>n&&n.part===t[0]),A=e.keypoints.findIndex(n=>n&&n.part===t[1]);e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[1]<e.keypoints[A].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of X5){let A=e.keypoints.findIndex(d=>d&&d.part===t[0]),n=e.keypoints.findIndex(d=>d&&d.part===t[1]),s=e.keypoints.findIndex(d=>d&&d.part===o[0]),a=e.keypoints.findIndex(d=>d&&d.part===o[1]);if(!e.keypoints[s]||!e.keypoints[a])continue;let i=e.keypoints[A]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[A].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[A].position[0])]:[0,0],x=e.keypoints[n]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0];if(i[0]>i[1]||x[0]>x[1]){let d=e.keypoints[A];e.keypoints[A]=e.keypoints[n],e.keypoints[n]=d}}}function En(e){for(let t=0;t<e.length;t++)if(e[t]&&d0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-d0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-d0.keypoints[t].positionRaw[1])];o[0]<kn&&o[1]<kn?e[t]=d0.keypoints[t]:d0.keypoints[t]=e[t]}else d0.keypoints[t]=e[t];return e}function zn(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;d0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=r.pad(e,d0.padding),o.resize=r.image.resizeBilinear(o.pad,[t,t]);let A=r.cast(o.resize,"int32");return Object.keys(o).forEach(n=>r.dispose(o[n])),A}function jn(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+d0.padding[2][0]+d0.padding[2][1])/t[0]-d0.padding[2][0],A.position[1]*(t[1]+d0.padding[1][0]+d0.padding[1][1])/t[1]-d0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=W0(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var f0,Mt=0,q5=Number.MAX_SAFE_INTEGER,ne={boxes:[],bodies:[],last:0};async function Sn(e){return w.initial&&(f0=null),f0?e.debug&&g("cached model:",f0.modelUrl):(ut(["size"],e),f0=await N(e.body.modelPath)),Mt=f0.inputs[0].shape?f0.inputs[0].shape[2]:0,Mt<64&&(Mt=256),f0}async function M2(e,t,o){let A=e[0][0],n=[],s=0;for(let l=0;l<A.length;l++)if(s=A[l][2],s>t.body.minConfidence){let y=[A[l][1],A[l][0]];n.push({score:Math.round(100*s)/100,part:bt[l],positionRaw:y,position:[Math.round((o.shape[2]||0)*y[0]),Math.round((o.shape[1]||0)*y[1])]})}s=n.reduce((l,y)=>y.score>l?y.score:l,0);let a=[],i=W0(n.map(l=>l.position),[o.shape[2],o.shape[1]]),x={};for(let[l,y]of Object.entries(gt)){let c=[];for(let f=0;f<y.length-1;f++){let h=n.find(v=>v.part===y[f]),m=n.find(v=>v.part===y[f+1]);h&&m&&h.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&c.push([h.position,m.position])}x[l]=c}let d={id:0,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:n,annotations:x};return D5(d),a.push(d),a}async function P2(e,t,o){let A=[];for(let n=0;n<e[0].length;n++){let s=e[0][n],a=Math.round(100*s[51+4])/100;if(a>t.body.minConfidence){let i=[];for(let y=0;y<17;y++){let c=s[3*y+2];if(c>t.body.minConfidence){let f=[s[3*y+1],s[3*y+0]];i.push({part:bt[y],score:Math.round(100*c)/100,positionRaw:f,position:[Math.round((o.shape[2]||0)*f[0]),Math.round((o.shape[1]||0)*f[1])]})}}let x=W0(i.map(y=>y.position),[o.shape[2],o.shape[1]]),d={};for(let[y,c]of Object.entries(gt)){let f=[];for(let h=0;h<c.length-1;h++){let m=i.find(b=>b.part===c[h]),v=i.find(b=>b.part===c[h+1]);m&&v&&m.score>(t.body.minConfidence||0)&&v.score>(t.body.minConfidence||0)&&f.push([m.position,v.position])}d[y]=f}let l={id:n,score:a,box:x.box,boxRaw:x.boxRaw,keypoints:[...i],annotations:d};D5(l),A.push(l)}}return A.sort((n,s)=>s.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function U5(e,t){if(!f0||!(f0!=null&&f0.inputs[0].shape))return[];t.skipAllowed||(ne.boxes.length=0),q5++;let o=(t.body.skipTime||0)>M()-ne.last,A=q5<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?ne.bodies:new Promise(async n=>{let s={};q5=0,s.input=zn(e,Mt),s.res=f0==null?void 0:f0.execute(s.input),ne.last=M();let a=await s.res.array();ne.bodies=s.res.shape[2]===17?await M2(a,t,e):await P2(a,t,e);for(let i of ne.bodies)jn(i,[e.shape[2]||1,e.shape[1]||1]),En(i.keypoints);Object.keys(s).forEach(i=>r.dispose(s[i])),n(ne.bodies)})}var Me,Pt=[],Cn=0,J5=Number.MAX_SAFE_INTEGER,Tt=0,vt=2.5;async function In(e){if(!Me||w.initial){Me=await N(e.object.modelPath);let t=Object.values(Me.modelSignature.inputs);Tt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&g("cached model:",Me.modelUrl);return Me}async function v2(e,t,o){let A=0,n=[];for(let x of[1,2,4])r.tidy(async()=>{let d=x*13,l=r.squeeze(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)===ce.length)),y=r.squeeze(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)<ce.length)),f=await y.reshape([-1,4,y.shape[1]/4]).argMax(2).array(),h=await l.array();for(let m=0;m<l.shape[0];m++)for(let v=0;v<l.shape[1];v++){let b=h[m][v];if(b>(o.object.minConfidence||0)&&v!==61){let P=(.5+Math.trunc(m%d))/d,p=(.5+Math.trunc(m/d))/d,u=f[m].map(H=>H*(d/x/Tt)),[E,W]=[P-vt/x*u[0],p-vt/x*u[1]],[C,S]=[P+vt/x*u[2]-E,p+vt/x*u[3]-W],O=[E,W,C,S];O=O.map(H=>Math.max(0,Math.min(H,1)));let V=[O[0]*t[0],O[1]*t[1],O[2]*t[0],O[3]*t[1]],G={id:A++,score:Math.round(100*b)/100,class:v+1,label:ce[v].label,box:V.map(H=>Math.trunc(H)),boxRaw:O};n.push(G)}}});e.forEach(x=>r.dispose(x));let s=n.map(x=>[x.boxRaw[1],x.boxRaw[0],x.boxRaw[3],x.boxRaw[2]]),a=n.map(x=>x.score),i=[];if(s&&s.length>0){let x=await r.image.nonMaxSuppressionAsync(s,a,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);i=await x.data(),r.dispose(x)}return n=n.filter((x,d)=>i.includes(d)).sort((x,d)=>d.score-x.score),n}async function Y5(e,t){let o=(t.object.skipTime||0)>M()-Cn,A=J5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Pt.length>0?(J5++,Pt):(J5=0,!w.kernels.includes("mod")||!w.kernels.includes("sparsetodense")?Pt:new Promise(async n=>{let s=[e.shape[2]||0,e.shape[1]||0],a=r.image.resizeBilinear(e,[Tt,Tt],!1),i=r.div(a,L.tf255),x=i.transpose([0,3,1,2]);r.dispose(i),r.dispose(a);let d;t.object.enabled&&(d=Me.execute(x)),Cn=M(),r.dispose(x);let l=await v2(d,s,t);Pt=l,n(l)}))}var He=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],T2=He.length,Be=He.reduce((e,t,o)=>(e[t]=o,e),{}),w2=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Va=w2.map(([e,t])=>[Be[e],Be[t]]),Nn=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Ln(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:s},{position:{x:a,y:i}})=>({maxX:Math.max(o,a),maxY:Math.max(A,i),minX:Math.min(n,a),minY:Math.min(s,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function Gn(e,[t,o],[A,n]){let s=t/A,a=o/n,i=(d,l)=>({id:l,score:d.score,boxRaw:[d.box[0]/n,d.box[1]/A,d.box[2]/n,d.box[3]/A],box:[Math.trunc(d.box[0]*a),Math.trunc(d.box[1]*s),Math.trunc(d.box[2]*a),Math.trunc(d.box[3]*s)],keypoints:d.keypoints.map(({score:y,part:c,position:f})=>({score:y,part:c,position:[Math.trunc(f.x*a),Math.trunc(f.y*s)],positionRaw:[f.x/A,f.y/A]})),annotations:{}});return e.map((d,l)=>i(d,l))}var wt=class{constructor(t,o){R(this,"priorityQueue");R(this,"numberOfElements");R(this,"getElementValue");this.priorityQueue=new 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K5(e,t,o,A){return{y:A.get(e,t,o),x:A.get(e,t,o+T2)}}function Q5(e,t,o){let{heatmapY:A,heatmapX:n,id:s}=e,{y:a,x:i}=K5(A,n,s,o);return{x:e.heatmapX*t+i,y:e.heatmapY*t+a}}function _5(e,t,o){return e<t?t:e>o?o:e}function Bn(e,t,o,A){let n=o-e,s=A-t;return n*n+s*s}function $5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var v0,k2=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],Rt=1,Pe=16,E2=50**2;function Hn(e,t,o,A,n,s,a=2){let i=b=>({y:s.get(b.y,b.x,e),x:s.get(b.y,b.x,s.shape[2]/2+e)}),x=(b,P,p)=>({y:_5(Math.round(b.y/Pe),0,P-1),x:_5(Math.round(b.x/Pe),0,p-1)}),[d,l]=A.shape,y=x(t.position,d,l),c=i(y),h=$5(t.position,c);for(let b=0;b<a;b++){let P=x(h,d,l),p=K5(P.y,P.x,o,n);h=$5({x:P.x*Pe,y:P.y*Pe},{x:p.x,y:p.y})}let m=x(h,d,l),v=A.get(m.y,m.x,o);return{position:h,part:He[o],score:v}}function z2(e,t,o,A,n){let 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y=Math.abs(l.annotations.leftEyeIris[3][0]-l.annotations.leftEyeIris[1][0])/2,c=Math.abs(l.annotations.leftEyeIris[4][1]-l.annotations.leftEyeIris[2][1])/2;n.ellipse(l.annotations.leftEyeIris[0][0],l.annotations.leftEyeIris[0][1],y,c,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(l.annotations&&l.annotations.rightEyeIris&&l.annotations.rightEyeIris[0]){n.strokeStyle=A.useDepth?"rgba(255, 200, 255, 0.3)":A.color,n.beginPath();let y=Math.abs(l.annotations.rightEyeIris[3][0]-l.annotations.rightEyeIris[1][0])/2,c=Math.abs(l.annotations.rightEyeIris[4][1]-l.annotations.rightEyeIris[2][1])/2;n.ellipse(l.annotations.rightEyeIris[0][0],l.annotations.rightEyeIris[0][1],y,c,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(A.drawGaze&&((s=l.rotation)==null?void 0:s.angle)&&typeof Path2D!="undefined"){n.strokeStyle="pink";let 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@@ -108,7 +108,7 @@ var It=Object.defineProperty;var ir=Object.getOwnPropertyDescriptor;var lr=Objec
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A=o0(k0,o);if(!t||!e)return;let n=re(e);if(!!n){n.lineJoin="round";for(let a=0;a<t.length;a++){if(n.strokeStyle=A.color,n.fillStyle=A.color,n.lineWidth=A.lineWidth,n.font=A.font,A.drawBoxes&&t[a].box&&((s=t[a].box)==null?void 0:s.length)===4&&(Fe(n,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],A),A.drawLabels&&(A.shadowColor&&A.shadowColor!==""&&(n.fillStyle=A.shadowColor,n.fillText(`body ${100*t[a].score}%`,t[a].box[0]+3,1+t[a].box[1]+A.lineHeight,t[a].box[2])),n.fillStyle=A.labelColor,n.fillText(`body ${100*t[a].score}%`,t[a].box[0]+2,0+t[a].box[1]+A.lineHeight,t[a].box[2]))),A.drawPoints&&t[a].keypoints)for(let i=0;i<t[a].keypoints.length;i++)!t[a].keypoints[i].score||t[a].keypoints[i].score===0||(n.fillStyle=A.useDepth&&t[a].keypoints[i].position[2]?`rgba(${127.5+2*(t[a].keypoints[i].position[2]||0)}, ${127.5-2*(t[a].keypoints[i].position[2]||0)}, 255, 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Y=c[k].annotations&&c[k].annotations.leftEyeIris&&c[k].annotations.leftEyeIris[0]&&c[k].annotations.rightEyeIris&&c[k].annotations.rightEyeIris[0]&&c[k].annotations.leftEyeIris.length>0&&c[k].annotations.rightEyeIris.length>0&&c[k].annotations.leftEyeIris[0]!==null&&c[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[k].annotations.leftEyeIris[3][0]-c[k].annotations.leftEyeIris[1][0]),Math.abs(c[k].annotations.rightEyeIris[4][1]-c[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,e0=(Q=e.config.face.detector)!=null&&Q.return?r.squeeze(c[k].tensor):null;r.dispose(c[k].tensor),c[k].tensor&&delete c[k].tensor;let I={...c[k],id:k};l!=null&&l.age&&(I.age=l.age),l!=null&&l.gender&&(I.gender=l.gender),l!=null&&l.genderScore&&(I.genderScore=l==null?void 0:l.genderScore),l!=null&&l.descriptor&&(I.embedding=l==null?void 0:l.descriptor),l!=null&&l.race&&(I.race=l==null?void 0:l.race),a&&(I.emotion=a),x&&(I.real=x),d&&(I.live=d),Y&&Y!==0&&(I.iris=Math.trunc(500/Y/11.7)/100),X&&(I.rotation=X),e0&&(I.tensor=e0),y.push(I),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var $n=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=e[o].keypoints.find(x=>x.part==="leftWrist"),n=e[o].keypoints.find(x=>x.part==="rightWrist"),s=e[o].keypoints.find(x=>x.part==="nose");s&&A&&n&&A.position[1]<s.position[1]&&n.position[1]<s.position[1]?t.push({body:o,gesture:"i give up"}):s&&A&&A.position[1]<s.position[1]?t.push({body:o,gesture:"raise left hand"}):s&&n&&n.position[1]<s.position[1]&&t.push({body:o,gesture:"raise right hand"});let a=e[o].keypoints.find(x=>x.part==="leftShoulder"),i=e[o].keypoints.find(x=>x.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},er=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));i>10&&t.push({face:o,gesture:`mouth ${Math.trunc(i)}% open`});let x=e[o].mesh[152][2]||0;Math.abs(x)>10&&t.push({face:o,gesture:`head ${x<0?"up":"down"}`})}return t},tr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let A=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],n=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],s=Math.abs(A*n),a=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],i=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],x=Math.abs(a*i),d=!1;Math.abs(s-x)/Math.max(s,x)<.25&&(d=!0,t.push({iris:o,gesture:"facing center"}));let y=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],c=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(y>.06||c>.06)&&(d=!1),y>c?y>.05&&t.push({iris:o,gesture:"looking right"}):c>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],h=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(h<.01||f<.01||h>.022||f>.022)&&(d=!1),(h<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(h>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),d&&t.push({iris:o,gesture:"looking center"})}return t},or=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=[];if(e[o].annotations)for(let[n,s]of Object.entries(e[o].annotations))n!=="palmBase"&&Array.isArray(s)&&s[0]&&A.push({name:n.toLowerCase(),position:s[0]});if(A&&A.length>0){let n=A.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:o,gesture:`${n.name} forward`});let s=A.reduce((a,i)=>a.position[1]<i.position[1]?a:i);t.push({hand:o,gesture:`${s.name} up`})}if(e[o].keypoints){let n=dn(e[o].keypoints);for(let s of n)t.push({hand:o,gesture:s.name})}}return t};var 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T=0;T<e.hand.length;T++){let Q=e.hand[T].box.map((e0,I)=>((n-1)*j.hand[T].box[I]+e0)/n),k=e.hand[T].boxRaw.map((e0,I)=>((n-1)*j.hand[T].boxRaw[I]+e0)/n);j.hand[T].keypoints.length!==e.hand[T].keypoints.length&&(j.hand[T].keypoints=e.hand[T].keypoints);let X=e.hand[T].keypoints&&e.hand[T].keypoints.length>0?e.hand[T].keypoints.map((e0,I)=>e0.map((F,E0)=>((n-1)*(j.hand[T].keypoints[I][E0]||1)+(F||0))/n)):[],Y={};if(Object.keys(j.hand[T].annotations).length!==Object.keys(e.hand[T].annotations).length)j.hand[T].annotations=e.hand[T].annotations,Y=j.hand[T].annotations;else if(e.hand[T].annotations)for(let e0 of Object.keys(e.hand[T].annotations))Y[e0]=e.hand[T].annotations[e0]&&e.hand[T].annotations[e0][0]?e.hand[T].annotations[e0].map((I,F)=>I.map((E0,z0)=>((n-1)*j.hand[T].annotations[e0][F][z0]+E0)/n)):null;j.hand[T]={...e.hand[T],box:Q,boxRaw:k,keypoints:X,annotations:Y}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let T=0;T<e.face.length;T++){let Q=e.face[T].box.map((X,Y)=>((n-1)*j.face[T].box[Y]+X)/n),k=e.face[T].boxRaw.map((X,Y)=>((n-1)*j.face[T].boxRaw[Y]+X)/n);if(e.face[T].rotation){let X={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};X.matrix=(c=e.face[T].rotation)==null?void 0:c.matrix,X.angle={roll:((n-1)*(((h=(f=j.face[T].rotation)==null?void 0:f.angle)==null?void 0:h.roll)||0)+(((v=(m=e.face[T].rotation)==null?void 0:m.angle)==null?void 0:v.roll)||0))/n,yaw:((n-1)*(((P=(b=j.face[T].rotation)==null?void 0:b.angle)==null?void 0:P.yaw)||0)+(((u=(p=e.face[T].rotation)==null?void 0:p.angle)==null?void 0:u.yaw)||0))/n,pitch:((n-1)*(((W=(E=j.face[T].rotation)==null?void 0:E.angle)==null?void 0:W.pitch)||0)+(((S=(C=e.face[T].rotation)==null?void 0:C.angle)==null?void 0:S.pitch)||0))/n},X.gaze={bearing:((n-1)*(((V=(O=j.face[T].rotation)==null?void 0:O.gaze)==null?void 0:V.bearing)||0)+(((H=(G=e.face[T].rotation)==null?void 0:G.gaze)==null?void 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s=0,a=[];for(let C of e){let S={id:s++,face:C,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let z of t)C.box[0]>z.box[0]&&C.box[0]<z.box[0]+z.box[2]&&C.box[1]+C.box[3]>z.box[1]&&C.box[1]+C.box[3]<z.box[1]+z.box[3]&&(S.body=z);if(S.body)for(let z of o)z.box[0]+z.box[2]>S.body.box[0]&&z.box[0]+z.box[2]<S.body.box[0]+S.body.box[2]&&z.box[1]+z.box[3]>S.body.box[1]&&z.box[1]+z.box[3]<S.body.box[1]+S.body.box[3]&&S.hands&&(S.hands.left=z),z.box[0]<S.body.box[0]+S.body.box[2]&&z.box[0]>S.body.box[0]&&z.box[1]+z.box[3]>S.body.box[1]&&z.box[1]+z.box[3]<S.body.box[1]+S.body.box[3]&&S.hands&&(S.hands.right=z);for(let z of A)z.face!==void 0&&z.face===C.id?(i=S.gestures)==null||i.push(z):z.iris!==void 0&&z.iris===C.id?(x=S.gestures)==null||x.push(z):z.body!==void 0&&z.body===((d=S.body)==null?void 0:d.id)?(l=S.gestures)==null||l.push(z):z.hand!==void 0&&z.hand===((c=(y=S.hands)==null?void 0:y.left)==null?void 0:c.id)?(f=S.gestures)==null||f.push(z):z.hand!==void 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A=M(),n=o0(k0,o),s=Promise.all([jt(e,t.face,n),St(e,t.body,n),Wt(e,t.hand,n),Ct(e,t.object,n),zt(e,t.gesture,n)]);return so=w.perfadd?so+Math.round(M()-A):Math.round(M()-A),t.performance.draw=so,s}var Te=.1,xo=.5;function G2(e,t,o){let A=!1,n=o.length-1;for(let s=0;s<o.length;n=s++)o[s].y>t!=o[n].y>t&&e<(o[n].x-o[s].x)*(t-o[s].y)/(o[n].y-o[s].y)+o[s].x&&(A=!A);return A}async function Qn(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,o=e.tensor.shape[1]||0,A=await e.tensor.buffer(),n=[];for(let a of b0.silhouette)n.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});Te&&Te>0&&(n=n.map(a=>({x:a.x>.5?a.x+Te:a.x-Te,y:a.y>.5?a.y+Te:a.y-Te})));for(let a=0;a<t;a++)for(let i=0;i<o;i++)G2(a/t,i/t,n)||(A.set(xo*A.get(0,i,a,0),0,i,a,0),A.set(xo*A.get(0,i,a,1),0,i,a,1),A.set(xo*A.get(0,i,a,2),0,i,a,2));let s=A.toTensor();return r.dispose(A),s}var H2=e=>{let 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e5(c[k].tensor||r.tensor([]),e.config,k,c.length):0,e.performance.antispoof=w.perfadd?(e.performance.antispoof||0)+Math.trunc(M()-o):Math.trunc(M()-o)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?d=(P=e.config.face.liveness)!=null&&P.enabled?F5(c[k].tensor||r.tensor([]),e.config,k,c.length):0:(e.state="run:liveness",o=M(),d=(p=e.config.face.liveness)!=null&&p.enabled?await F5(c[k].tensor||r.tensor([]),e.config,k,c.length):0,e.performance.liveness=w.perfadd?(e.performance.antispoof||0)+Math.trunc(M()-o):Math.trunc(M()-o)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?n=(u=e.config.face.gear)!=null&&u.enabled?Ut(c[k].tensor||r.tensor([]),e.config,k,c.length):null:(e.state="run:gear",o=M(),n=(E=e.config.face.gear)!=null&&E.enabled?await Ut(c[k].tensor||r.tensor([]),e.config,k,c.length):null,e.performance.gear=Math.trunc(M()-o)),e.analyze("End GEAR:"),e.analyze("Start 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Description:"),e.config.async?l=(H=e.config.face.description)!=null&&H.enabled?z5(c[k].tensor||r.tensor([]),e.config,k,c.length):null:(e.state="run:description",o=M(),l=(t0=e.config.face.description)!=null&&t0.enabled?await z5(c[k].tensor||r.tensor([]),e.config,k,c.length):null,e.performance.description=w.perfadd?(e.performance.description||0)+Math.trunc(M()-o):Math.trunc(M()-o)),e.analyze("End Description:"),e.config.async&&([A,s,a,i,l,n,x,d]=await Promise.all([A,s,a,i,l,n,x,d])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&A&&s&&(l={...l,age:A.age,gender:s.gender,genderScore:s.genderScore}),((m0=e.config.face.gear)==null?void 0:m0.enabled)&&n&&(l={...l,age:n.age,gender:n.gender,genderScore:n.genderScore,race:n.race}),((B0=e.config.face.mobilefacenet)==null?void 0:B0.enabled)&&i&&(l.descriptor=i),(T=e.config.face.iris)!=null&&T.enabled;let Y=c[k].annotations&&c[k].annotations.leftEyeIris&&c[k].annotations.leftEyeIris[0]&&c[k].annotations.rightEyeIris&&c[k].annotations.rightEyeIris[0]&&c[k].annotations.leftEyeIris.length>0&&c[k].annotations.rightEyeIris.length>0&&c[k].annotations.leftEyeIris[0]!==null&&c[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[k].annotations.leftEyeIris[3][0]-c[k].annotations.leftEyeIris[1][0]),Math.abs(c[k].annotations.rightEyeIris[4][1]-c[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,e0=(Q=e.config.face.detector)!=null&&Q.return?r.squeeze(c[k].tensor):null;r.dispose(c[k].tensor),c[k].tensor&&delete c[k].tensor;let I={...c[k],id:k};l!=null&&l.age&&(I.age=l.age),l!=null&&l.gender&&(I.gender=l.gender),l!=null&&l.genderScore&&(I.genderScore=l==null?void 0:l.genderScore),l!=null&&l.descriptor&&(I.embedding=l==null?void 0:l.descriptor),l!=null&&l.race&&(I.race=l==null?void 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a=e[o].keypoints.find(x=>x.part==="leftShoulder"),i=e[o].keypoints.find(x=>x.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},er=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));i>10&&t.push({face:o,gesture:`mouth ${Math.trunc(i)}% open`});let 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vt,this.draw={options:k0,canvas:(o,A)=>ao(o,A),face:(o,A,n)=>kt(o,A,n),body:(o,A,n)=>Et(o,A,n),hand:(o,A,n)=>zt(o,A,n),gesture:(o,A,n)=>Rt(o,A,n),object:(o,A,n)=>jt(o,A,n),person:(o,A,n)=>so(o,A,n),all:(o,A,n)=>io(o,A,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=ZA,this.faceUVMap=XA,this.gl=q,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(K0)),this.config.backend=t}validate(t){return Ot(K0,t||this.config)}now(){return M()}image(t,o=!0){return le(t,this.config,o)}async segmentation(t,o){return Xn(t,o,this.config)}enhance(t){return v5(t)}compare(t,o){return So(this.config,t,o)}async init(){await wt(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=M(),A=Object.values(this.models).filter(a=>a).length;t&&(this.config=o0(this.config,t)),this.env.initial&&(this.config.debug&&g(`version: ${this.version}`),this.config.debug&&g(`tfjs version: ${this.tf.version["tfjs-core"]}`),await wt(this)||g("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&g("configuration:",this.config),this.config.debug&&g("environment:",this.env),this.config.debug&&g("tf flags:",this.tf.ENV.flags))),await to(this),this.env.initial&&this.config.debug&&g("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(a=>a).length!==A&&(await oo(this),this.emit("load"));let s=Math.trunc(M()-o);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return Ar(t,this.config)}async warmup(t){let o=M(),A=await ar(this,t),n=M();return this.performance.warmup=Math.trunc(n-o),A}async profile(t,o){let A=await this.tf.profile(()=>this.detect(t,o)),n={};for(let i of A.kernels)n[i.name]?n[i.name]+=i.kernelTimeMs:n[i.name]=i.kernelTimeMs;let s=[];Object.entries(n).forEach(i=>s.push({name:i[0],ms:i[1]})),s.sort((i,x)=>x.ms-i.ms),s.length=20;let a={};for(let i of s)a[i.name]=i.ms;return a}async detect(t,o){return this.state="detect",new Promise(async A=>{var v,b,P,p,u,E,W,C,S,O,V,G,H,t0,z,m0,B0,T,Q,k,X,Y;this.state="config";let n;this.config=o0(this.config,o),this.state="check";let s=Ee(this,Ct).call(this,t);s&&(g(s,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:M(),persons:[],error:s}));let a=M();await wt(this),await this.load(),n=M(),this.state="image";let i=await le(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(M()-n):Math.trunc(M()-n),this.analyze("Get Image:"),!i.tensor){this.config.debug&&g("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:M(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=M(),this.config.skipAllowed=await jo(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(M()-n):Math.trunc(M()-n),this.analyze("Check Changed:");let x=[],d=[],l=[],y=[];this.state="detect:face",this.config.async?(x=this.config.face.enabled?yo(this,i.tensor):[],this.performance.face&&delete this.performance.face):(n=M(),x=this.config.face.enabled?await yo(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(x=await x),this.analyze("Start Body:"),this.state="detect:body";let c=this.config.body.maxDetected===-1?o0(this.config,{body:{maxDetected:this.config.face.enabled?1*x.length:1}}):this.config;this.config.async?((v=this.config.body.modelPath)!=null&&v.includes("posenet")?d=this.config.body.enabled?_5(i.tensor,c):[]:(b=this.config.body.modelPath)!=null&&b.includes("blazepose")?d=this.config.body.enabled?a5(i.tensor,c):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?d=this.config.body.enabled?f5(i.tensor,c):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(d=this.config.body.enabled?X5(i.tensor,c):[]),this.performance.body&&delete this.performance.body):(n=M(),(u=this.config.body.modelPath)!=null&&u.includes("posenet")?d=this.config.body.enabled?await _5(i.tensor,c):[]:(E=this.config.body.modelPath)!=null&&E.includes("blazepose")?d=this.config.body.enabled?await a5(i.tensor,c):[]:(W=this.config.body.modelPath)!=null&&W.includes("efficientpose")?d=this.config.body.enabled?await f5(i.tensor,c):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(d=this.config.body.enabled?await X5(i.tensor,c):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?o0(this.config,{hand:{maxDetected:this.config.face.enabled?2*x.length:1}}):this.config;this.config.async?((O=(S=this.config.hand.detector)==null?void 0:S.modelPath)!=null&&O.includes("handdetect")?l=this.config.hand.enabled?W5(i.tensor,f):[]:(G=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&G.includes("handtrack")&&(l=this.config.hand.enabled?N5(i.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(n=M(),(t0=(H=this.config.hand.detector)==null?void 0:H.modelPath)!=null&&t0.includes("handdetect")?l=this.config.hand.enabled?await W5(i.tensor,f):[]:(m0=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&m0.includes("handtrack")&&(l=this.config.hand.enabled?await N5(i.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((B0=this.config.object.modelPath)!=null&&B0.includes("nanodet")?y=this.config.object.enabled?q5(i.tensor,this.config):[]:(T=this.config.object.modelPath)!=null&&T.includes("centernet")&&(y=this.config.object.enabled?y5(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=M(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?y=this.config.object.enabled?await q5(i.tensor,this.config):[]:(k=this.config.object.modelPath)!=null&&k.includes("centernet")&&(y=this.config.object.enabled?await y5(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([x,d,l,y]=await Promise.all([x,d,l,y])),this.state="detect:gesture";let h=[];this.config.gesture.enabled&&(n=M(),h=[...er(x),...$n(d),...or(l),...tr(x)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(M()-a):Math.trunc(M()-a);let m=((Y=(X=this.process)==null?void 0:X.tensor)==null?void 0:Y.shape)||[];this.result={face:x,body:d,hand:l,gesture:h,object:y,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return sr(x,d,l,h,m)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};we=new WeakMap,Ze=new WeakMap,Xe=new WeakMap,Ct=new WeakMap;export{K2 as Human,K2 as default,K0 as defaults,Kn as draw,w as env,rr as match,Ao as models};
+2Q==`;async function q2(e){let t=(n,s="application/octet-stream")=>fetch(`data:${s};base64,${n}`).then(a=>a.blob()),o,A;switch(e.config.warmup){case"face":o=await t(It);break;case"body":case"full":o=await t(Ot);break;default:o=null}if(o){let n=await createImageBitmap(o);A=await e.detect(n,e.config),n.close()}return A}async function U2(e){return new Promise(t=>{let o;switch(e.config.warmup){case"face":o="data:image/jpeg;base64,"+It;break;case"full":case"body":o="data:image/jpeg;base64,"+Ot;break;default:o=null}let A;if(typeof Image!="undefined")A=new Image;else if(w.Image)A=new w.Image;else return;A.onload=async()=>{let n=s0(A.naturalWidth,A.naturalHeight);if(!n)g("Warmup: Canvas not found"),t(void 0);else{let s=n.getContext("2d");s&&s.drawImage(A,0,0);let a=await e.image(n),i=await e.detect(a.tensor,e.config);t(i)}},o?A.src=o:t(void 0)})}async function J2(e){let t=n=>Buffer.from(n,"base64"),o;e.config.warmup==="face"?o=t(It):o=t(Ot);let A;if("node"in r){let n=r.node.decodeJpeg(o),s=n.expandDims(0);e.tf.dispose(n),A=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&g("Warmup tfjs-node not loaded");return A}async function Y2(e){let t;return typeof createImageBitmap=="function"?t=await q2(e):typeof Image!="undefined"||w.Canvas!==void 0?t=await U2(e):t=await J2(e),t}async function ar(e,t){let o=M();return e.state="warmup",t&&(e.config=o0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:M(),persons:[],error:null}:new Promise(async A=>{let n=await Y2(e),s=M();e.config.debug&&g("warmup",e.config.warmup,Math.round(s-o),"ms"),e.emit("warmup"),A(n)})}var we,Xe,De,Nt,ir=class{constructor(t){R(this,"version");R(this,"config");R(this,"result");R(this,"state");R(this,"process");R(this,"tf");R(this,"env");R(this,"draw");R(this,"models");R(this,"events");R(this,"faceTriangulation");R(this,"faceUVMap");R(this,"performance");ze(this,we,void 0);ze(this,Xe,void 0);ze(this,De,void 0);R(this,"gl");R(this,"analyze",(...t)=>{if(!Ee(this,Xe))return;let o=this.tf.engine().state.numTensors,A=Ee(this,we);je(this,we,o);let n=o-A;n!==0&&g(...t,n)});ze(this,Nt,t=>{if(!Ee(this,De))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ae))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});R(this,"similarity",mo);R(this,"distance",Ze);R(this,"match",po);R(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new Event(t)))});this.env=w,Q0.wasmPath=Se["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${r.version_core}/dist/`,Q0.modelBasePath=w.browser?"../models/":"file://models/",Q0.backend=w.browser?"humangl":"tensorflow",this.version=Xt,Object.defineProperty(this,"version",{value:Xt}),this.config=JSON.parse(JSON.stringify(Q0)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=o0(this.config,t)),Co(this.config),this.tf=r,this.state="idle",je(this,we,0),je(this,Xe,!1),je(this,De,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new Fe,this.draw={options:k0,canvas:(o,A)=>lo(o,A),face:(o,A,n)=>jt(o,A,n),body:(o,A,n)=>St(o,A,n),hand:(o,A,n)=>Wt(o,A,n),gesture:(o,A,n)=>zt(o,A,n),object:(o,A,n)=>Ct(o,A,n),person:(o,A,n)=>io(o,A,n),all:(o,A,n)=>yo(o,A,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=ZA,this.faceUVMap=XA,this.gl=q,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Q0)),this.config.backend=t}validate(t){return Gt(Q0,t||this.config)}now(){return M()}image(t,o=!0){return le(t,this.config,o)}async segmentation(t,o){return Xn(t,o,this.config)}enhance(t){return E5(t)}compare(t,o){return Wo(this.config,t,o)}async init(){await Et(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=M(),A=Object.values(this.models).filter(a=>a).length;t&&(this.config=o0(this.config,t)),this.env.initial&&(this.config.debug&&g(`version: ${this.version}`),this.config.debug&&g(`tfjs version: ${this.tf.version["tfjs-core"]}`),await Et(this)||g("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&g("configuration:",this.config),this.config.debug&&g("environment:",this.env),this.config.debug&&g("tf flags:",this.tf.ENV.flags))),await Ao(this),this.env.initial&&this.config.debug&&g("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(a=>a).length!==A&&(await no(this),this.emit("load"));let s=Math.trunc(M()-o);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return Ar(t,this.config)}async warmup(t){let o=M(),A=await ar(this,t),n=M();return this.performance.warmup=Math.trunc(n-o),A}async profile(t,o){let A=await this.tf.profile(()=>this.detect(t,o)),n={};for(let i of A.kernels)n[i.name]?n[i.name]+=i.kernelTimeMs:n[i.name]=i.kernelTimeMs;let s=[];Object.entries(n).forEach(i=>s.push({name:i[0],ms:i[1]})),s.sort((i,x)=>x.ms-i.ms),s.length=20;let a={};for(let i of s)a[i.name]=i.ms;return a}async detect(t,o){return this.state="detect",new Promise(async A=>{var v,b,P,p,u,E,W,C,S,O,V,G,H,t0,z,m0,B0,T,Q,k,X,Y;this.state="config";let n;this.config=o0(this.config,o),this.state="check";let s=Ee(this,Nt).call(this,t);s&&(g(s,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:M(),persons:[],error:s}));let a=M();await Et(this),await this.load(),n=M(),this.state="image";let i=await le(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(M()-n):Math.trunc(M()-n),this.analyze("Get Image:"),!i.tensor){this.config.debug&&g("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:M(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=M(),this.config.skipAllowed=await So(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(M()-n):Math.trunc(M()-n),this.analyze("Check Changed:");let x=[],d=[],l=[],y=[];this.state="detect:face",this.config.async?(x=this.config.face.enabled?co(this,i.tensor):[],this.performance.face&&delete this.performance.face):(n=M(),x=this.config.face.enabled?await co(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(x=await x),this.analyze("Start Body:"),this.state="detect:body";let c=this.config.body.maxDetected===-1?o0(this.config,{body:{maxDetected:this.config.face.enabled?1*x.length:1}}):this.config;this.config.async?((v=this.config.body.modelPath)!=null&&v.includes("posenet")?d=this.config.body.enabled?eo(i.tensor,c):[]:(b=this.config.body.modelPath)!=null&&b.includes("blazepose")?d=this.config.body.enabled?c5(i.tensor,c):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?d=this.config.body.enabled?b5(i.tensor,c):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(d=this.config.body.enabled?U5(i.tensor,c):[]),this.performance.body&&delete this.performance.body):(n=M(),(u=this.config.body.modelPath)!=null&&u.includes("posenet")?d=this.config.body.enabled?await eo(i.tensor,c):[]:(E=this.config.body.modelPath)!=null&&E.includes("blazepose")?d=this.config.body.enabled?await c5(i.tensor,c):[]:(W=this.config.body.modelPath)!=null&&W.includes("efficientpose")?d=this.config.body.enabled?await b5(i.tensor,c):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(d=this.config.body.enabled?await U5(i.tensor,c):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?o0(this.config,{hand:{maxDetected:this.config.face.enabled?2*x.length:1}}):this.config;this.config.async?((O=(S=this.config.hand.detector)==null?void 0:S.modelPath)!=null&&O.includes("handdetect")?l=this.config.hand.enabled?O5(i.tensor,f):[]:(G=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&G.includes("handtrack")&&(l=this.config.hand.enabled?B5(i.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(n=M(),(t0=(H=this.config.hand.detector)==null?void 0:H.modelPath)!=null&&t0.includes("handdetect")?l=this.config.hand.enabled?await O5(i.tensor,f):[]:(m0=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&m0.includes("handtrack")&&(l=this.config.hand.enabled?await B5(i.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((B0=this.config.object.modelPath)!=null&&B0.includes("nanodet")?y=this.config.object.enabled?Y5(i.tensor,this.config):[]:(T=this.config.object.modelPath)!=null&&T.includes("centernet")&&(y=this.config.object.enabled?m5(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=M(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?y=this.config.object.enabled?await Y5(i.tensor,this.config):[]:(k=this.config.object.modelPath)!=null&&k.includes("centernet")&&(y=this.config.object.enabled?await m5(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([x,d,l,y]=await Promise.all([x,d,l,y])),this.state="detect:gesture";let h=[];this.config.gesture.enabled&&(n=M(),h=[...er(x),...$n(d),...or(l),...tr(x)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(M()-n):Math.trunc(M()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(M()-a):Math.trunc(M()-a);let m=((Y=(X=this.process)==null?void 0:X.tensor)==null?void 0:Y.shape)||[];this.result={face:x,body:d,hand:l,gesture:h,object:y,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return sr(x,d,l,h,m)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};we=new WeakMap,Xe=new WeakMap,De=new WeakMap,Nt=new WeakMap;export{ir as Human,ir as default,Q0 as defaults,Kn as draw,w as env,rr as match,ro as models};
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/human.esm.js b/dist/human.esm.js
index 1759ca73..9e01ca90 100644
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
@@ -215,31 +215,31 @@ var config = {
 // dist/tfjs.esm.js
 var tfjs_esm_exports = {};
 __export(tfjs_esm_exports, {
-  Abs: () => ao,
-  Acos: () => nl,
-  Acosh: () => sl,
+  Abs: () => io,
+  Acos: () => sl,
+  Acosh: () => rl,
   AdadeltaOptimizer: () => fb,
   AdagradOptimizer: () => mb,
   AdamOptimizer: () => gb,
   AdamaxOptimizer: () => bb,
   Add: () => Ir,
   AddN: () => ka,
-  All: () => rl,
-  Any: () => al,
+  All: () => al,
+  Any: () => il,
   ArgMax: () => Ia,
-  ArgMin: () => il,
-  Asin: () => ol,
-  Asinh: () => ul,
-  Atan: () => ll,
-  Atan2: () => dl,
-  Atanh: () => cl,
+  ArgMin: () => ol,
+  Asin: () => ul,
+  Asinh: () => ll,
+  Atan: () => cl,
+  Atan2: () => pl,
+  Atanh: () => dl,
   AvgPool: () => Sa,
   AvgPool3D: () => qd,
   AvgPool3DGrad: () => og,
   AvgPoolGrad: () => ig,
-  BackendWasm: () => wde,
+  BackendWasm: () => kde,
   BatchMatMul: () => Ca,
-  BatchToSpaceND: () => io,
+  BatchToSpaceND: () => oo,
   Bincount: () => ug,
   BroadcastArgs: () => lg,
   BroadcastTo: () => A$,
@@ -250,7 +250,7 @@ __export(tfjs_esm_exports, {
   ClipByValue: () => Sr,
   Complex: () => jd,
   ComplexAbs: () => Kd,
-  Concat: () => oo,
+  Concat: () => uo,
   Conv2D: () => $a,
   Conv2DBackpropFilter: () => cg,
   Conv2DBackpropInput: () => _a,
@@ -259,13 +259,13 @@ __export(tfjs_esm_exports, {
   Conv3DBackpropInputV2: () => pg,
   Cos: () => Aa,
   Cosh: () => Ea,
-  CropAndResize: () => lo,
-  Cumprod: () => pl,
-  Cumsum: () => uo,
+  CropAndResize: () => co,
+  Cumprod: () => hl,
+  Cumsum: () => lo,
   CustomCallback: () => BL,
   DataStorage: () => Ud,
   DenseBincount: () => hg,
-  DepthToSpace: () => co,
+  DepthToSpace: () => po,
   DepthwiseConv2dNative: () => Ra,
   DepthwiseConv2dNativeBackpropFilter: () => fg,
   DepthwiseConv2dNativeBackpropInput: () => mg,
@@ -279,14 +279,14 @@ __export(tfjs_esm_exports, {
   Elu: () => Fa,
   EluGrad: () => bg,
   Environment: () => S$,
-  Equal: () => po,
-  Erf: () => hl,
+  Equal: () => ho,
+  Erf: () => fl,
   Exp: () => Oa,
-  ExpandDims: () => ho,
-  Expm1: () => fo,
+  ExpandDims: () => fo,
+  Expm1: () => mo,
   FFT: () => yg,
-  Fill: () => fl,
-  FlipLeftRight: () => mo,
+  Fill: () => ml,
+  FlipLeftRight: () => go,
   Floor: () => Pa,
   FloorDiv: () => za,
   FromPixels: () => fd,
@@ -294,33 +294,33 @@ __export(tfjs_esm_exports, {
   FusedConv2D: () => aa,
   FusedDepthwiseConv2D: () => ia,
   GPGPUContext: () => Kf,
-  GatherNd: () => bo,
-  GatherV2: () => go,
+  GatherNd: () => yo,
+  GatherV2: () => bo,
   GraphModel: () => E4,
-  Greater: () => yo,
+  Greater: () => vo,
   GreaterEqual: () => La,
   History: () => LL,
   IFFT: () => vg,
   Identity: () => Ba,
   Imag: () => Zd,
   InputSpec: () => Dt,
-  IsFinite: () => ml,
-  IsInf: () => gl,
-  IsNan: () => bl,
-  KernelBackend: () => tl,
+  IsFinite: () => gl,
+  IsInf: () => bl,
+  IsNan: () => yl,
+  KernelBackend: () => nl,
   LRN: () => ep,
   LRNGrad: () => wg,
   LayerVariable: () => AL,
   LayersModel: () => pr,
   LeakyRelu: () => Va,
-  Less: () => vo,
-  LessEqual: () => xo,
+  Less: () => xo,
+  LessEqual: () => wo,
   LinSpace: () => xg,
   Log: () => Wa,
-  Log1p: () => yl,
+  Log1p: () => vl,
   LogSoftmax: () => E$,
-  LogicalAnd: () => wo,
-  LogicalNot: () => vl,
+  LogicalAnd: () => ko,
+  LogicalNot: () => xl,
   LogicalOr: () => Jd,
   MathBackendCPU: () => L0,
   MathBackendWebGL: () => M1,
@@ -335,88 +335,88 @@ __export(tfjs_esm_exports, {
   Min: () => ja,
   Minimum: () => Ka,
   MirrorPad: () => Xa,
-  Mod: () => xl,
+  Mod: () => wl,
   MomentumOptimizer: () => yb,
   Multinomial: () => Cg,
   Multiply: () => Ya,
-  Neg: () => ko,
-  NonMaxSuppressionV3: () => So,
-  NonMaxSuppressionV4: () => wl,
-  NonMaxSuppressionV5: () => Co,
-  NotEqual: () => Io,
+  Neg: () => Io,
+  NonMaxSuppressionV3: () => Co,
+  NonMaxSuppressionV4: () => kl,
+  NonMaxSuppressionV5: () => No,
+  NotEqual: () => So,
   OP_SCOPE_SUFFIX: () => l_,
-  OneHot: () => To,
-  OnesLike: () => No,
+  OneHot: () => $o,
+  OnesLike: () => To,
   Optimizer: () => _r,
   OptimizerConstructors: () => Wr,
-  Pack: () => $o,
+  Pack: () => _o,
   PadV2: () => Qa,
-  Pool: () => Tde,
+  Pool: () => $de,
   Pow: () => Za,
   Prelu: () => Ja,
-  Prod: () => _o,
+  Prod: () => Ao,
   RMSPropOptimizer: () => vb,
   RNN: () => Ar,
-  Range: () => kl,
+  Range: () => Il,
   Rank: () => e_,
   Real: () => np,
   RealDiv: () => Da,
-  Reciprocal: () => Il,
+  Reciprocal: () => Sl,
   Reduction: () => oO,
   Relu: () => ei,
   Relu6: () => ni,
-  Reshape: () => Ao,
+  Reshape: () => Eo,
   ResizeBilinear: () => ti,
   ResizeBilinearGrad: () => Tg,
-  ResizeNearestNeighbor: () => Sl,
+  ResizeNearestNeighbor: () => Cl,
   ResizeNearestNeighborGrad: () => Ng,
-  Reverse: () => Eo,
-  RotateWithOffset: () => Ho,
-  Round: () => Ro,
+  Reverse: () => Ro,
+  RotateWithOffset: () => qo,
+  Round: () => Do,
   Rsqrt: () => si,
   SGDOptimizer: () => Ip,
-  ScatterNd: () => Do,
-  Select: () => Fo,
-  Selu: () => Cl,
+  ScatterNd: () => Fo,
+  Select: () => Oo,
+  Selu: () => Nl,
   Sequential: () => Ub,
   Sigmoid: () => ai,
-  Sign: () => Nl,
+  Sign: () => Tl,
   Sin: () => ri,
-  Sinh: () => Po,
-  Slice: () => Oo,
+  Sinh: () => zo,
+  Slice: () => Po,
   Softmax: () => ui,
-  Softplus: () => Tl,
-  SpaceToBatchND: () => zo,
+  Softplus: () => $l,
+  SpaceToBatchND: () => Mo,
   SparseFillEmptyRows: () => sp,
-  SparseReshape: () => $l,
+  SparseReshape: () => _l,
   SparseSegmentMean: () => rp,
   SparseSegmentSum: () => ap,
   SparseToDense: () => ip,
-  SplitV: () => Mo,
+  SplitV: () => Lo,
   Sqrt: () => ii,
-  Square: () => _l,
+  Square: () => Al,
   SquaredDifference: () => li,
   Step: () => hi,
-  StridedSlice: () => Lo,
+  StridedSlice: () => Bo,
   StringNGrams: () => op,
   StringSplit: () => $g,
   StringToHashBucketFast: () => _g,
   Sub: () => ci,
   Sum: () => oi,
   SymbolicTensor: () => $s,
-  Tan: () => Bo,
+  Tan: () => Vo,
   Tanh: () => di,
   Tensor: () => et,
   TensorBuffer: () => Vt,
   Tile: () => Cr,
-  TopK: () => Vo,
-  Transform: () => Wo,
+  TopK: () => Wo,
+  Transform: () => Uo,
   Transpose: () => pi,
   Unique: () => Ag,
-  Unpack: () => Uo,
+  Unpack: () => Go,
   UnsortedSegmentSum: () => up,
   Variable: () => gd,
-  ZerosLike: () => Go,
+  ZerosLike: () => Ho,
   _FusedMatMul: () => ra,
   abs: () => Mt,
   acos: () => GA,
@@ -425,7 +425,7 @@ __export(tfjs_esm_exports, {
   addN: () => KA,
   all: () => qk,
   any: () => pm,
-  argMax: () => Gu,
+  argMax: () => Hu,
   argMin: () => JA,
   asin: () => tE,
   asinh: () => sE,
@@ -436,20 +436,20 @@ __export(tfjs_esm_exports, {
   avgPool3d: () => Yk,
   backend: () => zA,
   backend_util: () => S,
-  basicLSTMCell: () => Hde,
-  batchNorm: () => qu,
+  basicLSTMCell: () => qde,
+  batchNorm: () => ju,
   batchNorm2d: () => $E,
   batchNorm3d: () => AE,
   batchNorm4d: () => RE,
   batchToSpaceND: () => Hg,
   bincount: () => Qk,
-  booleanMaskAsync: () => vpe,
+  booleanMaskAsync: () => xpe,
   broadcastArgs: () => OE,
   broadcastTo: () => nd,
-  broadcast_util: () => qo,
+  broadcast_util: () => jo,
   browser: () => Nk,
   buffer: () => De,
-  callbacks: () => _pe,
+  callbacks: () => Ape,
   cast: () => ce,
   ceil: () => ME,
   clipByValue: () => Vn,
@@ -466,7 +466,7 @@ __export(tfjs_esm_exports, {
   conv2dTranspose: () => Jk,
   conv3d: () => eI,
   conv3dTranspose: () => tR,
-  copyRegisteredKernels: () => Ade,
+  copyRegisteredKernels: () => Ede,
   cos: () => jg,
   cosh: () => nI,
   cosineWindow: () => TI,
@@ -478,21 +478,21 @@ __export(tfjs_esm_exports, {
   deprecationWarn: () => Gk,
   depthToSpace: () => cR,
   depthwiseConv2d: () => hp,
-  deregisterOp: () => Epe,
+  deregisterOp: () => Rpe,
   device_util: () => dp,
-  diag: () => qde,
+  diag: () => jde,
   dilation2d: () => fR,
-  disableDeprecationWarnings: () => Fde,
+  disableDeprecationWarnings: () => Ode,
   dispose: () => Re,
-  disposeVariables: () => Ode,
+  disposeVariables: () => Pde,
   div: () => xe,
   divNoNan: () => vR,
-  dot: () => jde,
+  dot: () => Kde,
   dropout: () => nF,
   einsum: () => kR,
   elu: () => fp,
-  enableDebugMode: () => Dde,
-  enableProdMode: () => Rde,
+  enableDebugMode: () => Fde,
+  enableProdMode: () => Dde,
   enclosingPowerOfTwo: () => sF,
   engine: () => Ss,
   env: () => X,
@@ -503,58 +503,58 @@ __export(tfjs_esm_exports, {
   expm1: () => _R,
   eye: () => rI,
   fft: () => lb,
-  fill: () => Ol,
-  findBackend: () => Wde,
-  findBackendFactory: () => Ude,
+  fill: () => Pl,
+  findBackend: () => Ude,
+  findBackendFactory: () => Gde,
   floor: () => mp,
   floorDiv: () => Hk,
   forceHalfFloat: () => GX,
   fused: () => ha,
-  gather: () => ju,
+  gather: () => Ku,
   gatherND: () => J3,
   gather_util: () => $k,
-  getBackend: () => Bde,
+  getBackend: () => Vde,
   getGradient: () => nx,
   getKernel: () => Zf,
   getKernelsForBackend: () => Jf,
-  getThreadsCount: () => Vpe,
+  getThreadsCount: () => Wpe,
   gpgpu_util: () => RK,
-  grad: () => Yde,
-  grads: () => Qde,
+  grad: () => Qde,
+  grads: () => Zde,
   greater: () => Wn,
-  greaterEqual: () => jo,
+  greaterEqual: () => Ko,
   ifft: () => Id,
   imag: () => Kg,
   image: () => qn,
-  inTopKAsync: () => wpe,
+  inTopKAsync: () => kpe,
   initializers: () => qM,
   input: () => OB,
   io: () => En,
   irfft: () => xI,
-  isFinite: () => Kde,
-  isInf: () => Xde,
+  isFinite: () => Xde,
+  isInf: () => Yde,
   isNaN: () => BR,
   keep: () => Ht,
   kernel_impls: () => xs,
   layers: () => $L,
   leakyRelu: () => Xg,
   less: () => aI,
-  lessEqual: () => Ko,
+  lessEqual: () => Xo,
   linalg: () => VO,
   linspace: () => GR,
-  loadGraphModel: () => Rpe,
-  loadLayersModel: () => Tpe,
+  loadGraphModel: () => Dpe,
+  loadLayersModel: () => $pe,
   localResponseNormalization: () => qR,
   log: () => Yn,
   log1p: () => Yg,
-  logSigmoid: () => epe,
+  logSigmoid: () => tpe,
   logSoftmax: () => iI,
   logSumExp: () => iD,
   logicalAnd: () => Ds,
   logicalNot: () => Jg,
   logicalOr: () => cI,
-  logicalXor: () => tpe,
-  losses: () => Spe,
+  logicalXor: () => npe,
+  losses: () => Cpe,
   matMul: () => We,
   math: () => iA,
   max: () => As,
@@ -564,62 +564,62 @@ __export(tfjs_esm_exports, {
   maximum: () => $r,
   mean: () => St,
   memory: () => dm,
-  meshgrid: () => npe,
+  meshgrid: () => spe,
   metrics: () => rW,
   min: () => fm,
   minimum: () => bp,
   mirrorPad: () => xD,
   mod: () => kD,
-  model: () => Cpe,
+  model: () => Npe,
   models: () => xW,
   moments: () => tb,
-  movingAverage: () => xpe,
+  movingAverage: () => wpe,
   mul: () => V,
-  multiRNNCell: () => spe,
+  multiRNNCell: () => rpe,
   multinomial: () => TD,
   neg: () => kt,
   nextFrame: () => OI,
   norm: () => CI,
-  notEqual: () => Ku,
+  notEqual: () => Xu,
   oneHot: () => vd,
   ones: () => Mn,
   onesLike: () => Qn,
   op: () => L,
-  outerProduct: () => rpe,
+  outerProduct: () => ape,
   pad: () => fi,
-  pad1d: () => ape,
-  pad2d: () => ipe,
-  pad3d: () => ope,
-  pad4d: () => upe,
-  pool: () => lpe,
+  pad1d: () => ipe,
+  pad2d: () => ope,
+  pad3d: () => upe,
+  pad4d: () => lpe,
+  pool: () => cpe,
   pow: () => pa,
   prelu: () => sb,
   print: () => U_,
   prod: () => pI,
-  profile: () => Pde,
-  rand: () => cpe,
-  randomGamma: () => dpe,
+  profile: () => zde,
+  rand: () => dpe,
+  randomGamma: () => ppe,
   randomNormal: () => KD,
-  randomUniform: () => zl,
-  range: () => Xu,
-  ready: () => Lde,
+  randomUniform: () => Ml,
+  range: () => Yu,
+  ready: () => Bde,
   real: () => wd,
   reciprocal: () => ZD,
   registerBackend: () => pp,
-  registerCallbackConstructor: () => $pe,
+  registerCallbackConstructor: () => _pe,
   registerGradient: () => D$,
-  registerKernel: () => Al,
-  registerOp: () => Ape,
+  registerKernel: () => El,
+  registerOp: () => Epe,
   regularizers: () => wW,
   relu: () => Xs,
   relu6: () => hI,
-  removeBackend: () => Vde,
+  removeBackend: () => Wde,
   reshape: () => G,
   reverse: () => Zn,
-  reverse1d: () => ppe,
-  reverse2d: () => hpe,
-  reverse3d: () => fpe,
-  reverse4d: () => mpe,
+  reverse1d: () => hpe,
+  reverse2d: () => fpe,
+  reverse3d: () => mpe,
+  reverse4d: () => gpe,
   rfft: () => cb,
   round: () => fI,
   rsqrt: () => mI,
@@ -628,19 +628,19 @@ __export(tfjs_esm_exports, {
   scatter_util: () => Ak,
   selu: () => gI,
   separableConv2d: () => c3,
-  sequential: () => Npe,
+  sequential: () => Tpe,
   serialization: () => ae,
-  setBackend: () => Mde,
-  setPlatform: () => Gde,
-  setThreadsCount: () => Bpe,
-  setWasmPath: () => Mpe,
-  setWasmPaths: () => Lpe,
+  setBackend: () => Lde,
+  setPlatform: () => Hde,
+  setThreadsCount: () => Vpe,
+  setWasmPath: () => Lpe,
+  setWasmPaths: () => Bpe,
   setWebGLContext: () => S5,
   setdiff1dAsync: () => p3,
   shared: () => Zy,
   sigmoid: () => qs,
   sign: () => f3,
-  signal: () => Ipe,
+  signal: () => Spe,
   sin: () => bI,
   sinh: () => yI,
   slice: () => He,
@@ -650,11 +650,11 @@ __export(tfjs_esm_exports, {
   slice4d: () => kd,
   slice_util: () => wt,
   softmax: () => ub,
-  softplus: () => Pl,
+  softplus: () => zl,
   spaceToBatchND: () => nb,
   sparse: () => Wc,
   sparseToDense: () => NI,
-  spectral: () => kpe,
+  spectral: () => Ipe,
   split: () => Bn,
   sqrt: () => dn,
   square: () => ct,
@@ -668,44 +668,44 @@ __export(tfjs_esm_exports, {
   sum: () => ve,
   sumOutType: () => cp,
   tan: () => O3,
-  tanh: () => Hu,
+  tanh: () => qu,
   tensor: () => fs,
   tensor1d: () => Zt,
   tensor2d: () => Ki,
   tensor3d: () => pA,
-  tensor4d: () => gpe,
-  tensor5d: () => bpe,
-  tensor6d: () => ype,
+  tensor4d: () => bpe,
+  tensor5d: () => ype,
+  tensor6d: () => vpe,
   tensor_util: () => _s,
   test_util: () => _A,
   tidy: () => j,
   tile: () => ps,
-  time: () => zde,
+  time: () => Mde,
   topk: () => z3,
   train: () => Pi,
   transpose: () => qe,
   truncatedNormal: () => db,
   unique: () => hx,
-  unregisterGradient: () => _de,
-  unregisterKernel: () => $de,
+  unregisterGradient: () => Ade,
+  unregisterKernel: () => _de,
   unsortedSegmentSum: () => V3,
   unstack: () => Fs,
   upcastType: () => cn,
   util: () => w,
-  valueAndGrad: () => Zde,
-  valueAndGrads: () => Jde,
+  valueAndGrad: () => Jde,
+  valueAndGrads: () => epe,
   variable: () => U3,
   variableGrads: () => XR,
-  version: () => Upe,
-  version_converter: () => Dpe,
-  version_core: () => Ede,
-  version_cpu: () => Fpe,
+  version: () => Gpe,
+  version_converter: () => Fpe,
+  version_core: () => Rde,
+  version_cpu: () => Ope,
   version_layers: () => uS,
-  version_wasm: () => Wpe,
-  version_webgl: () => Ope,
-  webgl: () => Ppe,
+  version_wasm: () => Upe,
+  version_webgl: () => Ppe,
+  webgl: () => zpe,
   webgl_util: () => I5,
-  webgpu: () => soe,
+  webgpu: () => roe,
   where: () => vn,
   whereAsync: () => II,
   zeros: () => $t,
@@ -1332,7 +1332,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return Ce.buffer != nn && rs(Ce.buffer), oc;
       }
       function o() {
-        return Ce.buffer != nn && rs(Ce.buffer), pu;
+        return Ce.buffer != nn && rs(Ce.buffer), hu;
       }
       function u() {
         return Ce.buffer != nn && rs(Ce.buffer), uc;
@@ -1360,7 +1360,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }
       var P, R, F, $;
       function z(C) {
-        if (C instanceof wu)
+        if (C instanceof ku)
           return;
         ee("exiting due to exception: " + C);
       }
@@ -1378,7 +1378,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             ue ? Q(ue) : B(pe.buffer);
           });
         }, process.argv.length > 1 && (y = process.argv[1].replace(/\\/g, "/")), b = process.argv.slice(2), process.on("uncaughtException", function(D) {
-          if (!(D instanceof wu))
+          if (!(D instanceof ku))
             throw D;
         }), process.on("unhandledRejection", function(D) {
           throw D;
@@ -1435,8 +1435,8 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         for (var ue = 0; ue < Te.length; ++ue)
           pe.push(bt[Te[ue]]);
         ye == "v" ? pe.push(0) : pe = pe.concat([1, bt[ye]]), pe[1] = pe.length - 2;
-        var us = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(pe, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), ls = new WebAssembly.Module(us), Bc = new WebAssembly.Instance(ls, { e: { f: C } }), ku = Bc.exports.f;
-        return ku;
+        var us = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(pe, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), ls = new WebAssembly.Module(us), Bc = new WebAssembly.Instance(ls, { e: { f: C } }), Iu = Bc.exports.f;
+        return Iu;
       }
       var re = [], le;
       function me() {
@@ -1490,11 +1490,11 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             var Bc = pe[B[ls]];
             Bc ? (us === 0 && (us = Ef()), bt[ls] = Bc(Q[ls])) : bt[ls] = Q[ls];
           }
-        var ku = Te.apply(null, bt);
+        var Iu = Te.apply(null, bt);
         function MT($n) {
           return us !== 0 && Pc(us), ye($n);
         }
-        return ku = MT(ku), ku;
+        return Iu = MT(Iu), Iu;
       }
       function en(C, D, B, Q) {
         B = B || [];
@@ -1583,7 +1583,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       function Bs(C, D) {
         a().set(C, D);
       }
-      function du(C, D, B) {
+      function pu(C, D, B) {
         for (var Q = 0; Q < C.length; ++Q)
           a()[D++ >> 0] = C.charCodeAt(Q);
         B || (a()[D >> 0] = 0);
@@ -1591,10 +1591,10 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       function Ii(C, D) {
         return C % D > 0 && (C += D - C % D), C;
       }
-      var nn, ic, oc, pu, uc, lc, Fv, cc, dc;
+      var nn, ic, oc, hu, uc, lc, Fv, cc, dc;
       N && (nn = d.buffer);
       function rs(C) {
-        nn = C, d.HEAP8 = ic = new Int8Array(C), d.HEAP16 = pu = new Int16Array(C), d.HEAP32 = lc = new Int32Array(C), d.HEAPU8 = oc = new Uint8Array(C), d.HEAPU16 = uc = new Uint16Array(C), d.HEAPU32 = Fv = new Uint32Array(C), d.HEAPF32 = cc = new Float32Array(C), d.HEAPF64 = dc = new Float64Array(C);
+        nn = C, d.HEAP8 = ic = new Int8Array(C), d.HEAP16 = hu = new Int16Array(C), d.HEAP32 = lc = new Int32Array(C), d.HEAPU8 = oc = new Uint8Array(C), d.HEAPU16 = uc = new Uint16Array(C), d.HEAPU32 = Fv = new Uint32Array(C), d.HEAPF32 = cc = new Float32Array(C), d.HEAPF64 = dc = new Float64Array(C);
       }
       var pc = d.INITIAL_MEMORY || 16777216;
       if (N)
@@ -1614,7 +1614,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             Ov(d.preRun.shift());
         vc(Si);
       }
-      function hu() {
+      function fu() {
         Or = true, !N && vc(er);
       }
       function sh() {
@@ -1624,7 +1624,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         if (!N) {
           if (d.postRun)
             for (typeof d.postRun == "function" && (d.postRun = [d.postRun]); d.postRun.length; )
-              fu(d.postRun.shift());
+              mu(d.postRun.shift());
           vc(hc);
         }
       }
@@ -1634,11 +1634,11 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       function Pv(C) {
         er.unshift(C);
       }
-      function fu(C) {
+      function mu(C) {
         hc.unshift(C);
       }
       var tr = 0, mc = null, as = null;
-      function mu(C) {
+      function gu(C) {
         tr++, d.monitorRunDependencies && d.monitorRunDependencies(tr);
       }
       function zv(C) {
@@ -1707,7 +1707,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             });
           }
         }
-        N || mu("wasm-instantiate");
+        N || gu("wasm-instantiate");
         function B(ye) {
           D(ye.instance, ye.module);
         }
@@ -1784,7 +1784,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         OT(C);
       }
       function ch(C) {
-        if (C instanceof wu || C == "unwind")
+        if (C instanceof ku || C == "unwind")
           return Jt;
         v(1, C);
       }
@@ -1980,13 +1980,13 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           return Qv(C, ue, pe, D);
         });
       }
-      var gu = [];
+      var bu = [];
       function Th(C, D, B) {
-        gu.length = D;
+        bu.length = D;
         for (var Q = B >> 3, ue = 0; ue < D; ue++)
-          gu[ue] = p()[Q + ue];
+          bu[ue] = p()[Q + ue];
         var pe = C < 0, ye = pe ? oh[-C - 1] : qh[C];
-        return ye.apply(null, gu);
+        return ye.apply(null, bu);
       }
       function $h(C) {
         try {
@@ -2100,11 +2100,11 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         var D = Fh[C] || (typeof document != "undefined" ? document.querySelector(C) : void 0);
         return D;
       }
-      function bu(C) {
+      function yu(C) {
         return Oh(C);
       }
       function Cc(C, D, B) {
-        var Q = bu(C);
+        var Q = yu(C);
         if (!Q)
           return -4;
         if (Q.canvasSharedPtr && (l()[Q.canvasSharedPtr >> 2] = D, l()[Q.canvasSharedPtr + 4 >> 2] = B), Q.offscreenCanvas || !Q.controlTransferredOffscreen) {
@@ -2126,7 +2126,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return N ? Lr(2, 1, C, D, B) : Cc(C, D, B);
       }
       function Ph(C, D, B) {
-        var Q = bu(C);
+        var Q = yu(C);
         return Q ? Cc(C, D, B) : Nc(C, D, B);
       }
       function zh() {
@@ -2211,7 +2211,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         }
       } }, Wh = ["default", "low-power", "high-performance"];
       function Uh(C, D) {
-        var B = D >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Wh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, pe = bu(C);
+        var B = D >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Wh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, pe = yu(C);
         if (!pe || ue.explicitSwapControl)
           return 0;
         var ye = gt.createContext(pe, ue);
@@ -2320,18 +2320,18 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return (Rc = d._Floor = d.asm.Floor).apply(null, arguments);
       }, Dc = d._FloorDiv = function() {
         return (Dc = d._FloorDiv = d.asm.FloorDiv).apply(null, arguments);
-      }, yu = d._FusedBatchNorm = function() {
-        return (yu = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments);
+      }, vu = d._FusedBatchNorm = function() {
+        return (vu = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments);
       }, Sf = d._FusedConv2D = function() {
         return (Sf = d._FusedConv2D = d.asm.FusedConv2D).apply(null, arguments);
       }, Cf = d._FusedDepthwiseConv2D = function() {
         return (Cf = d._FusedDepthwiseConv2D = d.asm.FusedDepthwiseConv2D).apply(null, arguments);
       }, Ai = d._Gather = function() {
         return (Ai = d._Gather = d.asm.Gather).apply(null, arguments);
-      }, vu = d._GatherNd = function() {
-        return (vu = d._GatherNd = d.asm.GatherNd).apply(null, arguments);
-      }, xu = d._Greater = function() {
-        return (xu = d._Greater = d.asm.Greater).apply(null, arguments);
+      }, xu = d._GatherNd = function() {
+        return (xu = d._GatherNd = d.asm.GatherNd).apply(null, arguments);
+      }, wu = d._Greater = function() {
+        return (wu = d._Greater = d.asm.Greater).apply(null, arguments);
       }, Hv = d._GreaterEqual = function() {
         return (Hv = d._GreaterEqual = d.asm.GreaterEqual).apply(null, arguments);
       }, Ei = d._LeakyRelu = function() {
@@ -2487,9 +2487,9 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, FT = d.dynCall_jiji = function() {
         return (FT = d.dynCall_jiji = d.asm.dynCall_jiji).apply(null, arguments);
       }, Jv = d.__emscripten_allow_main_runtime_queued_calls = 21456;
-      d.cwrap = en, d.keepRuntimeAlive = Pr, d.PThread = $e, d.PThread = $e, d.wasmMemory = Ce, d.ExitStatus = wu;
+      d.cwrap = en, d.keepRuntimeAlive = Pr, d.PThread = $e, d.PThread = $e, d.wasmMemory = Ce, d.ExitStatus = ku;
       var zc;
-      function wu(C) {
+      function ku(C) {
         this.name = "ExitStatus", this.message = "Program terminated with exit(" + C + ")", this.status = C;
       }
       as = function C() {
@@ -2499,13 +2499,13 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         if (C = C || b, tr > 0)
           return;
         if (N) {
-          h(d), hu(), postMessage({ cmd: "loaded" });
+          h(d), fu(), postMessage({ cmd: "loaded" });
           return;
         }
         if (sn(), tr > 0)
           return;
         function D() {
-          zc || (zc = true, d.calledRun = true, !at && (hu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), rh()));
+          zc || (zc = true, d.calledRun = true, !at && (fu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), rh()));
         }
         d.setStatus ? (d.setStatus("Running..."), setTimeout(function() {
           setTimeout(function() {
@@ -2520,7 +2520,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         Pr() || sh(), PT(C);
       }
       function PT(C) {
-        Jt = C, Pr() || ($e.terminateAllThreads(), d.onExit && d.onExit(C), at = true), v(C, new wu(C));
+        Jt = C, Pr() || ($e.terminateAllThreads(), d.onExit && d.onExit(C), at = true), v(C, new ku(C));
       }
       if (d.preInit)
         for (typeof d.preInit == "function" && (d.preInit = [d.preInit]); d.preInit.length > 0; )
@@ -2575,7 +2575,7 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       var y, v, x, k;
       function T(H) {
-        if (H instanceof vu)
+        if (H instanceof xu)
           return;
         R("exiting due to exception: " + H);
       }
@@ -2592,12 +2592,12 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           Ie ? de(Ie) : J(Qe.buffer);
         });
       }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), c = process.argv.slice(2), process.on("uncaughtException", function(H) {
-        if (!(H instanceof vu))
+        if (!(H instanceof xu))
           throw H;
       }), process.on("unhandledRejection", function(H) {
         throw H;
       }), d = (H, J) => {
-        if (pu())
+        if (hu())
           throw process.exitCode = H, J;
         T(J), process.exit(H);
       }, a.inspect = function() {
@@ -2651,7 +2651,7 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       function Y(H, J) {
         for (var de = H; de < H + J; de++) {
-          var Ie = mu(de);
+          var Ie = gu(de);
           Ie && q.set(Ie, de);
         }
       }
@@ -2674,11 +2674,11 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           var nr = 0;
           if (an != null && an !== 0) {
             var Fc = (an.length << 2) + 1;
-            nr = yu(Fc), tt(an, nr, Fc);
+            nr = vu(Fc), tt(an, nr, Fc);
           }
           return nr;
         }, array: function(an) {
-          var nr = yu(an.length);
+          var nr = vu(an.length);
           return at(an, nr), nr;
         } };
         function Ve(an) {
@@ -2790,18 +2790,18 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       function Ls(H) {
         Cn = H, a.HEAP8 = Et = new Int8Array(H), a.HEAP16 = Nn = new Int16Array(H), a.HEAP32 = Yt = new Int32Array(H), a.HEAPU8 = en = new Uint8Array(H), a.HEAPU16 = Tn = new Uint16Array(H), a.HEAPU32 = Dn = new Uint32Array(H), a.HEAPF32 = tn = new Float32Array(H), a.HEAPF64 = Ms = new Float64Array(H);
       }
-      var ki = a.INITIAL_MEMORY || 16777216, Js, Bs = [], du = [], Ii = [], nn = false, ic = false, oc = 0;
-      function pu() {
+      var ki = a.INITIAL_MEMORY || 16777216, Js, Bs = [], pu = [], Ii = [], nn = false, ic = false, oc = 0;
+      function hu() {
         return se || oc > 0;
       }
       function uc() {
         if (a.preRun)
           for (typeof a.preRun == "function" && (a.preRun = [a.preRun]); a.preRun.length; )
             dc(a.preRun.shift());
-        fu(Bs);
+        mu(Bs);
       }
       function lc() {
-        nn = true, fu(du);
+        nn = true, mu(pu);
       }
       function Fv() {
         ic = true;
@@ -2810,13 +2810,13 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         if (a.postRun)
           for (typeof a.postRun == "function" && (a.postRun = [a.postRun]); a.postRun.length; )
             pc(a.postRun.shift());
-        fu(Ii);
+        mu(Ii);
       }
       function dc(H) {
         Bs.unshift(H);
       }
       function rs(H) {
-        du.unshift(H);
+        pu.unshift(H);
       }
       function pc(H) {
         Ii.unshift(H);
@@ -2846,7 +2846,7 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       var sn;
       sn = "tfjs-backend-wasm.wasm", fc(sn) || (sn = b(sn));
-      function hu(H) {
+      function fu(H) {
         try {
           if (H == sn && ee)
             return new Uint8Array(ee);
@@ -2865,7 +2865,7 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
                 throw "failed to load wasm binary file at '" + sn + "'";
               return H.arrayBuffer();
             }).catch(function() {
-              return hu(sn);
+              return fu(sn);
             });
           if (v)
             return new Promise(function(H, J) {
@@ -2875,7 +2875,7 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
             });
         }
         return Promise.resolve().then(function() {
-          return hu(sn);
+          return fu(sn);
         });
       }
       function rh() {
@@ -2915,7 +2915,7 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return Qe().catch(o), {};
       }
       var Ov, Pv;
-      function fu(H) {
+      function mu(H) {
         for (; H.length > 0; ) {
           var J = H.shift();
           if (typeof J == "function") {
@@ -2923,7 +2923,7 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
             continue;
           }
           var de = J.func;
-          typeof de == "number" ? J.arg === void 0 ? mu(de)() : mu(de)(J.arg) : de(J.arg === void 0 ? null : J.arg);
+          typeof de == "number" ? J.arg === void 0 ? gu(de)() : gu(de)(J.arg) : de(J.arg === void 0 ? null : J.arg);
         }
       }
       function tr(H) {
@@ -2937,7 +2937,7 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         });
       }
       var as = [];
-      function mu(H) {
+      function gu(H) {
         var J = as[H];
         return J || (H >= as.length && (as.length = H + 1), as[H] = J = Js.get(H)), J;
       }
@@ -3082,8 +3082,8 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return (Nh = a._Floor = a.asm.Floor).apply(null, arguments);
       }, Lr = a._FloorDiv = function() {
         return (Lr = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments);
-      }, gu = a._FusedBatchNorm = function() {
-        return (gu = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
+      }, bu = a._FusedBatchNorm = function() {
+        return (bu = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
       }, Th = a._FusedConv2D = function() {
         return (Th = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments);
       }, $h = a._FusedDepthwiseConv2D = function() {
@@ -3104,8 +3104,8 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return (Fh = a._LessEqual = a.asm.LessEqual).apply(null, arguments);
       }, Oh = a._Log = function() {
         return (Oh = a._Log = a.asm.Log).apply(null, arguments);
-      }, bu = a._LogicalAnd = function() {
-        return (bu = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
+      }, yu = a._LogicalAnd = function() {
+        return (yu = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
       }, Cc = a._Max = function() {
         return (Cc = a._Max = a.asm.Max).apply(null, arguments);
       }, Nc = a._MaxPool = function() {
@@ -3214,8 +3214,8 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         return (Rc = a.stackSave = a.asm.stackSave).apply(null, arguments);
       }, Dc = a.stackRestore = function() {
         return (Dc = a.stackRestore = a.asm.stackRestore).apply(null, arguments);
-      }, yu = a.stackAlloc = function() {
-        return (yu = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments);
+      }, vu = a.stackAlloc = function() {
+        return (vu = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments);
       }, Sf = a.dynCall_iijjiiii = function() {
         return (Sf = a.dynCall_iijjiiii = a.asm.dynCall_iijjiiii).apply(null, arguments);
       }, Cf = a.dynCall_jiji = function() {
@@ -3223,13 +3223,13 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       };
       a.cwrap = Se;
       var Ai;
-      function vu(H) {
+      function xu(H) {
         this.name = "ExitStatus", this.message = "Program terminated with exit(" + H + ")", this.status = H;
       }
       er = function H() {
-        Ai || xu(), Ai || (er = H);
+        Ai || wu(), Ai || (er = H);
       };
-      function xu(H) {
+      function wu(H) {
         if (H = H || c, Fn > 0 || (uc(), Fn > 0))
           return;
         function J() {
@@ -3241,14 +3241,14 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           }, 1), J();
         }, 1)) : J();
       }
-      a.run = xu;
+      a.run = wu;
       function Hv(H) {
-        re = H, pu() || (a.onExit && a.onExit(H), oe = true), d(H, new vu(H));
+        re = H, hu() || (a.onExit && a.onExit(H), oe = true), d(H, new xu(H));
       }
       if (a.preInit)
         for (typeof a.preInit == "function" && (a.preInit = [a.preInit]); a.preInit.length > 0; )
           a.preInit.pop()();
-      xu();
+      wu();
       var Ei;
       u && (Ei = { uncaughtException: process.listeners("uncaughtException").filter(function(H) {
         return !u.uncaughtException.indexOf(H) > -1;
@@ -3301,7 +3301,7 @@ var Ud = class {
     return this.dataIdsCount;
   }
 };
-var tl = class {
+var nl = class {
   refCount(e) {
     return On("refCount");
   }
@@ -3363,7 +3363,7 @@ function l$(e, t) {
   for (; n > 0; )
     s = Math.random() * n | 0, n--, pd(e, n, s), pd(t, n, s);
 }
-function Bu(e, t, n) {
+function Vu(e, t, n) {
   return Math.max(e, Math.min(t, n));
 }
 function c$(e) {
@@ -3455,7 +3455,7 @@ function b$(e) {
     t[n] = n;
   return Uw(t), t;
 }
-function Pu(e, t) {
+function zu(e, t) {
   return t <= e.length ? e : e + " ".repeat(t - e.length);
 }
 function y$(e, t = (s) => 0, n) {
@@ -3593,7 +3593,7 @@ function hd(e, t) {
       return n;
   return e;
 }
-function ro(e) {
+function ao(e) {
   let t = e.length;
   if (t < 2)
     return [];
@@ -3794,26 +3794,26 @@ function ag(e, t) {
     return n.set(e, s), n.get(e);
   }
 }
-var ao = "Abs";
-var nl = "Acos";
-var sl = "Acosh";
+var io = "Abs";
+var sl = "Acos";
+var rl = "Acosh";
 var Ir = "Add";
 var ka = "AddN";
-var rl = "All";
-var al = "Any";
+var al = "All";
+var il = "Any";
 var Ia = "ArgMax";
-var il = "ArgMin";
-var ol = "Asin";
-var ul = "Asinh";
-var ll = "Atan";
-var cl = "Atanh";
-var dl = "Atan2";
+var ol = "ArgMin";
+var ul = "Asin";
+var ll = "Asinh";
+var cl = "Atan";
+var dl = "Atanh";
+var pl = "Atan2";
 var Sa = "AvgPool";
 var ig = "AvgPoolGrad";
 var qd = "AvgPool3D";
 var og = "AvgPool3DGrad";
 var Ca = "BatchMatMul";
-var io = "BatchToSpaceND";
+var oo = "BatchToSpaceND";
 var ug = "Bincount";
 var A$ = "BroadcastTo";
 var lg = "BroadcastArgs";
@@ -3822,7 +3822,7 @@ var Ta = "Ceil";
 var Sr = "ClipByValue";
 var jd = "Complex";
 var Kd = "ComplexAbs";
-var oo = "Concat";
+var uo = "Concat";
 var $a = "Conv2D";
 var cg = "Conv2DBackpropFilter";
 var _a = "Conv2DBackpropInput";
@@ -3831,11 +3831,11 @@ var dg = "Conv3DBackpropFilterV2";
 var pg = "Conv3DBackpropInputV2";
 var Aa = "Cos";
 var Ea = "Cosh";
-var pl = "Cumprod";
-var uo = "Cumsum";
-var lo = "CropAndResize";
+var hl = "Cumprod";
+var lo = "Cumsum";
+var co = "CropAndResize";
 var hg = "DenseBincount";
-var co = "DepthToSpace";
+var po = "DepthToSpace";
 var Ra = "DepthwiseConv2dNative";
 var fg = "DepthwiseConv2dNativeBackpropFilter";
 var mg = "DepthwiseConv2dNativeBackpropInput";
@@ -3847,35 +3847,35 @@ var Da = "RealDiv";
 var Qd = "Einsum";
 var Fa = "Elu";
 var bg = "EluGrad";
-var hl = "Erf";
-var po = "Equal";
+var fl = "Erf";
+var ho = "Equal";
 var Oa = "Exp";
-var ho = "ExpandDims";
-var fo = "Expm1";
+var fo = "ExpandDims";
+var mo = "Expm1";
 var yg = "FFT";
-var fl = "Fill";
-var mo = "FlipLeftRight";
+var ml = "Fill";
+var go = "FlipLeftRight";
 var Pa = "Floor";
 var za = "FloorDiv";
 var Ma = "FusedBatchNorm";
-var go = "GatherV2";
-var bo = "GatherNd";
-var yo = "Greater";
+var bo = "GatherV2";
+var yo = "GatherNd";
+var vo = "Greater";
 var La = "GreaterEqual";
 var Ba = "Identity";
 var vg = "IFFT";
 var Zd = "Imag";
-var ml = "IsFinite";
-var gl = "IsInf";
-var bl = "IsNan";
+var gl = "IsFinite";
+var bl = "IsInf";
+var yl = "IsNan";
 var Va = "LeakyRelu";
-var vo = "Less";
-var xo = "LessEqual";
+var xo = "Less";
+var wo = "LessEqual";
 var xg = "LinSpace";
 var Wa = "Log";
-var yl = "Log1p";
-var wo = "LogicalAnd";
-var vl = "LogicalNot";
+var vl = "Log1p";
+var ko = "LogicalAnd";
+var xl = "LogicalNot";
 var Jd = "LogicalOr";
 var E$ = "LogSoftmax";
 var ep = "LRN";
@@ -3891,74 +3891,74 @@ var qa = "Mean";
 var ja = "Min";
 var Ka = "Minimum";
 var Xa = "MirrorPad";
-var xl = "Mod";
+var wl = "Mod";
 var Cg = "Multinomial";
 var Ya = "Multiply";
-var ko = "Neg";
-var Io = "NotEqual";
-var So = "NonMaxSuppressionV3";
-var wl = "NonMaxSuppressionV4";
-var Co = "NonMaxSuppressionV5";
-var No = "OnesLike";
-var To = "OneHot";
-var $o = "Pack";
+var Io = "Neg";
+var So = "NotEqual";
+var Co = "NonMaxSuppressionV3";
+var kl = "NonMaxSuppressionV4";
+var No = "NonMaxSuppressionV5";
+var To = "OnesLike";
+var $o = "OneHot";
+var _o = "Pack";
 var Qa = "PadV2";
-var Tde = "Pool";
+var $de = "Pool";
 var Za = "Pow";
 var Ja = "Prelu";
-var _o = "Prod";
-var kl = "Range";
+var Ao = "Prod";
+var Il = "Range";
 var np = "Real";
-var Il = "Reciprocal";
+var Sl = "Reciprocal";
 var ei = "Relu";
-var Ao = "Reshape";
-var Sl = "ResizeNearestNeighbor";
+var Eo = "Reshape";
+var Cl = "ResizeNearestNeighbor";
 var Ng = "ResizeNearestNeighborGrad";
 var ti = "ResizeBilinear";
 var Tg = "ResizeBilinearGrad";
 var ni = "Relu6";
-var Eo = "Reverse";
-var Ro = "Round";
+var Ro = "Reverse";
+var Do = "Round";
 var si = "Rsqrt";
-var Do = "ScatterNd";
-var Fo = "Select";
-var Cl = "Selu";
-var Oo = "Slice";
+var Fo = "ScatterNd";
+var Oo = "Select";
+var Nl = "Selu";
+var Po = "Slice";
 var ri = "Sin";
-var Po = "Sinh";
-var Nl = "Sign";
+var zo = "Sinh";
+var Tl = "Sign";
 var ai = "Sigmoid";
-var Tl = "Softplus";
+var $l = "Softplus";
 var ii = "Sqrt";
 var oi = "Sum";
-var zo = "SpaceToBatchND";
-var Mo = "SplitV";
+var Mo = "SpaceToBatchND";
+var Lo = "SplitV";
 var ui = "Softmax";
 var sp = "SparseFillEmptyRows";
-var $l = "SparseReshape";
+var _l = "SparseReshape";
 var rp = "SparseSegmentMean";
 var ap = "SparseSegmentSum";
 var ip = "SparseToDense";
 var li = "SquaredDifference";
-var _l = "Square";
-var Lo = "StridedSlice";
+var Al = "Square";
+var Bo = "StridedSlice";
 var op = "StringNGrams";
 var $g = "StringSplit";
 var _g = "StringToHashBucketFast";
 var ci = "Sub";
-var Bo = "Tan";
+var Vo = "Tan";
 var di = "Tanh";
 var Cr = "Tile";
-var Vo = "TopK";
-var Wo = "Transform";
+var Wo = "TopK";
+var Uo = "Transform";
 var pi = "Transpose";
 var Ag = "Unique";
-var Uo = "Unpack";
+var Go = "Unpack";
 var up = "UnsortedSegmentSum";
-var Go = "ZerosLike";
+var Ho = "ZerosLike";
 var hi = "Step";
 var fd = "FromPixels";
-var Ho = "RotateWithOffset";
+var qo = "RotateWithOffset";
 var ra = "_FusedMatMul";
 var aa = "FusedConv2D";
 var ia = "FusedDepthwiseConv2D";
@@ -3969,13 +3969,13 @@ function R$(...e) {
   X().getBool("IS_TEST") || X().getBool("PROD") || console.log(...e);
 }
 var Qi = ag("kernelRegistry", () => /* @__PURE__ */ new Map());
-var Vu = ag("gradRegistry", () => /* @__PURE__ */ new Map());
+var Wu = ag("gradRegistry", () => /* @__PURE__ */ new Map());
 function Zf(e, t) {
   let n = Eg(e, t);
   return Qi.get(n);
 }
 function nx(e) {
-  return Vu.get(e);
+  return Wu.get(e);
 }
 function Jf(e) {
   let t = Qi.entries(), n = [];
@@ -3988,44 +3988,44 @@ function Jf(e) {
   }
   return n;
 }
-function Al(e) {
+function El(e) {
   let { kernelName: t, backendName: n } = e, s = Eg(t, n);
   Qi.has(s) && ar(`The kernel '${t}' for backend '${n}' is already registered`), Qi.set(s, e);
 }
 function D$(e) {
   let { kernelName: t } = e;
-  Vu.has(t) && X().getBool("DEBUG") && ar(`Overriding the gradient for '${t}'`), Vu.set(t, e);
+  Wu.has(t) && X().getBool("DEBUG") && ar(`Overriding the gradient for '${t}'`), Wu.set(t, e);
 }
-function $de(e, t) {
+function _de(e, t) {
   let n = Eg(e, t);
   if (!Qi.has(n))
     throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);
   Qi.delete(n);
 }
-function _de(e) {
-  if (!Vu.has(e))
+function Ade(e) {
+  if (!Wu.has(e))
     throw new Error(`The gradient '${e}' for backend is not registered`);
-  Vu.delete(e);
+  Wu.delete(e);
 }
-function Ade(e, t) {
+function Ede(e, t) {
   Jf(e).forEach((s) => {
     let r = Object.assign({}, s, { backendName: t });
-    Al(r);
+    El(r);
   });
 }
 function Eg(e, t) {
   return `${t}_${e}`;
 }
 var w = {};
-Ae(w, { arraysEqual: () => kr, assert: () => O, assertNonNegativeIntegerDimensions: () => sg, assertNonNull: () => wa, assertShapesMatch: () => pn, bytesFromStringArray: () => Xw, bytesPerElement: () => Xf, checkConversionForErrors: () => jw, clamp: () => Bu, computeStrides: () => ro, createScalarValue: () => L$, createShuffledIndices: () => b$, decodeString: () => md, distSquared: () => h$, encodeString: () => Rl, fetch: () => V$, fingerPrint64: () => M$, flatten: () => sa, getArrayFromDType: () => qw, getTypedArrayFromDType: () => Hw, hasEncodingLoss: () => x$, hexToLong: () => El, indexToLoc: () => I$, inferDtype: () => Gd, inferFromImplicitShape: () => v$, isBoolean: () => Yw, isFunction: () => hr, isInt: () => Yi, isNumber: () => Qw, isPromise: () => rg, isScalarShape: () => f$, isString: () => ir, isTypedArray: () => Qt, isValidDtype: () => Kw, locToIndex: () => k$, makeOnesTypedArray: () => ng, makeZerosNestedTypedArray: () => w$, makeZerosTypedArray: () => Hd, nearestDivisor: () => hd, nearestLargerEven: () => c$, now: () => Wu, parseAxisParam: () => ts, randUniform: () => p$, repeatedTry: () => y$, rightPad: () => Pu, shuffle: () => Uw, shuffleCombo: () => l$, sizeFromShape: () => pt, sizeToSquarishShape: () => g$, squeezeShape: () => Gw, sum: () => d$, swap: () => pd, tanh: () => m$, toNestedArray: () => Hi, toTypedArray: () => lp });
+Ae(w, { arraysEqual: () => kr, assert: () => O, assertNonNegativeIntegerDimensions: () => sg, assertNonNull: () => wa, assertShapesMatch: () => pn, bytesFromStringArray: () => Xw, bytesPerElement: () => Xf, checkConversionForErrors: () => jw, clamp: () => Vu, computeStrides: () => ao, createScalarValue: () => L$, createShuffledIndices: () => b$, decodeString: () => md, distSquared: () => h$, encodeString: () => Dl, fetch: () => V$, fingerPrint64: () => M$, flatten: () => sa, getArrayFromDType: () => qw, getTypedArrayFromDType: () => Hw, hasEncodingLoss: () => x$, hexToLong: () => Rl, indexToLoc: () => I$, inferDtype: () => Gd, inferFromImplicitShape: () => v$, isBoolean: () => Yw, isFunction: () => hr, isInt: () => Yi, isNumber: () => Qw, isPromise: () => rg, isScalarShape: () => f$, isString: () => ir, isTypedArray: () => Qt, isValidDtype: () => Kw, locToIndex: () => k$, makeOnesTypedArray: () => ng, makeZerosNestedTypedArray: () => w$, makeZerosTypedArray: () => Hd, nearestDivisor: () => hd, nearestLargerEven: () => c$, now: () => Uu, parseAxisParam: () => ts, randUniform: () => p$, repeatedTry: () => y$, rightPad: () => zu, shuffle: () => Uw, shuffleCombo: () => l$, sizeFromShape: () => pt, sizeToSquarishShape: () => g$, squeezeShape: () => Gw, sum: () => d$, swap: () => pd, tanh: () => m$, toNestedArray: () => Hi, toTypedArray: () => lp });
 var sx = xa(HT());
 var qr = sx.default || sx;
-function El(e) {
+function Rl(e) {
   return qr.fromString(e, true, 16);
 }
-var tk = El("c3a5c85c97cb3127");
-var Gr = El("b492b66fbe98f273");
-var on = El("9ae16a3b2f90404f");
+var tk = Rl("c3a5c85c97cb3127");
+var Gr = Rl("b492b66fbe98f273");
+var on = Rl("9ae16a3b2f90404f");
 function em(e) {
   return e.xor(e.shru(47));
 }
@@ -4042,7 +4042,7 @@ function rx(e, t) {
 function Lt(e, t) {
   return t === 0 ? e : e.shru(t).or(e.shl(64 - t));
 }
-function ur(e, t, n = El("9ddfea08eb382d69")) {
+function ur(e, t, n = Rl("9ddfea08eb382d69")) {
   let s = e.xor(t).mul(n);
   s = s.xor(s.shru(47));
   let r = t.xor(s).mul(n);
@@ -4095,7 +4095,7 @@ function M$(e, t = e.length) {
   return u = c, o[0] = o[0].add(t - 1 & 63), i[0] = i[0].add(o[0]), o[0] = o[0].add(i[0]), s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(p), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(p), s = s.xor(o[1].mul(9)), r = r.add(i[0].mul(9).add(lt(e, u + 40))), a = Lt(a.add(o[0]), 33).mul(p), i = Vc(e, u, i[1].mul(p), s.add(o[0])), o = Vc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], ur(ur(i[0], o[0], p).add(em(r).mul(tk)).add(a), ur(i[1], o[1], p).add(s), p);
 }
 function L$(e, t) {
-  return t === "string" ? Rl(e) : lp([e], t);
+  return t === "string" ? Dl(e) : lp([e], t);
 }
 function B$(e, t) {
   return e instanceof Float32Array && t === "float32" || e instanceof Int32Array && t === "int32" || e instanceof Uint8Array && t === "bool";
@@ -4117,13 +4117,13 @@ function lp(e, t) {
   } else
     throw new Error(`Unknown data type ${t}`);
 }
-function Wu() {
+function Uu() {
   return X().platform.now();
 }
 function V$(e, t) {
   return X().platform.fetch(e, t);
 }
-function Rl(e, t = "utf-8") {
+function Dl(e, t = "utf-8") {
   return t = t || "utf-8", X().platform.encode(e, t);
 }
 function md(e, t = "utf-8") {
@@ -4136,14 +4136,14 @@ var W$ = class {
   profileKernel(e, t, n) {
     let s, r = () => {
       s = n();
-    }, a, i = Wu();
+    }, a, i = Uu();
     if (this.backendTimer.timerAvailable())
       a = this.backendTimer.time(r);
     else {
       r();
       for (let u of s)
         u.dataSync();
-      a = Promise.resolve({ kernelMs: Wu() - i });
+      a = Promise.resolve({ kernelMs: Uu() - i });
     }
     if (X().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
       for (let u = 0; u < s.length; u++) {
@@ -4175,7 +4175,7 @@ function U$(e, t, n) {
 }
 var G$ = class {
   logKernelProfile(e, t, n, s, r, a) {
-    let i = typeof s == "number" ? Pu(`${s}ms`, 9) : s.error, o = Pu(e, 25), u = t.rank, l = t.size, c = Pu(t.shape.toString(), 14), p = "";
+    let i = typeof s == "number" ? zu(`${s}ms`, 9) : s.error, o = zu(e, 25), u = t.rank, l = t.size, c = zu(t.shape.toString(), 14), p = "";
     for (let d in r) {
       let h = r[d];
       if (h != null) {
@@ -4258,27 +4258,27 @@ function q$(e, t, n, s) {
   }
 }
 var ax = 20;
-var Iu = 3;
+var Su = 3;
 var Ff = 7;
 function j$(e, t, n, s) {
-  let r = ro(t), a = K$(e, t, n, r), i = t.length, o = ed(e, t, n, r, a), u = ["Tensor"];
+  let r = ao(t), a = K$(e, t, n, r), i = t.length, o = ed(e, t, n, r, a), u = ["Tensor"];
   return s && (u.push(`  dtype: ${n}`), u.push(`  rank: ${i}`), u.push(`  shape: [${t}]`), u.push("  values:")), u.push(o.map((l) => "    " + l).join(`
 `)), u.join(`
 `);
 }
 function K$(e, t, n, s) {
-  let r = pt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? $u(e) : e;
+  let r = pt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? _u(e) : e;
   if (o > 1)
     for (let l = 0; l < r / a; l++) {
       let c = l * a;
       for (let p = 0; p < a; p++)
-        i[p] = Math.max(i[p], Tu(u[c + p], 0, n).length);
+        i[p] = Math.max(i[p], $u(u[c + p], 0, n).length);
     }
   return i;
 }
-function Tu(e, t, n) {
+function $u(e, t, n) {
   let s;
-  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Ff))} + ${parseFloat(e[1].toFixed(Ff))}j` : ir(e) ? s = `'${e}'` : n === "bool" ? s = sk(e) : s = parseFloat(e.toFixed(Ff)).toString(), Pu(s, t);
+  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Ff))} + ${parseFloat(e[1].toFixed(Ff))}j` : ir(e) ? s = `'${e}'` : n === "bool" ? s = sk(e) : s = parseFloat(e.toFixed(Ff)).toString(), zu(s, t);
 }
 function sk(e) {
   return e === 0 ? "false" : "true";
@@ -4287,27 +4287,27 @@ function ed(e, t, n, s, r, a = true) {
   let i = n === "complex64" ? 2 : 1, o = t[0], u = t.length;
   if (u === 0) {
     if (n === "complex64") {
-      let m = $u(e);
-      return [Tu(m[0], 0, n)];
+      let m = _u(e);
+      return [$u(m[0], 0, n)];
     }
     return n === "bool" ? [sk(e[0])] : [e[0].toString()];
   }
   if (u === 1) {
     if (o > ax) {
-      let g = Iu * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Iu) * i, o * i));
-      return n === "complex64" && (b = $u(b), y = $u(y)), ["[" + b.map((v, x) => Tu(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => Tu(v, r[o - Iu + x], n)).join(", ") + "]"];
+      let g = Su * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Su) * i, o * i));
+      return n === "complex64" && (b = _u(b), y = _u(y)), ["[" + b.map((v, x) => $u(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => $u(v, r[o - Su + x], n)).join(", ") + "]"];
     }
-    let m = n === "complex64" ? $u(e) : Array.from(e);
-    return ["[" + m.map((g, b) => Tu(g, r[b], n)).join(", ") + "]"];
+    let m = n === "complex64" ? _u(e) : Array.from(e);
+    return ["[" + m.map((g, b) => $u(g, r[b], n)).join(", ") + "]"];
   }
   let l = t.slice(1), c = s.slice(1), p = s[0] * i, d = [];
   if (o > ax) {
-    for (let m = 0; m < Iu; m++) {
+    for (let m = 0; m < Su; m++) {
       let g = m * p, b = g + p;
       d.push(...ed(e.slice(g, b), l, n, c, r, false));
     }
     d.push("...");
-    for (let m = o - Iu; m < o; m++) {
+    for (let m = o - Su; m < o; m++) {
       let g = m * p, b = g + p;
       d.push(...ed(e.slice(g, b), l, n, c, r, m === o - 1));
     }
@@ -4327,7 +4327,7 @@ function ed(e, t, n, s, r, a = true) {
 `;
   return d[d.length - 1] = " " + d[d.length - 1] + "]" + (a ? "" : f), d;
 }
-function $u(e) {
+function _u(e) {
   let t = [];
   for (let n = 0; n < e.length; n += 2)
     t.push([e[n], e[n + 1]]);
@@ -4341,7 +4341,7 @@ var Vt = class {
     }
     if (t === "complex64")
       throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");
-    this.values = n || qw(t, this.size), this.strides = ro(e);
+    this.values = n || qw(t, this.size), this.strides = ao(e);
   }
   set(e, ...t) {
     t.length === 0 && (t = [0]), O(t.length === this.rank, () => `The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);
@@ -4404,7 +4404,7 @@ function Z$(e) {
 }
 var et = class {
   constructor(e, t, n, s) {
-    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = pt(e), this.strides = ro(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
+    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = pt(e), this.strides = ao(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
   }
   get rank() {
     return this.shape.length;
@@ -4489,8 +4489,7 @@ function J$() {
 J$();
 var gd = class extends et {
   constructor(e, t, n, s) {
-    super(e.shape, e.dtype, e.dataId, s);
-    this.trainable = t, this.name = n;
+    super(e.shape, e.dtype, e.dataId, s), this.trainable = t, this.name = n;
   }
   assign(e) {
     if (e.dtype !== this.dtype)
@@ -4648,7 +4647,7 @@ var tm = class {
       throw new Error(`Cannot initialize backend ${e}, no registration found.`);
     try {
       let n = t.factory();
-      if (n && !(n instanceof tl) && typeof n.then == "function") {
+      if (n && !(n instanceof nl) && typeof n.then == "function") {
         let s = ++this.pendingBackendInitId, r = n.then((a) => s < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (s < this.pendingBackendInitId || (this.pendingBackendInit = null, ar(`Initialization of backend ${e} failed`), ar(a.stack || a.message)), false));
         return this.pendingBackendInit = r, { success: r, asyncInit: true };
       } else
@@ -4796,7 +4795,7 @@ var tm = class {
       throw new Error("Values passed to engine.makeTensor() are null");
     n = n || "float32", s = s || this.backend;
     let r = e;
-    n === "string" && ir(e[0]) && (r = e.map((o) => Rl(o)));
+    n === "string" && ir(e[0]) && (r = e.map((o) => Dl(o)));
     let a = s.write(r, t, n), i = new et(t, n, a, this.nextTensorId());
     if (this.trackTensor(i, s), n === "string") {
       let o = this.state.tensorInfo.get(a), u = Xw(r);
@@ -4941,8 +4940,8 @@ var tm = class {
     return this.state.tensorInfo.get(e).backend.readToGPU(e, t);
   }
   async time(e) {
-    let t = Wu(), n = await this.backend.time(e);
-    return n.wallMs = Wu() - t, n;
+    let t = Uu(), n = await this.backend.time(e);
+    return n.wallMs = Uu() - t, n;
   }
   track(e) {
     return this.state.activeScope != null && (e.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(e)), e;
@@ -5060,7 +5059,7 @@ function _(e, t, n, s = "numeric") {
   let o = r !== "string" ? lp(e, r) : sa(e, [], true);
   return M.makeTensor(o, a, r);
 }
-function Uu(e, t, n, s = "numeric") {
+function Gu(e, t, n, s = "numeric") {
   if (!Array.isArray(e))
     throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);
   return e.map((a, i) => _(a, `${t}[${i}]`, n, s));
@@ -5271,7 +5270,7 @@ async function Pg(e, t) {
   }
   return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), n;
 }
-function Dl(e) {
+function Fl(e) {
   if (e.modelTopology instanceof ArrayBuffer)
     throw new Error("Expected JSON model topology, received ArrayBuffer.");
   return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: e.modelTopology == null ? 0 : ux(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : ux(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
@@ -5390,7 +5389,7 @@ var ua = class {
             n(u.result.modelArtifacts);
           }, u.onerror = (l) => (a.close(), s(u.error)), i.oncomplete = () => a.close();
         } else {
-          let i = Dl(t), o = a.transaction(or, "readwrite"), u = o.objectStore(or), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
+          let i = Fl(t), o = a.transaction(or, "readwrite"), u = o.objectStore(or), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
           l.onsuccess = () => {
             c = a.transaction(Yr, "readwrite");
             let d = c.objectStore(Yr).put({ modelPath: this.modelPath, modelArtifacts: t, modelArtifactsInfo: i });
@@ -5493,7 +5492,7 @@ var la = class {
     if (e.modelTopology instanceof ArrayBuffer)
       throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
     {
-      let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Dl(e);
+      let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Fl(e);
       try {
         this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, h_(e.weightData));
         let r = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
@@ -5694,7 +5693,7 @@ ck();
 var G_ = { buffer: De, cast: ce, clone: lr, print: U_ };
 Q$(G_);
 var En = {};
-Ae(En, { browserFiles: () => Q_, browserHTTPRequest: () => nA, concatenateArrayBuffers: () => Og, copyModel: () => P_, decodeWeights: () => hk, encodeWeights: () => d_, fromMemory: () => rA, getLoadHandlers: () => k_, getModelArtifactsForJSON: () => Pg, getModelArtifactsInfoForJSON: () => Dl, getSaveHandlers: () => w_, http: () => Mg, isHTTPScheme: () => um, listModels: () => F_, loadWeights: () => Z_, moveModel: () => z_, registerLoadRouter: () => x_, registerSaveRouter: () => v_, removeModel: () => O_, weightsLoaderFactory: () => Ik, withSaveHandler: () => aA });
+Ae(En, { browserFiles: () => Q_, browserHTTPRequest: () => nA, concatenateArrayBuffers: () => Og, copyModel: () => P_, decodeWeights: () => hk, encodeWeights: () => d_, fromMemory: () => rA, getLoadHandlers: () => k_, getModelArtifactsForJSON: () => Pg, getModelArtifactsInfoForJSON: () => Fl, getSaveHandlers: () => w_, http: () => Mg, isHTTPScheme: () => um, listModels: () => F_, loadWeights: () => Z_, moveModel: () => z_, registerLoadRouter: () => x_, registerSaveRouter: () => v_, removeModel: () => O_, weightsLoaderFactory: () => Ik, withSaveHandler: () => aA });
 var H_ = "model";
 var q_ = ".json";
 var j_ = ".weights.bin";
@@ -5719,7 +5718,7 @@ var om = class {
         let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
         i.download = this.weightDataFileName, i.href = t, await cx(() => i.dispatchEvent(new MouseEvent("click")));
       }
-      return { modelArtifactsInfo: Dl(e) };
+      return { modelArtifactsInfo: Fl(e) };
     }
   }
 };
@@ -5875,7 +5874,7 @@ var zg = class {
     t.body.append("model.json", new Blob([JSON.stringify(s)], { type: eA }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: J_ }), "model.weights.bin");
     let r = await this.fetch(this.path, t);
     if (r.ok)
-      return { modelArtifactsInfo: Dl(e), responses: [r] };
+      return { modelArtifactsInfo: Fl(e), responses: [r] };
     throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`);
   }
   async load() {
@@ -5968,7 +5967,7 @@ function uA(e, t, n = 1, s = 0) {
   if (t < 2)
     throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);
   let a = { indices: _(e, "indices", "oneHot", "int32") }, i = { depth: t, onValue: n, offValue: s };
-  return M.runKernel(To, a, i);
+  return M.runKernel($o, a, i);
 }
 var vd = L({ oneHot_: uA });
 function lA(e, t) {
@@ -5988,8 +5987,8 @@ function cA(e, t, n) {
   return ce(u, "int32");
 }
 var dA = L({ confusionMatrix_: cA });
-var qo = {};
-Ae(qo, { assertAndGetBroadcastShape: () => ot, getBroadcastDims: () => Ck, getReductionAxes: () => _t });
+var jo = {};
+Ae(jo, { assertAndGetBroadcastShape: () => ot, getBroadcastDims: () => Ck, getReductionAxes: () => _t });
 function Ck(e, t) {
   let n = e.length, s = [];
   for (let r = 0; r < n; r++) {
@@ -6175,7 +6174,7 @@ function _k(e, t) {
   let l = 1;
   for (let p = a; p < n; ++p)
     l *= o[p], u.push(o[p]);
-  let c = [...ro(e.shape).map((p) => p / l), 1].slice(0, a);
+  let c = [...ao(e.shape).map((p) => p / l), 1].slice(0, a);
   return [u, i, l, c];
 }
 var Ak = {};
@@ -6216,7 +6215,7 @@ function Ek(e, t, n) {
   let s = t.shape.length, r = s > 1 ? t.shape[s - 1] : 1, a = n.length, i = 1;
   for (let p = r; p < a; ++p)
     i *= n[p];
-  let o = r < 1 ? 1 : r, u = pt(t.shape) / o, l = [...ro(n.slice(0, r)), 1], c = pt(n);
+  let o = r < 1 ? 1 : r, u = pt(t.shape) / o, l = [...ao(n.slice(0, r)), 1], c = pt(n);
   return { sliceRank: r, numUpdates: u, sliceSize: i, strides: l, outputSize: c };
 }
 var wt = {};
@@ -6290,7 +6289,7 @@ function Pk(e, t, n, s, r) {
     }
   for (let o = 0; o < a.length; o++) {
     let u = r[o];
-    a[o] < 0 && (a[o] += u), a[o] = Bu(0, a[o], r[o]);
+    a[o] < 0 && (a[o] += u), a[o] = Vu(0, a[o], r[o]);
   }
   return a;
 }
@@ -6302,13 +6301,13 @@ function Mk(e, t, n, s, r, a) {
   let i = t[r], o = n[r] || 1;
   (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MIN_SAFE_INTEGER : i = Number.MAX_SAFE_INTEGER);
   let u = s[r];
-  return i < 0 && (i += u), i = Bu(0, i, u - 1), i;
+  return i < 0 && (i += u), i = Vu(0, i, u - 1), i;
 }
 function Lk(e, t, n, s, r, a) {
   let i = t[r], o = n[r] || 1;
   (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MAX_SAFE_INTEGER : i = Number.MIN_SAFE_INTEGER);
   let u = s[r];
-  return i < 0 && (i += u), o > 0 ? i = Bu(0, i, u) : i = Bu(-1, i, u - 1), i;
+  return i < 0 && (i += u), o > 0 ? i = Vu(0, i, u) : i = Vu(-1, i, u - 1), i;
 }
 function CA(e, t, n) {
   let s = n.length;
@@ -6498,25 +6497,25 @@ function PA(e, t) {
 function Uk(e) {
   for (let t = 0; t < e.length; t++) {
     let n = e[t];
-    Array.isArray(n) ? Uk(n) : e[t] = Rl(n);
+    Array.isArray(n) ? Uk(n) : e[t] = Dl(n);
   }
   return e;
 }
-var Ede = "0.0.0";
-function Rde() {
+var Rde = "0.0.0";
+function Dde() {
   X().set("PROD", true);
 }
-function Dde() {
+function Fde() {
   X().set("DEBUG", true);
 }
-function Fde() {
+function Ode() {
   X().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
 }
 function Gk(e) {
   X().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(e + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
 }
 Z$(Gk);
-function Ode() {
+function Pde() {
   M.disposeVariables();
 }
 function Ss() {
@@ -6525,7 +6524,7 @@ function Ss() {
 function dm() {
   return M.memory();
 }
-function Pde(e) {
+function zde(e) {
   return M.profile(e);
 }
 function j(e, t) {
@@ -6537,25 +6536,25 @@ function Re(e) {
 function Ht(e) {
   return M.keep(e);
 }
-function zde(e) {
+function Mde(e) {
   return M.time(e);
 }
-function Mde(e) {
+function Lde(e) {
   return M.setBackend(e);
 }
-function Lde() {
+function Bde() {
   return M.ready();
 }
-function Bde() {
+function Vde() {
   return M.backendName;
 }
-function Vde(e) {
+function Wde(e) {
   M.removeBackend(e);
 }
-function Wde(e) {
+function Ude(e) {
   return M.findBackend(e);
 }
-function Ude(e) {
+function Gde(e) {
   return M.findBackendFactory(e);
 }
 function pp(e, t, n = 1) {
@@ -6564,7 +6563,7 @@ function pp(e, t, n = 1) {
 function zA() {
   return M.backend;
 }
-function Gde(e, t) {
+function Hde(e, t) {
   X().setPlatform(e, t);
 }
 function MA(e, t) {
@@ -6603,18 +6602,18 @@ function WA(e) {
     return M.runKernel(Kd, n);
   } else {
     let n = { x: t };
-    return M.runKernel(ao, n);
+    return M.runKernel(io, n);
   }
 }
 var Mt = L({ abs_: WA });
 function UA(e) {
   let n = { x: _(e, "x", "acos") };
-  return M.runKernel(nl, n);
+  return M.runKernel(sl, n);
 }
 var GA = L({ acos_: UA });
 function HA(e) {
   let n = { x: _(e, "x", "acosh") };
-  return M.runKernel(sl, n);
+  return M.runKernel(rl, n);
 }
 var qA = L({ acosh_: HA });
 function jA(e) {
@@ -6633,54 +6632,54 @@ function jA(e) {
 var KA = L({ addN_: jA });
 function XA(e, t = null, n = false) {
   let r = { x: _(e, "x", "all", "bool") }, a = { axis: t, keepDims: n };
-  return M.runKernel(rl, r, a);
+  return M.runKernel(al, r, a);
 }
 var qk = L({ all_: XA });
 function YA(e, t = null, n = false) {
   let r = { x: _(e, "x", "any", "bool") }, a = { axis: t, keepDims: n };
-  return M.runKernel(al, r, a);
+  return M.runKernel(il, r, a);
 }
 var pm = L({ any_: YA });
 function QA(e, t = 0) {
   let s = { x: _(e, "x", "argMax") }, r = { axis: t };
   return M.runKernel(Ia, s, r);
 }
-var Gu = L({ argMax_: QA });
+var Hu = L({ argMax_: QA });
 function ZA(e, t = 0) {
   let s = { x: _(e, "x", "argMin") }, r = { axis: t };
-  return M.runKernel(il, s, r);
+  return M.runKernel(ol, s, r);
 }
 var JA = L({ argMin_: ZA });
 function eE(e) {
   let n = { x: _(e, "x", "asin") };
-  return M.runKernel(ol, n);
+  return M.runKernel(ul, n);
 }
 var tE = L({ asin_: eE });
 function nE(e) {
   let n = { x: _(e, "x", "asinh") };
-  return M.runKernel(ul, n);
+  return M.runKernel(ll, n);
 }
 var sE = L({ asinh_: nE });
 function rE(e) {
   let n = { x: _(e, "x", "atan") };
-  return M.runKernel(ll, n);
+  return M.runKernel(cl, n);
 }
 var aE = L({ atan_: rE });
 function iE(e, t) {
   let n = _(e, "a", "atan2"), s = _(t, "b", "atan2");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(dl, r);
+  return M.runKernel(pl, r);
 }
 var oE = L({ atan2_: iE });
 function uE(e) {
   let n = { x: _(e, "x", "atanh") };
-  return M.runKernel(cl, n);
+  return M.runKernel(dl, n);
 }
 var lE = L({ atanh_: uE });
 function cE(e, t, n, s, r = "NHWC", a) {
   let i = e[3], o = [...t, i], u = Xk(r);
-  return Fl(e, o, n, a, s, null, null, u);
+  return Ol(e, o, n, a, s, null, null, u);
 }
 function jk(e, t, n, s, r, a, i = "channelsLast") {
   let [o, u] = xd(t), l;
@@ -6690,7 +6689,7 @@ function jk(e, t, n, s, r, a, i = "channelsLast") {
     l = [o, u, e[1], e[1]];
   else
     throw new Error(`Unknown dataFormat ${i}`);
-  return Fl(e, l, n, s, r, a, false, i);
+  return Ol(e, l, n, s, r, a, false, i);
 }
 function dE(e, t, n, s, r, a, i = "NDHWC") {
   let [o, u, l] = hm(t), c, p;
@@ -6702,7 +6701,7 @@ function dE(e, t, n, s, r, a, i = "NDHWC") {
     throw new Error(`Unknown dataFormat ${i}`);
   return Kk(e, c, n, s, r, false, p, a);
 }
-function Fl(e, t, n, s, r, a, i = false, o = "channelsLast") {
+function Ol(e, t, n, s, r, a, i = false, o = "channelsLast") {
   let [u, l, c, p] = [-1, -1, -1, -1];
   if (o === "channelsLast")
     [u, l, c, p] = e;
@@ -6828,7 +6827,7 @@ function hn(e, t, n) {
 }
 function gE(e, t) {
   let s = { x: _(e, "x", "reshape", "string_or_numeric") }, r = { shape: t };
-  return M.runKernel(Ao, s, r);
+  return M.runKernel(Eo, s, r);
 }
 var G = L({ reshape_: gE });
 function bE(e, t, n, s, r) {
@@ -6849,7 +6848,7 @@ function yE(e, t, n, s, r, a = "NDHWC") {
 var Yk = L({ avgPool3d_: yE });
 function vE(e, t = 0) {
   O(e.length >= 1, () => "Pass at least one tensor to concat");
-  let n = Uu(e, "tensors", "concat", "string_or_numeric");
+  let n = Gu(e, "tensors", "concat", "string_or_numeric");
   if (n[0].dtype === "complex64" && n.forEach((a) => {
     if (a.dtype !== "complex64")
       throw new Error(`Cannot concatenate complex64 tensors with a tensor
@@ -6857,7 +6856,7 @@ function vE(e, t = 0) {
   }), n.length === 1)
     return lr(n[0]);
   let s = n, r = { axis: t };
-  return M.runKernel(oo, s, r);
+  return M.runKernel(uo, s, r);
 }
 var Ft = L({ concat_: vE });
 function xE(e) {
@@ -6870,24 +6869,24 @@ function wE(e, t, n) {
   if (s.rank === 0)
     throw new Error("Slicing scalar is not possible");
   let r = { x: s }, a = { begin: t, size: n };
-  return M.runKernel(Oo, r, a);
+  return M.runKernel(Po, r, a);
 }
 var He = L({ slice_: wE });
 function kE(e) {
   let n = { x: _(e, "x", "tanh", "float32") };
   return M.runKernel(di, n);
 }
-var Hu = L({ tanh_: kE });
+var qu = L({ tanh_: kE });
 function IE(e, t, n, s, r, a) {
-  let i = _(e, "forgetBias", "basicLSTMCell"), o = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = We(d, o), f = ie(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = He(f, [0, 0], b), v = He(f, [0, g], b), x = He(f, [0, g * 2], b), k = He(f, [0, g * 3], b), T = ie(V(qs(y), Hu(v)), V(c, qs(ie(i, x)))), N = V(Hu(T), qs(k));
+  let i = _(e, "forgetBias", "basicLSTMCell"), o = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = We(d, o), f = ie(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = He(f, [0, 0], b), v = He(f, [0, g], b), x = He(f, [0, g * 2], b), k = He(f, [0, g * 3], b), T = ie(V(qs(y), qu(v)), V(c, qs(ie(i, x)))), N = V(qu(T), qs(k));
   return [T, N];
 }
-var Hde = L({ basicLSTMCell_: IE });
+var qde = L({ basicLSTMCell_: IE });
 function SE(e, t, n) {
   let s = _(e, "x", "batchToSpaceND"), r = t.reduce((o, u) => o * u);
   O(s.rank >= 1 + t.length, () => `input rank is ${s.rank} but should be > than blockShape.length ${t.length}`), O(n.length === t.length, () => `crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`), O(s.shape[0] % r === 0, () => `input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);
   let a = { x: s }, i = { blockShape: t, crops: n };
-  return M.runKernel(io, a, i);
+  return M.runKernel(oo, a, i);
 }
 var Hg = L({ batchToSpaceND_: SE });
 function CE(e) {
@@ -6903,26 +6902,26 @@ function NE(e, t, n, s, r, a) {
   let d = { x: CE(i), scale: l, offset: c, mean: o, variance: u }, h = { varianceEpsilon: a }, f = M.runKernel(Ma, d, h);
   return G(f, i.shape);
 }
-var qu = L({ batchNorm_: NE });
+var ju = L({ batchNorm_: NE });
 function TE(e, t, n, s, r, a) {
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), O(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), O(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), qu(i, o, u, c, l, a);
+  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), O(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), O(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), ju(i, o, u, c, l, a);
 }
 var $E = L({ batchNorm2d_: TE });
 function _E(e, t, n, s, r, a) {
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), O(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), O(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), qu(i, o, u, c, l, a);
+  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), O(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), O(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), ju(i, o, u, c, l, a);
 }
 var AE = L({ batchNorm3d_: _E });
 function EE(e, t, n, s, r, a) {
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), O(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), O(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), qu(i, o, u, c, l, a);
+  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), O(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), O(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), ju(i, o, u, c, l, a);
 }
 var RE = L({ batchNorm4d_: EE });
 function DE(e, t, n) {
@@ -7059,12 +7058,12 @@ function sR(e) {
 var nI = L({ cosh_: sR });
 function rR(e, t = 0, n = false, s = false) {
   let a = { x: _(e, "x", "cumprod") }, i = { axis: t, exclusive: n, reverse: s };
-  return M.runKernel(pl, a, i);
+  return M.runKernel(hl, a, i);
 }
 var aR = L({ cumprod_: rR });
 function iR(e, t = 0, n = false, s = false) {
   let a = { x: _(e, "x", "cumsum") }, i = { axis: t, exclusive: n, reverse: s };
-  return M.runKernel(uo, a, i);
+  return M.runKernel(lo, a, i);
 }
 var sI = L({ cumsum_: iR });
 function oR(e, t, n, s = false) {
@@ -7082,7 +7081,7 @@ function lR(e, t, n = "NHWC") {
     ${a} and ${t} for depthToSpace with input shape
         ${s.shape}`), O(i % (t * t) === 0, () => `Dimension size must be evenly divisible by ${t * t} but is ${i} for depthToSpace with input shape ${s.shape}`);
   let o = { x: s }, u = { blockSize: t, dataFormat: n };
-  return M.runKernel(co, o, u);
+  return M.runKernel(po, o, u);
 }
 var cR = L({ depthToSpace_: lR });
 function dR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
@@ -7096,7 +7095,7 @@ function pR(e) {
   let n = { x: _(e, "x", "diag") };
   return M.runKernel(gg, n);
 }
-var qde = L({ diag_: pR });
+var jde = L({ diag_: pR });
 function hR(e, t, n, s, r = [1, 1], a = "NHWC") {
   let i = _(e, "x", "dilation2d"), o = _(t, "filter", "dilation2d");
   O(i.rank === 3 || i.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`), O(o.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`), O(a === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);
@@ -7110,17 +7109,17 @@ function mR(e, t) {
   let n = _(e, "a", "equal", "string_or_numeric"), s = _(t, "b", "equal", "string_or_numeric");
   [n, s] = vt(n, s), ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(po, r);
+  return M.runKernel(ho, r);
 }
 var Kn = L({ equal_: mR });
 function gR(e, t, n) {
   let s = _(t, "a", "where"), r = _(n, "b", "where"), a = _(e, "condition", "where", "bool"), i = ot(ot(a.shape, s.shape), r.shape), o = nd(a, i), u = nd(s, i), l = nd(r, i), c = { condition: o, t: u, e: l };
-  return M.runKernel(Fo, c);
+  return M.runKernel(Oo, c);
 }
 var vn = L({ where_: gR });
 function bR(e) {
   let n = { x: _(e, "x", "zerosLike") };
-  return M.runKernel(Go, n);
+  return M.runKernel(Ho, n);
 }
 var je = L({ zerosLike_: bR });
 function yR(e, t) {
@@ -7148,7 +7147,7 @@ function xR(e, t) {
     return We(n, i);
   }
 }
-var jde = L({ dot_: xR });
+var Kde = L({ dot_: xR });
 function wR(e, ...t) {
   let n = t.map((r, a) => _(r, `tensors${a}`, "einsum")), s = { equation: e };
   return M.runKernel(Qd, n, s);
@@ -7163,7 +7162,7 @@ function SR(e) {
   let t = _(e, "x", "erf");
   O(t.dtype === "int32" || t.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), t.dtype === "int32" && (t = ce(t, "float32"));
   let n = { x: t };
-  return M.runKernel(hl, n);
+  return M.runKernel(fl, n);
 }
 var CR = L({ erf_: SR });
 function NR(e) {
@@ -7175,12 +7174,12 @@ function TR(e, t = 0) {
   let n = _(e, "x", "expandDims", "string_or_numeric");
   O(t <= n.rank, () => "Axis must be <= rank of the tensor");
   let s = { input: n }, r = { dim: t };
-  return M.runKernel(ho, s, r);
+  return M.runKernel(fo, s, r);
 }
 var Pn = L({ expandDims_: TR });
 function $R(e) {
   let n = { x: _(e, "x", "expm1") };
-  return M.runKernel(fo, n);
+  return M.runKernel(mo, n);
 }
 var _R = L({ expm1_: $R });
 function AR(e, t) {
@@ -7207,9 +7206,9 @@ function ER(e, t, n, s = "float32") {
   throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`);
 }
 var rI = L({ eye_: ER });
-function Ol(e, t, n) {
+function Pl(e, t, n) {
   let s = { shape: e, value: t, dtype: n };
-  return M.runKernel(fl, {}, s);
+  return M.runKernel(ml, {}, s);
 }
 function RR(e) {
   let n = { x: _(e, "x", "floor", "float32") };
@@ -7218,14 +7217,14 @@ function RR(e) {
 var mp = L({ floor_: RR });
 function DR(e, t, n = 0, s = 0) {
   let r = _(e, "x", "gather"), a = _(t, "indices", "gather", "int32"), i = { x: r, indices: a }, o = { axis: n, batchDims: s };
-  return M.runKernel(go, i, o);
+  return M.runKernel(bo, i, o);
 }
-var ju = L({ gather_: DR });
+var Ku = L({ gather_: DR });
 function FR(e, t) {
   let n = _(e, "a", "greater", "string_or_numeric"), s = _(t, "b", "greater", "string_or_numeric");
   [n, s] = vt(n, s), ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(yo, r);
+  return M.runKernel(vo, r);
 }
 var Wn = L({ greater_: FR });
 function OR(e, t) {
@@ -7234,7 +7233,7 @@ function OR(e, t) {
   let r = { a: n, b: s };
   return M.runKernel(La, r);
 }
-var jo = L({ greaterEqual_: OR });
+var Ko = L({ greaterEqual_: OR });
 function PR(e) {
   let n = { input: _(e, "input", "imag") };
   return M.runKernel(Zd, n);
@@ -7242,17 +7241,17 @@ function PR(e) {
 var Kg = L({ imag_: PR });
 function zR(e) {
   let n = { x: _(e, "x", "isFinite") };
-  return M.runKernel(ml, n);
-}
-var Kde = L({ isFinite_: zR });
-function MR(e) {
-  let n = { x: _(e, "x", "isInf") };
   return M.runKernel(gl, n);
 }
-var Xde = L({ isInf_: MR });
+var Xde = L({ isFinite_: zR });
+function MR(e) {
+  let n = { x: _(e, "x", "isInf") };
+  return M.runKernel(bl, n);
+}
+var Yde = L({ isInf_: MR });
 function LR(e) {
   let n = { x: _(e, "x", "isNaN") };
-  return M.runKernel(bl, n);
+  return M.runKernel(yl, n);
 }
 var BR = L({ isNaN_: LR });
 function VR(e, t = 0.2) {
@@ -7264,16 +7263,16 @@ function WR(e, t) {
   let n = _(e, "a", "less", "string_or_numeric"), s = _(t, "b", "less", "string_or_numeric");
   [n, s] = vt(n, s), ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(vo, r);
+  return M.runKernel(xo, r);
 }
 var aI = L({ less_: WR });
 function UR(e, t) {
   let n = _(e, "a", "lessEqual", "string_or_numeric"), s = _(t, "b", "lessEqual", "string_or_numeric");
   [n, s] = vt(n, s), ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(xo, r);
+  return M.runKernel(wo, r);
 }
-var Ko = L({ lessEqual_: UR });
+var Xo = L({ lessEqual_: UR });
 function GR(e, t, n) {
   if (n <= 0)
     throw new Error("The number of values should be positive.");
@@ -7297,10 +7296,10 @@ function jR(e) {
 var Yn = L({ log_: jR });
 function KR(e) {
   let n = { x: _(e, "x", "log1p") };
-  return M.runKernel(yl, n);
+  return M.runKernel(vl, n);
 }
 var Yg = L({ log1p_: KR });
-function Yde(e) {
+function Qde(e) {
   return O(hr(e), () => "The f passed in grad(f) must be a function"), (t, n) => {
     let s = _(t, "x", "tf.grad", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grad") : null;
     return M.tidy(() => {
@@ -7309,24 +7308,24 @@ function Yde(e) {
     });
   };
 }
-function Qde(e) {
+function Zde(e) {
   return O(hr(e), () => "The f passed in grads(f) must be a function"), (t, n) => {
     O(Array.isArray(t), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
-    let s = Uu(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
+    let s = Gu(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
     return M.tidy(() => {
       let { value: a, grads: i } = M.gradients(() => e(...s), s, r);
       return r != null && pn(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), gp(i), i;
     });
   };
 }
-function Zde(e) {
+function Jde(e) {
   return O(hr(e), () => "The f passed in valueAndGrad(f) must be a function"), (t, n) => {
     O(t instanceof et, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
     let { grads: s, value: r } = M.gradients(() => e(t), [t], n);
     return gp(s), { grad: s[0], value: r };
   };
 }
-function Jde(e) {
+function epe(e) {
   return O(hr(e), () => "The f passed in valueAndGrads(f) must be a function"), (t, n) => {
     O(Array.isArray(t) && t.every((r) => r instanceof et), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
     let s = M.gradients(() => e(...t), t, n);
@@ -7360,19 +7359,19 @@ function gp(e) {
 }
 function YR(e) {
   let n = { x: _(e, "x", "neg") };
-  return M.runKernel(ko, n);
+  return M.runKernel(Io, n);
 }
 var kt = L({ neg_: YR });
 function QR(e) {
   let n = { x: _(e, "x", "softplus") };
-  return M.runKernel(Tl, n);
+  return M.runKernel($l, n);
 }
-var Pl = L({ softplus_: QR });
+var zl = L({ softplus_: QR });
 function ZR(e) {
   let t = _(e, "x", "logSigmoid");
-  return js((s) => ({ value: kt(Pl(kt(s))), gradFunc: (i) => V(i, qs(kt(s))) }))(t);
+  return js((s) => ({ value: kt(zl(kt(s))), gradFunc: (i) => V(i, qs(kt(s))) }))(t);
 }
-var epe = L({ logSigmoid_: ZR });
+var tpe = L({ logSigmoid_: ZR });
 function JR(e, t = null, n = false) {
   let r = { x: _(e, "x", "max") }, a = { reductionIndices: t, keepDims: n };
   return M.runKernel(Ua, r, a);
@@ -7461,12 +7460,12 @@ function oD(e, t) {
   let n = _(e, "a", "logicalAnd", "bool"), s = _(t, "b", "logicalAnd", "bool");
   ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(wo, r);
+  return M.runKernel(ko, r);
 }
 var Ds = L({ logicalAnd_: oD });
 function uD(e) {
   let n = { x: _(e, "x", "logicalNot", "bool") };
-  return M.runKernel(vl, n);
+  return M.runKernel(xl, n);
 }
 var Jg = L({ logicalNot_: uD });
 function lD(e, t) {
@@ -7480,7 +7479,7 @@ function cD(e, t) {
   let n = _(e, "a", "logicalXor", "bool"), s = _(t, "b", "logicalXor", "bool");
   return ot(n.shape, s.shape), Ds(cI(e, t), Jg(Ds(e, t)));
 }
-var tpe = L({ logicalXor_: cD });
+var npe = L({ logicalXor_: cD });
 function dD(e, t, n, s, r) {
   let a = _(e, "x", "maxPool"), i = 1, o = a, u = false;
   a.rank === 3 && (u = true, o = G(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(o.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.rank}.`), O(Ps(n, i), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`), hn("maxPool", s, r);
@@ -7528,7 +7527,7 @@ function Mn(e, t = "float32") {
   let n = ng(pt(e), t);
   return M.makeTensor(n, e, t);
 }
-function npe(e, t, { indexing: n = "xy" } = {}) {
+function spe(e, t, { indexing: n = "xy" } = {}) {
   if (n !== "xy" && n !== "ij")
     throw new TypeError(`${n} is not a valid third argument to meshgrid`);
   if (e === void 0)
@@ -7568,7 +7567,7 @@ function wD(e, t) {
   let n = _(e, "a", "mod"), s = _(t, "b", "mod");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(xl, r);
+  return M.runKernel(wl, r);
 }
 var kD = L({ mod_: wD });
 function ID(e) {
@@ -7585,7 +7584,7 @@ function SD(e, t = null, n = false) {
 }
 var tb = L({ moments_: SD });
 function CD(e, t, n, s) {
-  let r = _(t, "data", "multiRNNCell"), a = Uu(n, "c", "multiRNNCell"), i = Uu(s, "h", "multiRNNCell"), o = r, u = [];
+  let r = _(t, "data", "multiRNNCell"), a = Gu(n, "c", "multiRNNCell"), i = Gu(s, "h", "multiRNNCell"), o = r, u = [];
   for (let p = 0; p < e.length; p++) {
     let d = e[p](o, a[p], i[p]);
     u.push(d[0]), u.push(d[1]), o = d[1];
@@ -7595,7 +7594,7 @@ function CD(e, t, n, s) {
     l.push(u[p]), c.push(u[p + 1]);
   return [l, c];
 }
-var spe = L({ multiRNNCell_: CD });
+var rpe = L({ multiRNNCell_: CD });
 function ND(e, t, n, s = false) {
   let r = _(e, "logits", "multinomial"), a = r.size, i = r.rank;
   if (a < 2)
@@ -7611,12 +7610,12 @@ function $D(e, t) {
   let n = _(e, "a", "notEqual", "string_or_numeric"), s = _(t, "b", "notEqual", "string_or_numeric");
   [n, s] = vt(n, s), ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(Io, r);
+  return M.runKernel(So, r);
 }
-var Ku = L({ notEqual_: $D });
+var Xu = L({ notEqual_: $D });
 function _D(e) {
   let n = { x: _(e, "x", "onesLike") };
-  return M.runKernel(No, n);
+  return M.runKernel(To, n);
 }
 var Qn = L({ onesLike_: _D });
 function AD(e, t) {
@@ -7625,7 +7624,7 @@ function AD(e, t) {
   let r = G(n, [-1, 1]), a = G(s, [1, -1]);
   return We(r, a);
 }
-var rpe = L({ outerProduct_: AD });
+var ape = L({ outerProduct_: AD });
 function ED(e, t, n = 0) {
   let s = _(e, "x", "pad");
   if (s.rank === 0)
@@ -7637,24 +7636,24 @@ var fi = L({ pad_: ED });
 function RD(e, t, n = 0) {
   return O(t.length === 2, () => "Invalid number of paddings. Must be length of 2."), fi(e, [t], n);
 }
-var ape = L({ pad1d_: RD });
+var ipe = L({ pad1d_: RD });
 function DD(e, t, n = 0) {
   return O(t.length === 2 && t[0].length === 2 && t[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), fi(e, t, n);
 }
-var ipe = L({ pad2d_: DD });
+var ope = L({ pad2d_: DD });
 function FD(e, t, n = 0) {
   return O(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."), fi(e, t, n);
 }
-var ope = L({ pad3d_: FD });
+var upe = L({ pad3d_: FD });
 function OD(e, t, n = 0) {
   return O(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."), fi(e, t, n);
 }
-var upe = L({ pad4d_: OD });
+var lpe = L({ pad4d_: OD });
 function PD(e, t, n) {
   let s = _(e, "x", "spaceToBatchND");
   O(s.rank >= 1 + t.length, () => `input rank ${s.rank} should be > than [blockShape] ${t.length}`), O(n.length === t.length, () => `paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`), O(s.shape.reduce((i, o, u) => u > 0 && u <= t.length ? i && (o + n[u - 1][0] + n[u - 1][1]) % t[u - 1] === 0 : i, true), () => `input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);
   let r = { x: s }, a = { blockShape: t, paddings: n };
-  return M.runKernel(zo, r, a);
+  return M.runKernel(Mo, r, a);
 }
 var nb = L({ spaceToBatchND_: PD });
 function zD(e, t, n, s, r, a, i) {
@@ -7674,7 +7673,7 @@ function LD(e, t) {
   let s = e.map((i, o) => i + (i - 1) * (t[o] - 1)).map((i) => i - 1), r = s.map((i) => Math.floor(i / 2)), a = s.map((i, o) => i - r[o]);
   return s.map((i, o) => [r[o], a[o]]);
 }
-var lpe = L({ pool_: zD });
+var cpe = L({ pool_: zD });
 function BD(e, t) {
   let n = _(e, "base", "pow"), s = _(t, "exp", "pow");
   [n, s] = vt(n, s);
@@ -7691,7 +7690,7 @@ function WD(e, t = null, n = false) {
   let s = _(e, "x", "prod");
   s.dtype === "bool" && (s = ce(s, "int32"));
   let r = { x: s }, a = { axis: t, keepDims: n };
-  return M.runKernel(_o, r, a);
+  return M.runKernel(Ao, r, a);
 }
 var pI = L({ prod_: WD });
 function UD(e, t, n) {
@@ -7708,7 +7707,7 @@ function UD(e, t, n) {
     r[a] = t();
   return M.makeTensor(r, e, n);
 }
-var cpe = L({ rand_: UD });
+var dpe = L({ rand_: UD });
 var rb = xa(Wd());
 var ab = class {
   constructor(e, t, n, s, r) {
@@ -7781,7 +7780,7 @@ function qD(e, t, n = 1, s = "float32", r) {
     i.values[o] = a.nextValue();
   return i.toTensor();
 }
-var dpe = L({ randomGamma_: qD });
+var ppe = L({ randomGamma_: qD });
 function jD(e, t = 0, n = 1, s, r) {
   if (s != null && s === "bool")
     throw new Error(`Unsupported data type ${s}`);
@@ -7797,12 +7796,12 @@ function XD(e, t = 0, n = 1, s = "float32", r) {
     a.values[o] = i.nextValue();
   return a.toTensor();
 }
-var zl = L({ randomUniform_: XD });
-function Xu(e, t, n = 1, s = "float32") {
+var Ml = L({ randomUniform_: XD });
+function Yu(e, t, n = 1, s = "float32") {
   if (n === 0)
     throw new Error("Cannot have a step of zero");
   let r = { start: e, stop: t, step: n, dtype: s };
-  return M.runKernel(kl, {}, r);
+  return M.runKernel(Il, {}, r);
 }
 function YD(e) {
   let n = { input: _(e, "input", "real") };
@@ -7811,7 +7810,7 @@ function YD(e) {
 var wd = L({ real_: YD });
 function QD(e) {
   let n = { x: _(e, "x", "reciprocal") };
-  return M.runKernel(Il, n);
+  return M.runKernel(Sl, n);
 }
 var ZD = L({ reciprocal_: QD });
 function JD(e) {
@@ -7826,32 +7825,32 @@ function e3(e) {
 var hI = L({ relu6_: e3 });
 function t3(e, t) {
   let s = { x: _(e, "x", "reverse") }, r = { dims: t };
-  return M.runKernel(Eo, s, r);
+  return M.runKernel(Ro, s, r);
 }
 var Zn = L({ reverse_: t3 });
 function n3(e) {
   let t = _(e, "x", "reverse");
   return O(t.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`), Zn(t, 0);
 }
-var ppe = L({ reverse1d_: n3 });
+var hpe = L({ reverse1d_: n3 });
 function s3(e, t) {
   let n = _(e, "x", "reverse");
   return O(n.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`), Zn(n, t);
 }
-var hpe = L({ reverse2d_: s3 });
+var fpe = L({ reverse2d_: s3 });
 function r3(e, t) {
   let n = _(e, "x", "reverse");
   return O(n.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`), Zn(n, t);
 }
-var fpe = L({ reverse3d_: r3 });
+var mpe = L({ reverse3d_: r3 });
 function a3(e, t) {
   let n = _(e, "x", "reverse");
   return O(n.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`), Zn(n, t);
 }
-var mpe = L({ reverse4d_: a3 });
+var gpe = L({ reverse4d_: a3 });
 function i3(e) {
   let n = { x: _(e, "x", "round") };
-  return M.runKernel(Ro, n);
+  return M.runKernel(Do, n);
 }
 var fI = L({ round_: i3 });
 function o3(e) {
@@ -7868,7 +7867,7 @@ function we(e, t) {
 }
 function u3(e) {
   let n = { x: _(e, "x", "selu") };
-  return M.runKernel(Cl, n);
+  return M.runKernel(Nl, n);
 }
 var gI = L({ selu_: u3 });
 function l3(e, t, n, s, r, a = [1, 1], i = "NHWC") {
@@ -7896,7 +7895,7 @@ async function d3(e, t) {
 var p3 = d3;
 function h3(e) {
   let n = { x: _(e, "x", "sign") };
-  return M.runKernel(Nl, n);
+  return M.runKernel(Tl, n);
 }
 var f3 = L({ sign_: h3 });
 function m3(e) {
@@ -7906,7 +7905,7 @@ function m3(e) {
 var bI = L({ sin_: m3 });
 function g3(e) {
   let n = { x: _(e, "x", "sinh") };
-  return M.runKernel(Po, n);
+  return M.runKernel(zo, n);
 }
 var yI = L({ sinh_: g3 });
 function b3(e, t, n) {
@@ -7967,7 +7966,7 @@ function S3(e) {
 var xI = L({ irfft_: S3 });
 function C3(e, t, n = 0) {
   let r = { x: _(e, "x", "split") }, a = { numOrSizeSplits: t, axis: n };
-  return M.runKernel(Mo, r, a);
+  return M.runKernel(Lo, r, a);
 }
 var Bn = L({ split_: C3 });
 function N3(e, t) {
@@ -8003,10 +8002,10 @@ function _3(e, t) {
 }
 var mr = L({ squeeze_: _3 });
 function A3(e, t = 0) {
-  let n = Uu(e, "tensors", "stack", "string_or_numeric");
+  let n = Gu(e, "tensors", "stack", "string_or_numeric");
   O(n.length >= 1, () => "Pass at least one tensor to tf.stack"), n.length > 0 && O(t <= n[0].rank, () => "Axis must be <= rank of the tensor");
   let s = n, r = { axis: t };
-  return M.runKernel($o, s, r);
+  return M.runKernel(_o, s, r);
 }
 var Jn = L({ stack_: A3 });
 function E3(e, t = 0) {
@@ -8016,12 +8015,12 @@ function E3(e, t = 0) {
 var yp = L({ step_: E3 });
 function R3(e, t, n, s, r = 0, a = 0, i = 0, o = 0, u = 0) {
   let c = { x: _(e, "x", "stridedSlice", "string_or_numeric") }, p = { begin: t, end: n, strides: s, beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
-  return M.runKernel(Lo, c, p);
+  return M.runKernel(Bo, c, p);
 }
 var D3 = L({ stridedSlice_: R3 });
 function F3(e) {
   let n = { x: _(e, "x", "tan", "float32") };
-  return M.runKernel(Bo, n);
+  return M.runKernel(Vo, n);
 }
 var O3 = L({ tan_: F3 });
 function Zt(e, t) {
@@ -8041,7 +8040,7 @@ function Ki(e, t, n) {
     throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
   return Nr(e, t, s, n);
 }
-function gpe(e, t, n) {
+function bpe(e, t, n) {
   if (wa(e), t != null && t.length !== 4)
     throw new Error("tensor4d() requires shape to have four numbers");
   let s = Rs(e, n);
@@ -8051,7 +8050,7 @@ function gpe(e, t, n) {
     throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
   return Nr(e, t, s, n);
 }
-function bpe(e, t, n) {
+function ype(e, t, n) {
   if (wa(e), t != null && t.length !== 5)
     throw new Error("tensor5d() requires shape to have five numbers");
   let s = Rs(e, n);
@@ -8061,7 +8060,7 @@ function bpe(e, t, n) {
     throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
   return Nr(e, t, s, n);
 }
-function ype(e, t, n) {
+function vpe(e, t, n) {
   if (wa(e), t != null && t.length !== 6)
     throw new Error("tensor6d() requires shape to have six numbers");
   let s = Rs(e, n);
@@ -8080,7 +8079,7 @@ function P3(e, t = 1, n = true) {
     throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);
   if (t > r)
     throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);
-  let a = { x: s }, i = { k: t, sorted: n }, [o, u] = M.runKernel(Vo, a, i);
+  let a = { x: s }, i = { k: t, sorted: n }, [o, u] = M.runKernel(Wo, a, i);
   return { values: o, indices: u };
 }
 var z3 = L({ topk_: P3 });
@@ -8111,7 +8110,7 @@ function W3(e, t = 0) {
   let n = _(e, "x", "unstack", "string_or_numeric");
   O(t >= -n.shape.length && t < n.shape.length, () => `Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);
   let s = { value: n }, r = { axis: t };
-  return M.runKernel(Uo, s, r);
+  return M.runKernel(Go, s, r);
 }
 var Fs = L({ unstack_: W3 });
 function U3(e, t = true, n, s) {
@@ -8139,10 +8138,10 @@ async function H3(e, t, n) {
   let u = 1;
   for (let m = a; m < a + i; m++)
     u *= o[m];
-  let l = o.slice(0, a).concat([u], o.slice(a + i)), c = G(s, l), p = G(r, [-1]), d = await II(p), h = mr(d, [1]), f = ju(c, h, a);
+  let l = o.slice(0, a).concat([u], o.slice(a + i)), c = G(s, l), p = G(r, [-1]), d = await II(p), h = mr(d, [1]), f = Ku(c, h, a);
   return e !== s && s.dispose(), t !== r && r.dispose(), h.dispose(), c.dispose(), p.dispose(), d.dispose(), f;
 }
-var vpe = H3;
+var xpe = H3;
 function q3(e, t = "euclidean", n = null, s = false) {
   e = _(e, "x", "norm");
   let r = SI(e, t, n), a = r.shape;
@@ -8193,12 +8192,12 @@ function j3(e, t, n, s, r = true) {
   }
   return ie(a, c);
 }
-var xpe = L({ movingAverage_: j3 });
+var wpe = L({ movingAverage_: j3 });
 function K3(e, t, n) {
   let s = _(e, "indices", "scatterND", "int32"), r = _(t, "updates", "scatterND");
   Bg(r, s, n);
   let a = { indices: s, updates: r }, i = { shape: n };
-  return M.runKernel(Do, a, i);
+  return M.runKernel(Fo, a, i);
 }
 var X3 = L({ scatterND_: K3 });
 function Y3(e, t, n, s) {
@@ -8224,7 +8223,7 @@ function Q3(e, t, n, s = 0) {
 var NI = L({ sparseToDense_: Q3 });
 function Z3(e, t) {
   let n = _(t, "indices", "gatherND", "int32"), r = { params: _(e, "x", "gatherND", "string_or_numeric"), indices: n };
-  return M.runKernel(bo, r);
+  return M.runKernel(yo, r);
 }
 var J3 = L({ gatherND_: Z3 });
 function eF(e, t) {
@@ -8244,7 +8243,7 @@ function tF(e, t, n, s) {
   let r = _(e, "x", "dropout");
   if (O(r.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`), O(t >= 0 && t < 1, () => `rate must be a float in the range [0, 1), but got ${t}.`), t === 0)
     return e instanceof et ? r.clone() : r;
-  let a = eF(r, n), i = 1 - t, o = xe(mp(ie(zl(a, 0, 1, "float32", s), i)), i);
+  let a = eF(r, n), i = 1 - t, o = xe(mp(ie(Ml(a, 0, 1, "float32", s), i)), i);
   return V(r, o);
 }
 var nF = L({ dropout_: tF });
@@ -8278,7 +8277,7 @@ async function rF(e, t, n = 1) {
   }
   return e !== s && s.dispose(), t !== r && r.dispose(), fs(c, r.shape, "bool");
 }
-var wpe = rF;
+var kpe = rF;
 var ha = {};
 Ae(ha, { conv2d: () => oF, depthwiseConv2d: () => dF, matMul: () => hF });
 function aF(e, t, n, s, r, a = "NHWC", i) {
@@ -8328,7 +8327,7 @@ function iF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilat
   }
   let p = _(e, "x", "conv2d", "float32"), d = _(t, "filter", "conv2d", "float32"), h = p, f = false;
   p.rank === 3 && (f = true, h = G(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`), hn("fused conv2d", s, i), O(h.shape[3] === d.shape[2], () => `Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${d.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NHWC", () => `Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);
-  let m = Fl(h.shape, d.shape, n, a, s, i), g;
+  let m = Ol(h.shape, d.shape, n, a, s, i), g;
   o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), ot(m.outShape, g.shape));
   let b;
   l != null && (b = _(l, "prelu weights", "fused conv2d"));
@@ -8374,7 +8373,7 @@ function cF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilat
   }
   let p = _(e, "x", "depthwiseConv2d", "float32"), d = _(t, "filter", "depthwiseConv2d", "float32"), h = p, f = false;
   p.rank === 3 && (f = true, h = G(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`), O(h.shape[3] === d.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`), a == null && (a = [1, 1]), O(Ps(n, a), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), hn("fused depthwiseConv2d", s, i);
-  let m = Fl(h.shape, d.shape, n, a, s, i, true), g;
+  let m = Ol(h.shape, d.shape, n, a, s, i, true), g;
   o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), ot(m.outShape, g.shape));
   let b;
   l != null && (b = _(l, "prelu weights", "fused depthwiseConv2d"));
@@ -8440,7 +8439,7 @@ function bF(e, t, n, s = false, r = 0) {
     i.push(He(e, a, t)), a += n;
   if (s)
     for (; a < e.size; ) {
-      let o = a + t - e.size, u = Ft([He(e, a, t - o), Ol([o], r)]);
+      let o = a + t - e.size, u = Ft([He(e, a, t - o), Pl([o], r)]);
       i.push(u), a += n;
     }
   return i.length === 0 ? Ki([], [0, t]) : G(Ft(i), [i.length, t]);
@@ -8456,14 +8455,14 @@ function xF(e, t, n, s, r = "bilinear", a = 0) {
   let i = _(e, "image", "cropAndResize"), o = _(t, "boxes", "cropAndResize", "float32"), u = _(n, "boxInd", "cropAndResize", "int32"), l = o.shape[0];
   O(i.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && o.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${l},4] but had shape ${o.shape}.`), O(u.rank === 1 && u.shape[0] === l, () => `Error in cropAndResize: boxInd must be have size [${l}] but had shape ${o.shape}.`), O(s.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`), O(s[0] >= 1 && s[1] >= 1, () => `cropSize must be atleast [1,1], but was ${s}`), O(r === "bilinear" || r === "nearest", () => `method must be bilinear or nearest, but was ${r}`);
   let c = { image: i, boxes: o, boxInd: u }, p = { method: r, extrapolationValue: a, cropSize: s };
-  return M.runKernel(lo, c, p);
+  return M.runKernel(co, c, p);
 }
 var wF = L({ cropAndResize_: xF });
 function kF(e) {
   let t = _(e, "image", "flipLeftRight", "float32");
   O(t.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);
   let n = { image: t };
-  return M.runKernel(mo, n, {});
+  return M.runKernel(go, n, {});
 }
 var IF = L({ flipLeftRight_: kF });
 function SF(e) {
@@ -8477,19 +8476,19 @@ function NF(e, t, n = 0, s = 0.5) {
   let r = _(e, "image", "rotateWithOffset", "float32");
   O(r.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);
   let a = { image: r }, i = { radians: t, fillValue: n, center: s };
-  return M.runKernel(Ho, a, i);
+  return M.runKernel(qo, a, i);
 }
 var TF = L({ rotateWithOffset_: NF });
-function Xo(e, t, n, s, r, a) {
+function Yo(e, t, n, s, r, a) {
   s == null && (s = 0.5), r == null && (r = Number.NEGATIVE_INFINITY), a == null && (a = 0);
   let i = e.shape[0];
   return n = Math.min(n, i), O(0 <= s && s <= 1, () => `iouThreshold must be in [0, 1], but was '${s}'`), O(e.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${e.rank}'`), O(e.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`), O(t.rank === 1, () => "scores must be a 1D tensor"), O(t.shape[0] === i, () => `scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`), O(0 <= a && a <= 1, () => `softNmsSigma must be in [0, 1], but was '${a}'`), { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a };
 }
 function $F(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
-  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = Xo(a, i, n, s, r);
+  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = Yo(a, i, n, s, r);
   n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
   let u = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r };
-  return M.runKernel(So, { boxes: a, scores: i }, u);
+  return M.runKernel(Co, { boxes: a, scores: i }, u);
 }
 var _F = L({ nonMaxSuppression_: $F });
 function AF(e, t, n) {
@@ -8562,33 +8561,33 @@ function fx(e, t) {
   return e.score - t.score || e.score === t.score && t.boxIndex - e.boxIndex;
 }
 async function PF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
-  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = Xo(a, i, n, s, r);
+  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = Yo(a, i, n, s, r);
   n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
   let u = await Promise.all([a.data(), i.data()]), l = u[0], c = u[1], { selectedIndices: p } = RI(l, c, n, s, r);
   return a !== e && a.dispose(), i !== t && i.dispose(), Zt(p, "int32");
 }
 var zF = PF;
 function MF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
-  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Xo(i, o, n, s, r, a);
+  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Yo(i, o, n, s, r, a);
   n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
-  let l = { boxes: i, scores: o }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = M.runKernel(Co, l, c);
+  let l = { boxes: i, scores: o }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = M.runKernel(No, l, c);
   return { selectedIndices: p[0], selectedScores: p[1] };
 }
 var LF = L({ nonMaxSuppressionWithScore_: MF });
 async function BF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
-  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Xo(i, o, n, s, r, a);
+  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Yo(i, o, n, s, r, a);
   n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
   let l = await Promise.all([i.data(), o.data()]), c = l[0], p = l[1], { selectedIndices: d, selectedScores: h } = FI(c, p, n, s, r, a);
   return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Zt(d, "int32"), selectedScores: Zt(h) };
 }
 var VF = BF;
 function WF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
-  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Xo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: i, scores: o }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = M.runKernel(wl, d, h);
+  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Yo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: i, scores: o }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = M.runKernel(kl, d, h);
   return { selectedIndices: f[0], validOutputs: f[1] };
 }
 var UF = L({ nonMaxSuppressionPadded_: WF });
 async function GF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
-  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Xo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, [d, h] = await Promise.all([i.data(), o.data()]), { selectedIndices: f, validOutputs: m } = DI(d, h, l, c, p, a);
+  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Yo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, [d, h] = await Promise.all([i.data(), o.data()]), { selectedIndices: f, validOutputs: m } = DI(d, h, l, c, p, a);
   return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Zt(f, "int32"), validOutputs: we(m, "int32") };
 }
 var HF = GF;
@@ -8606,7 +8605,7 @@ function KF(e, t, n = false, s = false) {
   O(r.rank === 3 || r.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`), O(r.dtype === "float32" || r.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), O(s === false || n === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
   let a = r, i = false;
   r.rank === 3 && (i = true, a = G(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
-  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(Sl, o, u);
+  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(Cl, o, u);
   return i ? G(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
 }
 var XF = L({ resizeNearestNeighbor_: KF });
@@ -8622,16 +8621,16 @@ function YF(e, t = "binary", n = false, s = 0.5) {
     let g = Qk(ce(fI(h), "int32"), fs([]), 256);
     l = QF(g, u);
   }
-  let f = n ? Ko(h, l) : Wn(h, l);
+  let f = n ? Xo(h, l) : Wn(h, l);
   return ce(V(f, 255), "int32");
 }
 function QF(e, t) {
   let n = Zt([-1]), s = Zt([0]), r = Zt([0]), a, i, o, u, l, c;
   for (let p = 0; p < e.size - 1; p++) {
     a = He(e, 0, p + 1), i = He(e, p + 1), l = xe(ve(a), t), c = xe(ve(i), t);
-    let d = ve(V(a, Xu(0, a.size)));
+    let d = ve(V(a, Yu(0, a.size)));
     o = xe(d, ve(a));
-    let h = Ol(i.shape, a.size), f = ie(Xu(0, i.size), h), m = V(i, f);
+    let h = Pl(i.shape, a.size), f = ie(Yu(0, i.size), h), m = V(i, f);
     u = xe(ve(m), ve(i));
     let g = ge(o, u), b = ge(o, u), y = V(l, c);
     r = V(V(y, g), b);
@@ -8645,7 +8644,7 @@ function JF(e, t, n = "nearest", s = "constant", r = 0, a) {
   let i = _(e, "image", "transform", "float32"), o = _(t, "transforms", "transform", "float32");
   O(i.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && (o.shape[0] === i.shape[0] || o.shape[0] === 1) && o.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), O(a == null || a.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${a}.`);
   let u = { image: i, transforms: o }, l = { interpolation: n, fillMode: s, fillValue: r, outputShape: a };
-  return M.runKernel(Wo, u, l);
+  return M.runKernel(Uo, u, l);
 }
 var eO = L({ transform_: JF });
 function tO(e, t, n) {
@@ -8658,7 +8657,7 @@ function tO(e, t, n) {
   if (!(n <= i))
     throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);
   t < 0 && (t = a), n < 0 && (n = i);
-  let o = G(Xu(0, a, 1, "int32"), [-1, 1]), u = Xu(0, i, 1, "int32"), l = ge(o, u), c = Ds(Ko(l, we(+t, "int32")), jo(l, we(-n, "int32"))), p = $t([a, i], s.dtype);
+  let o = G(Yu(0, a, 1, "int32"), [-1, 1]), u = Yu(0, i, 1, "int32"), l = ge(o, u), c = Ds(Xo(l, we(+t, "int32")), Ko(l, we(-n, "int32"))), p = $t([a, i], s.dtype);
   return G(Jn(Fs(G(s, [-1, a, i])).map((d) => vn(c, d, p))), r);
 }
 var nO = L({ bandPart_: tO });
@@ -8750,7 +8749,7 @@ function uO(e, t, n = 3) {
     if (r == null)
       return xe(ve(a), we(s.size));
     {
-      let i = V(r, Mn(s.shape)), o = ce(ve(Ku(i, we(0))), "float32");
+      let i = V(r, Mn(s.shape)), o = ce(ve(Xu(i, we(0))), "float32");
       return xe(ve(a), o);
     }
   }
@@ -8864,7 +8863,7 @@ function _O(e, t, n) {
     throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);
   if (a.rank !== 1)
     throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);
-  let i = { inputIndices: s, inputShape: r, newShape: a }, o = M.runKernel($l, i);
+  let i = { inputIndices: s, inputShape: r, newShape: a }, o = M.runKernel(_l, i);
   return { outputIndices: o[0], outputShape: o[1] };
 }
 var AO = L({ sparseReshape_: _O });
@@ -8927,11 +8926,11 @@ function LO(e, t) {
   return M.runKernel(_g, r, s);
 }
 var BO = L({ stringToHashBucketFast_: LO });
-var kpe = { fft: lb, ifft: Id, rfft: cb, irfft: xI };
-var Ipe = { hammingWindow: mF, hannWindow: AI, frame: EI, stft: vF };
+var Ipe = { fft: lb, ifft: Id, rfft: cb, irfft: xI };
+var Spe = { hammingWindow: mF, hannWindow: AI, frame: EI, stft: vF };
 var qn = { flipLeftRight: IF, grayscaleToRGB: CF, resizeNearestNeighbor: XF, resizeBilinear: jF, rotateWithOffset: TF, cropAndResize: wF, nonMaxSuppression: _F, nonMaxSuppressionAsync: zF, nonMaxSuppressionWithScore: LF, nonMaxSuppressionWithScoreAsync: VF, nonMaxSuppressionPadded: UF, nonMaxSuppressionPaddedAsync: HF, threshold: ZF, transform: eO };
 var VO = { bandPart: nO, gramSchmidt: rO, qr: iO };
-var Spe = { absoluteDifference: cO, computeWeightedLoss: Ys, cosineDistance: pO, hingeLoss: fO, huberLoss: gO, logLoss: yO, meanSquaredError: xO, sigmoidCrossEntropy: IO, softmaxCrossEntropy: NO };
+var Cpe = { absoluteDifference: cO, computeWeightedLoss: Ys, cosineDistance: pO, hingeLoss: fO, huberLoss: gO, logLoss: yO, meanSquaredError: xO, sigmoidCrossEntropy: IO, softmaxCrossEntropy: NO };
 var Wc = { sparseFillEmptyRows: $O, sparseReshape: AO, sparseSegmentMean: RO, sparseSegmentSum: FO };
 var Mf = { stringNGrams: PO, stringSplit: MO, stringToHashBucketFast: BO };
 var _r = class extends Vk {
@@ -8972,8 +8971,7 @@ var _r = class extends Vk {
 Object.defineProperty(_r, Symbol.hasInstance, { value: (e) => e.minimize != null && e.computeGradients != null && e.applyGradients != null });
 var fb = class extends _r {
   constructor(e, t, n = null) {
-    super();
-    this.learningRate = e, this.rho = t, this.epsilon = n, this.accumulatedGrads = [], this.accumulatedUpdates = [], n == null && (this.epsilon = M.backend.epsilon());
+    super(), this.learningRate = e, this.rho = t, this.epsilon = n, this.accumulatedGrads = [], this.accumulatedUpdates = [], n == null && (this.epsilon = M.backend.epsilon());
   }
   applyGradients(e) {
     (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
@@ -9014,13 +9012,12 @@ fb.className = "Adadelta";
 Tr(fb);
 var mb = class extends _r {
   constructor(e, t = 0.1) {
-    super();
-    this.learningRate = e, this.initialAccumulatorValue = t, this.accumulatedGrads = [];
+    super(), this.learningRate = e, this.initialAccumulatorValue = t, this.accumulatedGrads = [];
   }
   applyGradients(e) {
     (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
       let r = M.registeredVariables[n];
-      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accumulator`, variable: j(() => Ol(r.shape, this.initialAccumulatorValue).variable(false)) });
+      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accumulator`, variable: j(() => Pl(r.shape, this.initialAccumulatorValue).variable(false)) });
       let a = Array.isArray(e) ? e[s].tensor : e[n];
       if (a == null)
         return;
@@ -9055,8 +9052,7 @@ mb.className = "Adagrad";
 Tr(mb);
 var gb = class extends _r {
   constructor(e, t, n, s = null) {
-    super();
-    this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], j(() => {
+    super(), this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], j(() => {
       this.accBeta1 = we(t).variable(), this.accBeta2 = we(n).variable();
     }), s == null && (this.epsilon = M.backend.epsilon());
   }
@@ -9102,8 +9098,7 @@ gb.className = "Adam";
 Tr(gb);
 var bb = class extends _r {
   constructor(e, t, n, s = null, r = 0) {
-    super();
-    this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.decay = r, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], j(() => {
+    super(), this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.decay = r, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], j(() => {
       this.iteration = we(0).variable(), this.accBeta1 = we(t).variable();
     }), s == null && (this.epsilon = M.backend.epsilon());
   }
@@ -9144,8 +9139,7 @@ bb.className = "Adamax";
 Tr(bb);
 var Ip = class extends _r {
   constructor(e) {
-    super();
-    this.learningRate = e, this.setLearningRate(e);
+    super(), this.learningRate = e, this.setLearningRate(e);
   }
   applyGradients(e) {
     (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
@@ -9183,8 +9177,7 @@ Ip.className = "SGD";
 Tr(Ip);
 var yb = class extends Ip {
   constructor(e, t, n = false) {
-    super(e);
-    this.learningRate = e, this.momentum = t, this.useNesterov = n, this.accumulations = [], this.m = we(this.momentum);
+    super(e), this.learningRate = e, this.momentum = t, this.useNesterov = n, this.accumulations = [], this.m = we(this.momentum);
   }
   applyGradients(e) {
     (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
@@ -9222,8 +9215,7 @@ yb.className = "Momentum";
 Tr(yb);
 var vb = class extends _r {
   constructor(e, t = 0.9, n = 0, s = null, r = false) {
-    super();
-    if (this.learningRate = e, this.decay = t, this.momentum = n, this.epsilon = s, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = r, s == null && (this.epsilon = M.backend.epsilon()), e == null)
+    if (super(), this.learningRate = e, this.decay = t, this.momentum = n, this.epsilon = s, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = r, s == null && (this.epsilon = M.backend.epsilon()), e == null)
       throw new Error("learningRate for RMSPropOptimizer must be defined.");
   }
   applyGradients(e) {
@@ -9300,7 +9292,7 @@ function OI() {
   return new Promise((e) => WO(() => e()));
 }
 var S = {};
-Ae(S, { ERF_A1: () => JO, ERF_A2: () => eP, ERF_A3: () => tP, ERF_A4: () => nP, ERF_A5: () => sP, ERF_P: () => ZO, PARALLELIZE_THRESHOLD: () => xb, SELU_SCALE: () => zI, SELU_SCALEALPHA: () => PI, applyActivation: () => wp, assertAndGetBroadcastShape: () => ot, assertAxesAreInnerMostDims: () => sD, assertParamsConsistent: () => UO, assignToTypedArray: () => lP, axesAreInnerMostDims: () => Qg, calculateShapes: () => Ek, checkEinsumDimSizes: () => mP, checkPadOnDimRoundingMode: () => hn, combineLocations: () => oI, complexWithEvenIndex: () => iP, complexWithOddIndex: () => oP, computeConv2DInfo: () => Fl, computeConv3DInfo: () => Kk, computeDefaultPad: () => Ug, computeDilation2DInfo: () => cE, computeOptimalWindowSize: () => HO, computeOutAndReduceShapes: () => uI, computeOutShape: () => GO, computePool2DInfo: () => jk, computePool3DInfo: () => dE, convertConv2DDataFormat: () => Xk, decodeEinsumEquation: () => hP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => da, exponent: () => dP, exponents: () => cP, fromStringArrayToUint8: () => PP, fromUint8ToStringArray: () => OP, getAxesPermutation: () => lI, getBroadcastDims: () => Ck, getComplexWithIndex: () => uP, getEinsumComputePath: () => gP, getEinsumPermutation: () => fP, getFusedBiasGradient: () => xp, getFusedDyActivation: () => vp, getImageCenter: () => qO, getInnerMostAxes: () => rD, getPermuted: () => KO, getReductionAxes: () => _t, getReshaped: () => jO, getReshapedPermuted: () => XO, getSliceBeginCoords: () => YO, getSliceSize: () => QO, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => xP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => wP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => kP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => CP, getSparseReshapeInputOutputMismatchErrorMessage: () => TP, getSparseReshapeInputOutputMultipleErrorMessage: () => NP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => IP, getSparseReshapeNegativeOutputDimErrorMessage: () => SP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => EP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => $P, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => _P, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => AP, getUndoAxesPermutation: () => Zg, isIdentityPermutation: () => bP, log: () => R$, mergeRealAndImagArrays: () => rP, prepareAndValidate: () => _k, prepareSplitSize: () => vP, segment_util: () => MI, shouldFuse: () => kp, slice_util: () => wt, splitRealAndImagArrays: () => aP, tupleValuesAreOne: () => fr, upcastType: () => cn, validateInput: () => Bg, validateUpdateShape: () => Lg, warn: () => ar });
+Ae(S, { ERF_A1: () => JO, ERF_A2: () => eP, ERF_A3: () => tP, ERF_A4: () => nP, ERF_A5: () => sP, ERF_P: () => ZO, PARALLELIZE_THRESHOLD: () => xb, SELU_SCALE: () => zI, SELU_SCALEALPHA: () => PI, applyActivation: () => wp, assertAndGetBroadcastShape: () => ot, assertAxesAreInnerMostDims: () => sD, assertParamsConsistent: () => UO, assignToTypedArray: () => lP, axesAreInnerMostDims: () => Qg, calculateShapes: () => Ek, checkEinsumDimSizes: () => mP, checkPadOnDimRoundingMode: () => hn, combineLocations: () => oI, complexWithEvenIndex: () => iP, complexWithOddIndex: () => oP, computeConv2DInfo: () => Ol, computeConv3DInfo: () => Kk, computeDefaultPad: () => Ug, computeDilation2DInfo: () => cE, computeOptimalWindowSize: () => HO, computeOutAndReduceShapes: () => uI, computeOutShape: () => GO, computePool2DInfo: () => jk, computePool3DInfo: () => dE, convertConv2DDataFormat: () => Xk, decodeEinsumEquation: () => hP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => da, exponent: () => dP, exponents: () => cP, fromStringArrayToUint8: () => PP, fromUint8ToStringArray: () => OP, getAxesPermutation: () => lI, getBroadcastDims: () => Ck, getComplexWithIndex: () => uP, getEinsumComputePath: () => gP, getEinsumPermutation: () => fP, getFusedBiasGradient: () => xp, getFusedDyActivation: () => vp, getImageCenter: () => qO, getInnerMostAxes: () => rD, getPermuted: () => KO, getReductionAxes: () => _t, getReshaped: () => jO, getReshapedPermuted: () => XO, getSliceBeginCoords: () => YO, getSliceSize: () => QO, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => xP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => wP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => kP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => CP, getSparseReshapeInputOutputMismatchErrorMessage: () => TP, getSparseReshapeInputOutputMultipleErrorMessage: () => NP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => IP, getSparseReshapeNegativeOutputDimErrorMessage: () => SP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => EP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => $P, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => _P, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => AP, getUndoAxesPermutation: () => Zg, isIdentityPermutation: () => bP, log: () => R$, mergeRealAndImagArrays: () => rP, prepareAndValidate: () => _k, prepareSplitSize: () => vP, segment_util: () => MI, shouldFuse: () => kp, slice_util: () => wt, splitRealAndImagArrays: () => aP, tupleValuesAreOne: () => fr, upcastType: () => cn, validateInput: () => Bg, validateUpdateShape: () => Lg, warn: () => ar });
 function UO(e, t) {
   let n = e[0].length;
   e.forEach((r, a) => {
@@ -9608,22 +9600,22 @@ function OP(e) {
   }
 }
 function PP(e) {
-  return e.map((t) => Rl(t));
+  return e.map((t) => Dl(t));
 }
 var xs = {};
 Ae(xs, { nonMaxSuppressionV3Impl: () => RI, nonMaxSuppressionV4Impl: () => DI, nonMaxSuppressionV5Impl: () => FI, whereImpl: () => kI });
-var LI = { kernelName: ao, inputsToSave: ["x"], gradFunc: (e, t) => {
+var LI = { kernelName: io, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, yp(ce(n, "float32"), -1)) };
 } };
-var zP = { kernelName: nl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var zP = { kernelName: sl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
     let s = ct(ce(n, "float32")), r = dn(ge(we(1), s));
     return kt(xe(e, r));
   } };
 } };
-var MP = { kernelName: sl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var MP = { kernelName: rl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
     let s = dn(ge(ct(ce(n, "float32")), 1));
@@ -9650,22 +9642,22 @@ var VP = { kernelName: Ia, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => je(n) };
 } };
-var WP = { kernelName: il, inputsToSave: ["x"], gradFunc: (e, t) => {
+var WP = { kernelName: ol, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => je(n) };
 } };
-var UP = { kernelName: ol, inputsToSave: ["x"], gradFunc: (e, t) => {
+var UP = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, dn(ge(we(1), ct(ce(n, "float32"))))) };
 } };
-var GP = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
+var GP = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
     let s = dn(ie(we(1), ct(ce(n, "float32"))));
     return xe(e, s);
   } };
 } };
-var HP = { kernelName: dl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+var HP = { kernelName: pl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t, r = ot(n.shape, s.shape);
   return { a: () => {
     let o = ie(ct(n), ct(s)), u = V(e, xe(s, o)), l = _t(n.shape, r);
@@ -9675,11 +9667,11 @@ var HP = { kernelName: dl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
     return l.length > 0 && (u = ve(u, l)), G(u, s.shape);
   } };
 } };
-var qP = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
+var qP = { kernelName: cl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, ie(ct(ce(n, "float32")), 1)) };
 } };
-var jP = { kernelName: cl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var jP = { kernelName: dl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, ge(we(1), ct(ce(n, "float32")))) };
 } };
@@ -9711,7 +9703,7 @@ var ez = { kernelName: Ca, inputsToSave: ["a", "b"], gradFunc: (e, t, n) => {
   let [s, r] = t, { transposeA: a, transposeB: i } = n;
   return !a && !i ? { a: () => We(e, r, false, true), b: () => We(s, e, true, false) } : !a && i ? { a: () => We(e, r, false, false), b: () => We(e, s, true, false) } : a && !i ? { a: () => We(r, e, false, true), b: () => We(s, e, false, false) } : { a: () => We(r, e, true, true), b: () => We(e, s, true, true) };
 } };
-var tz = { kernelName: io, gradFunc: (e, t, n) => {
+var tz = { kernelName: oo, gradFunc: (e, t, n) => {
   let { blockShape: s, crops: r } = n;
   return { x: () => nb(e, s, r) };
 } };
@@ -9731,10 +9723,10 @@ var sz = { kernelName: Na, gradFunc: (e) => ({ x: () => e.clone() }) };
 var rz = { kernelName: Ta, gradFunc: (e) => ({ x: () => je(e) }) };
 var az = { kernelName: Sr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { clipValueMin: r, clipValueMax: a } = n;
-  return { x: () => vn(Ds(jo(s, r), Ko(s, a)), e, je(e)) };
+  return { x: () => vn(Ds(Ko(s, r), Xo(s, a)), e, je(e)) };
 } };
 var iz = { kernelName: Kd, inputsToSave: ["x"], gradFunc: LI.gradFunc };
-var oz = { kernelName: oo, saveAllInputs: true, gradFunc: (e, t, n) => {
+var oz = { kernelName: uo, saveAllInputs: true, gradFunc: (e, t, n) => {
   let s = t.map((u) => u.shape), { axis: r } = n, a = ts(r, t[0].shape)[0], i = s.map((u) => u[a]);
   return Bn(e, i, a).map((u) => () => u);
 } };
@@ -9769,7 +9761,7 @@ var fz = { kernelName: Ea, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(yI(ce(n, "float32")), e) };
 } };
-var mz = { kernelName: uo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var mz = { kernelName: lo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { axis: r, exclusive: a, reverse: i } = n;
   return { x: () => {
     let o = lI([r], s.rank), u = sI(e, r, a, !i);
@@ -9790,7 +9782,7 @@ var yz = { kernelName: Fa, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t, s = { dy: e, y: n };
   return { x: () => M.runKernel(bg, s) };
 } };
-var vz = { kernelName: hl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var vz = { kernelName: fl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t, s = V(Xn(kt(ct(n))), 2 / Math.sqrt(Math.PI));
   return { x: () => V(e, s) };
 } };
@@ -9798,11 +9790,11 @@ var xz = { kernelName: Oa, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, n) };
 } };
-var wz = { kernelName: ho, inputsToSave: ["input"], gradFunc: (e, t) => {
+var wz = { kernelName: fo, inputsToSave: ["input"], gradFunc: (e, t) => {
   let [n] = t;
   return { input: () => G(e, n.shape) };
 } };
-var kz = { kernelName: fo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var kz = { kernelName: mo, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, Xn(n)) };
 } };
@@ -9841,7 +9833,7 @@ var Cz = { kernelName: Ma, inputsToSave: ["x", "mean", "variance", "scale"], gra
     return a.rank === 1 && (x = ve(x, l)), G(x, a.shape);
   } };
 } };
-var Nz = { kernelName: go, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
+var Nz = { kernelName: bo, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
   let [s, r] = t, { axis: a } = n, i = ts(a, s.shape)[0];
   return { x: () => {
     let u = s.shape, l = r.size, c = u.slice(0, i), p = c.length, d = u.slice(a, u.length).slice(1), h = d.length, f = yx(0, p), m = yx(p + 1, p + 1 + h), g = vx([c, [l], d]), b = G(e, g), y = G(r, [l]), v = vx([[p], f, m]), x = qe(b, v), k = V3(x, y, s.shape[i]), T = Zg(v);
@@ -9866,14 +9858,14 @@ var Tz = { kernelName: La, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   return { a: () => je(n), b: () => je(s) };
 } };
 var $z = { kernelName: Ba, gradFunc: (e) => ({ x: () => ce(e, "float32") }) };
-var _z = { kernelName: ml, gradFunc: (e) => ({ x: () => je(e) }) };
-var Az = { kernelName: gl, gradFunc: (e) => ({ x: () => je(e) }) };
-var Ez = { kernelName: bl, gradFunc: (e) => ({ x: () => je(e) }) };
+var _z = { kernelName: gl, gradFunc: (e) => ({ x: () => je(e) }) };
+var Az = { kernelName: bl, gradFunc: (e) => ({ x: () => je(e) }) };
+var Ez = { kernelName: yl, gradFunc: (e) => ({ x: () => je(e) }) };
 var Rz = { kernelName: Va, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { alpha: r } = n, a = Wn(s, 0);
   return { x: () => vn(a, e, V(e, r)) };
 } };
-var Dz = { kernelName: yl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Dz = { kernelName: vl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, ie(n, 1)) };
 } };
@@ -9906,7 +9898,7 @@ var xx = { kernelName: Ua, inputsToSave: ["x"], outputsToSave: [true], gradFunc:
 } };
 var Lz = { kernelName: Ga, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t;
-  return { a: () => V(e, ce(jo(n, s), "float32")), b: () => V(e, ce(aI(n, s), "float32")) };
+  return { a: () => V(e, ce(Ko(n, s), "float32")), b: () => V(e, ce(aI(n, s), "float32")) };
 } };
 function Bz(e, t, n, s, r, a, i) {
   let o = _(e, "dy", "maxPool3dGrad"), u = _(t, "input", "maxPool3dGrad"), l = _(n, "output", "maxPool3dGrad"), c = o, p = u, d = l, h = false;
@@ -9947,13 +9939,13 @@ var jz = { kernelName: ja, inputsToSave: ["x"], outputsToSave: [true], gradFunc:
 } };
 var Kz = { kernelName: Ka, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t;
-  return { a: () => V(e, ce(Ko(n, s), "float32")), b: () => V(e, ce(Wn(n, s), "float32")) };
+  return { a: () => V(e, ce(Xo(n, s), "float32")), b: () => V(e, ce(Wn(n, s), "float32")) };
 } };
 var Xz = { kernelName: Xa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
   return { x: () => He(e, a, s.shape) };
 } };
-var Yz = { kernelName: xl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+var Yz = { kernelName: wl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t, r = ot(n.shape, s.shape);
   return { a: () => {
     let o = _t(n.shape, r);
@@ -9973,13 +9965,13 @@ var Qz = { kernelName: Ya, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
     return u.length > 0 ? G(ve(o, u), s.shape) : o;
   } };
 } };
-var Zz = { kernelName: ko, gradFunc: (e) => ({ x: () => kt(e) }) };
-var Jz = { kernelName: To, inputsToSave: ["indices"], gradFunc: (e, t) => {
+var Zz = { kernelName: Io, gradFunc: (e) => ({ x: () => kt(e) }) };
+var Jz = { kernelName: $o, inputsToSave: ["indices"], gradFunc: (e, t) => {
   let n = t[0];
   return { indices: () => $t(n.shape, "float32") };
 } };
-var eM = { kernelName: No, gradFunc: (e) => ({ x: () => je(e) }) };
-var tM = { kernelName: $o, saveAllInputs: true, gradFunc: (e, t, n) => {
+var eM = { kernelName: To, gradFunc: (e) => ({ x: () => je(e) }) };
+var tM = { kernelName: _o, saveAllInputs: true, gradFunc: (e, t, n) => {
   let { axis: s } = n;
   return Fs(e, s).map((a) => () => a);
 } };
@@ -10016,19 +10008,19 @@ var rM = { kernelName: Da, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
     return kt(xe(o, ce(l, "float32")));
   } };
 } };
-var aM = { kernelName: Il, inputsToSave: ["x"], gradFunc: (e, t) => {
+var aM = { kernelName: Sl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, kt(ct(n))) };
 } };
 var iM = { kernelName: ni, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t, s = V(Ko(n, 6), yp(n));
+  let [n] = t, s = V(Xo(n, 6), yp(n));
   return { x: () => V(e, ce(s, "float32")) };
 } };
 var oM = { kernelName: ei, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, ce(yp(n), "float32")) };
 } };
-var uM = { kernelName: Ao, inputsToSave: ["x"], gradFunc: (e, t) => {
+var uM = { kernelName: Eo, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => G(e, n.shape) };
 } };
@@ -10036,24 +10028,24 @@ var lM = { kernelName: ti, inputsToSave: ["images"], gradFunc: (e, t, n) => {
   let [s] = t, r = { dy: e, images: s };
   return { images: () => M.runKernel(Tg, r, n) };
 } };
-var cM = { kernelName: Sl, inputsToSave: ["images"], gradFunc: (e, t, n) => {
+var cM = { kernelName: Cl, inputsToSave: ["images"], gradFunc: (e, t, n) => {
   let [s] = t, r = { dy: e, images: s };
   return { images: () => M.runKernel(Ng, r, n) };
 } };
-var dM = { kernelName: Eo, gradFunc: (e, t, n) => {
+var dM = { kernelName: Ro, gradFunc: (e, t, n) => {
   let { dims: s } = n, r = ts(s, e.shape);
   return { x: () => Zn(e, r) };
 } };
-var pM = { kernelName: Ro, gradFunc: (e) => ({ x: () => je(e) }) };
+var pM = { kernelName: Do, gradFunc: (e) => ({ x: () => je(e) }) };
 var hM = { kernelName: si, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => kt(xe(e, V(pa(n, 1.5), 2))) };
 } };
-var fM = { kernelName: Fo, inputsToSave: ["condition"], gradFunc: (e, t) => {
+var fM = { kernelName: Oo, inputsToSave: ["condition"], gradFunc: (e, t) => {
   let [n] = t;
   return { condition: () => ce(je(n), "float32"), t: () => V(e, ce(n, e.dtype)), e: () => V(e, ce(Jg(n), e.dtype)) };
 } };
-var mM = { kernelName: Cl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var mM = { kernelName: Nl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
     let s = Wn(n, we(0)), r = we(PI), a = we(zI), i = V(e, a), o = V(V(e, r), Xn(ce(n, "float32")));
@@ -10064,16 +10056,16 @@ var gM = { kernelName: ai, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, V(n, ge(we(1), n))) };
 } };
-var bM = { kernelName: Nl, gradFunc: (e) => ({ x: () => je(e) }) };
+var bM = { kernelName: Tl, gradFunc: (e) => ({ x: () => je(e) }) };
 var yM = { kernelName: ri, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(jg(ce(n, "float32")), e) };
 } };
-var vM = { kernelName: Po, inputsToSave: ["x"], gradFunc: (e, t) => {
+var vM = { kernelName: zo, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(nI(ce(n, "float32")), e) };
 } };
-var xM = { kernelName: Oo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var xM = { kernelName: Po, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { begin: r, size: a } = n, i = s.shape, [o, u] = Bk(s, r, a), l = [];
   for (let c = 0; c < e.rank; c++)
     l.push([o[c], i[c] - o[c] - u[c]]);
@@ -10083,15 +10075,15 @@ var wM = { kernelName: ui, outputsToSave: [true], gradFunc: (e, t, n) => {
   let [s] = t, { dim: r } = n, a = true, i = V(e, s);
   return { logits: () => ge(i, V(ve(i, [r], a), s)) };
 } };
-var kM = { kernelName: Tl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var kM = { kernelName: $l, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, qs(n)) };
 } };
-var kx = { kernelName: zo, gradFunc: (e, t, n) => {
+var kx = { kernelName: Mo, gradFunc: (e, t, n) => {
   let { blockShape: s, paddings: r } = n;
   return { x: () => Hg(e, s, r) };
 } };
-var Ix = { kernelName: Mo, gradFunc: (e, t, n) => {
+var Ix = { kernelName: Lo, gradFunc: (e, t, n) => {
   let { axis: s } = n;
   return { x: () => Ft(e, s) };
 } };
@@ -10099,7 +10091,7 @@ var IM = { kernelName: ii, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, V(dn(ce(n, "float32")), 2)) };
 } };
-var SM = { kernelName: _l, inputsToSave: ["x"], gradFunc: (e, t) => {
+var SM = { kernelName: Al, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, V(ce(n, "float32"), 2)) };
 } };
@@ -10126,7 +10118,7 @@ var $M = { kernelName: oi, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let o = G(e, r), u = V(o, Mn(s.shape, "float32"));
   return { x: () => u };
 } };
-var _M = { kernelName: Bo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var _M = { kernelName: Vo, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, ct(jg(n))) };
 } };
@@ -10165,7 +10157,7 @@ var RM = { kernelName: pi, gradFunc: (e, t, n) => {
   let s = n, { perm: r } = s, a = Zg(r);
   return { x: () => qe(e, a) };
 } };
-var DM = { kernelName: Uo, gradFunc: (e, t, n) => {
+var DM = { kernelName: Go, gradFunc: (e, t, n) => {
   let s = n, { axis: r } = s;
   return { value: () => Jn(e, r) };
 } };
@@ -10174,14 +10166,14 @@ var FM = { kernelName: up, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
   return { x: () => OM(e, n) };
 } };
 function OM(e, t) {
-  let n = $r(t, je(t)), s = ju(e, n), r = jo(t, we(0, "int32")), a = s.rank - r.rank;
+  let n = $r(t, je(t)), s = Ku(e, n), r = Ko(t, we(0, "int32")), a = s.rank - r.rank;
   for (let o = 0; o < a; ++o)
     r = Pn(r, o + 1);
   r = Ds(r, Mn(s.shape, "bool"));
   let i = je(s);
   return vn(r, s, i);
 }
-var PM = { kernelName: Go, gradFunc: (e) => ({ x: () => je(e) }) };
+var PM = { kernelName: Ho, gradFunc: (e) => ({ x: () => je(e) }) };
 var zM = [LI, zP, MP, LP, BP, VP, WP, UP, GP, HP, qP, jP, YP, JP, ez, tz, nz, sz, rz, az, iz, oz, lz, uz, pz, hz, fz, mz, gz, bz, rM, yz, vz, xz, wz, kz, Sz, Iz, Cz, Nz, Tz, $z, _z, Az, Ez, Rz, Dz, Fz, Oz, Mz, xx, xx, Lz, Wz, Hz, qz, jz, Kz, Xz, Yz, Qz, Zz, Jz, eM, tM, wx, wx, nM, sM, aM, iM, oM, uM, lM, cM, dM, pM, hM, fM, mM, gM, bM, yM, vM, xM, wM, kM, kx, kx, Ix, Ix, IM, CM, SM, NM, TM, $M, _M, AM, EM, RM, DM, FM, PM];
 for (let e of zM)
   D$(e);
@@ -10196,32 +10188,27 @@ function bs() {
 }
 var Vs = class extends Error {
   constructor(e) {
-    super(e);
-    Object.setPrototypeOf(this, Vs.prototype);
+    super(e), Object.setPrototypeOf(this, Vs.prototype);
   }
 };
 var hs = class extends Error {
   constructor(e) {
-    super(e);
-    Object.setPrototypeOf(this, hs.prototype);
+    super(e), Object.setPrototypeOf(this, hs.prototype);
   }
 };
 var U = class extends Error {
   constructor(e) {
-    super(e);
-    Object.setPrototypeOf(this, U.prototype);
+    super(e), Object.setPrototypeOf(this, U.prototype);
   }
 };
 var Fe = class extends Error {
   constructor(e) {
-    super(e);
-    Object.setPrototypeOf(this, Fe.prototype);
+    super(e), Object.setPrototypeOf(this, Fe.prototype);
   }
 };
 var VI = class extends Error {
   constructor(e) {
-    super(e);
-    Object.setPrototypeOf(this, VI.prototype);
+    super(e), Object.setPrototypeOf(this, VI.prototype);
   }
 };
 function fa(e, t) {
@@ -10277,7 +10264,7 @@ function mm(e) {
       }
     }
 }
-function Ml(e, t = {}, n = {}, s = "object", r = false) {
+function Ll(e, t = {}, n = {}, s = "object", r = false) {
   if (typeof e == "string") {
     let a = e, i;
     if (a in n)
@@ -10370,15 +10357,14 @@ function UI(e) {
 function Ib(e, t) {
   return j(() => dn(ve(V(e, e), t, true)));
 }
-var Ll = class extends ae.Serializable {
+var Bl = class extends ae.Serializable {
   getConfig() {
     return {};
   }
 };
-var Sb = class extends Ll {
+var Sb = class extends Bl {
   constructor(e) {
-    super();
-    this.defaultMaxValue = 2, this.defaultAxis = 0, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.axis = e.axis != null ? e.axis : this.defaultAxis;
+    super(), this.defaultMaxValue = 2, this.defaultAxis = 0, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.axis = e.axis != null ? e.axis : this.defaultAxis;
   }
   apply(e) {
     return j(() => {
@@ -10392,10 +10378,9 @@ var Sb = class extends Ll {
 };
 Sb.className = "MaxNorm";
 ae.registerClass(Sb);
-var Cb = class extends Ll {
+var Cb = class extends Bl {
   constructor(e) {
-    super();
-    this.defaultAxis = 0, this.axis = e.axis != null ? e.axis : this.defaultAxis;
+    super(), this.defaultAxis = 0, this.axis = e.axis != null ? e.axis : this.defaultAxis;
   }
   apply(e) {
     return j(() => xe(e, ie(Rt(), Ib(e, this.axis))));
@@ -10406,17 +10391,16 @@ var Cb = class extends Ll {
 };
 Cb.className = "UnitNorm";
 ae.registerClass(Cb);
-var Nb = class extends Ll {
+var Nb = class extends Bl {
   apply(e) {
     return Xs(e);
   }
 };
 Nb.className = "NonNeg";
 ae.registerClass(Nb);
-var Tb = class extends Ll {
+var Tb = class extends Bl {
   constructor(e) {
-    super();
-    this.defaultMinValue = 0, this.defaultMaxValue = 1, this.defaultRate = 1, this.defaultAxis = 0, this.minValue = e.minValue != null ? e.minValue : this.defaultMinValue, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.rate = e.rate != null ? e.rate : this.defaultRate, this.axis = e.axis != null ? e.axis : this.defaultAxis;
+    super(), this.defaultMinValue = 0, this.defaultMaxValue = 1, this.defaultRate = 1, this.defaultAxis = 0, this.minValue = e.minValue != null ? e.minValue : this.defaultMinValue, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.rate = e.rate != null ? e.rate : this.defaultRate, this.axis = e.axis != null ? e.axis : this.defaultAxis;
   }
   apply(e) {
     return j(() => {
@@ -10435,7 +10419,7 @@ function Ot(e) {
   return wb(e);
 }
 function Nx(e, t = {}) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "constraint");
+  return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "constraint");
 }
 function Pt(e) {
   if (e == null)
@@ -10444,7 +10428,7 @@ function Pt(e) {
     let n = { className: e in Cx ? Cx[e] : e, config: {} };
     return Nx(n);
   } else
-    return e instanceof Ll ? e : Nx(e);
+    return e instanceof Bl ? e : Nx(e);
 }
 function WM(e) {
   return new Sb(e);
@@ -10478,19 +10462,19 @@ function Un(e) {
 function GI(e) {
   mi(YM, "PoolMode", e);
 }
-var zu = [];
+var Mu = [];
 var Tx = "/";
 function ea(e, t) {
-  zu.push(e);
+  Mu.push(e);
   try {
     let n = t();
-    return zu.pop(), n;
+    return Mu.pop(), n;
   } catch (n) {
-    throw zu.pop(), n;
+    throw Mu.pop(), n;
   }
 }
 function JM() {
-  return zu.length === 0 ? "" : zu.join(Tx) + Tx;
+  return Mu.length === 0 ? "" : Mu.join(Tx) + Tx;
 }
 function HI(e) {
   if (!jI(e))
@@ -10553,7 +10537,7 @@ function ys(e, t) {
 function Sp(e, t) {
   return ce(e, t);
 }
-function Bl(e, t = -1) {
+function Vl(e, t = -1) {
   let n = e.shape.slice();
   return t < 0 && (t = n.length + t + 1), n.splice(t, 0, 1), G(e, n);
 }
@@ -10561,7 +10545,7 @@ function nL(e, t) {
   return j(() => {
     if (e.shape.length !== 2)
       throw new U(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);
-    let n = Bl(e, 1);
+    let n = Vl(e, 1);
     return gm(n, [1, t, 1]);
   });
 }
@@ -10700,9 +10684,9 @@ function Es(e, t, n, s) {
   }
 }
 function KI(e, t, n) {
-  return j(() => (Array.isArray(t) ? t = Zt(t, "int32") : t = ce(t, "int32"), ju(e, t, n)));
+  return j(() => (Array.isArray(t) ? t = Zt(t, "int32") : t = ce(t, "int32"), Ku(e, t, n)));
 }
-function Vl(e) {
+function Wl(e) {
   return V(e, e);
 }
 function bm(e, t, n) {
@@ -10748,7 +10732,7 @@ function oL(e) {
     return Vn(t, 0, 1);
   });
 }
-function Wl(e, t, n = false) {
+function Ul(e, t, n = false) {
   return n ? e() : t();
 }
 var uL = ["fanIn", "fanOut", "fanAvg"];
@@ -10783,8 +10767,7 @@ Np.className = "Ones";
 ae.registerClass(Np);
 var Ab = class extends ns {
   constructor(e) {
-    super();
-    if (typeof e != "object")
+    if (super(), typeof e != "object")
       throw new U(`Expected argument of type ConstantConfig but got ${e}`);
     if (e.value === void 0)
       throw new U(`config must have value set but got ${e}`);
@@ -10801,11 +10784,10 @@ Ab.className = "Constant";
 ae.registerClass(Ab);
 var Eb = class extends ns {
   constructor(e) {
-    super();
-    this.DEFAULT_MINVAL = -0.05, this.DEFAULT_MAXVAL = 0.05, this.minval = e.minval || this.DEFAULT_MINVAL, this.maxval = e.maxval || this.DEFAULT_MAXVAL, this.seed = e.seed;
+    super(), this.DEFAULT_MINVAL = -0.05, this.DEFAULT_MAXVAL = 0.05, this.minval = e.minval || this.DEFAULT_MINVAL, this.maxval = e.maxval || this.DEFAULT_MAXVAL, this.seed = e.seed;
   }
   apply(e, t) {
-    return zl(e, this.minval, this.maxval, t);
+    return Ml(e, this.minval, this.maxval, t);
   }
   getConfig() {
     return { minval: this.minval, maxval: this.maxval, seed: this.seed };
@@ -10815,8 +10797,7 @@ Eb.className = "RandomUniform";
 ae.registerClass(Eb);
 var Rb = class extends ns {
   constructor(e) {
-    super();
-    this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
+    super(), this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
   }
   apply(e, t) {
     if (t = t || "float32", t !== "float32" && t !== "int32")
@@ -10831,8 +10812,7 @@ Rb.className = "RandomNormal";
 ae.registerClass(Rb);
 var Db = class extends ns {
   constructor(e) {
-    super();
-    this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
+    super(), this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
   }
   apply(e, t) {
     if (t = t || "float32", t !== "float32" && t !== "int32")
@@ -10847,8 +10827,7 @@ Db.className = "TruncatedNormal";
 ae.registerClass(Db);
 var Fb = class extends ns {
   constructor(e) {
-    super();
-    this.gain = e.gain != null ? e.gain : 1;
+    super(), this.gain = e.gain != null ? e.gain : 1;
   }
   apply(e, t) {
     return j(() => {
@@ -10883,8 +10862,7 @@ function pL(e, t = "channelsLast") {
 }
 var xn = class extends ns {
   constructor(e) {
-    super();
-    if (e.scale < 0)
+    if (super(), e.scale < 0)
       throw new U(`scale must be a positive float. Got: ${e.scale}`);
     this.scale = e.scale == null ? 1 : e.scale, this.mode = e.mode == null ? "fanIn" : e.mode, cL(this.mode), this.distribution = e.distribution == null ? "normal" : e.distribution, dL(this.distribution), this.seed = e.seed;
   }
@@ -10897,7 +10875,7 @@ var xn = class extends ns {
       return db(e, 0, i, t, this.seed);
     } else {
       let i = Math.sqrt(3 * a);
-      return zl(e, -i, i, t);
+      return Ml(e, -i, i, t);
     }
   }
   getConfig() {
@@ -10968,8 +10946,7 @@ Rp.className = "LeCunNormal";
 ae.registerClass(Rp);
 var Ob = class extends ns {
   constructor(e) {
-    super();
-    if (this.DEFAULT_GAIN = 1, this.gain = e.gain == null ? this.DEFAULT_GAIN : e.gain, this.seed = e.seed, this.seed != null)
+    if (super(), this.DEFAULT_GAIN = 1, this.gain = e.gain == null ? this.DEFAULT_GAIN : e.gain, this.seed = e.seed, this.seed != null)
       throw new Fe("Random seed is not implemented for Orthogonal Initializer yet.");
   }
   apply(e, t) {
@@ -10989,7 +10966,7 @@ Ob.className = "Orthogonal";
 ae.registerClass(Ob);
 var _x = { 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 Ax(e, t = {}) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "initializer");
+  return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "initializer");
 }
 function yt(e) {
   return wb(e);
@@ -11062,7 +11039,7 @@ function TL(e) {
   return new Ob(e);
 }
 var $L = {};
-Ae($L, { Layer: () => Ge, RNN: () => Ar, RNNCell: () => Hl, activation: () => cV, add: () => vV, alphaDropout: () => nW, average: () => xV, averagePooling1d: () => Hy, averagePooling2d: () => qy, averagePooling3d: () => jy, avgPool1d: () => _V, avgPool2d: () => EV, avgPool3d: () => DV, avgPooling1d: () => AV, avgPooling2d: () => RV, avgPooling3d: () => FV, batchNormalization: () => NV, bidirectional: () => KV, concatenate: () => wV, conv1d: () => tV, conv2d: () => nV, conv2dTranspose: () => sV, conv3d: () => rV, conv3dTranspose: () => aV, convLstm2d: () => GV, convLstm2dCell: () => HV, cropping2D: () => oV, dense: () => dV, depthwiseConv2d: () => lV, dot: () => CV, dropout: () => pV, elu: () => XB, embedding: () => yV, flatten: () => fV, gaussianDropout: () => tW, gaussianNoise: () => eW, globalAveragePooling1d: () => OV, globalAveragePooling2d: () => PV, globalMaxPool1d: () => YV, globalMaxPool2d: () => QV, globalMaxPooling1d: () => GS, globalMaxPooling2d: () => HS, gru: () => MV, gruCell: () => LV, input: () => OB, inputLayer: () => KB, layerNormalization: () => TV, leakyReLU: () => QB, lstm: () => BV, lstmCell: () => VV, masking: () => sW, maxPool1d: () => ZV, maxPool2d: () => JV, maxPooling1d: () => qS, maxPooling2d: () => jS, maxPooling3d: () => zV, maximum: () => kV, minimum: () => IV, multiply: () => SV, permute: () => bV, prelu: () => ZB, reLU: () => YB, repeatVector: () => mV, reshape: () => gV, rnn: () => qV, separableConv2d: () => iV, simpleRNN: () => WV, simpleRNNCell: () => UV, softmax: () => JB, spatialDropout1d: () => hV, stackedRNNCells: () => jV, thresholdedReLU: () => eV, timeDistributed: () => XV, upSampling2d: () => uV, zeroPadding2d: () => $V });
+Ae($L, { Layer: () => Ge, RNN: () => Ar, RNNCell: () => ql, activation: () => cV, add: () => vV, alphaDropout: () => nW, average: () => xV, averagePooling1d: () => Hy, averagePooling2d: () => qy, averagePooling3d: () => jy, avgPool1d: () => _V, avgPool2d: () => EV, avgPool3d: () => DV, avgPooling1d: () => AV, avgPooling2d: () => RV, avgPooling3d: () => FV, batchNormalization: () => NV, bidirectional: () => KV, concatenate: () => wV, conv1d: () => tV, conv2d: () => nV, conv2dTranspose: () => sV, conv3d: () => rV, conv3dTranspose: () => aV, convLstm2d: () => GV, convLstm2dCell: () => HV, cropping2D: () => oV, dense: () => dV, depthwiseConv2d: () => lV, dot: () => CV, dropout: () => pV, elu: () => XB, embedding: () => yV, flatten: () => fV, gaussianDropout: () => tW, gaussianNoise: () => eW, globalAveragePooling1d: () => OV, globalAveragePooling2d: () => PV, globalMaxPool1d: () => YV, globalMaxPool2d: () => QV, globalMaxPooling1d: () => GS, globalMaxPooling2d: () => HS, gru: () => MV, gruCell: () => LV, input: () => OB, inputLayer: () => KB, layerNormalization: () => TV, leakyReLU: () => QB, lstm: () => BV, lstmCell: () => VV, masking: () => sW, maxPool1d: () => ZV, maxPool2d: () => JV, maxPooling1d: () => qS, maxPooling2d: () => jS, maxPooling3d: () => zV, maximum: () => kV, minimum: () => IV, multiply: () => SV, permute: () => bV, prelu: () => ZB, reLU: () => YB, repeatVector: () => mV, reshape: () => gV, rnn: () => qV, separableConv2d: () => iV, simpleRNN: () => WV, simpleRNNCell: () => UV, softmax: () => JB, spatialDropout1d: () => hV, stackedRNNCells: () => jV, thresholdedReLU: () => eV, timeDistributed: () => XV, upSampling2d: () => uV, zeroPadding2d: () => $V });
 var _L = 0;
 function YI() {
   return _L++;
@@ -11166,8 +11143,7 @@ var Fp = class {
 var DL = 0;
 var Ge = class extends ae.Serializable {
   constructor(e = {}) {
-    super();
-    this._callHook = null, this._addedWeightNames = [], this._stateful = false, this.id = DL++, this.activityRegularizer = null, this.inputSpec = null, this.supportsMasking = false, this._trainableWeights = [], this._nonTrainableWeights = [], this._losses = [], this._updates = [], this._built = false, this.inboundNodes = [], this.outboundNodes = [];
+    super(), this._callHook = null, this._addedWeightNames = [], this._stateful = false, this.id = DL++, this.activityRegularizer = null, this.inputSpec = null, this.supportsMasking = false, this._trainableWeights = [], this._nonTrainableWeights = [], this._losses = [], this._updates = [], this._built = false, this.inboundNodes = [], this.outboundNodes = [];
     let t = e.name;
     if (!t) {
       let n = this.getClassName();
@@ -11488,10 +11464,9 @@ function QI(e, t, n) {
     }
   }
 }
-var Yo = class extends Ge {
+var Qo = class extends Ge {
   constructor(e) {
-    super({ dtype: e.dtype, name: e.name != null ? e.name : Dp("input").toString() });
-    if (e.batchSize == null && (e.batchSize = null), e.sparse == null && (e.sparse = false), this.trainable = false, this.built = true, this.sparse = e.sparse, e.inputShape != null && e.batchInputShape != null)
+    if (super({ dtype: e.dtype, name: e.name != null ? e.name : Dp("input").toString() }), e.batchSize == null && (e.batchSize = null), e.sparse == null && (e.sparse = false), this.trainable = false, this.built = true, this.sparse = e.sparse, e.inputShape != null && e.batchInputShape != null)
       throw new U("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");
     let t = e.batchInputShape;
     if (t == null) {
@@ -11515,8 +11490,8 @@ var Yo = class extends Ge {
     return { batchInputShape: this.batchInputShape, dtype: this.dtype, sparse: this.sparse, name: this.name };
   }
 };
-Yo.className = "InputLayer";
-ae.registerClass(Yo);
+Qo.className = "InputLayer";
+ae.registerClass(Qo);
 function ZI(e) {
   if (e.batchShape == null && e.shape == null)
     throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
@@ -11525,7 +11500,7 @@ function ZI(e) {
   let t = e.batchShape;
   e.shape != null && t == null && (t = [null].concat(e.shape));
   let n = e.dtype;
-  return n == null && (n = "float32"), new Yo({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
+  return n == null && (n = "float32"), new Qo({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
 }
 async function rr(e) {
   if (e == null)
@@ -11679,8 +11654,7 @@ var LL = class extends Ji {
 };
 var BL = class extends Ji {
   constructor(e, t) {
-    super();
-    if (this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || OI, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = PL), this.yieldEvery === "never" && e.onYield != null)
+    if (super(), this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || OI, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = PL), this.yieldEvery === "never" && e.onYield != null)
       throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");
     w.isNumber(this.yieldEvery) && (this.maybeWait = VM(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc)), this.trainBegin = e.onTrainBegin, this.trainEnd = e.onTrainEnd, this.epochBegin = e.onEpochBegin, this.epochEnd = e.onEpochEnd, this.batchBegin = e.onBatchBegin, this.batchEnd = e.onBatchEnd, this.yield = e.onYield;
   }
@@ -11746,22 +11720,22 @@ function tS(e, t, n, s, r, a, i, o, u) {
   return p.setParams({ epochs: n, initialEpoch: s, samples: r, steps: a, batchSize: i, verbose: t, doValidation: o, metrics: u }), { callbackList: p, history: l };
 }
 function ms(e, t = {}, n = false) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "layer", n);
+  return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "layer", n);
 }
 function Nd(e, t) {
   return j(() => {
     e.dtype !== "float32" && (e = ce(e, "float32"));
-    let n = ve(Vl(e), t, true), s = Ol(n.shape, Rt()), r = dn($r(n, s));
+    let n = ve(Wl(e), t, true), s = Pl(n.shape, Rt()), r = dn($r(n, s));
     return xe(e, r);
   });
 }
 function gi(e, t) {
-  return j(() => St(Vl(ge(t, e)), -1));
+  return j(() => St(Wl(ge(t, e)), -1));
 }
 function Op(e, t) {
   return j(() => St(Mt(ge(t, e)), -1));
 }
-function Qo(e, t) {
+function Zo(e, t) {
   return j(() => {
     let n = ge(e, t), s = Vn(Mt(e), Rt(), Number.MAX_VALUE), r = Mt(xe(n, s));
     return V(100, St(r, -1));
@@ -11770,13 +11744,13 @@ function Qo(e, t) {
 function VL(e, t) {
   return j(() => {
     let n = Vn(t, Rt(), Number.MAX_VALUE), s = Yn(ie(1, n)), r = Vn(e, Rt(), Number.MAX_VALUE), a = Yn(ie(1, r));
-    return St(Vl(ge(s, a)), -1);
+    return St(Wl(ge(s, a)), -1);
   });
 }
 function WL(e, t) {
   return j(() => {
     let n = $r(0, ge(1, V(e, t)));
-    return St(Vl(n), -1);
+    return St(Wl(n), -1);
   });
 }
 function UL(e, t) {
@@ -11793,11 +11767,11 @@ function GL(e, t) {
 }
 function HL(e, t) {
   return j(() => {
-    let n = Math.log(2), s = ge(t, e), r = ge(ie(s, Pl(V(-2, s))), n);
+    let n = Math.log(2), s = ge(t, e), r = ge(ie(s, zl(V(-2, s))), n);
     return St(r, -1);
   });
 }
-function Yu(e, t, n = false) {
+function Qu(e, t, n = false) {
   return j(() => {
     if (n)
       t = ub(t);
@@ -11813,7 +11787,7 @@ function Td(e, t, n = false) {
     let s = ce(mp(sL(e)), "int32");
     t = Vn(t, Rt(), 1 - Rt());
     let r = t.shape, a = G(vd(s, r[r.length - 1]), r);
-    return Yu(a, t, n);
+    return Qu(a, t, n);
   });
 }
 function qL(e, t) {
@@ -11848,7 +11822,7 @@ function Mb(e, t) {
     return kt(ve(r, -1));
   });
 }
-var $d = { meanSquaredError: gi, meanAbsoluteError: Op, meanAbsolutePercentageError: Qo, meanSquaredLogarithmicError: VL, squaredHinge: WL, hinge: UL, categoricalHinge: GL, logcosh: HL, categoricalCrossentropy: Yu, sparseCategoricalCrossentropy: Td, binaryCrossentropy: Pp, kullbackLeiblerDivergence: jL, poisson: KL, cosineProximity: Mb };
+var $d = { meanSquaredError: gi, meanAbsoluteError: Op, meanAbsolutePercentageError: Zo, meanSquaredLogarithmicError: VL, squaredHinge: WL, hinge: UL, categoricalHinge: GL, logcosh: HL, categoricalCrossentropy: Qu, sparseCategoricalCrossentropy: Td, binaryCrossentropy: Pp, kullbackLeiblerDivergence: jL, poisson: KL, cosineProximity: Mb };
 function Wf(e) {
   if (typeof e == "string") {
     if (e in $d)
@@ -11865,7 +11839,7 @@ function Lb(e, t) {
   });
 }
 function Bb(e, t) {
-  return j(() => Sp(Kn(Gu(e, -1), Gu(t, -1)), "float32"));
+  return j(() => Sp(Kn(Hu(e, -1), Hu(t, -1)), "float32"));
 }
 function nS(e, t) {
   return j(() => ce(ve(Ds(Kn(e, 1), Kn(t, 1))), "float32"));
@@ -11892,15 +11866,15 @@ function rS(e, t) {
   return Pp(e, t);
 }
 function aS(e, t) {
-  return e.rank === t.rank && (e = mr(e, [e.rank - 1])), t = Gu(t, -1), t.dtype !== e.dtype && (t = ce(t, e.dtype)), ce(Kn(e, t), "float32");
+  return e.rank === t.rank && (e = mr(e, [e.rank - 1])), t = Hu(t, -1), t.dtype !== e.dtype && (t = ce(t, e.dtype)), ce(Kn(e, t), "float32");
 }
 var ZL = gi;
 var JL = gi;
 var eB = Op;
 var tB = Op;
-var nB = Qo;
-var sB = Qo;
-var Vb = Yu;
+var nB = Zo;
+var sB = Zo;
+var Vb = Qu;
 var rB = Mb;
 var iS = Td;
 var _d = { binaryAccuracy: Lb, categoricalAccuracy: Bb, precision: sS, categoricalCrossentropy: Vb, sparseCategoricalCrossentropy: iS, mse: ZL, MSE: JL, mae: eB, MAE: tB, mape: nB, MAPE: sB, cosine: rB };
@@ -12063,7 +12037,7 @@ function dB(e, t, n, s) {
 function oS(e, t, n) {
   return (e === "inboundNodes" || e === "outputLayers" || e === "inputLayers") && t === 0 && typeof n == "string";
 }
-function Qu(e, t) {
+function Zu(e, t) {
   if (e === null)
     return null;
   if (typeof e == "string")
@@ -12074,7 +12048,7 @@ function Qu(e, t) {
     let n = [], s = e.length;
     for (let r = 0; r < s; ++r) {
       let a = e[r];
-      oS(t, r, a) ? n.push(a) : n.push(Qu(a, t));
+      oS(t, r, a) ? n.push(a) : n.push(Zu(a, t));
     }
     return n;
   } else {
@@ -12085,7 +12059,7 @@ function Qu(e, t) {
         n[s] = r;
       else {
         let a = Kr(s);
-        n[a] = Qu(r, a);
+        n[a] = Zu(r, a);
       }
     }
     return n;
@@ -12182,7 +12156,7 @@ var Qr = class {
 };
 var Uf = {};
 var Fx = {};
-function _u(e, t, n, s) {
+function Au(e, t, n, s) {
   let r = n == null ? false : n.training, a = Array.isArray(e), i = a ? e : [e], o = i.map((f) => f.name), u = [], l = t.names();
   for (let f of o)
     l.indexOf(f) !== -1 ? u.push(t.getValue(f)) : u.push(null);
@@ -12200,7 +12174,7 @@ function _u(e, t, n, s) {
       A > s.maxNumTensors && (s.maxNumTensors = A), A < s.minNumTensors && (s.minNumTensors = A);
     }
     let m = p[f], g = m.sourceLayer;
-    if (g instanceof Yo)
+    if (g instanceof Qo)
       continue;
     let b = [], y = [], v = [], x = false;
     for (let A of m.inputs) {
@@ -12284,8 +12258,7 @@ function mB(e) {
 }
 var Is = class extends Ge {
   constructor(e) {
-    super({});
-    if (this.containerNodes = /* @__PURE__ */ new Set(), this.name = e.name, this.name == null) {
+    if (super({}), this.containerNodes = /* @__PURE__ */ new Set(), this.name = e.name, this.name == null) {
       let b = this.getClassName().toLowerCase();
       this.name = Dp(b);
     }
@@ -12303,7 +12276,7 @@ var Is = class extends Ge {
     this.inputNames = [], this.outputNames = [], this.feedInputShapes = [], this.feedInputNames = [], this.feedOutputNames = [];
     for (let b = 0; b < this.inputLayers.length; b++) {
       let y = this.inputLayers[b];
-      if (!(y instanceof Yo))
+      if (!(y instanceof Qo))
         throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);
       this.inputNames.push(y.name), this.feedInputShapes.push(y.batchInputShape), this.feedInputNames.push(y.name);
     }
@@ -12475,7 +12448,7 @@ var Is = class extends Ge {
       let n = new Qr();
       for (let s = 0; s < this.inputs.length; ++s)
         n.add(this.inputs[s], e[s]);
-      return _u(this.outputs, n, t);
+      return Au(this.outputs, n, t);
     });
   }
   computeMask(e, t) {
@@ -12753,7 +12726,7 @@ async function cS(e, t, n, s) {
         return lr(e);
       if (e.shape.length === 2) {
         if (e.shape[1] > 1)
-          return Gu(e, 1);
+          return Hu(e, 1);
         if (e.shape[1] === 1)
           return G(e, [e.shape[0]]);
         throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
@@ -12918,7 +12891,7 @@ async function IB(e, t, n) {
 function km(e) {
   w.assert(e > 0 && Number.isInteger(e), () => `batchSize is required to be a positive integer, but got ${e}`);
 }
-function Au(e, t, n) {
+function Eu(e, t, n) {
   return e == null ? [null] : Array.isArray(e) ? e.map((s) => ta(s, t, n - t)) : ta(e, t, n - t);
 }
 function Wb(e, t) {
@@ -12997,7 +12970,7 @@ async function CB(e, t, n, s = {}) {
     } else if (s.validationSplit != null && s.validationSplit > 0 && s.validationSplit < 1) {
       g = true;
       let A = Math.floor(r[0].shape[0] * (1 - s.validationSplit)), P = r[0].shape[0];
-      c = Au(r, A, P), i = r, r = Au(r, 0, A), p = Au(a, A, P), o = a, a = Au(a, 0, A), b = c.concat(p);
+      c = Eu(r, A, P), i = r, r = Eu(r, 0, A), p = Eu(a, A, P), o = a, a = Eu(a, 0, A), b = c.concat(p);
     } else
       s.validationSteps != null && (g = true);
     let y = r.concat(a).concat(d);
@@ -13016,7 +12989,7 @@ function pS(e) {
   for (let n = 0; n < e.length; ++n) {
     let s = e[n];
     if (s.rank === 1)
-      t.push(Bl(s, 1));
+      t.push(Vl(s, 1));
     else {
       if (s.rank === 0)
         throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");
@@ -13130,11 +13103,11 @@ function TB(e, t, n) {
     throw new U(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`);
 }
 function $B(e, t, n) {
-  let s = [gi, Pp, Yu];
+  let s = [gi, Pp, Qu];
   for (let r = 0; r < e.length; ++r) {
     let a = e[r], i = t[r], o = n[r];
     if (i != null) {
-      if (i === Yu && a.shape[a.shape.length - 1] === 1)
+      if (i === Qu && a.shape[a.shape.length - 1] === 1)
         throw new U(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);
       if (s.indexOf(i) !== -1) {
         let u = a.shape.slice(1), l = o.slice(1);
@@ -13198,8 +13171,7 @@ function _B(e, t) {
 var AB = "layers-model";
 var pr = class extends Is {
   constructor(e) {
-    super(e);
-    this.isTraining = false;
+    super(e), this.isTraining = false;
   }
   summary(e, t, n = console.log) {
     if (!this.built)
@@ -13319,7 +13291,7 @@ var pr = class extends Is {
           throw new U(`No value is provided for the model's input ${o.name}`);
         a.add(o, u);
       }
-    let i = _u(r, a);
+    let i = Au(r, a);
     return n ? i : i[0];
   }
   retrieveSymbolicTensors(e) {
@@ -13350,14 +13322,14 @@ var pr = class extends Is {
       let r = Im(s, t), a = this.outputs.map((i) => []);
       for (let i = 0; i < r.length; ++i)
         j(() => {
-          let u = r[i][0], l = r[i][1], c = Au(e, u, l), p = [];
+          let u = r[i][0], l = r[i][1], c = Eu(e, u, l), p = [];
           if (Array.isArray(c))
             for (let h = 0; h < c.length; ++h)
               p.push({ key: this.inputs[h], value: c[h] });
           else
             p.push({ key: this.inputs[0], value: c });
           let d = new Qr(p);
-          return _u(this.outputs, d);
+          return Au(this.outputs, d);
         }).forEach((u, l) => a[l].push(u));
       return bn(a.map((i) => Ft(i, 0)));
     });
@@ -13441,7 +13413,7 @@ var pr = class extends Is {
         let c = [];
         for (let f = 0; f < this.inputs.length; ++f)
           c.push({ key: this.inputs[f], value: n[f] });
-        let p = new Qr(c), d = _u(this.outputs, p, { training: true }), h;
+        let p = new Qr(c), d = Au(this.outputs, p, { training: true }), h;
         for (let f = 0; f < this.lossFunctions.length; ++f) {
           let g = this.lossFunctions[f](s[f], d[f]);
           r[f] != null && (g = bB(g, r[f]));
@@ -13470,7 +13442,7 @@ var pr = class extends Is {
       let t = [], n, s = e.slice(0, this.inputs.length), r = e.slice(this.inputs.length, this.inputs.length + this.outputs.length), a = [];
       for (let u = 0; u < this.inputs.length; ++u)
         a.push({ key: this.inputs[u], value: s[u] });
-      let i = new Qr(a), o = _u(this.outputs, i);
+      let i = new Qr(a), o = Au(this.outputs, i);
       for (let u = 0; u < this.lossFunctions.length; ++u) {
         let l = this.lossFunctions[u], c = St(l(r[u], o[u]));
         u === 0 ? n = c : n = ie(n, c), t.push(n);
@@ -13565,7 +13537,7 @@ var pr = class extends Is {
       throw new Error("Loading loss_weights is not supported yet.");
     if (e.sample_weight_mode != null)
       throw new Error("Loading sample_weight_mode is not supported yet.");
-    let t = Qu(e.optimizer_config), n = ms(t), s;
+    let t = Zu(e.optimizer_config), n = ms(t), s;
     if (typeof e.loss == "string")
       s = Kr(e.loss);
     else if (Array.isArray(e.loss))
@@ -13621,7 +13593,7 @@ async function EB(e, t) {
   "modelTopology" in e || (e = { modelTopology: e }), e = e;
   let n = e.modelTopology;
   n.model_config != null && (n = n.model_config);
-  let s = Qu(n), r = ms(s, t);
+  let s = Zu(n), r = ms(s, t);
   if (e.weightsManifest != null) {
     let a = await En.loadWeights(e.weightsManifest, e.pathPrefix, r.weights.map((o) => o.originalName)), i = {};
     for (let o of r.weights)
@@ -13646,7 +13618,7 @@ async function DB(e, t, n) {
     throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
   let s = await e.load(), r = s.modelTopology;
   r.model_config != null && (r = r.model_config);
-  let a = n.strict == null ? true : n.strict, i = s.weightData != null && s.weightSpecs != null && a, o = ms(Qu(r), t, i), u = s.trainingConfig;
+  let a = n.strict == null ? true : n.strict, i = s.weightData != null && s.weightSpecs != null && a, o = ms(Zu(r), t, i), u = s.trainingConfig;
   if (u != null && o.loadTrainingConfig(u), s.userDefinedMetadata != null && o.setUserDefinedMetadata(s.userDefinedMetadata), s.weightData != null) {
     if (s.weightSpecs == null)
       throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
@@ -13663,8 +13635,7 @@ function FB(e, t) {
 }
 var Cm = class extends pr {
   constructor(e) {
-    super({ inputs: [], outputs: [] });
-    if (e = e || {}, this.trainable = true, this.built = false, this.name = e.name != null ? e.name : Dp("sequential_"), e.layers != null)
+    if (super({ inputs: [], outputs: [] }), e = e || {}, this.trainable = true, this.built = false, this.name = e.name != null ? e.name : Dp("sequential_"), e.layers != null)
       for (let t of e.layers)
         this.add(t);
   }
@@ -13809,19 +13780,19 @@ var Cm = class extends pr {
 var Ub = Cm;
 Ub.className = "Sequential";
 ae.registerClass(Ub);
-function Cpe(e) {
+function Npe(e) {
   return new pr(e);
 }
-function Npe(e) {
+function Tpe(e) {
   return new Ub(e);
 }
-function Tpe(e, t) {
+function $pe(e, t) {
   return t == null && (t = {}), RB(e, t);
 }
 function OB(e) {
   return ZI(e);
 }
-function $pe(e, t) {
+function _pe(e, t) {
   zb.registerCallbackConstructor(e, t);
 }
 var In = class extends ae.Serializable {
@@ -13880,7 +13851,7 @@ xS.className = "hardSigmoid";
 ae.registerClass(xS);
 var wS = class extends In {
   apply(e) {
-    return Pl(e);
+    return zl(e);
   }
 };
 wS.className = "softplus";
@@ -13894,7 +13865,7 @@ kS.className = "softsign";
 ae.registerClass(kS);
 var IS = class extends In {
   apply(e) {
-    return Hu(e);
+    return qu(e);
   }
 };
 IS.className = "tanh";
@@ -13922,7 +13893,7 @@ CS.className = "swish";
 ae.registerClass(CS);
 var NS = class extends In {
   apply(e) {
-    return j(() => V(e, Hu(Pl(e))));
+    return j(() => V(e, qu(zl(e))));
   }
 };
 NS.className = "mish";
@@ -13931,7 +13902,7 @@ function br(e) {
   return e.getClassName();
 }
 function Gf(e, t = {}) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "activation");
+  return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "activation");
 }
 function yr(e) {
   if (e == null) {
@@ -13950,15 +13921,14 @@ function Hb(e) {
 }
 var TS = class extends ae.Serializable {
 };
-var Ul = class extends TS {
+var Gl = class extends TS {
   constructor(e) {
-    super();
-    Hb(e), this.l1 = e == null || e.l1 == null ? 0.01 : e.l1, this.l2 = e == null || e.l2 == null ? 0.01 : e.l2, this.hasL1 = this.l1 !== 0, this.hasL2 = this.l2 !== 0;
+    super(), Hb(e), this.l1 = e == null || e.l1 == null ? 0.01 : e.l1, this.l2 = e == null || e.l2 == null ? 0.01 : e.l2, this.hasL1 = this.l1 !== 0, this.hasL2 = this.l2 !== 0;
   }
   apply(e) {
     return j(() => {
       let t = $t([1]);
-      return this.hasL1 && (t = ie(t, ve(V(this.l1, Mt(e))))), this.hasL2 && (t = ie(t, ve(V(this.l2, Vl(e))))), G(t, []);
+      return this.hasL1 && (t = ie(t, ve(V(this.l1, Mt(e))))), this.hasL2 && (t = ie(t, ve(V(this.l2, Wl(e))))), G(t, []);
     });
   }
   getConfig() {
@@ -13968,20 +13938,20 @@ var Ul = class extends TS {
     return new e({ l1: t.l1, l2: t.l2 });
   }
 };
-Ul.className = "L1L2";
-ae.registerClass(Ul);
+Gl.className = "L1L2";
+ae.registerClass(Gl);
 function PB(e) {
-  return Hb(e), new Ul({ l1: e != null ? e.l1 : null, l2: 0 });
+  return Hb(e), new Gl({ l1: e != null ? e.l1 : null, l2: 0 });
 }
 function zB(e) {
-  return Hb(e), new Ul({ l2: e != null ? e.l2 : null, l1: 0 });
+  return Hb(e), new Gl({ l2: e != null ? e.l2 : null, l1: 0 });
 }
 var Vx = { l1l2: "L1L2" };
 function it(e) {
   return wb(e);
 }
 function Wx(e, t = {}) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "regularizer");
+  return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "regularizer");
 }
 function ft(e) {
   if (e == null)
@@ -13994,8 +13964,7 @@ function ft(e) {
 }
 var qb = class extends Ge {
   constructor(e) {
-    super(e == null ? {} : e);
-    this.supportsMasking = true, e != null && (this.maxValue = e.maxValue);
+    super(e == null ? {} : e), this.supportsMasking = true, e != null && (this.maxValue = e.maxValue);
   }
   call(e, t) {
     e = Oe(e);
@@ -14014,8 +13983,7 @@ qb.className = "ReLU";
 ae.registerClass(qb);
 var jb = class extends Ge {
   constructor(e) {
-    super(e == null ? {} : e);
-    this.DEFAULT_ALPHA = 0.3, e == null && (e = {}), this.alpha = e.alpha == null ? this.DEFAULT_ALPHA : e.alpha;
+    super(e == null ? {} : e), this.DEFAULT_ALPHA = 0.3, e == null && (e = {}), this.alpha = e.alpha == null ? this.DEFAULT_ALPHA : e.alpha;
   }
   call(e, t) {
     let n = Oe(e);
@@ -14033,8 +14001,7 @@ jb.className = "LeakyReLU";
 ae.registerClass(jb);
 var Kb = class extends Ge {
   constructor(e) {
-    super(e == null ? {} : e);
-    if (this.DEFAULT_ALPHA_INITIALIZER = "zeros", e == null && (e = {}), this.supportsMasking = true, this.alphaInitializer = ht(e.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER), this.alphaRegularizer = ft(e.alphaRegularizer), this.alphaConstraint = Pt(e.alphaConstraint), e.sharedAxes == null)
+    if (super(e == null ? {} : e), this.DEFAULT_ALPHA_INITIALIZER = "zeros", e == null && (e = {}), this.supportsMasking = true, this.alphaInitializer = ht(e.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER), this.alphaRegularizer = ft(e.alphaRegularizer), this.alphaConstraint = Pt(e.alphaConstraint), e.sharedAxes == null)
       this.sharedAxes = null;
     else if (Array.isArray(e.sharedAxes))
       this.sharedAxes = e.sharedAxes;
@@ -14068,8 +14035,7 @@ Kb.className = "PReLU";
 ae.registerClass(Kb);
 var Xb = class extends Ge {
   constructor(e) {
-    super(e == null ? {} : e);
-    if (this.DEFAULT_ALPHA = 1, e == null && (e = {}), e.alpha != null && e.alpha !== this.DEFAULT_ALPHA)
+    if (super(e == null ? {} : e), this.DEFAULT_ALPHA = 1, e == null && (e = {}), e.alpha != null && e.alpha !== this.DEFAULT_ALPHA)
       throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);
     this.alpha = e.alpha == null ? this.DEFAULT_ALPHA : e.alpha;
   }
@@ -14089,8 +14055,7 @@ Xb.className = "ELU";
 ae.registerClass(Xb);
 var Yb = class extends Ge {
   constructor(e) {
-    super(e == null ? {} : e);
-    this.DEFAULT_THETA = 1, e == null && (e = {}), this.theta = e.theta == null ? this.DEFAULT_THETA : e.theta;
+    super(e == null ? {} : e), this.DEFAULT_THETA = 1, e == null && (e = {}), this.theta = e.theta == null ? this.DEFAULT_THETA : e.theta;
   }
   call(e, t) {
     let n = Oe(e);
@@ -14108,8 +14073,7 @@ Yb.className = "ThresholdedReLU";
 ae.registerClass(Yb);
 var Qb = class extends Ge {
   constructor(e) {
-    super(e == null ? {} : e);
-    this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new Gb().apply, this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis;
+    super(e == null ? {} : e), this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new Gb().apply, this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis;
   }
   call(e, t) {
     let n = Oe(e);
@@ -14200,8 +14164,7 @@ function LB(e, t, n, s = [1, 1, 1], r = "valid", a, i) {
 }
 var Jb = class extends Ge {
   constructor(e, t) {
-    super(t);
-    if (this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", Jb.verifyArgs(t), this.rank = e, Bt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
+    if (super(t), this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", Jb.verifyArgs(t), this.rank = e, Bt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
       throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
     if (this.kernelSize = Xi(t.kernelSize, e, "kernelSize"), this.strides = Xi(t.strides == null ? 1 : t.strides, e, "strides"), this.padding = t.padding == null ? "valid" : t.padding, Un(this.padding), this.dataFormat = t.dataFormat == null ? "channelsLast" : t.dataFormat, Ct(this.dataFormat), this.activation = yr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ht(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = Pt(t.biasConstraint), this.biasRegularizer = ft(t.biasRegularizer), this.activityRegularizer = ft(t.activityRegularizer), this.dilationRate = Xi(t.dilationRate == null ? 1 : t.dilationRate, e, "dilationRate"), this.rank === 1 && Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)
       throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);
@@ -14226,10 +14189,9 @@ var Jb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-var Gl = class extends Jb {
+var Hl = class extends Jb {
   constructor(e, t) {
-    super(e, t);
-    this.kernel = null, Gl.verifyArgs(t), this.filters = t.filters, Bt(this.filters, "filters"), this.kernelInitializer = ht(t.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.kernelConstraint = Pt(t.kernelConstraint), this.kernelRegularizer = ft(t.kernelRegularizer);
+    super(e, t), this.kernel = null, Hl.verifyArgs(t), this.filters = t.filters, Bt(this.filters, "filters"), this.kernelInitializer = ht(t.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.kernelConstraint = Pt(t.kernelConstraint), this.kernelRegularizer = ft(t.kernelRegularizer);
   }
   build(e) {
     e = nt(e);
@@ -14278,10 +14240,9 @@ var Gl = class extends Jb {
       throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`);
   }
 };
-var _S = class extends Gl {
+var _S = class extends Hl {
   constructor(e) {
-    super(2, e);
-    _S.verifyArgs(e);
+    super(2, e), _S.verifyArgs(e);
   }
   getConfig() {
     let e = super.getConfig();
@@ -14295,10 +14256,9 @@ var _S = class extends Gl {
 var zp = _S;
 zp.className = "Conv2D";
 ae.registerClass(zp);
-var AS = class extends Gl {
+var AS = class extends Hl {
   constructor(e) {
-    super(3, e);
-    AS.verifyArgs(e);
+    super(3, e), AS.verifyArgs(e);
   }
   getConfig() {
     let e = super.getConfig();
@@ -14314,8 +14274,7 @@ Mp.className = "Conv3D";
 ae.registerClass(Mp);
 var ey = class extends zp {
   constructor(e) {
-    super(e);
-    if (this.inputSpec = [new Dt({ ndim: 4 })], this.padding !== "same" && this.padding !== "valid")
+    if (super(e), this.inputSpec = [new Dt({ ndim: 4 })], this.padding !== "same" && this.padding !== "valid")
       throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
   }
   build(e) {
@@ -14356,8 +14315,7 @@ ey.className = "Conv2DTranspose";
 ae.registerClass(ey);
 var ty = class extends Mp {
   constructor(e) {
-    super(e);
-    if (this.inputSpec = [new Dt({ ndim: 5 })], this.padding !== "same" && this.padding !== "valid")
+    if (super(e), this.inputSpec = [new Dt({ ndim: 5 })], this.padding !== "same" && this.padding !== "valid")
       throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
   }
   build(e) {
@@ -14396,10 +14354,9 @@ var ty = class extends Mp {
 };
 ty.className = "Conv3DTranspose";
 ae.registerClass(ty);
-var ES = class extends Gl {
+var ES = class extends Hl {
   constructor(e, t) {
-    super(e, t);
-    if (this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform", this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform", this.depthwiseKernel = null, this.pointwiseKernel = null, t.filters == null)
+    if (super(e, t), this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform", this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform", this.depthwiseKernel = null, this.pointwiseKernel = null, t.filters == null)
       throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");
     if (t.kernelInitializer != null || t.kernelRegularizer != null || t.kernelConstraint != null)
       throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");
@@ -14442,10 +14399,9 @@ var ny = class extends ES {
 };
 ny.className = "SeparableConv2D";
 ae.registerClass(ny);
-var RS = class extends Gl {
+var RS = class extends Hl {
   constructor(e) {
-    super(1, e);
-    RS.verifyArgs(e), this.inputSpec = [{ ndim: 3 }];
+    super(1, e), RS.verifyArgs(e), this.inputSpec = [{ ndim: 3 }];
   }
   getConfig() {
     let e = super.getConfig();
@@ -14461,8 +14417,7 @@ sy.className = "Conv1D";
 ae.registerClass(sy);
 var ry = class extends Ge {
   constructor(e) {
-    super(e);
-    typeof e.cropping == "number" ? this.cropping = [[e.cropping, e.cropping], [e.cropping, e.cropping]] : typeof e.cropping[0] == "number" ? this.cropping = [[e.cropping[0], e.cropping[0]], [e.cropping[1], e.cropping[1]]] : this.cropping = e.cropping, this.dataFormat = e.dataFormat === void 0 ? "channelsLast" : e.dataFormat, this.inputSpec = [{ ndim: 4 }];
+    super(e), typeof e.cropping == "number" ? this.cropping = [[e.cropping, e.cropping], [e.cropping, e.cropping]] : typeof e.cropping[0] == "number" ? this.cropping = [[e.cropping[0], e.cropping[0]], [e.cropping[1], e.cropping[1]]] : this.cropping = e.cropping, this.dataFormat = e.dataFormat === void 0 ? "channelsLast" : e.dataFormat, this.inputSpec = [{ ndim: 4 }];
   }
   computeOutputShape(e) {
     return this.dataFormat === "channelsFirst" ? [e[0], e[1], e[2] - this.cropping[0][0] - this.cropping[0][1], e[3] - this.cropping[1][0] - this.cropping[1][1]] : [e[0], e[1] - this.cropping[0][0] - this.cropping[0][1], e[2] - this.cropping[1][0] - this.cropping[1][1], e[3]];
@@ -14487,8 +14442,7 @@ ry.className = "Cropping2D";
 ae.registerClass(ry);
 var ay = class extends Ge {
   constructor(e) {
-    super(e);
-    this.DEFAULT_SIZE = [2, 2], this.inputSpec = [{ ndim: 4 }], this.size = e.size == null ? this.DEFAULT_SIZE : e.size, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.interpolation = e.interpolation == null ? "nearest" : e.interpolation, ZM(this.interpolation);
+    super(e), this.DEFAULT_SIZE = [2, 2], this.inputSpec = [{ ndim: 4 }], this.size = e.size == null ? this.DEFAULT_SIZE : e.size, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.interpolation = e.interpolation == null ? "nearest" : e.interpolation, ZM(this.interpolation);
   }
   computeOutputShape(e) {
     if (this.dataFormat === "channelsFirst") {
@@ -14532,8 +14486,7 @@ function BB(e, t, n = [1, 1], s = "valid", r, a) {
 }
 var iy = class extends Jb {
   constructor(e) {
-    super(2, e);
-    this.depthwiseKernel = null, this.depthMultiplier = e.depthMultiplier == null ? 1 : e.depthMultiplier, this.depthwiseInitializer = ht(e.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.depthwiseConstraint = Pt(e.depthwiseConstraint), this.depthwiseRegularizer = ft(e.depthwiseRegularizer);
+    super(2, e), this.depthwiseKernel = null, this.depthMultiplier = e.depthMultiplier == null ? 1 : e.depthMultiplier, this.depthwiseInitializer = ht(e.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.depthwiseConstraint = Pt(e.depthwiseConstraint), this.depthwiseRegularizer = ft(e.depthwiseRegularizer);
   }
   build(e) {
     if (e = nt(e), e.length < 4)
@@ -14739,7 +14692,7 @@ var OS = class extends Ge {
   getInitialState(e) {
     return j(() => {
       let t = $t(e.shape);
-      return t = ve(t, [1, 2]), t = Bl(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? gm(t, [1, n]) : t) : this.cell.stateSize > 1 ? [gm(t, [1, this.cell.stateSize])] : [t];
+      return t = ve(t, [1, 2]), t = Vl(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? gm(t, [1, n]) : t) : this.cell.stateSize > 1 ? [gm(t, [1, this.cell.stateSize])] : [t];
     });
   }
   get trainableWeights() {
@@ -14765,12 +14718,11 @@ var OS = class extends Ge {
 var Ar = OS;
 Ar.className = "RNN";
 ae.registerClass(Ar);
-var Hl = class extends Ge {
+var ql = class extends Ge {
 };
-var Lp = class extends Hl {
+var Lp = class extends ql {
   constructor(e) {
-    super(e);
-    this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
+    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     e = nt(e), this.kernel = this.addWeight("kernel", [e[e.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint) : this.bias = null, this.built = true;
@@ -14798,8 +14750,7 @@ Lp.className = "SimpleRNNCell";
 ae.registerClass(Lp);
 var oy = class extends Ar {
   constructor(e) {
-    e.cell = new Lp(e);
-    super(e);
+    e.cell = new Lp(e), super(e);
   }
   call(e, t) {
     return j(() => {
@@ -14814,10 +14765,9 @@ var oy = class extends Ar {
 };
 oy.className = "SimpleRNN";
 ae.registerClass(oy);
-var Bp = class extends Hl {
+var Bp = class extends ql {
   constructor(e) {
-    super(e);
-    if (this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.resetAfter)
+    if (super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.resetAfter)
       throw new U("GRUCell does not support reset_after parameter set to true.");
     this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
@@ -14853,8 +14803,7 @@ Bp.className = "GRUCell";
 ae.registerClass(Bp);
 var uy = class extends Ar {
   constructor(e) {
-    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Bp(e);
-    super(e);
+    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Bp(e), super(e);
   }
   call(e, t) {
     return j(() => {
@@ -14869,10 +14818,9 @@ var uy = class extends Ar {
 };
 uy.className = "GRU";
 ae.registerClass(uy);
-var ql = class extends Hl {
+var jl = class extends ql {
   constructor(e) {
-    super(e);
-    this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
+    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     var t;
@@ -14918,12 +14866,11 @@ var ql = class extends Hl {
     return { ...e, ...t };
   }
 };
-ql.className = "LSTMCell";
-ae.registerClass(ql);
+jl.className = "LSTMCell";
+ae.registerClass(jl);
 var ly = class extends Ar {
   constructor(e) {
-    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new ql(e);
-    super(e);
+    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new jl(e), super(e);
   }
   call(e, t) {
     return j(() => {
@@ -14938,10 +14885,9 @@ var ly = class extends Ar {
 };
 ly.className = "LSTM";
 ae.registerClass(ly);
-var Vp = class extends Hl {
+var Vp = class extends ql {
   constructor(e) {
-    super(e);
-    this.cells = e.cells;
+    super(e), this.cells = e.cells;
   }
   get stateSize() {
     let e = [];
@@ -15025,7 +14971,7 @@ var Vp = class extends Hl {
 Vp.className = "StackedRNNCells";
 ae.registerClass(Vp);
 function vr(e) {
-  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : XI(t(), n), o = () => Wl(i, t, s);
+  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : XI(t(), n), o = () => Ul(i, t, s);
   return !r || r <= 1 ? Ht(o().clone()) : Array(r).fill(void 0).map(o).map((l) => Ht(l.clone()));
 }
 var PS = class extends Ar {
@@ -15034,8 +14980,7 @@ var PS = class extends Ar {
       throw new Fe("Unrolling is not possible with convolutional RNNs.");
     if (Array.isArray(e.cell))
       throw new Fe("It is not possible at the moment to stack convolutional cells.");
-    super(e);
-    this.inputSpec = [new Dt({ ndim: 5 })];
+    super(e), this.inputSpec = [new Dt({ ndim: 5 })];
   }
   call(e, t) {
     return j(() => {
@@ -15086,11 +15031,10 @@ var PS = class extends Ar {
   }
 };
 PS.className = "ConvRNN2D";
-var Wp = class extends ql {
+var Wp = class extends jl {
   constructor(e) {
     let { filters: t, kernelSize: n, strides: s, padding: r, dataFormat: a, dilationRate: i } = e;
-    super({ ...e, units: t });
-    this.filters = t, Bt(this.filters, "filters"), this.kernelSize = Xi(n, 2, "kernelSize"), this.kernelSize.forEach((o) => Bt(o, "kernelSize")), this.strides = Xi(s || 1, 2, "strides"), this.strides.forEach((o) => Bt(o, "strides")), this.padding = r || "valid", Un(this.padding), this.dataFormat = a || "channelsLast", Ct(this.dataFormat), this.dilationRate = Xi(i || 1, 2, "dilationRate"), this.dilationRate.forEach((o) => Bt(o, "dilationRate"));
+    super({ ...e, units: t }), this.filters = t, Bt(this.filters, "filters"), this.kernelSize = Xi(n, 2, "kernelSize"), this.kernelSize.forEach((o) => Bt(o, "kernelSize")), this.strides = Xi(s || 1, 2, "strides"), this.strides.forEach((o) => Bt(o, "strides")), this.padding = r || "valid", Un(this.padding), this.dataFormat = a || "channelsLast", Ct(this.dataFormat), this.dilationRate = Xi(i || 1, 2, "dilationRate"), this.dilationRate.forEach((o) => Bt(o, "dilationRate"));
   }
   build(e) {
     var t;
@@ -15160,8 +15104,7 @@ cy.className = "ConvLSTM2D";
 ae.registerClass(cy);
 var Up = class extends Ge {
   constructor(e) {
-    super(e);
-    this.rate = Math.max(Math.min(e.rate, 1), 0), this.noiseShape = e.noiseShape, this.seed = e.seed, this.supportsMasking = true;
+    super(e), this.rate = Math.max(Math.min(e.rate, 1), 0), this.noiseShape = e.noiseShape, this.seed = e.seed, this.supportsMasking = true;
   }
   getNoiseShape(e) {
     if (this.noiseShape == null)
@@ -15177,7 +15120,7 @@ var Up = class extends Ge {
       let n = Oe(e);
       if (0 < this.rate && this.rate < 1) {
         let s = t.training == null ? false : t.training, r = this.getNoiseShape(n);
-        return Wl(() => XI(n, this.rate, r, this.seed), () => n, s);
+        return Ul(() => XI(n, this.rate, r, this.seed), () => n, s);
       }
       return e;
     });
@@ -15194,8 +15137,7 @@ Up.className = "Dropout";
 ae.registerClass(Up);
 var dy = class extends Up {
   constructor(e) {
-    super(e);
-    this.inputSpec = [{ ndim: 3 }];
+    super(e), this.inputSpec = [{ ndim: 3 }];
   }
   getNoiseShape(e) {
     let t = e.shape;
@@ -15206,8 +15148,7 @@ dy.className = "SpatialDropout1D";
 ae.registerClass(dy);
 var py = class extends Ge {
   constructor(e) {
-    super(e);
-    if (this.activation = null, this.useBias = true, this.kernel = null, this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.batchInputShape == null && e.inputShape == null && e.inputDim != null) {
+    if (super(e), this.activation = null, this.useBias = true, this.kernel = null, this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.batchInputShape == null && e.inputShape == null && e.inputDim != null) {
       let t = null;
       e.batchSize != null && (t = e.batchSize), this.batchInputShape = [t, e.inputDim];
     }
@@ -15239,9 +15180,7 @@ py.className = "Dense";
 ae.registerClass(py);
 var hy = class extends Ge {
   constructor(e) {
-    e = e || {};
-    super(e);
-    this.inputSpec = [{ minNDim: 3 }], this.dataFormat = e.dataFormat;
+    e = e || {}, super(e), this.inputSpec = [{ minNDim: 3 }], this.dataFormat = e.dataFormat;
   }
   computeOutputShape(e) {
     e = nt(e);
@@ -15274,8 +15213,7 @@ hy.className = "Flatten";
 ae.registerClass(hy);
 var fy = class extends Ge {
   constructor(e) {
-    super(e);
-    this.supportsMasking = true, this.activation = yr(e.activation);
+    super(e), this.supportsMasking = true, this.activation = yr(e.activation);
   }
   call(e, t) {
     return j(() => {
@@ -15293,8 +15231,7 @@ fy.className = "Activation";
 ae.registerClass(fy);
 var my = class extends Ge {
   constructor(e) {
-    super(e);
-    this.n = e.n, this.inputSpec = [{ ndim: 2 }];
+    super(e), this.n = e.n, this.inputSpec = [{ ndim: 2 }];
   }
   computeOutputShape(e) {
     return [e[0], this.n, e[1]];
@@ -15311,8 +15248,7 @@ my.className = "RepeatVector";
 ae.registerClass(my);
 var gy = class extends Ge {
   constructor(e) {
-    super(e);
-    this.targetShape = e.targetShape;
+    super(e), this.targetShape = e.targetShape;
     for (let t = 0; t < this.targetShape.length; ++t)
       this.isUnknown(this.targetShape[t]) && (this.targetShape[t] = null);
   }
@@ -15365,8 +15301,7 @@ gy.className = "Reshape";
 ae.registerClass(gy);
 var by = class extends Ge {
   constructor(e) {
-    super(e);
-    if (e.dims == null)
+    if (super(e), e.dims == null)
       throw new Error("Required configuration field `dims` is missing during Permute constructor call.");
     if (!Array.isArray(e.dims))
       throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);
@@ -15394,8 +15329,7 @@ by.className = "Permute";
 ae.registerClass(by);
 var yy = class extends Ge {
   constructor(e) {
-    super(e == null ? {} : e);
-    this.supportsMasking = true, e != null ? this.maskValue = e.maskValue == null ? 0 : e.maskValue : this.maskValue = 0;
+    super(e == null ? {} : e), this.supportsMasking = true, e != null ? this.maskValue = e.maskValue == null ? 0 : e.maskValue : this.maskValue = 0;
   }
   computeOutputShape(e) {
     return e;
@@ -15406,12 +15340,12 @@ var yy = class extends Ge {
   }
   computeMask(e, t) {
     let n = Oe(e), s = -1;
-    return pm(Ku(n, this.maskValue), s);
+    return pm(Xu(n, this.maskValue), s);
   }
   call(e, t) {
     return j(() => {
       this.invokeCallHook(e, t);
-      let n = Oe(e), s = -1, r = true, a = pm(Ku(n, this.maskValue), s, r);
+      let n = Oe(e), s = -1, r = true, a = pm(Xu(n, this.maskValue), s, r);
       return V(n, ce(a, n.dtype));
     });
   }
@@ -15420,8 +15354,7 @@ yy.className = "Masking";
 ae.registerClass(yy);
 var vy = class extends Ge {
   constructor(e) {
-    super(e);
-    if (this.embeddings = null, this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform", e.batchInputShape == null && e.inputShape == null) {
+    if (super(e), this.embeddings = null, this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform", e.batchInputShape == null && e.inputShape == null) {
       let t = null;
       e.batchSize != null && (t = e.batchSize), e.inputLength == null ? this.batchInputShape = [t, null] : this.batchInputShape = [t].concat(dt(e.inputLength));
     }
@@ -15433,7 +15366,7 @@ var vy = class extends Ge {
   warnOnIncompatibleInputShape(e) {
   }
   computeMask(e, t) {
-    return j(() => this.maskZero ? (e = Oe(e), Ku(e, je(e))) : null);
+    return j(() => this.maskZero ? (e = Oe(e), Xu(e, je(e))) : null);
   }
   computeOutputShape(e) {
     if (e = nt(e), this.inputLength == null)
@@ -15470,8 +15403,7 @@ vy.className = "Embedding";
 ae.registerClass(vy);
 var bi = class extends Ge {
   constructor(e) {
-    super(e || {});
-    this.supportsMasking = true;
+    super(e || {}), this.supportsMasking = true;
   }
   mergeFunction(e) {
     throw new Fe();
@@ -15525,7 +15457,7 @@ var bi = class extends Ge {
           for (let a of e) {
             let i = a.rank;
             for (let o = 0; o < r - i; ++o)
-              a = Bl(a, 1);
+              a = Vl(a, 1);
             n.push(a);
           }
           return this.mergeFunction(n);
@@ -15668,8 +15600,7 @@ Sy.className = "Minimum";
 ae.registerClass(Sy);
 var Cy = class extends bi {
   constructor(e) {
-    super(e);
-    this.DEFAULT_AXIS = -1, e == null && (e = {}), this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis, this.supportsMasking = true, this.reshapeRequired = false;
+    super(e), this.DEFAULT_AXIS = -1, e == null && (e = {}), this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis, this.supportsMasking = true, this.reshapeRequired = false;
   }
   build(e) {
     if (!(Array.isArray(e) && Array.isArray(e[0])) || e.length === 1)
@@ -15746,7 +15677,7 @@ var Cy = class extends bi {
 };
 Cy.className = "Concatenate";
 ae.registerClass(Cy);
-function Su(e, t) {
+function Cu(e, t) {
   for (; e < 0; )
     e += t;
   return e;
@@ -15795,8 +15726,7 @@ function VB(e, t, n) {
 }
 var Ny = class extends bi {
   constructor(e) {
-    super(e);
-    this.axes = e.axes, this.normalize = e.normalize == null ? false : e.normalize, this.supportsMasking = true, this.reshapeRequired = false;
+    super(e), this.axes = e.axes, this.normalize = e.normalize == null ? false : e.normalize, this.supportsMasking = true, this.reshapeRequired = false;
   }
   build(e) {
     w.assert(Array.isArray(e) && e.length === 2 && Array.isArray(e[0]) && Array.isArray(e[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
@@ -15811,11 +15741,11 @@ var Ny = class extends bi {
     if (e.length !== 2)
       throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);
     let t = e[0], n = e[1], s;
-    return Array.isArray(this.axes) ? s = this.axes.map((r, a) => Su(r, e[a].shape.length)) : s = [Su(this.axes, t.shape.length), Su(this.axes, n.shape.length)], this.normalize && (t = Nd(t, s[0]), n = Nd(n, s[1])), VB(t, n, s);
+    return Array.isArray(this.axes) ? s = this.axes.map((r, a) => Cu(r, e[a].shape.length)) : s = [Cu(this.axes, t.shape.length), Cu(this.axes, n.shape.length)], this.normalize && (t = Nd(t, s[0]), n = Nd(n, s[1])), VB(t, n, s);
   }
   interpretAxes(e, t) {
     let n;
-    return Array.isArray(this.axes) ? n = this.axes : n = [Su(this.axes, e.length), Su(this.axes, t.length)], n;
+    return Array.isArray(this.axes) ? n = this.axes : n = [Cu(this.axes, e.length), Cu(this.axes, t.length)], n;
   }
   computeOutputShape(e) {
     w.assert(Array.isArray(e) && e.length === 2 && Array.isArray(e[0]) && Array.isArray(e[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
@@ -15839,8 +15769,7 @@ Ny.className = "Dot";
 ae.registerClass(Ny);
 var Ty = class extends Ge {
   constructor(e) {
-    super(e);
-    this.supportsMasking = true, this.stddev = e.stddev;
+    super(e), this.supportsMasking = true, this.stddev = e.stddev;
   }
   computeOutputShape(e) {
     return e;
@@ -15853,7 +15782,7 @@ var Ty = class extends Ge {
     return j(() => {
       this.invokeCallHook(e, t);
       let n = Oe(e);
-      return Wl(() => ie(Cp(n.shape, 0, this.stddev), n), () => n, t.training || false);
+      return Ul(() => ie(Cp(n.shape, 0, this.stddev), n), () => n, t.training || false);
     });
   }
 };
@@ -15861,8 +15790,7 @@ Ty.className = "GaussianNoise";
 ae.registerClass(Ty);
 var $y = class extends Ge {
   constructor(e) {
-    super(e);
-    this.supportsMasking = true, this.rate = e.rate;
+    super(e), this.supportsMasking = true, this.rate = e.rate;
   }
   computeOutputShape(e) {
     return e;
@@ -15875,7 +15803,7 @@ var $y = class extends Ge {
     return j(() => {
       this.invokeCallHook(e, t);
       let n = Oe(e);
-      return this.rate > 0 && this.rate < 1 ? Wl(() => {
+      return this.rate > 0 && this.rate < 1 ? Ul(() => {
         let r = Math.sqrt(this.rate / (1 - this.rate));
         return V(n, Cp(n.shape, 1, r));
       }, () => n, t.training || false) : n;
@@ -15886,8 +15814,7 @@ $y.className = "GaussianDropout";
 ae.registerClass($y);
 var _y = class extends Ge {
   constructor(e) {
-    super(e);
-    this.supportsMasking = true, this.rate = e.rate, this.noiseShape = e.noiseShape;
+    super(e), this.supportsMasking = true, this.rate = e.rate, this.noiseShape = e.noiseShape;
   }
   _getNoiseShape(e) {
     return this.noiseShape || Oe(e).shape;
@@ -15903,8 +15830,8 @@ var _y = class extends Ge {
     return j(() => {
       if (this.rate < 1 && this.rate > 0) {
         let n = this._getNoiseShape(e);
-        return Wl(() => {
-          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = jo(zl(n), this.rate);
+        return Ul(() => {
+          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = Ko(Ml(n), this.rate);
           u = Sp(u, "float32");
           let l = ((1 - this.rate) * (1 + this.rate * o ** 2)) ** -0.5, c = -l * o * this.rate, p = ie(V(r, u), V(ie(u, -1), o));
           return ie(V(p, l), c);
@@ -15916,7 +15843,7 @@ var _y = class extends Ge {
 };
 _y.className = "AlphaDropout";
 ae.registerClass(_y);
-function Zu(e, t, n, s, r, a = 1e-3) {
+function Ju(e, t, n, s, r, a = 1e-3) {
   let i;
   if (e.rank === 2)
     i = $E(e, t, n, s, r, a);
@@ -15931,7 +15858,7 @@ function Zu(e, t, n, s, r, a = 1e-3) {
 function WB(e, t, n, s, r = 1e-3) {
   return j(() => {
     let a = tb(e, s), i = a.mean, o = a.variance;
-    return [Zu(e, i, o, n, t, r), i, o];
+    return [Ju(e, i, o, n, t, r), i, o];
   });
 }
 function UB(e, t, n, s, r = 1e-3) {
@@ -15940,7 +15867,7 @@ function UB(e, t, n, s, r = 1e-3) {
     for (let f of ys(0, e.rank))
       s.indexOf(f) !== -1 ? u.push(1) : u.push(e.shape[f]);
     let l = G(i, u), c = G(o, u), p = t == null ? null : G(t, u), d = n == null ? null : G(n, u);
-    return [Zu(e, l, c, d, p, r), i, o];
+    return [Ju(e, l, c, d, p, r), i, o];
   });
 }
 function GB(e, t, n, s, r = 1e-3) {
@@ -15948,9 +15875,7 @@ function GB(e, t, n, s, r = 1e-3) {
 }
 var Ay = class extends Ge {
   constructor(e) {
-    e == null && (e = {});
-    super(e);
-    this.supportsMasking = true, this.axis = e.axis == null ? -1 : e.axis, this.momentum = e.momentum == null ? 0.99 : e.momentum, this.epsilon = e.epsilon == null ? 1e-3 : e.epsilon, this.center = e.center == null ? true : e.center, this.scale = e.scale == null ? true : e.scale, this.betaInitializer = ht(e.betaInitializer || "zeros"), this.gammaInitializer = ht(e.gammaInitializer || "ones"), this.movingMeanInitializer = ht(e.movingMeanInitializer || "zeros"), this.movingVarianceInitializer = ht(e.movingVarianceInitializer || "ones"), this.betaConstraint = Pt(e.betaConstraint), this.gammaConstraint = Pt(e.gammaConstraint), this.betaRegularizer = ft(e.betaRegularizer), this.gammaRegularizer = ft(e.gammaRegularizer);
+    e == null && (e = {}), super(e), this.supportsMasking = true, this.axis = e.axis == null ? -1 : e.axis, this.momentum = e.momentum == null ? 0.99 : e.momentum, this.epsilon = e.epsilon == null ? 1e-3 : e.epsilon, this.center = e.center == null ? true : e.center, this.scale = e.scale == null ? true : e.scale, this.betaInitializer = ht(e.betaInitializer || "zeros"), this.gammaInitializer = ht(e.gammaInitializer || "ones"), this.movingMeanInitializer = ht(e.movingMeanInitializer || "zeros"), this.movingVarianceInitializer = ht(e.movingVarianceInitializer || "ones"), this.betaConstraint = Pt(e.betaConstraint), this.gammaConstraint = Pt(e.gammaConstraint), this.betaRegularizer = ft(e.betaRegularizer), this.gammaRegularizer = ft(e.gammaRegularizer);
   }
   build(e) {
     e = nt(e);
@@ -15972,9 +15897,9 @@ var Ay = class extends Ge {
       let c = !w.arraysEqual(l, ys(0, a).slice(0, a - 1)), p = () => {
         if (c) {
           let b = G(this.movingMean.read(), u), y = G(this.movingVariance.read(), u), v = this.center ? G(this.beta.read(), u) : null, x = this.scale ? G(this.gamma.read(), u) : null;
-          return Zu(s, b, y, v, x, this.epsilon);
+          return Ju(s, b, y, v, x, this.epsilon);
         } else
-          return Zu(s, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
+          return Ju(s, 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 p();
@@ -15998,9 +15923,7 @@ Ay.className = "BatchNormalization";
 ae.registerClass(Ay);
 var Ey = class extends Ge {
   constructor(e) {
-    e == null && (e = {});
-    super(e);
-    if (this.axis = e.axis == null ? -1 : e.axis, typeof this.axis == "number") {
+    if (e == null && (e = {}), super(e), this.axis = e.axis == null ? -1 : e.axis, typeof this.axis == "number") {
       if (!Number.isInteger(this.axis))
         throw new Error(`Expected axis to be an integer, but received ${this.axis}`);
     } else if (Array.isArray(this.axis)) {
@@ -16034,7 +15957,7 @@ var Ey = class extends Ge {
       let l = (f) => f != null && f.shape.length !== r ? G(f, u) : f, c = l(this.gamma.read()), p = l(this.beta.read()), d = [], h = [];
       for (let f = 0; f < r; ++f)
         this.axis.indexOf(f) !== -1 ? (d.push(s[f]), h.push(1)) : (d.push(1), h.push(s[f]));
-      return i = ps(i, d), o = ps(o, d), c = ps(c, h), p = ps(p, h), Zu(n, i, o, p, c, this.epsilon);
+      return i = ps(i, d), o = ps(o, d), c = ps(c, h), p = ps(p, h), Ju(n, i, o, p, c, this.epsilon);
     });
   }
   getConfig() {
@@ -16058,9 +15981,7 @@ function HB(e, t, n) {
 }
 var Ry = class extends Ge {
   constructor(e) {
-    e == null && (e = {});
-    super(e);
-    if (this.dataFormat = e.dataFormat == null ? bs() : e.dataFormat, e.padding == null)
+    if (e == null && (e = {}), super(e), this.dataFormat = e.dataFormat == null ? bs() : e.dataFormat, e.padding == null)
       this.padding = [[1, 1], [1, 1]];
     else if (typeof e.padding == "number")
       this.padding = [[e.padding, e.padding], [e.padding, e.padding]];
@@ -16112,9 +16033,7 @@ function zS(e, t, n, s, r, a) {
 }
 var MS = class extends Ge {
   constructor(e) {
-    e.poolSize == null && (e.poolSize = 2);
-    super(e);
-    if (typeof e.poolSize == "number")
+    if (e.poolSize == null && (e.poolSize = 2), super(e), typeof e.poolSize == "number")
       this.poolSize = [e.poolSize];
     else if (Array.isArray(e.poolSize) && e.poolSize.length === 1 && typeof e.poolSize[0] == "number")
       this.poolSize = e.poolSize;
@@ -16137,7 +16056,7 @@ var MS = class extends Ge {
   }
   call(e, t) {
     return j(() => {
-      this.invokeCallHook(e, t), e = Bl(Oe(e), 2);
+      this.invokeCallHook(e, t), e = Vl(Oe(e), 2);
       let n = this.poolingFunction(Oe(e), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
       return mr(n, [2]);
     });
@@ -16169,9 +16088,7 @@ Fy.className = "AveragePooling1D";
 ae.registerClass(Fy);
 var LS = class extends Ge {
   constructor(e) {
-    e.poolSize == null && (e.poolSize = [2, 2]);
-    super(e);
-    if (this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize], e.strides == null)
+    if (e.poolSize == null && (e.poolSize = [2, 2]), super(e), this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize], e.strides == null)
       this.strides = this.poolSize;
     else if (Array.isArray(e.strides)) {
       if (e.strides.length !== 2)
@@ -16216,9 +16133,7 @@ Py.className = "AveragePooling2D";
 ae.registerClass(Py);
 var BS = class extends Ge {
   constructor(e) {
-    e.poolSize == null && (e.poolSize = [2, 2, 2]);
-    super(e);
-    if (this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize, e.poolSize], e.strides == null)
+    if (e.poolSize == null && (e.poolSize = [2, 2, 2]), super(e), this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize, e.poolSize], e.strides == null)
       this.strides = this.poolSize;
     else if (Array.isArray(e.strides)) {
       if (e.strides.length !== 3)
@@ -16263,8 +16178,7 @@ My.className = "AveragePooling3D";
 ae.registerClass(My);
 var VS = class extends Ge {
   constructor(e) {
-    super(e);
-    this.inputSpec = [new Dt({ ndim: 3 })];
+    super(e), this.inputSpec = [new Dt({ ndim: 3 })];
   }
   computeOutputShape(e) {
     return [e[0], e[2]];
@@ -16301,8 +16215,7 @@ By.className = "GlobalMaxPooling1D";
 ae.registerClass(By);
 var WS = class extends Ge {
   constructor(e) {
-    super(e);
-    this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.inputSpec = [new Dt({ ndim: 4 })];
+    super(e), this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.inputSpec = [new Dt({ ndim: 4 })];
   }
   computeOutputShape(e) {
     return e = e, this.dataFormat === "channelsLast" ? [e[0], e[3]] : [e[0], e[1]];
@@ -16337,8 +16250,7 @@ Wy.className = "GlobalMaxPooling2D";
 ae.registerClass(Wy);
 var US = class extends Ge {
   constructor(e) {
-    super(e);
-    this.layer = e.layer;
+    super(e), this.layer = e.layer;
   }
   build(e) {
     this.built = true;
@@ -16383,8 +16295,7 @@ var US = class extends Ge {
 };
 var Uy = class extends US {
   constructor(e) {
-    super(e);
-    this.supportsMasking = true;
+    super(e), this.supportsMasking = true;
   }
   build(e) {
     if (e = nt(e), e.length < 3)
@@ -16524,7 +16435,7 @@ var Gy = class extends US {
 Gy.className = "Bidirectional";
 ae.registerClass(Gy);
 function KB(e) {
-  return new Yo(e);
+  return new Qo(e);
 }
 function XB(e) {
   return new Xb(e);
@@ -16686,7 +16597,7 @@ function BV(e) {
   return new ly(e);
 }
 function VV(e) {
-  return new ql(e);
+  return new jl(e);
 }
 function WV(e) {
   return new oy(e);
@@ -16758,13 +16669,13 @@ function hW(e, t) {
   return Op(e, t);
 }
 function fW(e, t) {
-  return Qo(e, t);
+  return Zo(e, t);
 }
 function mW(e, t) {
-  return Qo(e, t);
+  return Zo(e, t);
 }
 function gW(e, t) {
-  return Qo(e, t);
+  return Zo(e, t);
 }
 function bW(e, t) {
   return gi(e, t);
@@ -16780,7 +16691,7 @@ Ae(xW, { modelFromJSON: () => EB });
 var wW = {};
 Ae(wW, { l1: () => IW, l1l2: () => kW, l2: () => SW });
 function kW(e) {
-  return new Ul(e);
+  return new Gl(e);
 }
 function IW(e) {
   return PB(e);
@@ -16790,8 +16701,7 @@ function SW(e) {
 }
 var CW = class extends Ji {
   constructor() {
-    super(...arguments);
-    this.model = null;
+    super(...arguments), this.model = null;
   }
   setModel(e) {
     if (!(e instanceof pr))
@@ -16807,8 +16717,7 @@ function Gx(e, t) {
 }
 var NW = class extends CW {
   constructor(e) {
-    super();
-    if (e == null && (e = {}), e.restoreBestWeights)
+    if (super(), e == null && (e = {}), e.restoreBestWeights)
       throw new Fe("restoreBestWeights = True is not implemented in EarlyStopping yet.");
     this.monitor = e.monitor || "val_loss", this.minDelta = Math.abs(e.minDelta || 0), this.patience = e.patience || 0, this.verbose = e.verbose || 0, this.mode = e.mode || "auto", this.baseline = e.baseline, ["auto", "min", "max"].indexOf(this.mode) === -1 && (console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`), this.mode = "auto"), this.mode === "min" ? this.monitorFunc = jc : this.mode === "max" ? this.monitorFunc = Gx : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = Gx : this.monitorFunc = jc, this.monitorFunc === jc && (this.minDelta *= -1);
   }
@@ -16832,7 +16741,7 @@ var NW = class extends CW {
 function TW(e) {
   return new NW(e);
 }
-var _pe = { earlyStopping: TW };
+var Ape = { earlyStopping: TW };
 var $W = X();
 $W.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (e) => {
   e && 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.");
@@ -16846,14 +16755,14 @@ var Hx;
   })(t = e.CheckpointFormatVersion || (e.CheckpointFormatVersion = {}));
 })(Hx || (Hx = {}));
 var Ky = {};
-function Ape(e, t) {
+function Epe(e, t) {
   let n = { tfOpName: e, category: "custom", inputs: [], attrs: [], customExecutor: t };
   Ky[e] = n;
 }
 function XS(e) {
   return Ky[e];
 }
-function Epe(e) {
+function Rpe(e) {
   delete Ky[e];
 }
 function I(e, t, n, s, r) {
@@ -16919,7 +16828,7 @@ Ae(QS, { json: () => EW });
 var EW = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { 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: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { 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: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Prod", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axes", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
 var ZS = {};
 Ae(ZS, { json: () => RW });
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}], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { 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: true }] }, { 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: 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{ tfOpName: "StatelessIf", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "If", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "StatelessWhile", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "While", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "TensorListScatter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListScatterV2", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }, { start: 3, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGather", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListSetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListReserve", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListFromTensor", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListStack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }, { tfName: "num_elements", name: "numElements", type: "dtype" }] }, { tfOpName: "TensorListSplit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }, { start: 2, name: "lengths", type: "number[]" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcat", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPopBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPushBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListLength", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }] }];
+var RW = [{ 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: true }, { 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: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { 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: true }] }, { 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: true }] }, { 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: 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"TensorListStack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }, { tfName: "num_elements", name: "numElements", type: "dtype" }] }, { tfOpName: "TensorListSplit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }, { start: 2, name: "lengths", type: "number[]" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcat", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPopBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPushBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { 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 JS = {};
 Ae(JS, { json: () => DW });
 var DW = [{ 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: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [], notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }] }, { 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: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { 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: true }, { 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: true }, { 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: true }, { 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: true }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number" }] }, { tfOpName: "Conv2DBackpropInput", category: "convolution", inputs: [{ start: 2, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 0, name: "outputShape", type: "number[]" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]", notSupported: true }] }, { 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: true }, { 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" }] }];
@@ -17311,13 +17220,13 @@ var ZW = (e, t, n) => {
     case "Sinh":
       return [yI(I("x", e, t, n))];
     case "Softplus":
-      return [Pl(I("x", e, t, n))];
+      return [zl(I("x", e, t, n))];
     case "Sqrt":
       return [dn(I("x", e, t, n))];
     case "Square":
       return [ct(I("x", e, t, n))];
     case "Tanh":
-      return [Hu(I("x", e, t, n))];
+      return [qu(I("x", e, t, n))];
     case "Tan":
       return [O3(I("x", e, t, n))];
     case "ClipByValue":
@@ -17350,7 +17259,7 @@ function Hn(e, t, n = "") {
 function Kx(e) {
   return !(typeof e == "number" || e.some((t) => t < 0));
 }
-function Cu(e, t, n) {
+function Nu(e, t, n) {
   let s = Pm(e, n), r = !Kx(s);
   if (r && t.length === 0)
     throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);
@@ -17488,7 +17397,7 @@ var JW = class {
     this.writeMany(i, a);
   }
 };
-var jl = class {
+var eo = class {
   constructor(e, t, n, s = -1) {
     this.tensors = e, this.elementShape = t, this.elementDtype = n, e != null && e.forEach((r) => {
       if (n !== r.dtype)
@@ -17500,7 +17409,7 @@ var jl = class {
     return this.idTensor.id;
   }
   copy() {
-    return new jl([...this.tensors], this.elementShape, this.elementDtype);
+    return new eo([...this.tensors], this.elementShape, this.elementDtype);
   }
   clearAndClose(e) {
     this.tensors.forEach((t) => {
@@ -17516,7 +17425,7 @@ var jl = class {
     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(e, this.elementShape, "TensorList shape mismatch: ");
-    let s = Cu(this.elementShape, this.tensors, e);
+    let s = Nu(this.elementShape, this.tensors, e);
     return j(() => {
       let r = this.tensors.map((a) => G(a, s));
       return Jn(r, 0);
@@ -17527,7 +17436,7 @@ var jl = class {
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
     if (this.size() === 0)
       throw new Error("Trying to pop from an empty list.");
-    let n = Cu(this.elementShape, this.tensors, e), s = this.tensors.pop();
+    let n = Nu(this.elementShape, this.tensors, e), s = this.tensors.pop();
     return Hn(s.shape, e, "TensorList shape mismatch: "), G(s, n);
   }
   pushBack(e) {
@@ -17542,7 +17451,11 @@ var jl = class {
       throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);
     if (this.maxNumElements !== -1 && e > this.maxNumElements)
       throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);
-    this.tensors.length = e;
+    let t = new eo([], this.elementShape, this.elementDtype, this.maxNumElements);
+    t.tensors.length = e;
+    for (let n = 0; n < Math.min(this.tensors.length, e); ++n)
+      t.tensors[n] = this.tensors[n];
+    return t;
   }
   getItem(e, t, n) {
     if (n !== this.elementDtype)
@@ -17552,7 +17465,7 @@ var jl = class {
     if (this.tensors[e] == null)
       throw new Error(`element at index ${e} is null.`);
     Hn(this.tensors[e].shape, t, "TensorList shape mismatch: ");
-    let s = Cu(this.elementShape, this.tensors, t);
+    let s = Nu(this.elementShape, this.tensors, t);
     return G(this.tensors[e], s);
   }
   setItem(e, t) {
@@ -17566,7 +17479,7 @@ var jl = class {
     if (t !== this.elementDtype)
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
     Hn(this.elementShape, n, "TensorList shape mismatch: "), e = e.slice(0, this.size());
-    let s = Cu(this.elementShape, this.tensors, n);
+    let s = Nu(this.elementShape, this.tensors, n);
     return e.length === 0 ? fs([], [0].concat(s)) : j(() => {
       let r = e.map((a) => G(this.tensors[a], s));
       return Jn(r, 0);
@@ -17576,7 +17489,7 @@ var jl = class {
     if (!!e && e !== this.elementDtype)
       throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
     Hn(this.elementShape, t, "TensorList shape mismatch: ");
-    let n = Cu(this.elementShape, this.tensors, t);
+    let n = Nu(this.elementShape, this.tensors, t);
     return this.size() === 0 ? fs([], [0].concat(n)) : j(() => {
       let s = this.tensors.map((r) => G(r, n));
       return Ft(s, 0);
@@ -17592,10 +17505,10 @@ function e4(e, t, n) {
   let r = e.shape.slice(1);
   Hn(r, t, "TensorList shape mismatch: ");
   let a = Fs(e);
-  return new jl(a, t, s);
+  return new eo(a, t, s);
 }
 function t4(e, t, n) {
-  return new jl([], e, t, n);
+  return new eo([], e, t, n);
 }
 function n4(e, t, n, s) {
   if (t.length !== e.shape[0])
@@ -17603,7 +17516,7 @@ function n4(e, t, n, s) {
   let r = Math.max(...t);
   if (s != null && s !== -1 && r >= s)
     throw new Error(`Max index must be < array size (${r}  vs. ${s})`);
-  let a = new jl([], n, e.dtype, s), i = Fs(e, 0);
+  let a = new eo([], n, e.dtype, s), i = Fs(e, 0);
   return t.forEach((o, u) => {
     a.setItem(o, i[u]);
   }), a;
@@ -17622,7 +17535,7 @@ function s4(e, t, n) {
       c[p] = G(He(e, h, f), i);
     }
     return e.dispose(), c;
-  }), l = new jl([], n, e.dtype, t.length);
+  }), l = new eo([], n, e.dtype, t.length);
   for (let c = 0; c < u.length; c++)
     l.setItem(c, u[c]);
   return l;
@@ -17771,6 +17684,10 @@ var r4 = async (e, t, n) => {
       let s = I("tensorListId", e, t, n), r = n.getTensorList(s.id);
       return [we(r.size(), "int32")];
     }
+    case "TensorListResize": {
+      let s = I("tensorListId", e, t, n), r = I("size", e, t, n), i = n.getTensorList(s.id).resize(r);
+      return n.addTensorList(i), [i.idTensor];
+    }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
@@ -17854,7 +17771,7 @@ var i4 = (e, t, n) => {
   switch (e.op) {
     case "Fill": {
       let s = I("shape", e, t, n), r = I("dtype", e, t, n), a = I("value", e, t, n);
-      return [Ol(s, a, r)];
+      return [Pl(s, a, r)];
     }
     case "LinSpace": {
       let s = I("start", e, t, n), r = I("stop", e, t, n), a = I("num", e, t, n);
@@ -17873,10 +17790,10 @@ var i4 = (e, t, n) => {
     case "OnesLike":
       return [Qn(I("x", e, t, n))];
     case "RandomUniform":
-      return [zl(I("shape", e, t, n), I("minval", e, t, n), I("maxval", e, t, n), I("dtype", e, t, n))];
+      return [Ml(I("shape", e, t, n), I("minval", e, t, n), I("maxval", e, t, n), I("dtype", e, t, n))];
     case "Range": {
       let s = I("start", e, t, n), r = I("stop", e, t, n), a = I("step", e, t, n);
-      return [Xu(s, r, a, I("dtype", e, t, n))];
+      return [Yu(s, r, a, I("dtype", e, t, n))];
     }
     case "TruncatedNormal": {
       let s = I("shape", e, t, n), r = I("mean", e, t, n), a = I("stdDev", e, t, n), i = I("seed", e, t, n);
@@ -18082,15 +17999,15 @@ var h4 = (e, t, n) => {
     case "Equal":
       return [Kn(I("a", e, t, n), I("b", e, t, n))];
     case "NotEqual":
-      return [Ku(I("a", e, t, n), I("b", e, t, n))];
+      return [Xu(I("a", e, t, n), I("b", e, t, n))];
     case "Greater":
       return [Wn(I("a", e, t, n), I("b", e, t, n))];
     case "GreaterEqual":
-      return [jo(I("a", e, t, n), I("b", e, t, n))];
+      return [Ko(I("a", e, t, n), I("b", e, t, n))];
     case "Less":
       return [aI(I("a", e, t, n), I("b", e, t, n))];
     case "LessEqual":
-      return [Ko(I("a", e, t, n), I("b", e, t, n))];
+      return [Xo(I("a", e, t, n), I("b", e, t, n))];
     case "LogicalAnd":
       return [Ds(I("a", e, t, n), I("b", e, t, n))];
     case "LogicalNot":
@@ -18132,9 +18049,9 @@ var m4 = (e, t, n) => {
   switch (e.op) {
     case "FusedBatchNorm":
     case "FusedBatchNormV2":
-      return [qu(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
+      return [ju(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
     case "FusedBatchNormV3":
-      return [qu(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
+      return [ju(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
     case "LRN":
       return [qR(I("x", e, t, n), I("radius", e, t, n), I("bias", e, t, n), I("alpha", e, t, n), I("beta", e, t, n))];
     case "Softmax":
@@ -18175,7 +18092,7 @@ var g4 = (e, t, n) => {
     }
     case "ArgMax": {
       let i = I("axis", e, t, n);
-      return [Gu(I("x", e, t, n), i)];
+      return [Hu(I("x", e, t, n), i)];
     }
     case "ArgMin": {
       let i = I("axis", e, t, n);
@@ -18213,11 +18130,11 @@ var b4 = (e, t, n) => {
     }
     case "Gather": {
       let s = I("x", e, t, n), r = I("indices", e, t, n);
-      return [ju(s, ce(r, "int32"), 0)];
+      return [Ku(s, ce(r, "int32"), 0)];
     }
     case "GatherV2": {
       let s = I("axis", e, t, n), r = I("batchDims", e, t, n), a = I("x", e, t, n), i = I("indices", e, t, n);
-      return [ju(a, ce(i, "int32"), s, r)];
+      return [Ku(a, ce(i, "int32"), s, r)];
     }
     case "Reverse": {
       let s = I("dims", e, t, n), r = [];
@@ -18896,16 +18813,16 @@ var E4 = class {
     this.executor.dispose(), this.initializer && this.initializer.dispose(), this.resourceManager.dispose();
   }
 };
-async function Rpe(e, t = {}) {
+async function Dpe(e, t = {}) {
   if (e == null)
     throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
   t == null && (t = {}), t.fromTFHub && e.load == null && (e.endsWith("/") || (e = e + "/"), e = `${e}${A4}${_4}`);
   let n = new E4(e, t);
   return await n.load(), n;
 }
-var Dpe = "0.0.0";
+var Fpe = "0.0.0";
 var R4 = {};
-Ae(R4, { CSVDataset: () => _0, Dataset: () => Zo, FileDataSource: () => P0, TextLineDataset: () => $0, URLDataSource: () => z0, array: () => tU, csv: () => pU, func: () => hU, generator: () => fU, microphone: () => gU, version_data: () => bU, webcam: () => mU, zip: () => nU });
+Ae(R4, { CSVDataset: () => _0, Dataset: () => Jo, FileDataSource: () => P0, TextLineDataset: () => $0, URLDataSource: () => z0, array: () => tU, csv: () => pU, func: () => hU, generator: () => fU, microphone: () => gU, version_data: () => bU, webcam: () => mU, zip: () => nU });
 var D4 = xa(Wd());
 var F4 = xa(Wd());
 function O4(e, t) {
@@ -18924,7 +18841,7 @@ function Rd(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set())
   if (r.recurse && r.value !== null)
     throw new Error("A deep map function may not return both a value and recurse=true.");
   if (r.recurse)
-    if (eo(e)) {
+    if (to(e)) {
       let a = Array.isArray(e) ? [] : {};
       s.add(e);
       for (let i in e) {
@@ -18948,7 +18865,7 @@ function y0(e, t, n = /* @__PURE__ */ new Set()) {
   if (r.recurse && r.value !== null)
     throw new Error("A deep zip function may not return both a value and recurse=true.");
   if (r.recurse)
-    if (eo(s)) {
+    if (to(s)) {
       let a = Array.isArray(s) ? [] : {};
       n.add(s);
       for (let i in s) {
@@ -18962,7 +18879,7 @@ function y0(e, t, n = /* @__PURE__ */ new Set()) {
     return r.value;
 }
 function v0(e) {
-  return e === null ? null : eo(e[0]) ? { value: null, recurse: true } : { value: e, recurse: false };
+  return e === null ? null : to(e[0]) ? { value: null, recurse: true } : { value: e, recurse: false };
 }
 async function x0(e, t) {
   let n = /* @__PURE__ */ new Map();
@@ -18976,7 +18893,7 @@ async function x0(e, t) {
   }
   return Rd(e, t, n);
 }
-function eo(e) {
+function to(e) {
   let t = false;
   if (X().get("IS_BROWSER"))
     t = e instanceof TextDecoder;
@@ -18996,7 +18913,7 @@ function L4(e) {
   return O4(e, B4);
 }
 function B4(e) {
-  return e instanceof et ? { value: e.clone(), recurse: false } : eo(e) ? { value: null, recurse: true } : { value: e, recurse: false };
+  return e instanceof et ? { value: e.clone(), recurse: false } : to(e) ? { value: null, recurse: true } : { value: e, recurse: false };
 }
 var w0 = class {
   constructor(e) {
@@ -19173,8 +19090,7 @@ var Ut = class {
 };
 var U4 = class extends Ut {
   constructor(e) {
-    super();
-    this.items = e, this.trav = 0;
+    super(), this.items = e, this.trav = 0;
   }
   summary() {
     return `Array of ${this.items.length} items`;
@@ -19188,8 +19104,7 @@ var U4 = class extends Ut {
 };
 var G4 = class extends Ut {
   constructor(e) {
-    super();
-    this.nextFn = e;
+    super(), this.nextFn = e;
   }
   summary() {
     return "Function call";
@@ -19204,8 +19119,7 @@ var G4 = class extends Ut {
 };
 var H4 = class extends Ut {
   constructor(e) {
-    super();
-    this.upstream = e, this.lastRead = Promise.resolve({ value: null, done: false });
+    super(), this.upstream = e, this.lastRead = Promise.resolve({ value: null, done: false });
   }
   summary() {
     return `${this.upstream.summary()} -> Serial`;
@@ -19219,8 +19133,7 @@ var H4 = class extends Ut {
 };
 var q4 = class extends Ut {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.maxCount = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
+    super(), this.upstream = e, this.maxCount = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
   }
   summary() {
     return `${this.upstream.summary()} -> Skip`;
@@ -19240,8 +19153,7 @@ var q4 = class extends Ut {
 };
 var j4 = class extends Ut {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.maxCount = t, this.count = 0;
+    super(), this.upstream = e, this.maxCount = t, this.count = 0;
   }
   summary() {
     return `${this.upstream.summary()} -> Take`;
@@ -19252,8 +19164,7 @@ var j4 = class extends Ut {
 };
 var K4 = class extends Ut {
   constructor(e, t, n = true) {
-    super();
-    this.upstream = e, this.batchSize = t, this.enableSmallLastBatch = n, this.lastRead = Promise.resolve({ value: null, done: false });
+    super(), this.upstream = e, this.batchSize = t, this.enableSmallLastBatch = n, this.lastRead = Promise.resolve({ value: null, done: false });
   }
   summary() {
     return `${this.upstream.summary()} -> RowMajorBatch`;
@@ -19274,8 +19185,7 @@ var K4 = class extends Ut {
 };
 var X4 = class extends Ut {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.predicate = t, this.lastRead = Promise.resolve({ value: null, done: false });
+    super(), this.upstream = e, this.predicate = t, this.lastRead = Promise.resolve({ value: null, done: false });
   }
   summary() {
     return `${this.upstream.summary()} -> Filter`;
@@ -19294,8 +19204,7 @@ var X4 = class extends Ut {
 };
 var Y4 = class extends Ut {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.transform = t;
+    super(), this.upstream = e, this.transform = t;
   }
   summary() {
     return `${this.upstream.summary()} -> Map`;
@@ -19312,8 +19221,7 @@ var Y4 = class extends Ut {
 };
 var Q4 = class extends Ut {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.handler = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
+    super(), this.upstream = e, this.handler = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
   }
   summary() {
     return `${this.upstream.summary()} -> handleErrors`;
@@ -19333,8 +19241,7 @@ var Q4 = class extends Ut {
 };
 var Jx = class extends Ut {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.transform = t;
+    super(), this.upstream = e, this.transform = t;
   }
   summary() {
     return `${this.upstream.summary()} -> AsyncMap`;
@@ -19351,8 +19258,7 @@ var Jx = class extends Ut {
 };
 var Qy = class extends Ut {
   constructor() {
-    super();
-    this.outputQueue = new I0(), this.lastRead = Promise.resolve({ value: null, done: false });
+    super(), this.outputQueue = new I0(), this.lastRead = Promise.resolve({ value: null, done: false });
   }
   async next() {
     return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
@@ -19366,8 +19272,7 @@ var Qy = class extends Ut {
 };
 var Z4 = class extends Qy {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.transform = t;
+    super(), this.upstream = e, this.transform = t;
   }
   summary() {
     return `${this.upstream.summary()} -> Flatmap`;
@@ -19385,8 +19290,7 @@ var Z4 = class extends Qy {
 };
 var C0 = class extends Ut {
   constructor(e, t) {
-    super();
-    this.baseErrorHandler = t, this.lastRead = null, this.iterator = null, this.moreIterators = e;
+    super(), this.baseErrorHandler = t, this.lastRead = null, this.iterator = null, this.moreIterators = e;
   }
   summary() {
     return "TODO: fill in upstream of chained summaries -> Chained";
@@ -19408,8 +19312,7 @@ var C0 = class extends Ut {
 var N0 = ((e) => (e[e.FAIL = 0] = "FAIL", e[e.SHORTEST = 1] = "SHORTEST", e[e.LONGEST = 2] = "LONGEST", e))(N0 || {});
 var J4 = class extends Ut {
   constructor(e, t = 0) {
-    super();
-    this.iterators = e, this.mismatchMode = t, this.count = 0, this.currentPromise = null;
+    super(), this.iterators = e, this.mismatchMode = t, this.count = 0, this.currentPromise = null;
   }
   summary() {
     return "{TODO: fill in upstream of zip summaries} -> Zip";
@@ -19440,8 +19343,7 @@ var J4 = class extends Ut {
 };
 var T0 = class extends Ut {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.bufferSize = t, this.buffer = new w0(t);
+    super(), this.upstream = e, this.bufferSize = t, this.buffer = new w0(t);
   }
   summary() {
     return `${this.upstream.summary()} -> Prefetch`;
@@ -19458,8 +19360,7 @@ var T0 = class extends Ut {
 };
 var eU = class extends T0 {
   constructor(e, t, n) {
-    super(e, t);
-    this.upstream = e, this.windowSize = t, this.upstreamExhausted = false, this.random = F4.alea(n || w.now().toString()), this.lastRead = Promise.resolve({ value: null, done: false });
+    super(e, t), this.upstream = e, this.windowSize = t, this.upstreamExhausted = false, this.random = F4.alea(n || w.now().toString()), this.lastRead = Promise.resolve({ value: null, done: false });
   }
   async next() {
     return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
@@ -19481,7 +19382,7 @@ var eU = class extends T0 {
     return { value: null, done: true };
   }
 };
-var Zo = class {
+var Jo = class {
   constructor() {
     this.size = null;
   }
@@ -19552,12 +19453,11 @@ var Zo = class {
     return (await this.iterator()).toArrayForTest();
   }
 };
-Zo.MAX_BUFFER_SIZE = 1e4;
+Jo.MAX_BUFFER_SIZE = 1e4;
 function _n(e, t = null) {
-  return new class extends Zo {
+  return new class extends Jo {
     constructor() {
-      super(...arguments);
-      this.size = t;
+      super(...arguments), this.size = t;
     }
     async iterator() {
       return e();
@@ -19568,7 +19468,7 @@ function tU(e) {
   return _n(async () => S0(e), e.length);
 }
 function nU(e) {
-  if (!eo(e))
+  if (!to(e))
     throw new Error("The argument to zip() must be an object or array.");
   let t;
   if (Array.isArray(e))
@@ -19579,9 +19479,9 @@ function nU(e) {
       t = t == null ? e[n].size : Math.min(t, e[n].size);
   return _n(async () => {
     let n = await x0(e, (s) => {
-      if (s instanceof Zo)
+      if (s instanceof Jo)
         return { value: s.iterator(), recurse: false };
-      if (eo(s))
+      if (to(s))
         return { value: null, recurse: true };
       throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
     });
@@ -19599,10 +19499,9 @@ function rU(e) {
     throw new Error("Can't make a batch of zero elements.");
   return e[0] instanceof et ? Jn(e) : fs(e);
 }
-var $0 = class extends Zo {
+var $0 = class extends Jo {
   constructor(e) {
-    super();
-    this.input = e;
+    super(), this.input = e;
   }
   async iterator() {
     return (await this.input.iterator()).decodeUTF8().split(`
@@ -19610,15 +19509,14 @@ var $0 = class extends Zo {
   }
 };
 var Kc = '"';
-var Nu = Symbol("out");
+var Tu = Symbol("out");
 var ew = Symbol("field");
 var Xc = Symbol("quote");
 var qf = Symbol("quoteafterquote");
 var tw = Symbol("quoteinquote");
-var _0 = class extends Zo {
+var _0 = class extends Jo {
   constructor(e, t) {
-    super();
-    this.input = e, this.hasHeader = true, this.fullColumnNames = null, this.columnNamesValidated = false, this.columnConfigs = null, this.configuredColumnsOnly = false, this.delimiter = ",", this.delimWhitespace = false, this.base = new $0(e), t || (t = {}), this.hasHeader = t.hasHeader !== false, this.fullColumnNames = t.columnNames, this.columnConfigs = t.columnConfigs, this.configuredColumnsOnly = t.configuredColumnsOnly, t.delimWhitespace ? (w.assert(t.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."), this.delimWhitespace = true, this.delimiter = " ") : this.delimiter = t.delimiter ? t.delimiter : ",";
+    super(), this.input = e, this.hasHeader = true, this.fullColumnNames = null, this.columnNamesValidated = false, this.columnConfigs = null, this.configuredColumnsOnly = false, this.delimiter = ",", this.delimWhitespace = false, this.base = new $0(e), t || (t = {}), this.hasHeader = t.hasHeader !== false, this.fullColumnNames = t.columnNames, this.columnConfigs = t.columnConfigs, this.configuredColumnsOnly = t.configuredColumnsOnly, t.delimWhitespace ? (w.assert(t.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."), this.delimWhitespace = true, this.delimiter = " ") : this.delimiter = t.delimiter ? t.delimiter : ",";
   }
   async columnNames() {
     return this.columnNamesValidated || await this.setColumnNames(), this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;
@@ -19695,10 +19593,10 @@ var _0 = class extends Zo {
     return e === "1" || e.toLowerCase() === "true" ? 1 : 0;
   }
   parseRow(e, t = true) {
-    let n = [], s = 0, r = e.length, a = Nu;
+    let n = [], s = 0, r = e.length, a = Tu;
     for (let i = 0; i < r; i++)
       switch (a) {
-        case Nu:
+        case Tu:
           switch (e.charAt(i)) {
             case Kc:
               s = i + 1, a = Xc;
@@ -19706,7 +19604,7 @@ var _0 = class extends Zo {
             case this.delimiter:
               if (s = i + 1, this.delimiter === " " && this.delimWhitespace)
                 break;
-              n.push(""), a = Nu;
+              n.push(""), a = Tu;
               break;
             default:
               a = ew, s = i;
@@ -19716,7 +19614,7 @@ var _0 = class extends Zo {
         case ew:
           switch (e.charAt(i)) {
             case this.delimiter:
-              n.push(e.substring(s, i)), a = Nu, s = i + 1;
+              n.push(e.substring(s, i)), a = Tu, s = i + 1;
               break;
             default:
           }
@@ -19732,7 +19630,7 @@ var _0 = class extends Zo {
         case qf:
           switch (e.charAt(i)) {
             case this.delimiter:
-              n.push(e.substring(s, i - 1)), a = Nu, s = i + 1;
+              n.push(e.substring(s, i - 1)), a = Tu, s = i + 1;
               break;
             case Kc:
               a = Xc;
@@ -19759,8 +19657,7 @@ var _0 = class extends Zo {
 };
 var A0 = class extends Ut {
   constructor(e) {
-    super();
-    this.microphoneConfig = e, this.isClosed = false, this.fftSize = e.fftSize || 1024;
+    super(), this.microphoneConfig = e, this.isClosed = false, this.fftSize = e.fftSize || 1024;
     let t = Math.log2(this.fftSize);
     if (this.fftSize < 0 || t < 4 || t > 14 || !Number.isInteger(t))
       throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);
@@ -19837,8 +19734,7 @@ var A0 = class extends Ut {
 };
 var E0 = class extends Ut {
   constructor(e, t) {
-    super();
-    if (this.webcamVideoElement = e, this.webcamConfig = t, this.isClosed = true, this.resize = false, this.needToResize())
+    if (super(), this.webcamVideoElement = e, this.webcamConfig = t, this.isClosed = true, this.resize = false, this.needToResize())
       if (this.resize = true, this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth], this.cropBoxInd = Zt([0], "int32"), this.webcamConfig.centerCrop) {
         let n = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width, s = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height, r = (1 - n) / 2, a = (1 - s) / 2, i = r + n, o = s + a;
         this.cropBox = Ki([a, r, o, i], [1, 4]);
@@ -19935,8 +19831,7 @@ var D0 = class extends Ut {
 };
 var aU = class extends D0 {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.impl = new iU(e, t);
+    super(), this.upstream = e, this.impl = new iU(e, t);
   }
   summary() {
     return this.impl.summary();
@@ -19947,8 +19842,7 @@ var aU = class extends D0 {
 };
 var iU = class extends Qy {
   constructor(e, t) {
-    super();
-    this.upstream = e, this.separator = t, this.carryover = "";
+    super(), this.upstream = e, this.separator = t, this.carryover = "";
   }
   summary() {
     return `${this.upstream.summary()} -> Split('${this.separator}')`;
@@ -19971,8 +19865,7 @@ var oU = class extends Ut {
 };
 var uU = class extends D0 {
   constructor(e) {
-    super();
-    this.upstream = e, this.impl = new lU(e);
+    super(), this.upstream = e, this.impl = new lU(e);
   }
   summary() {
     return this.impl.summary();
@@ -19983,8 +19876,7 @@ var uU = class extends D0 {
 };
 var lU = class extends Qy {
   constructor(e) {
-    super();
-    if (this.upstream = e, X().get("IS_BROWSER"))
+    if (super(), this.upstream = e, X().get("IS_BROWSER"))
       this.decoder = new TextDecoder("utf-8");
     else {
       let { StringDecoder: t } = Ww();
@@ -20005,8 +19897,7 @@ var lU = class extends Qy {
 };
 var F0 = class extends oU {
   constructor(e, t = {}) {
-    super();
-    this.file = e, this.options = t, w.assert(e instanceof Uint8Array || (X().get("IS_BROWSER") ? e instanceof File || e instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now."), this.offset = t.offset || 0, this.chunkSize = t.chunkSize || 1024 * 1024;
+    super(), this.file = e, this.options = t, w.assert(e instanceof Uint8Array || (X().get("IS_BROWSER") ? e instanceof File || e instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now."), this.offset = t.offset || 0, this.chunkSize = t.chunkSize || 1024 * 1024;
   }
   summary() {
     return `FileChunks ${this.file}`;
@@ -20047,8 +19938,7 @@ function O0(e) {
 }
 var P0 = class extends R0 {
   constructor(e, t = {}) {
-    super();
-    this.input = e, this.options = t;
+    super(), this.input = e, this.options = t;
   }
   async iterator() {
     if (O0(this.input) && X().get("IS_NODE")) {
@@ -20060,8 +19950,7 @@ var P0 = class extends R0 {
 };
 var z0 = class extends R0 {
   constructor(e, t = {}) {
-    super();
-    this.url = e, this.fileOptions = t;
+    super(), this.url = e, this.fileOptions = t;
   }
   async iterator() {
     return O0(this.url) ? new P0(this.url, this.fileOptions).iterator() : cU(this.url, this.fileOptions);
@@ -20093,10 +19982,9 @@ function be(e, t) {
   });
 }
 var yU = xs.whereImpl;
-var M0 = class extends tl {
+var M0 = class extends nl {
   constructor() {
-    super();
-    this.blockSize = 48, this.firstUse = true, this.data = new Ud(this, Ss());
+    super(), this.blockSize = 48, this.firstUse = true, this.data = new Ud(this, Ss());
   }
   nextDataId() {
     return M0.nextDataId++;
@@ -20211,7 +20099,7 @@ var vU = (e) => {
   let s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId).values;
   return s = B0(r), n.makeOutput(s, t.shape, t.dtype);
 };
-var xU = { kernelName: ao, backendName: "cpu", kernelFunc: vU };
+var xU = { kernelName: io, backendName: "cpu", kernelFunc: vU };
 function At(e) {
   return (t, n, s, r, a) => {
     let i = S.assertAndGetBroadcastShape(t, n), o = i.length, u = w.computeStrides(i), l = w.sizeFromShape(i), c = w.getTypedArrayFromDType(a, l), p = t.length, d = n.length, h = w.computeStrides(t), f = w.computeStrides(n), m = S.getBroadcastDims(t, i), g = S.getBroadcastDims(n, i);
@@ -20361,7 +20249,7 @@ function st(e, t, n) {
     return o.makeTensorInfo(i.shape, c, p);
   };
 }
-function Jo(e, t, n) {
+function eu(e, t, n) {
   return ({ inputs: s, attrs: r, backend: a }) => {
     let { x: i } = s;
     if (be(i, e), i.dtype === "string" || n === "string")
@@ -20371,7 +20259,7 @@ function Jo(e, t, n) {
   };
 }
 var U0 = Er((e) => Math.ceil(e));
-var TU = Jo(Ta, U0);
+var TU = eu(Ta, U0);
 var $U = { kernelName: Ta, backendName: "cpu", kernelFunc: TU };
 function tv(e, t, n, s) {
   let r = w.getArrayFromDType(n, w.sizeFromShape(t));
@@ -20396,16 +20284,16 @@ function tv(e, t, n, s) {
   return r;
 }
 var G0 = At((e, t) => e === t ? 1 : 0);
-var H0 = Gt(po, G0, null, "bool");
-var _U = { kernelName: po, backendName: "cpu", kernelFunc: H0 };
+var H0 = Gt(ho, G0, null, "bool");
+var _U = { kernelName: ho, backendName: "cpu", kernelFunc: H0 };
 var q0 = Er((e) => Math.exp(e));
-var j0 = Jo(Oa, q0, "float32");
+var j0 = eu(Oa, q0, "float32");
 var AU = { kernelName: Oa, backendName: "cpu", kernelFunc: j0 };
 var K0 = Er((e) => Math.expm1(e));
-var EU = Jo(fo, K0);
-var RU = { kernelName: fo, backendName: "cpu", kernelFunc: EU };
+var EU = eu(mo, K0);
+var RU = { kernelName: mo, backendName: "cpu", kernelFunc: EU };
 var X0 = Er((e) => Math.floor(e));
-var DU = Jo(Pa, X0);
+var DU = eu(Pa, X0);
 var FU = { kernelName: Pa, backendName: "cpu", kernelFunc: DU };
 function Y0(e, t, n, s, r, a, i, o, u) {
   let l = De([s, a], n);
@@ -20433,17 +20321,17 @@ function Q0(e, t, n) {
   return s;
 }
 var Z0 = At((e, t) => e > t ? 1 : 0);
-var OU = Gt(yo, Z0, null, "bool");
-var PU = { kernelName: yo, backendName: "cpu", kernelFunc: OU };
+var OU = Gt(vo, Z0, null, "bool");
+var PU = { kernelName: vo, backendName: "cpu", kernelFunc: OU };
 var J0 = At((e, t) => e >= t ? 1 : 0);
 var zU = Gt(La, J0, null, "bool");
 var MU = { kernelName: La, backendName: "cpu", kernelFunc: zU };
 var eC = At((e, t) => e < t ? 1 : 0);
-var LU = Gt(vo, eC, null, "bool");
-var BU = { kernelName: vo, backendName: "cpu", kernelFunc: LU };
+var LU = Gt(xo, eC, null, "bool");
+var BU = { kernelName: xo, backendName: "cpu", kernelFunc: LU };
 var tC = At((e, t) => e <= t ? 1 : 0);
-var VU = Gt(xo, tC, null, "bool");
-var WU = { kernelName: xo, backendName: "cpu", kernelFunc: VU };
+var VU = Gt(wo, tC, null, "bool");
+var WU = { kernelName: wo, backendName: "cpu", kernelFunc: VU };
 function nC(e, t, n) {
   let s = (t - e) / (n - 1), r = w.makeZerosTypedArray(n, "float32");
   r[0] = e;
@@ -20452,7 +20340,7 @@ function nC(e, t, n) {
   return r;
 }
 var sC = Er((e) => Math.log(e));
-var UU = Jo(Wa, sC);
+var UU = eu(Wa, sC);
 var GU = { kernelName: Wa, backendName: "cpu", kernelFunc: UU };
 function rC(e, t, n, s) {
   let r = w.getTypedArrayFromDType(s, w.sizeFromShape(n));
@@ -20486,10 +20374,10 @@ function QU(e) {
   let r = n.data.get(s.dataId).values, [a, i] = oC(r, s.shape, s.dtype);
   return n.makeTensorInfo(i, s.dtype, a);
 }
-var ZU = { kernelName: ko, backendName: "cpu", kernelFunc: QU };
+var ZU = { kernelName: Io, backendName: "cpu", kernelFunc: QU };
 var uC = At((e, t) => e !== t ? 1 : 0);
-var JU = Gt(Io, uC, null, "bool");
-var eG = { kernelName: Io, backendName: "cpu", kernelFunc: JU };
+var JU = Gt(So, uC, null, "bool");
+var eG = { kernelName: So, backendName: "cpu", kernelFunc: JU };
 function sv(e, t, n, s, r) {
   let a = t.length, i = w.sizeFromShape(t), o = w.computeStrides(t), u = w.computeStrides(r), l = w.getTypedArrayFromDType(n, w.sizeFromShape(r));
   for (let c = 0; c < i; ++c) {
@@ -20529,7 +20417,7 @@ function nG(e) {
   let h = n.data.get(p.dataId).values, { outVals: f, outShape: m, outDtype: g } = lC(p.shape, p.dtype, h, c), b = m;
   return i && (b = S.expandShapeToKeepDim(m, u)), d.forEach((y) => n.disposeIntermediateTensorInfo(y)), n.makeTensorInfo(b, g, f);
 }
-var sG = { kernelName: _o, backendName: "cpu", kernelFunc: nG };
+var sG = { kernelName: Ao, backendName: "cpu", kernelFunc: nG };
 function rv(e, t, n, s) {
   let r = e === t, a = e < t && n < 0, i = t < e && n > 1;
   if (r || a || i)
@@ -20541,7 +20429,7 @@ function rv(e, t, n, s) {
   return u;
 }
 var cC = Er((e) => 1 / Math.sqrt(e));
-var rG = Jo(si, cC);
+var rG = eu(si, cC);
 var aG = { kernelName: si, backendName: "cpu", kernelFunc: rG };
 var iG = Er((e) => 1 / (1 + Math.exp(-e)));
 var dC = st(ai, (e) => 1 / (1 + Math.exp(-e)));
@@ -20567,7 +20455,7 @@ function ga(e) {
   let l = n.data.get(r.dataId).values, c = Fd(l, o, u, r.shape, r.dtype);
   return n.makeTensorInfo(u, r.dtype, c);
 }
-var uG = { kernelName: Oo, backendName: "cpu", kernelFunc: ga };
+var uG = { kernelName: Po, backendName: "cpu", kernelFunc: ga };
 function pC(e, t, n, s, r, a, i) {
   let o = t[0], u = a[0], l = new Array(u), c = new Array(o), p = t[1];
   if (u === 0) {
@@ -20865,7 +20753,7 @@ function xC(e, t) {
   }
   return s;
 }
-var Eu = (e, t) => {
+var Ru = (e, t) => {
   let n = t.value - e.value;
   return n === 0 ? e.index - t.index : n;
 };
@@ -20876,20 +20764,20 @@ function wC(e, t, n = 0, s = e.length - 1) {
       wC(e, t, d, h);
     }
     let r = e[t], a = n, i = s;
-    for (w.swap(e, n, t), Eu(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
-      for (w.swap(e, a, i), a++, i--; Eu(e[a], r) < 0; )
+    for (w.swap(e, n, t), Ru(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
+      for (w.swap(e, a, i), a++, i--; Ru(e[a], r) < 0; )
         a = a + 1;
-      for (; Eu(e[i], r) > 0; )
+      for (; Ru(e[i], r) > 0; )
         i = i - 1;
     }
-    Eu(e[n], r) === 0 ? w.swap(e, n, i) : (i = i + 1, w.swap(e, i, s)), i <= t && (n = i + 1), t <= i && (s = i - 1);
+    Ru(e[n], r) === 0 ? w.swap(e, n, i) : (i = i + 1, w.swap(e, i, s)), i <= t && (n = i + 1), t <= i && (s = i - 1);
   }
 }
 function kC(e, t, n, s, r) {
   let a = t[t.length - 1], [i, o] = [e.length / a, a], u = w.getTypedArrayFromDType(n, i * s), l = w.getTypedArrayFromDType("int32", i * s);
   for (let p = 0; p < i; p++) {
     let d = p * o, h = e.subarray(d, d + o), f = new Array(h.length);
-    h.forEach((y, v) => f[v] = { value: y, index: v }), s < f.length && (wC(f, s), f = f.slice(0, s)), r && f.sort(Eu);
+    h.forEach((y, v) => f[v] = { value: y, index: v }), s < f.length && (wC(f, s), f = f.slice(0, s)), r && f.sort(Ru);
     let m = p * s, g = u.subarray(m, m + s), b = l.subarray(m, m + s);
     for (let y = 0; y < s; y++)
       g[y] = f[y].value, b[y] = f[y].index;
@@ -20934,7 +20822,7 @@ function IC(e, t, n, s) {
   let h = n.slice();
   return h[r] = p[1], { outputValues: d.values, outputShape: h, indices: o };
 }
-var Fpe = "0.0.0";
+var Ope = "0.0.0";
 pp("cpu", () => new L0(), 1);
 var SC = st(Fa, (e) => e >= 0 ? e : Math.exp(e) - 1);
 var yG = { kernelName: Fa, backendName: "cpu", kernelFunc: SC };
@@ -20986,11 +20874,11 @@ function mt(e) {
   }
   return { dataId: r.dataId, shape: o, dtype: r.dtype };
 }
-var SG = { kernelName: Ao, backendName: "cpu", kernelFunc: mt };
+var SG = { kernelName: Eo, backendName: "cpu", kernelFunc: mt };
 function _C(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
   be([r, a], "matMul");
-  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
+  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = jo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
   w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
   let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], T = mt({ inputs: { x: r }, backend: n, attrs: { shape: x } }), N = mt({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = i ? T.shape[1] : T.shape[2], A = i ? T.shape[2] : T.shape[1], P = o ? N.shape[1] : N.shape[2], R = Math.max(g, b), F = n.data.get(T.dataId).values, $ = n.data.get(N.dataId).values, z = w.computeStrides(T.shape), W = w.computeStrides(N.shape), [q, K, Y] = i ? [z[0], 1, z[1]] : [z[0], z[1], 1], [Z, te, ee] = o ? [1, W[1], W[0]] : [W[1], 1, W[0]], se = A * P, ne = De([R, A, P], T.dtype), oe = ne.values, re = n.blockSize;
   for (let le = 0; le < R; le++)
@@ -21019,10 +20907,10 @@ function NG(e) {
   return d;
 }
 var TG = { kernelName: ra, backendName: "cpu", kernelFunc: NG };
-var $G = st(nl, (e) => Math.acos(e));
-var _G = { kernelName: nl, backendName: "cpu", kernelFunc: $G };
-var AG = st(sl, (e) => Math.acosh(e));
-var EG = { kernelName: sl, backendName: "cpu", kernelFunc: AG };
+var $G = st(sl, (e) => Math.acos(e));
+var _G = { kernelName: sl, backendName: "cpu", kernelFunc: $G };
+var AG = st(rl, (e) => Math.acosh(e));
+var EG = { kernelName: rl, backendName: "cpu", kernelFunc: AG };
 function RG(e) {
   let { inputs: t, backend: n } = e, s = t;
   be(t, "addN");
@@ -21057,7 +20945,7 @@ function FG(e) {
   }
   return g;
 }
-var OG = { kernelName: rl, backendName: "cpu", kernelFunc: FG };
+var OG = { kernelName: al, backendName: "cpu", kernelFunc: FG };
 function PG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   be(r, "any");
@@ -21080,7 +20968,7 @@ function PG(e) {
   }
   return g;
 }
-var zG = { kernelName: al, backendName: "cpu", kernelFunc: PG };
+var zG = { kernelName: il, backendName: "cpu", kernelFunc: PG };
 function MG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
   be(r, "argMax");
@@ -21114,18 +21002,18 @@ function BG(e) {
   }
   return l.forEach((g) => n.disposeIntermediateTensorInfo(g)), n.makeTensorInfo(c, "int32", h);
 }
-var VG = { kernelName: il, backendName: "cpu", kernelFunc: BG };
-var WG = st(ol, (e) => Math.asin(e));
-var UG = { kernelName: ol, backendName: "cpu", kernelFunc: WG };
-var GG = st(ul, (e) => Math.asinh(e));
-var HG = { kernelName: ul, backendName: "cpu", kernelFunc: GG };
-var qG = st(ll, (e) => Math.atan(e));
-var jG = { kernelName: ll, backendName: "cpu", kernelFunc: qG };
+var VG = { kernelName: ol, backendName: "cpu", kernelFunc: BG };
+var WG = st(ul, (e) => Math.asin(e));
+var UG = { kernelName: ul, backendName: "cpu", kernelFunc: WG };
+var GG = st(ll, (e) => Math.asinh(e));
+var HG = { kernelName: ll, backendName: "cpu", kernelFunc: GG };
+var qG = st(cl, (e) => Math.atan(e));
+var jG = { kernelName: cl, backendName: "cpu", kernelFunc: qG };
 var KG = At((e, t) => Math.atan2(e, t));
-var XG = Gt(dl, KG);
-var YG = { kernelName: dl, backendName: "cpu", kernelFunc: XG };
-var QG = st(cl, (e) => Math.atanh(e));
-var ZG = { kernelName: cl, backendName: "cpu", kernelFunc: QG };
+var XG = Gt(pl, KG);
+var YG = { kernelName: pl, backendName: "cpu", kernelFunc: XG };
+var QG = st(dl, (e) => Math.atanh(e));
+var ZG = { kernelName: dl, backendName: "cpu", kernelFunc: QG };
 function uv(e, t, n, s, r, a) {
   let i = r.strideHeight, o = r.strideWidth, u = r.dilationHeight, l = r.dilationWidth, c = r.effectiveFilterHeight, p = r.effectiveFilterWidth, d = r.padInfo.top, h = r.padInfo.left, f = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, m = De(r.outShape, n), g = m.values, b = r.outShape[1] * r.outShape[2] * r.outShape[3], y = r.outShape[2] * r.outShape[3], v = r.outShape[3];
   for (let x = 0; x < r.batchSize; ++x) {
@@ -21347,7 +21235,7 @@ function cH(e) {
   let o = a.reduce((b, y) => b * y), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = mt({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wn({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ga({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
 }
-var dH = { kernelName: io, backendName: "cpu", kernelFunc: cH };
+var dH = { kernelName: oo, backendName: "cpu", kernelFunc: cH };
 function pH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = ev(o, u, a.dtype, a.shape, i);
   return n.makeTensorInfo([i], a.dtype, l);
@@ -21372,12 +21260,12 @@ var yH = (e) => {
   return n.makeOutput(s, t.shape, "float32");
 };
 var vH = { kernelName: Kd, backendName: "cpu", kernelFunc: yH };
-function to(e) {
+function no(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.data.get(s.dataId).complexTensorInfos.imag, a = n.data.get(r.dataId).values;
   return n.makeTensorInfo(r.shape, r.dtype, a);
 }
-var xH = { kernelName: Zd, backendName: "cpu", kernelFunc: to };
-function no(e) {
+var xH = { kernelName: Zd, backendName: "cpu", kernelFunc: no };
+function so(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = S.computeOutShape(t.map((m) => m.shape), a);
   if (w.sizeFromShape(i) === 0)
     return n.makeTensorInfo(i, t[0].dtype, []);
@@ -21386,7 +21274,7 @@ function no(e) {
     return Os({ inputs: { x: o[0] }, backend: n });
   let u = o.map((m) => m.shape);
   if (S.assertParamsConsistent(u, a), o[0].dtype === "complex64") {
-    let m = o.map((x) => ma({ inputs: { input: x }, backend: n })), g = o.map((x) => to({ inputs: { input: x }, backend: n })), b = no({ inputs: m, backend: n, attrs: { axis: a } }), y = no({ inputs: g, backend: n, attrs: { axis: a } }), v = Rn({ inputs: { real: b, imag: y }, backend: n });
+    let m = o.map((x) => ma({ inputs: { input: x }, backend: n })), g = o.map((x) => no({ inputs: { input: x }, backend: n })), b = so({ inputs: m, backend: n, attrs: { axis: a } }), y = so({ inputs: g, backend: n, attrs: { axis: a } }), v = Rn({ inputs: { real: b, imag: y }, backend: n });
     return m.forEach((x) => n.disposeIntermediateTensorInfo(x)), g.forEach((x) => n.disposeIntermediateTensorInfo(x)), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(y), v;
   }
   let l = o.map((m) => {
@@ -21397,7 +21285,7 @@ function no(e) {
   let p = l[0].shape[0] === 1, d = tv(c, i, t[0].dtype, p), h = S.computeOutShape(o.map((m) => m.shape), a), f = n.makeTensorInfo(h, t[0].dtype, d);
   return l.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
 }
-var wH = { kernelName: oo, backendName: "cpu", kernelFunc: no };
+var wH = { kernelName: uo, backendName: "cpu", kernelFunc: so };
 function RC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = s;
   be([r, a], "conv2d");
@@ -21585,8 +21473,8 @@ function EH(e) {
                 for (let Ms = Et; Ms < en; ++Ms) {
                   let Ls = Ms * le - Cn, ki = v * Ee + x * Tn + k * Dn + T * Ms, Js = E * ($ - 1 - Yt) + A * (z - 1 - tn) + P * (W - 1 - Ls) + R * Pe;
                   for (let Bs = 0; Bs < te; ++Bs) {
-                    let du = y[ki + Bs], Ii = N[Js + Bs];
-                    Nn += du * Ii;
+                    let pu = y[ki + Bs], Ii = N[Js + Bs];
+                    Nn += pu * Ii;
                   }
                 }
               }
@@ -21662,7 +21550,7 @@ function zH(e) {
   }
   return n.makeTensorInfo(b.shape, b.dtype, b.values);
 }
-var MH = { kernelName: lo, backendName: "cpu", kernelFunc: zH };
+var MH = { kernelName: co, backendName: "cpu", kernelFunc: zH };
 function LH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
   be(r, "cumprod");
@@ -21689,7 +21577,7 @@ function LH(e) {
   }
   return g;
 }
-var BH = { kernelName: pl, backendName: "cpu", kernelFunc: LH };
+var BH = { kernelName: hl, backendName: "cpu", kernelFunc: LH };
 function VH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
   be(r, "cumsum");
@@ -21716,7 +21604,7 @@ function VH(e) {
   }
   return g;
 }
-var WH = { kernelName: uo, backendName: "cpu", kernelFunc: VH };
+var WH = { kernelName: lo, backendName: "cpu", kernelFunc: VH };
 function UH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
   if (r.shape.length === 1) {
@@ -21746,7 +21634,7 @@ function HH(e) {
     }
   return n.makeTensorInfo([o, p, d, h], r.dtype, m);
 }
-var qH = { kernelName: co, backendName: "cpu", kernelFunc: HH };
+var qH = { kernelName: po, backendName: "cpu", kernelFunc: HH };
 function DC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s;
   be([r, a], "depthwiseConv2DNative");
@@ -21983,16 +21871,16 @@ var c6 = S.ERF_A2;
 var d6 = S.ERF_A3;
 var p6 = S.ERF_A4;
 var h6 = S.ERF_A5;
-var f6 = st(hl, (e) => {
+var f6 = st(fl, (e) => {
   let t = Math.sign(e), n = Math.abs(e), s = 1 / (1 + u6 * n);
   return t * (1 - ((((h6 * s + p6) * s + d6) * s + c6) * s + l6) * s * Math.exp(-n * n));
 });
-var m6 = { kernelName: hl, backendName: "cpu", kernelFunc: f6 };
+var m6 = { kernelName: fl, backendName: "cpu", kernelFunc: f6 };
 function Od(e) {
   let { inputs: t, backend: n, attrs: s } = e, { input: r } = t, { dim: a } = s, i = r.shape.length, o = r.shape.slice(), u = a;
   return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), mt({ inputs: { x: r }, backend: n, attrs: { shape: o } });
 }
-var g6 = { kernelName: ho, backendName: "cpu", kernelFunc: Od };
+var g6 = { kernelName: fo, backendName: "cpu", kernelFunc: Od };
 var b6 = At((e, t) => e / t);
 var lv = Gt(Da, b6);
 var Mm = { kernelName: Da, backendName: "cpu", kernelFunc: lv };
@@ -22029,7 +21917,7 @@ function v6(e) {
 function Lm(e, t, n, s, r) {
   if (n === 1)
     return { real: e, imag: t };
-  let a = S.mergeRealAndImagArrays(e, t), i = n / 2, o = S.complexWithEvenIndex(a), u = o.real, l = o.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = Rn({ inputs: { real: p, imag: d }, backend: r }), f = S.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = Rn({ inputs: { real: y, imag: v }, backend: r }), k = Lm(u, l, i, s, r), T = k.real, N = k.imag, E = [T.length], A = r.makeTensorInfo(E, "float32", T), P = r.makeTensorInfo(E, "float32", N), R = Rn({ inputs: { real: A, imag: P }, backend: r }), F = Lm(m, g, i, s, r), $ = F.real, z = F.imag, W = [$.length], q = r.makeTensorInfo(W, "float32", $), K = r.makeTensorInfo(W, "float32", z), Y = Rn({ inputs: { real: q, imag: K }, backend: r }), Z = S.exponents(n, s), te = [Z.real.length], ee = r.makeTensorInfo(te, "float32", Z.real), se = r.makeTensorInfo(te, "float32", Z.imag), ne = Rn({ inputs: { real: ee, imag: se }, backend: r }), oe = Hp({ inputs: { a: ne, b: Y }, backend: r }), re = Kl({ inputs: { a: R, b: oe }, backend: r }), le = iv({ inputs: { a: R, b: oe }, backend: r }), me = ma({ inputs: { input: re }, backend: r }), ke = ma({ inputs: { input: le }, backend: r }), Se = to({ inputs: { input: re }, backend: r }), Ee = to({ inputs: { input: le }, backend: r }), Pe = no({ inputs: [me, ke], backend: r, attrs: { axis: 0 } }), Xe = no({ inputs: [Se, Ee], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
+  let a = S.mergeRealAndImagArrays(e, t), i = n / 2, o = S.complexWithEvenIndex(a), u = o.real, l = o.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = Rn({ inputs: { real: p, imag: d }, backend: r }), f = S.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = Rn({ inputs: { real: y, imag: v }, backend: r }), k = Lm(u, l, i, s, r), T = k.real, N = k.imag, E = [T.length], A = r.makeTensorInfo(E, "float32", T), P = r.makeTensorInfo(E, "float32", N), R = Rn({ inputs: { real: A, imag: P }, backend: r }), F = Lm(m, g, i, s, r), $ = F.real, z = F.imag, W = [$.length], q = r.makeTensorInfo(W, "float32", $), K = r.makeTensorInfo(W, "float32", z), Y = Rn({ inputs: { real: q, imag: K }, backend: r }), Z = S.exponents(n, s), te = [Z.real.length], ee = r.makeTensorInfo(te, "float32", Z.real), se = r.makeTensorInfo(te, "float32", Z.imag), ne = Rn({ inputs: { real: ee, imag: se }, backend: r }), oe = Hp({ inputs: { a: ne, b: Y }, backend: r }), re = Kl({ inputs: { a: R, b: oe }, backend: r }), le = iv({ inputs: { a: R, b: oe }, backend: r }), me = ma({ inputs: { input: re }, backend: r }), ke = ma({ inputs: { input: le }, backend: r }), Se = no({ inputs: { input: re }, backend: r }), Ee = no({ inputs: { input: le }, backend: r }), Pe = so({ inputs: [me, ke], backend: r, attrs: { axis: 0 } }), Xe = so({ inputs: [Se, Ee], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
   return r.disposeIntermediateTensorInfo(p), r.disposeIntermediateTensorInfo(d), r.disposeIntermediateTensorInfo(h), r.disposeIntermediateTensorInfo(y), r.disposeIntermediateTensorInfo(v), r.disposeIntermediateTensorInfo(x), r.disposeIntermediateTensorInfo(A), r.disposeIntermediateTensorInfo(P), r.disposeIntermediateTensorInfo(R), r.disposeIntermediateTensorInfo(q), r.disposeIntermediateTensorInfo(K), r.disposeIntermediateTensorInfo(Y), r.disposeIntermediateTensorInfo(ee), r.disposeIntermediateTensorInfo(se), r.disposeIntermediateTensorInfo(ne), r.disposeIntermediateTensorInfo(oe), r.disposeIntermediateTensorInfo(re), r.disposeIntermediateTensorInfo(le), r.disposeIntermediateTensorInfo(me), r.disposeIntermediateTensorInfo(Se), r.disposeIntermediateTensorInfo(ke), r.disposeIntermediateTensorInfo(Ee), r.disposeIntermediateTensorInfo(Pe), r.disposeIntermediateTensorInfo(Xe), { real: Je, imag: Ye };
 }
 function x6(e, t, n) {
@@ -22053,11 +21941,11 @@ function cv(e) {
   let { backend: t, attrs: n } = e, { shape: s, value: r, dtype: a } = n, i = a || w.inferDtype(r), o = w.getArrayFromDType(i, w.sizeFromShape(s));
   return S6(o, r, i), t.makeTensorInfo(s, i, o);
 }
-var I6 = { kernelName: fl, backendName: "cpu", kernelFunc: cv };
+var I6 = { kernelName: ml, backendName: "cpu", kernelFunc: cv };
 function S6(e, t, n) {
   e.fill(t);
 }
-var C6 = { kernelName: mo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var C6 = { kernelName: go, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { image: s } = e, r = n, a = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [i, o, u, l] = s.shape, c = r.data.get(s.dataId).values;
   for (let d = 0; d < i; d++) {
     let h = d * u * o * l;
@@ -22114,7 +22002,7 @@ function D6(e) {
   let d = n.data.get(r.dataId).values, h = n.bufferSync(s), f = Y0(d, h, s.dtype, l, o, c, p, s.shape, a);
   return n.makeTensorInfo(u, s.dtype, f.values);
 }
-var F6 = { kernelName: bo, backendName: "cpu", kernelFunc: D6 };
+var F6 = { kernelName: yo, backendName: "cpu", kernelFunc: D6 };
 function O6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s;
   be([r, a], "gatherV2");
@@ -22128,30 +22016,30 @@ function O6(e) {
   let d = w.sizeFromShape(a.shape), h = S.segment_util.collectGatherOpShapeInfo(r, a, u, p), f = mt({ inputs: { x: r }, backend: n, attrs: { shape: [h.batchSize, h.outerSize, h.dimSize, h.sliceSize] } }), m = mt({ inputs: { x: a }, backend: n, attrs: { shape: [h.batchSize, d / h.batchSize] } }), g = [h.batchSize, h.outerSize, d / h.batchSize, h.sliceSize], b = n.bufferSync(m), y = n.bufferSync(f), v = Q0(y, b, g);
   return n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), n.makeTensorInfo(h.outputShape, v.dtype, v.values);
 }
-var P6 = { kernelName: go, backendName: "cpu", kernelFunc: O6 };
+var P6 = { kernelName: bo, backendName: "cpu", kernelFunc: O6 };
 function z6(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = FC(o, true, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
   return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
 }
 var M6 = { kernelName: vg, backendName: "cpu", kernelFunc: z6 };
-var L6 = st(ml, (e) => Number.isFinite(e) ? 1 : 0, "bool");
-var B6 = { kernelName: ml, backendName: "cpu", kernelFunc: L6 };
-var V6 = st(gl, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
-var W6 = { kernelName: gl, backendName: "cpu", kernelFunc: V6 };
-var U6 = st(bl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
-var G6 = { kernelName: bl, backendName: "cpu", kernelFunc: U6 };
+var L6 = st(gl, (e) => Number.isFinite(e) ? 1 : 0, "bool");
+var B6 = { kernelName: gl, backendName: "cpu", kernelFunc: L6 };
+var V6 = st(bl, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
+var W6 = { kernelName: bl, backendName: "cpu", kernelFunc: V6 };
+var U6 = st(yl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
+var G6 = { kernelName: yl, backendName: "cpu", kernelFunc: U6 };
 function H6(e) {
   let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = nC(s, r, a);
   return t.makeTensorInfo([i.length], "float32", i);
 }
 var q6 = { kernelName: xg, backendName: "cpu", kernelFunc: H6 };
-var j6 = st(yl, (e) => Math.log1p(e));
-var K6 = { kernelName: yl, backendName: "cpu", kernelFunc: j6 };
+var j6 = st(vl, (e) => Math.log1p(e));
+var K6 = { kernelName: vl, backendName: "cpu", kernelFunc: j6 };
 var X6 = At((e, t) => e && t);
-var Y6 = Gt(wo, X6, null, "bool");
-var Q6 = { kernelName: wo, backendName: "cpu", kernelFunc: Y6 };
-var Z6 = st(vl, (e) => e ? 0 : 1, "bool");
-var J6 = { kernelName: vl, backendName: "cpu", kernelFunc: Z6 };
+var Y6 = Gt(ko, X6, null, "bool");
+var Q6 = { kernelName: ko, backendName: "cpu", kernelFunc: Y6 };
+var Z6 = st(xl, (e) => e ? 0 : 1, "bool");
+var J6 = { kernelName: xl, backendName: "cpu", kernelFunc: Z6 };
 var eq = At((e, t) => e || t);
 var tq = Gt(Jd, eq, null, "bool");
 var nq = { kernelName: Jd, backendName: "cpu", kernelFunc: tq };
@@ -22348,8 +22236,8 @@ var Sq = At((e, t) => {
   let n = e % t;
   return e < 0 && t < 0 || e >= 0 && t >= 0 ? n : (n + t) % t;
 });
-var Cq = Gt(xl, Sq);
-var Nq = { kernelName: xl, backendName: "cpu", kernelFunc: Cq };
+var Cq = Gt(wl, Sq);
+var Nq = { kernelName: wl, backendName: "cpu", kernelFunc: Cq };
 var Tq = xa(Wd());
 function PC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = r.shape.length, o = a;
@@ -22389,7 +22277,7 @@ function Rq(e) {
   let l = n.data.get(r.dataId).values, c = n.data.get(a.dataId).values, { selectedIndices: p } = Eq(l, c, i, o, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var Dq = { kernelName: So, backendName: "cpu", kernelFunc: Rq };
+var Dq = { kernelName: Co, backendName: "cpu", kernelFunc: Rq };
 var Fq = xs.nonMaxSuppressionV4Impl;
 function Oq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s;
@@ -22397,7 +22285,7 @@ function Oq(e) {
   let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, { selectedIndices: d, validOutputs: h } = Fq(c, p, i, o, u, l);
   return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
 }
-var Pq = { kernelName: wl, backendName: "cpu", kernelFunc: Oq };
+var Pq = { kernelName: kl, backendName: "cpu", kernelFunc: Oq };
 var zq = xs.nonMaxSuppressionV5Impl;
 function Mq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s;
@@ -22405,7 +22293,7 @@ function Mq(e) {
   let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = zq(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var Lq = { kernelName: Co, backendName: "cpu", kernelFunc: Mq };
+var Lq = { kernelName: No, backendName: "cpu", kernelFunc: Mq };
 function Bq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s;
   be(r, "oneHot");
@@ -22416,29 +22304,29 @@ function Bq(e) {
     c[p] >= 0 && c[p] < a && (l[p * a + c[p]] = i);
   return n.makeTensorInfo([...r.shape, a], "int32", l);
 }
-var Vq = { kernelName: To, backendName: "cpu", kernelFunc: Bq };
+var Vq = { kernelName: $o, backendName: "cpu", kernelFunc: Bq };
 function Pd(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("zerosLike is not supported for string tensors");
   if (s.dtype === "complex64") {
-    let r = ma({ inputs: { input: s }, backend: n }), a = Pd({ inputs: { x: r }, backend: n }), i = to({ inputs: { input: s }, backend: n }), o = Pd({ inputs: { x: i }, backend: n }), u = Rn({ inputs: { real: a, imag: o }, backend: n });
+    let r = ma({ inputs: { input: s }, backend: n }), a = Pd({ inputs: { x: r }, backend: n }), i = no({ inputs: { input: s }, backend: n }), o = Pd({ inputs: { x: i }, backend: n }), u = Rn({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
     return cv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
 }
-var Wq = { kernelName: Go, backendName: "cpu", kernelFunc: Pd };
+var Wq = { kernelName: Ho, backendName: "cpu", kernelFunc: Pd };
 function zC(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported for string tensors");
   if (s.dtype === "complex64") {
-    let r = ma({ inputs: { input: s }, backend: n }), a = zC({ inputs: { x: r }, backend: n }), i = to({ inputs: { input: s }, backend: n }), o = Pd({ inputs: { x: i }, backend: n }), u = Rn({ inputs: { real: a, imag: o }, backend: n });
+    let r = ma({ inputs: { input: s }, backend: n }), a = zC({ inputs: { x: r }, backend: n }), i = no({ inputs: { input: s }, backend: n }), o = Pd({ inputs: { x: i }, backend: n }), u = Rn({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
     return cv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
 }
-var Uq = { kernelName: No, backendName: "cpu", kernelFunc: zC };
+var Uq = { kernelName: To, backendName: "cpu", kernelFunc: zC };
 function MC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
@@ -22450,10 +22338,10 @@ function MC(e) {
   let o = [], u = t.map((c) => {
     let p = Od({ inputs: { input: c }, backend: n, attrs: { dim: r } });
     return o.push(p), p;
-  }), l = no({ inputs: u, backend: n, attrs: { axis: r } });
+  }), l = so({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
 }
-var Gq = { kernelName: $o, backendName: "cpu", kernelFunc: MC };
+var Gq = { kernelName: _o, backendName: "cpu", kernelFunc: MC };
 function Hq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
   be(r, "pad");
@@ -22473,9 +22361,9 @@ function Xq(e) {
   let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: i } = n, o = rv(s, r, i, a);
   return t.makeTensorInfo([o.length], a, o);
 }
-var Yq = { kernelName: kl, backendName: "cpu", kernelFunc: Xq };
-var Qq = st(Il, (e) => 1 / e);
-var Zq = { kernelName: Il, backendName: "cpu", kernelFunc: Qq };
+var Yq = { kernelName: Il, backendName: "cpu", kernelFunc: Xq };
+var Qq = st(Sl, (e) => 1 / e);
+var Zq = { kernelName: Sl, backendName: "cpu", kernelFunc: Qq };
 function Jq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
   be(r, "resizeBilinear");
@@ -22541,7 +22429,7 @@ function sj(e) {
   }
   return n.makeTensorInfo([p, l, c, f], r.dtype, g);
 }
-var rj = { kernelName: Sl, backendName: "cpu", kernelFunc: sj };
+var rj = { kernelName: Cl, backendName: "cpu", kernelFunc: sj };
 function aj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
   be([a, r], "resizeNearestNeighborGrad");
@@ -22589,8 +22477,8 @@ function oj(e) {
   }
   return n.makeTensorInfo(u.shape, u.dtype, u.values);
 }
-var uj = { kernelName: Eo, backendName: "cpu", kernelFunc: oj };
-var lj = { kernelName: Ho, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var uj = { kernelName: Ro, backendName: "cpu", kernelFunc: oj };
+var lj = { kernelName: qo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [l, c, p, d] = s.shape, [h, f] = S.getImageCenter(i, c, p), m = 255, g = Math.sin(r), b = Math.cos(r), y = o.data.get(s.dataId).values;
   for (let x = 0; x < l; x++) {
     let k = x * p * c * d;
@@ -22614,11 +22502,11 @@ var lj = { kernelName: Ho, backendName: "cpu", kernelFunc: ({ inputs: e, attrs:
   }
   return { dataId: o.write(u, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
 } };
-var cj = st(Ro, (e) => {
+var cj = st(Do, (e) => {
   let t = Math.floor(e);
   return e - t < 0.5 ? Math.floor(e) : e - t > 0.5 ? Math.ceil(e) : t % 2 === 0 ? t : t + 1;
 });
-var dj = { kernelName: Ro, backendName: "cpu", kernelFunc: cj };
+var dj = { kernelName: Do, backendName: "cpu", kernelFunc: cj };
 function BC(e, t, n, s, r, a, i, o, u, l) {
   let c = [s / r, r], p = e.values, d = t.values;
   if (s === 0)
@@ -22642,7 +22530,7 @@ function pj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = S.calculateShapes(a, r, i), d = true, h = n.bufferSync(r), f = n.bufferSync(a), m = BC(h, f, i, p, l, u, o, c, 0, d);
   return n.makeTensorInfo(i, m.dtype, m.values);
 }
-var hj = { kernelName: Do, backendName: "cpu", kernelFunc: pj };
+var hj = { kernelName: Fo, backendName: "cpu", kernelFunc: pj };
 function fj(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t;
   be([s, r, a], "select");
@@ -22652,24 +22540,24 @@ function fj(e) {
       o[f] === 1 ? p[d++] = u[f] : p[d++] = l[f];
   return n.makeTensorInfo(r.shape, c, p);
 }
-var mj = { kernelName: Fo, backendName: "cpu", kernelFunc: fj };
+var mj = { kernelName: Oo, backendName: "cpu", kernelFunc: fj };
 var gj = S.SELU_SCALEALPHA;
 var bj = S.SELU_SCALE;
-var yj = st(Cl, (e) => e >= 0 ? bj * e : gj * (Math.exp(e) - 1));
-var vj = { kernelName: Cl, backendName: "cpu", kernelFunc: yj };
-var xj = st(Nl, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
-var wj = { kernelName: Nl, backendName: "cpu", kernelFunc: xj };
+var yj = st(Nl, (e) => e >= 0 ? bj * e : gj * (Math.exp(e) - 1));
+var vj = { kernelName: Nl, backendName: "cpu", kernelFunc: yj };
+var xj = st(Tl, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
+var wj = { kernelName: Tl, backendName: "cpu", kernelFunc: xj };
 var kj = st(ri, (e) => Math.sin(e));
 var Ij = { kernelName: ri, backendName: "cpu", kernelFunc: kj };
-var Sj = st(Po, (e) => Math.sinh(e));
-var Cj = { kernelName: Po, backendName: "cpu", kernelFunc: Sj };
+var Sj = st(zo, (e) => Math.sinh(e));
+var Cj = { kernelName: zo, backendName: "cpu", kernelFunc: Sj };
 var Nj = 11920928955078125e-23;
 var nw = Math.log(Nj) + 2;
-var Tj = st(Tl, (e) => {
+var Tj = st($l, (e) => {
   let t = e > -nw, n = e < nw, s = Math.exp(e), r;
   return n ? r = s : t ? r = e : r = Math.log(1 + s), r;
 });
-var $j = { kernelName: Tl, backendName: "cpu", kernelFunc: Tj };
+var $j = { kernelName: $l, backendName: "cpu", kernelFunc: Tj };
 function _j(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
   be([r], "spaceToBatchND");
@@ -22680,7 +22568,7 @@ function _j(e) {
   let l = LC.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = S.getReshaped(l.shape, a, o, false), p = S.getPermuted(c.length, a.length, false), d = S.getReshapedPermuted(l.shape, a, o, false), m = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wn({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = mt({ inputs: { x: y }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(y), k;
 }
-var Aj = { kernelName: zo, backendName: "cpu", kernelFunc: _j };
+var Aj = { kernelName: Mo, backendName: "cpu", kernelFunc: _j };
 function Ej(e) {
   let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
   if (a.shape.length !== 1)
@@ -22712,7 +22600,7 @@ function Dj(e) {
   let i = Array.from(n.data.get(r.dataId).values), o = n.data.get(s.dataId).values, u = Array.from(n.data.get(a.dataId).values), [l, c, p] = hC(o, s.shape, s.dtype, i, u);
   return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
 }
-var Fj = { kernelName: $l, backendName: "cpu", kernelFunc: Dj };
+var Fj = { kernelName: _l, backendName: "cpu", kernelFunc: Dj };
 function Oj(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
@@ -22759,8 +22647,8 @@ function Vj(e) {
     return l[o] += p, h;
   });
 }
-var Wj = { kernelName: Mo, backendName: "cpu", kernelFunc: Vj };
-var Uj = { kernelName: _l, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
+var Wj = { kernelName: Lo, backendName: "cpu", kernelFunc: Vj };
+var Uj = { kernelName: Al, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
   let { x: n } = e, s = t;
   be(n, "square");
   let r = s.data.get(n.dataId).values, a = new Float32Array(r.length);
@@ -22791,7 +22679,7 @@ function qj(e) {
   }
   return k;
 }
-var jj = { kernelName: Lo, backendName: "cpu", kernelFunc: qj };
+var jj = { kernelName: Bo, backendName: "cpu", kernelFunc: qj };
 function Kj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.data.get(c.dataId).values, h = n.data.get(p.dataId).values, [f, m] = gC(d, h, r, a, i, o, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
@@ -22819,8 +22707,8 @@ function Zj(e) {
   return n.makeTensorInfo(a.shape, "int32", o);
 }
 var Jj = { kernelName: _g, backendName: "cpu", kernelFunc: Zj };
-var e5 = st(Bo, (e) => Math.tan(e));
-var t5 = { kernelName: Bo, backendName: "cpu", kernelFunc: e5 };
+var e5 = st(Vo, (e) => Math.tan(e));
+var t5 = { kernelName: Vo, backendName: "cpu", kernelFunc: e5 };
 var n5 = st(di, (e) => Math.tanh(e));
 var s5 = { kernelName: di, backendName: "cpu", kernelFunc: n5 };
 function r5(e) {
@@ -22836,7 +22724,7 @@ function i5(e) {
   let o = n.data.get(r.dataId).values, [u, l] = kC(o, r.shape, r.dtype, a, i);
   return [n.makeTensorInfo(u.shape, u.dtype, u.values), n.makeTensorInfo(l.shape, l.dtype, l.values)];
 }
-var o5 = { kernelName: Vo, backendName: "cpu", kernelFunc: i5 };
+var o5 = { kernelName: Wo, backendName: "cpu", kernelFunc: i5 };
 function u5(e) {
   let { inputs: t, attrs: n, backend: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = n, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = w.computeStrides(r.shape), y = b[0], v = b[1], x = b[2], k = w.getTypedArrayFromDType(r.dtype, w.sizeFromShape(g));
   k.fill(u);
@@ -22867,7 +22755,7 @@ function u5(e) {
   }
   return { dataId: s.write(k, g, r.dtype), shape: r.shape, dtype: r.dtype };
 }
-var l5 = { kernelName: Wo, backendName: "cpu", kernelFunc: u5 };
+var l5 = { kernelName: Uo, backendName: "cpu", kernelFunc: u5 };
 function sw(e, t, n) {
   switch (n) {
     case "reflect":
@@ -22923,16 +22811,16 @@ function p5(e, t) {
 function h5(e, t) {
   return w.clamp(0, e, t - 1);
 }
-function Ru(e, t, n, s, r, a, i, o, u, l, c) {
+function Du(e, t, n, s, r, a, i, o, u, l, c) {
   let p = i * s + o * r + u * a + l;
   return 0 <= o && o < t && 0 <= u && u < n ? e[p] : c;
 }
 function f5(e, t, n, s, r, a, i, o, u, l, c) {
   let p = Math.round(o), d = Math.round(u);
-  return Ru(e, t, n, s, r, a, i, p, d, l, c);
+  return Du(e, t, n, s, r, a, i, p, d, l, c);
 }
 function m5(e, t, n, s, r, a, i, o, u, l, c) {
-  let p = Math.floor(o), d = Math.floor(u), h = p + 1, f = d + 1, m = (f - u) * Ru(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * Ru(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * Ru(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * Ru(e, t, n, s, r, a, i, h, f, l, c);
+  let p = Math.floor(o), d = Math.floor(u), h = p + 1, f = d + 1, m = (f - u) * Du(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * Du(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * Du(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * Du(e, t, n, s, r, a, i, h, f, l, c);
   return (h - o) * m + (o - p) * g;
 }
 function g5(e) {
@@ -22958,7 +22846,7 @@ function y5(e) {
   }
   return d;
 }
-var v5 = { kernelName: Uo, backendName: "cpu", kernelFunc: y5 };
+var v5 = { kernelName: Go, backendName: "cpu", kernelFunc: y5 };
 function x5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: i } = s;
   be(r, "unsortedSegmentSum");
@@ -22977,9 +22865,9 @@ function x5(e) {
 var w5 = { kernelName: up, backendName: "cpu", kernelFunc: x5 };
 var k5 = [TG, xU, _G, EG, NU, DG, OG, zG, LG, VG, UG, HG, jG, YG, ZG, tH, sH, aH, oH, CG, lH, dH, hH, mH, SU, $U, bH, wU, vH, wH, kH, SH, NH, $H, AH, RH, FH, PH, MH, BH, WH, GH, qH, jH, XH, QH, JH, e6, t6, n6, a6, yG, o6, _U, m6, AU, g6, RU, k6, I6, C6, FU, $6, A6, R6, F6, P6, PU, MU, kU, M6, xH, B6, W6, G6, vG, BU, WU, q6, GU, K6, Q6, J6, nq, rq, iq, oq, qU, lq, dq, hq, mq, bq, vq, wq, KU, Iq, Nq, Aq, YU, ZU, Dq, Pq, Lq, eG, Vq, Uq, Gq, LC, Kq, wG, sG, Yq, IU, Mm, Zq, kG, IG, SG, ej, nj, rj, ij, uj, lj, dj, aG, hj, mj, vj, oG, wj, Ij, Cj, uG, $q, $j, Aj, Rj, Fj, Pj, Mj, Bj, Wj, dG, Uj, hG, Hj, jj, Xj, Qj, Jj, bG, s6, t5, s5, a5, o5, l5, tG, b5, v5, w5, Wq];
 for (let e of k5)
-  Al(e);
+  El(e);
 var I5 = {};
-Ae(I5, { assertNotComplex: () => tu, bindCanvasToFramebuffer: () => O5, bindColorTextureToFramebuffer: () => ad, bindTextureToProgramUniformSampler: () => t1, bindTextureUnit: () => ZC, bindVertexBufferToProgramAttribute: () => Bm, callAndCheck: () => fe, canBeRepresented: () => VC, createFragmentShader: () => GC, createFramebuffer: () => QC, createProgram: () => HC, createStaticIndexBuffer: () => KC, createStaticVertexBuffer: () => jC, createTexture: () => XC, createVertexShader: () => UC, getBatchDim: () => ba, getExtensionOrThrow: () => Du, getFramebufferErrorMessage: () => n1, getMaxTexturesInShader: () => i1, getNumChannels: () => D5, getProgramUniformLocation: () => e1, getProgramUniformLocationOrThrow: () => JC, getRowsCols: () => ya, getShapeAs3D: () => id, getTextureShapeFromLogicalShape: () => r1, getWebGLDisjointQueryTimerVersion: () => o1, getWebGLErrorMessage: () => WC, getWebGLMaxTextureSize: () => a1, hasExtension: () => Ln, isCapableOfRenderingToFloatTexture: () => u1, isDownloadFloatTextureEnabled: () => l1, isReshapeFree: () => Ju, isWebGLFenceEnabled: () => c1, isWebGLVersionEnabled: () => Wm, linkProgram: () => qC, logShaderSourceAndInfoLog: () => pv, resetMaxTextureSize: () => P5, resetMaxTexturesInShader: () => z5, unbindColorTextureFromFramebuffer: () => Vm, unbindTextureUnit: () => F5, validateFramebuffer: () => Fu, validateProgram: () => rd, validateTextureSize: () => YC });
+Ae(I5, { assertNotComplex: () => nu, bindCanvasToFramebuffer: () => O5, bindColorTextureToFramebuffer: () => ad, bindTextureToProgramUniformSampler: () => t1, bindTextureUnit: () => ZC, bindVertexBufferToProgramAttribute: () => Bm, callAndCheck: () => fe, canBeRepresented: () => VC, createFragmentShader: () => GC, createFramebuffer: () => QC, createProgram: () => HC, createStaticIndexBuffer: () => KC, createStaticVertexBuffer: () => jC, createTexture: () => XC, createVertexShader: () => UC, getBatchDim: () => ba, getExtensionOrThrow: () => Fu, getFramebufferErrorMessage: () => n1, getMaxTexturesInShader: () => i1, getNumChannels: () => D5, getProgramUniformLocation: () => e1, getProgramUniformLocationOrThrow: () => JC, getRowsCols: () => ya, getShapeAs3D: () => id, getTextureShapeFromLogicalShape: () => r1, getWebGLDisjointQueryTimerVersion: () => o1, getWebGLErrorMessage: () => WC, getWebGLMaxTextureSize: () => a1, hasExtension: () => Ln, isCapableOfRenderingToFloatTexture: () => u1, isDownloadFloatTextureEnabled: () => l1, isReshapeFree: () => el, isWebGLFenceEnabled: () => c1, isWebGLVersionEnabled: () => Wm, linkProgram: () => qC, logShaderSourceAndInfoLog: () => pv, resetMaxTextureSize: () => P5, resetMaxTexturesInShader: () => z5, unbindColorTextureFromFramebuffer: () => Vm, unbindTextureUnit: () => F5, validateFramebuffer: () => Ou, validateProgram: () => rd, validateTextureSize: () => YC });
 var Xr = {};
 var jf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
 function S5(e, t) {
@@ -23021,11 +22909,11 @@ function Yc(e) {
   let t = w.sizeFromShape(e), n = Math.ceil(t / 4);
   return w.sizeToSquarishShape(n);
 }
-function eu(e, t) {
+function tu(e, t) {
   return [Math.max(1, Math.ceil(t / 2)), Math.max(1, Math.ceil(e / 2))];
 }
 function $5(e, t) {
-  let [n, s] = eu(e, t);
+  let [n, s] = tu(e, t);
   return n * s * 4;
 }
 function dv(e, t) {
@@ -23066,7 +22954,7 @@ function WC(e, t) {
       return `Unknown error code ${t}`;
   }
 }
-function Du(e, t) {
+function Fu(e, t) {
   return Qs(e, () => e.getExtension(t), 'Extension "' + t + '" not supported on this browser.');
 }
 function UC(e, t) {
@@ -23167,7 +23055,7 @@ function ad(e, t, n) {
 function Vm(e, t) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, t)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, null, 0));
 }
-function Fu(e) {
+function Ou(e) {
   let t = e.checkFramebufferStatus(e.FRAMEBUFFER);
   if (t !== e.FRAMEBUFFER_COMPLETE)
     throw new Error("Error binding framebuffer: " + n1(e, t));
@@ -23236,7 +23124,7 @@ function r1(e, t = false) {
 function Qc(e) {
   return e % 2 === 0;
 }
-function Ju(e, t) {
+function el(e, t) {
   if (e = e.slice(-2), t = t.slice(-2), w.arraysEqual(e, t) || !e.length || !t.length || e[0] === 0 || e[1] === 0 || t[0] === 0 || t[1] === 0)
     return true;
   if (e.length !== t.length) {
@@ -23339,7 +23227,7 @@ function M5(e, t) {
 function c1(e) {
   return e !== 2 ? false : vs(e).fenceSync != null;
 }
-function tu(e, t) {
+function nu(e, t) {
   Array.isArray(e) || (e = [e]), e.forEach((n) => {
     n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the WebGL backend.`);
   });
@@ -23572,7 +23460,7 @@ function V5(e, t, n) {
   return t.isPacked ? (l = U5(t.logicalShape, i, n.enableShapeUniforms), c = j5(o)) : (l = G5(t.logicalShape, i, n.enableShapeUniforms), c = q5(o)), n.packedInputs && (p += Z5), [p, u, c, r, l, a, n.userCode].join(`
 `);
 }
-function nu(e, t = false) {
+function su(e, t = false) {
   let n = e.shapeInfo.logicalShape;
   switch (n.length) {
     case 0:
@@ -23609,7 +23497,7 @@ function h1(e, t) {
 }
 function W5(e, t, n = false, s) {
   let r = "";
-  n ? r += h1(e, s) : r += nu(e, s);
+  n ? r += h1(e, s) : r += su(e, s);
   let a = e.shapeInfo.logicalShape, i = t.logicalShape;
   return a.length <= i.length && (n ? r += wK(e, t) : r += kK(e, t)), r;
 }
@@ -24165,7 +24053,7 @@ function pK(e, t) {
   if (e.shapeInfo.isUniform)
     return `
       float ${s}(int index) {
-        ${su(e)}
+        ${ru(e)}
       }
     `;
   let r = e.shapeInfo.texShape, a = r[0], i = r[1];
@@ -24261,11 +24149,11 @@ function fK(e, t) {
   }
   let { newShape: i, keptDims: o } = w.squeezeShape(n), u = i;
   if (u.length < n.length) {
-    let d = ru(e, u), h = ["row", "col"];
+    let d = au(e, u), h = ["row", "col"];
     return `
-      ${nu(d, t)}
+      ${su(d, t)}
       float ${r}(int row, int col) {
-        return ${r}(${au(h, o)});
+        return ${r}(${iu(h, o)});
       }
     `;
   }
@@ -24273,7 +24161,7 @@ function fK(e, t) {
     return `
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
-        ${su(e)}
+        ${ru(e)}
       }
     `;
   let l = a[0], c = a[1], p = vi(s);
@@ -24320,11 +24208,11 @@ function fK(e, t) {
 function mK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, i = [Math.ceil(a[0] / 2), Math.ceil(a[1] / 2)];
   if (n[0] === 1) {
-    let d = n.slice(1), h = [1, 2], f = ru(e, d), m = ["b", "row", "col"];
+    let d = n.slice(1), h = [1, 2], f = au(e, d), m = ["b", "row", "col"];
     return `
         ${h1(f, t)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${au(m, h)});
+          return ${r}(${iu(m, h)});
         }
       `;
   }
@@ -24352,11 +24240,11 @@ function mK(e, t) {
 function gK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[1] * n[2], i = n[2], { newShape: o, keptDims: u } = w.squeezeShape(n), l = o;
   if (l.length < n.length) {
-    let m = ru(e, l), g = ["row", "col", "depth"];
+    let m = au(e, l), g = ["row", "col", "depth"];
     return `
-        ${nu(m, t)}
+        ${su(m, t)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${au(g, u)});
+          return ${r}(${iu(g, u)});
         }
       `;
   }
@@ -24365,7 +24253,7 @@ function gK(e, t) {
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${a}, ${i}, 1)));
-        ${su(e)}
+        ${ru(e)}
       }
     `;
   let c = e.shapeInfo.texShape, p = c[0], d = c[1], h = e.shapeInfo.flatOffset;
@@ -24455,11 +24343,11 @@ function bK(e, t) {
 function yK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[3], i = n[2] * a, o = n[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(n);
   if (u.length < n.length) {
-    let y = ru(e, u), v = ["row", "col", "depth", "depth2"];
+    let y = au(e, u), v = ["row", "col", "depth", "depth2"];
     return `
-      ${nu(y, t)}
+      ${su(y, t)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${au(v, l)});
+        return ${r}(${iu(v, l)});
       }
     `;
   }
@@ -24468,7 +24356,7 @@ function yK(e, t) {
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${o}, ${i}, ${a}, 1)));
-        ${su(e)}
+        ${ru(e)}
       }
     `;
   let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1], f = `int stride2 = ${s}Shape[3];`, m = `int stride1 = ${s}Shape[2] * stride2;`, g = `int stride0 = ${s}Shape[1] * stride1;`;
@@ -24541,11 +24429,11 @@ function yK(e, t) {
 function vK(e) {
   let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = t[4], a = t[3] * r, i = t[2] * a, o = t[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(t);
   if (u.length < t.length) {
-    let m = ru(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
+    let m = au(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
     return `
-      ${nu(m)}
+      ${su(m)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${au(g, l)});
+        return ${s}(${iu(g, l)});
       }
     `;
   }
@@ -24556,7 +24444,7 @@ function vK(e) {
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${a}, ${r})) +
           depth3;
-        ${su(e)}
+        ${ru(e)}
       }
     `;
   let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1];
@@ -24598,12 +24486,12 @@ function vK(e) {
 function xK(e) {
   let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), { newShape: r, keptDims: a } = w.squeezeShape(t);
   if (r.length < t.length) {
-    let g = ru(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
+    let g = au(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
     return `
-      ${nu(g)}
+      ${su(g)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${au(b, a)});
+        return ${s}(${iu(b, a)});
       }
     `;
   }
@@ -24618,7 +24506,7 @@ function xK(e) {
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${su(e)}
+        ${ru(e)}
       }
     `;
   let p = e.shapeInfo.flatOffset, d = e.shapeInfo.texShape, h = d[0], f = d[1];
@@ -24662,7 +24550,7 @@ function xK(e) {
     }
   `;
 }
-function su(e) {
+function ru(e) {
   let t = e.name, n = w.sizeFromShape(e.shapeInfo.logicalShape);
   return n < 2 ? `return ${t};` : `
     for (int i = 0; i < ${n}; i++) {
@@ -24741,11 +24629,11 @@ function mv(e, t, n) {
   let { newShape: s, keptDims: r } = w.squeezeShape(t), a = t.length, i = e && a === 3 && t[0] === 1, o = i ? t.slice(1) : s, u = !e && a > 1 && !w.arraysEqual(t, n) && s.length < a || i;
   return { useSqueezeShape: u, uniformShape: u ? o : t, keptDims: r };
 }
-function ru(e, t) {
+function au(e, t) {
   let n = JSON.parse(JSON.stringify(e));
   return n.shapeInfo.logicalShape = t, n;
 }
-function au(e, t) {
+function iu(e, t) {
   return t.map((n) => e[n]).join(", ");
 }
 function IK(e, t, n, s) {
@@ -25136,14 +25024,14 @@ function vv(e) {
   return e.internalFormatPackedFloat;
 }
 function k1(e, t, n, s) {
-  let [r, a] = eu(t, n);
+  let [r, a] = tu(t, n);
   return Ql(e, r, a, vv(s), e.RGBA, e.FLOAT);
 }
 function xv(e) {
   return e.internalFormatPackedHalfFloat;
 }
 function I1(e, t, n, s) {
-  let [r, a] = eu(t, n);
+  let [r, a] = tu(t, n);
   return Ql(e, r, a, xv(s), e.RGBA, s.textureTypeHalfFloat);
 }
 function S1(e, t, n) {
@@ -25187,12 +25075,12 @@ var Kf = class {
     let n = "WEBGL_color_buffer_float", s = "EXT_color_buffer_half_float";
     if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), X().getNumber("WEBGL_VERSION") === 1) {
       let r = "OES_texture_float", a = "OES_texture_half_float";
-      if (this.textureFloatExtension = Du(this.gl, r), Ln(this.gl, a))
-        this.textureHalfFloatExtension = Du(this.gl, a);
+      if (this.textureFloatExtension = Fu(this.gl, r), Ln(this.gl, a))
+        this.textureHalfFloatExtension = Fu(this.gl, a);
       else if (X().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), Ln(this.gl, s))
-        this.colorBufferHalfFloatExtension = Du(this.gl, s);
+        this.colorBufferHalfFloatExtension = Fu(this.gl, s);
       else if (X().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", Ln(this.gl, n))
@@ -25300,7 +25188,7 @@ var Kf = class {
   }
   setOutputPackedMatrixTexture(e, t, n) {
     this.throwIfDisposed();
-    let [s, r] = eu(t, n);
+    let [s, r] = tu(t, n);
     this.setOutputMatrixTextureDriver(e, s, r);
   }
   setOutputMatrixWriteRegion(e, t, n, s) {
@@ -25310,7 +25198,7 @@ var Kf = class {
     throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
   }
   debugValidate() {
-    this.program != null && rd(this.gl, this.program), Fu(this.gl);
+    this.program != null && rd(this.gl, this.program), Ou(this.gl);
   }
   executeProgram() {
     this.throwIfDisposed(), this.throwIfNoProgram();
@@ -25321,7 +25209,7 @@ var Kf = class {
     this.throwIfDisposed(), fe(this.gl, () => this.gl.finish());
   }
   getQueryTimerExtension() {
-    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Du(this.gl, X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension;
+    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Fu(this.gl, X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension;
   }
   getQueryTimerExtensionWebGL2() {
     return this.getQueryTimerExtension();
@@ -25388,10 +25276,10 @@ var Kf = class {
     this.itemsToPoll.push({ isDoneFn: e, resolveFn: t }), !(this.itemsToPoll.length > 1) && w.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0));
   }
   bindTextureToFrameBuffer(e) {
-    this.throwIfDisposed(), ad(this.gl, e, this.framebuffer), this.debug && Fu(this.gl);
+    this.throwIfDisposed(), ad(this.gl, e, this.framebuffer), this.debug && Ou(this.gl);
   }
   unbindTextureToFrameBuffer() {
-    this.outputTexture != null ? (ad(this.gl, this.outputTexture, this.framebuffer), this.debug && Fu(this.gl)) : Vm(this.gl, this.framebuffer);
+    this.outputTexture != null ? (ad(this.gl, this.outputTexture, this.framebuffer), this.debug && Ou(this.gl)) : Vm(this.gl, this.framebuffer);
   }
   downloadMatrixDriver(e, t) {
     this.bindTextureToFrameBuffer(e);
@@ -25401,7 +25289,7 @@ var Kf = class {
   setOutputMatrixTextureDriver(e, t, n) {
     this.throwIfDisposed();
     let s = this.gl;
-    ad(s, e, this.framebuffer), this.debug && Fu(s), this.outputTexture = e, fe(s, () => s.viewport(0, 0, t, n)), fe(s, () => s.scissor(0, 0, t, n));
+    ad(s, e, this.framebuffer), this.debug && Ou(s), this.outputTexture = e, fe(s, () => s.viewport(0, 0, t, n)), fe(s, () => s.scissor(0, 0, t, n));
   }
   setOutputMatrixWriteRegionDriver(e, t, n, s) {
     this.throwIfDisposed(), fe(this.gl, () => this.gl.scissor(e, t, n, s));
@@ -25630,7 +25518,7 @@ function kX(e, t) {
 function aw(e, t, n, s, r) {
   let a = IX(t, s), i;
   if (r) {
-    let [u, l] = eu(e[0], e[1]);
+    let [u, l] = tu(e[0], e[1]);
     i = u * l;
   } else {
     let [u, l] = Yl(e[0], e[1]);
@@ -25774,10 +25662,9 @@ var VX = 600;
 function WX() {
   return X().global.screen == null ? 1024 : X().global.screen.height * X().global.screen.width * window.devicePixelRatio * VX / 1024 / 1024;
 }
-var z1 = class extends tl {
+var z1 = class extends nl {
   constructor(e) {
-    super();
-    if (this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !X().getBool("HAS_WEBGL"))
+    if (super(), this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !X().getBool("HAS_WEBGL"))
       throw new Error("WebGL is not supported on this device");
     let t;
     if (e != null) {
@@ -26069,7 +25956,7 @@ var z1 = class extends tl {
       }
       if (this.uploadToGPU(g.dataId), !!b.isPacked != !!e.packedInputs)
         g = b.isPacked ? this.unpackTensor(g) : this.packTensor(g), u.push(g), b = this.texData.get(g.dataId);
-      else if (b.isPacked && !Ju(b.shape, g.shape)) {
+      else if (b.isPacked && !el(b.shape, g.shape)) {
         let y = g, v = g.shape;
         g.shape = b.shape, g = this.packedReshape(g, v), u.push(g), b = this.texData.get(g.dataId), y.shape = v;
       }
@@ -26127,7 +26014,7 @@ var z1 = class extends tl {
     let c = t.texShape;
     if (c == null && (c = r1(n, o), t.texShape = c), r != null) {
       let p = id(n), d, h = c[1], f = c[0], m = r instanceof Uint8Array || r instanceof Uint8ClampedArray;
-      (o || !m) && ([h, f] = eu(c[0], c[1])), o ? d = new EK(p, m) : d = new AK(p, m);
+      (o || !m) && ([h, f] = tu(c[0], c[1])), o ? d = new EK(p, m) : d = new AK(p, m);
       let g = m ? [f, h] : c, b = this.makeTensorInfo(g, s), y = this.texData.get(b.dataId);
       m ? y.usage = 2 : y.usage = 1, y.texShape = g, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), h, f, r);
       let v = [[f, h]], x = true, k = this.runWebGLProgram(d, [b], s, v, x), T = this.texData.get(k.dataId);
@@ -26203,17 +26090,17 @@ function UX(e, t) {
   } else
     throw new Error(`Unknown dtype ${t}`);
 }
-var Ope = "0.0.0";
+var Ppe = "0.0.0";
 function GX() {
   X().set("WEBGL_FORCE_F16_TEXTURES", true);
 }
 dp.isBrowser() && pp("webgl", () => new M1(), 2);
-var Ppe = { forceHalfFloat: GX };
+var zpe = { forceHalfFloat: GX };
 var L1 = `
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `;
-var so = class {
+var ro = class {
   constructor(e, t, n) {
     this.variableNames = ["A", "B"], this.outputShape = S.assertAndGetBroadcastShape(t, n), this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
       float binaryOperation(float a, float b) {
@@ -26312,7 +26199,7 @@ var V1 = `
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
 `;
 function jX(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), o = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(V1, r.shape, i.shape) : new so(B1, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), o = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(V1, r.shape, i.shape) : new ro(B1, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
   return n.disposeIntermediateTensorInfo(i), u;
 }
 var KX = { kernelName: Va, backendName: "webgl", kernelFunc: jX };
@@ -26322,11 +26209,11 @@ var U1 = `
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
 `;
 function XX(e) {
-  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(U1, s.shape, r.shape) : new so(W1, s.shape, r.shape);
+  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(U1, s.shape, r.shape) : new ro(W1, s.shape, r.shape);
   return n.runWebGLProgram(a, [s, r], "float32");
 }
 var YX = { kernelName: Ja, backendName: "webgl", kernelFunc: XX };
-var iu = "if (isnan(x)) return x;";
+var ou = "if (isnan(x)) return x;";
 var QX = `
   if (isnan(a)) return a;
   if (isnan(b)) return b;
@@ -26353,7 +26240,7 @@ function jt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, sup
     let { a: u, b: l } = i, c = o;
     if (s && u.dtype === "complex64") {
       let f = c.texData.get(u.dataId), m = c.texData.get(l.dataId), [g, b] = [[f.complexTensorInfos.real, m.complexTensorInfos.real], [f.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((v) => {
-        let [x, k] = v, T = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, N = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, E = new so(e, u.shape, l.shape);
+        let [x, k] = v, T = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, N = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, E = new ro(e, u.shape, l.shape);
         return c.runWebGLProgram(E, [T, N], cn(x.dtype, k.dtype));
       }), y = Rr({ inputs: { real: g, imag: b }, backend: c });
       return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(b), y;
@@ -26364,7 +26251,7 @@ function jt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, sup
       return k.values = y, x;
     }
     let d = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") && t != null, h;
-    return d ? h = new Zl(t, u.shape, l.shape, n) : h = new so(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
+    return d ? h = new Zl(t, u.shape, l.shape, n) : h = new ro(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
   };
 }
 function Kp(e, t = false) {
@@ -26465,7 +26352,7 @@ function kv(e) {
     return d.values = l, p;
   }
   let i;
-  return X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? i = new Zl(dw, s.shape, r.shape) : i = new so(dw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
+  return X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? i = new Zl(dw, s.shape, r.shape) : i = new ro(dw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
 }
 var JX = { kernelName: Ya, backendName: "webgl", kernelFunc: kv };
 function e8(e, t, n) {
@@ -26476,9 +26363,9 @@ function he(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = n, o = w.sizeFromShape(r.shape), u = w.inferFromImplicitShape(a, o), l = w.sizeFromShape(u);
   w.assert(o === l, () => `The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);
   let c = i.texData.get(r.dataId);
-  return c.isPacked && !Ju(r.shape, u) && !(c.texture !== null && Ju(c.shape, u)) ? e8(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
+  return c.isPacked && !el(r.shape, u) && !(c.texture !== null && el(c.shape, u)) ? e8(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
 }
-var t8 = { kernelName: Ao, backendName: "webgl", kernelFunc: he };
+var t8 = { kernelName: Eo, backendName: "webgl", kernelFunc: he };
 var pw = class {
   constructor(e, t) {
     this.variableNames = ["x"];
@@ -26760,7 +26647,7 @@ function qt(e) {
 var l8 = { kernelName: pi, backendName: "webgl", kernelFunc: qt };
 var H1 = 1e3;
 function zd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
-  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = qo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
+  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = jo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
   w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
   let k = n ? [b, p, h] : [b, h, p], T = s ? [y, f, d] : [y, d, f], N = he({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = he({ inputs: { x: t }, backend: r, attrs: { shape: T } }), A = [N, E], P = Math.max(b, y), R = n ? N.shape[1] : N.shape[2], F = a != null, $ = i != null, z = u === "leakyrelu", W = u != null ? Kp(u, true) : null, q = F || $ || z || W != null, K;
   if ((h === 1 || f === 1) && R > H1 && q === false) {
@@ -26801,7 +26688,7 @@ function p8(e) {
   let r;
   return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Zr(s.shape, hw) : r = new Hs(s.shape, hw), n.runWebGLProgram(r, [s], s.dtype);
 }
-var h8 = { kernelName: ao, backendName: "webgl", kernelFunc: p8 };
+var h8 = { kernelName: io, backendName: "webgl", kernelFunc: p8 };
 var f8 = ss + `
   if (abs(x) > 1.) {
     return NAN;
@@ -26809,12 +26696,12 @@ var f8 = ss + `
   return acos(x);
 `;
 var m8 = Ke({ opSnippet: f8 });
-var g8 = { kernelName: nl, backendName: "webgl", kernelFunc: m8 };
+var g8 = { kernelName: sl, backendName: "webgl", kernelFunc: m8 };
 var b8 = ss + `
   if (x < 1.0) return NAN;
 return log(x + sqrt(x * x - 1.0));`;
 var y8 = Ke({ opSnippet: b8 });
-var v8 = { kernelName: sl, backendName: "webgl", kernelFunc: y8 };
+var v8 = { kernelName: rl, backendName: "webgl", kernelFunc: y8 };
 var fw = "return a + b;";
 var x8 = jt({ opSnippet: fw, packedOpSnippet: fw, supportsComplex: true, cpuKernelImpl: FK });
 var w8 = { kernelName: Ir, backendName: "webgl", kernelFunc: x8 };
@@ -26879,7 +26766,7 @@ function C8(e) {
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var N8 = { kernelName: rl, backendName: "webgl", kernelFunc: C8 };
+var N8 = { kernelName: al, backendName: "webgl", kernelFunc: C8 };
 function T8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = S.getAxesPermutation(l, o), p = r;
   c != null && (p = qt({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = S.getInnerMostAxes(l.length, o)), S.assertAxesAreInnerMostDims("any", l, o);
@@ -26891,7 +26778,7 @@ function T8(e) {
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var $8 = { kernelName: al, backendName: "webgl", kernelFunc: T8 };
+var $8 = { kernelName: il, backendName: "webgl", kernelFunc: T8 };
 var _8 = class {
   constructor(e, t, n) {
     this.variableNames = ["A"];
@@ -27045,7 +26932,7 @@ function D8(e) {
   let c = K1(n, u, i[0], "min");
   return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
 }
-var F8 = { kernelName: il, backendName: "webgl", kernelFunc: D8 };
+var F8 = { kernelName: ol, backendName: "webgl", kernelFunc: D8 };
 var O8 = ss + `
   if (abs(x) > 1.) {
     return NAN;
@@ -27053,15 +26940,15 @@ var O8 = ss + `
   return asin(x);
 `;
 var P8 = Ke({ opSnippet: O8 });
-var z8 = { kernelName: ol, backendName: "webgl", kernelFunc: P8 };
+var z8 = { kernelName: ul, backendName: "webgl", kernelFunc: P8 };
 var M8 = ss + "return log(x + sqrt(x * x + 1.0));";
 var L8 = Ke({ opSnippet: M8 });
-var B8 = { kernelName: ul, backendName: "webgl", kernelFunc: L8 };
+var B8 = { kernelName: ll, backendName: "webgl", kernelFunc: L8 };
 var V8 = ss + `
   return atan(x);
 `;
 var W8 = Ke({ opSnippet: V8 });
-var U8 = { kernelName: ll, backendName: "webgl", kernelFunc: W8 };
+var U8 = { kernelName: cl, backendName: "webgl", kernelFunc: W8 };
 var G8 = QX + `
   return atan(a, b);
 `;
@@ -27072,13 +26959,13 @@ var H8 = `
   return result;
 `;
 var q8 = jt({ opSnippet: G8, packedOpSnippet: H8 });
-var j8 = { kernelName: dl, backendName: "webgl", kernelFunc: q8 };
+var j8 = { kernelName: pl, backendName: "webgl", kernelFunc: q8 };
 var K8 = ss + `
   if ((x < -1.0) || (x > 1.0)) return NAN;
 return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
 var X8 = Ke({ opSnippet: K8 });
-var Y8 = { kernelName: cl, backendName: "webgl", kernelFunc: X8 };
-var el = class {
+var Y8 = { kernelName: dl, backendName: "webgl", kernelFunc: X8 };
+var tl = class {
   constructor(e, t, n, s = false, r = false) {
     if (this.variableNames = ["x"], t === "avg" && n)
       throw new Error("Cannot compute positions for average pool.");
@@ -27420,13 +27307,13 @@ var Iv = class {
 };
 function Q8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
-  tu(r, "avgPool");
+  nu(r, "avgPool");
   let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
   w.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(r.shape, a, i, l, o, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     return kn({ inputs: { x: r }, backend: n });
-  let p = new el(c, "avg", false);
+  let p = new tl(c, "avg", false);
   return n.runWebGLProgram(p, [r], "float32");
 }
 var Z8 = { kernelName: Sa, backendName: "webgl", kernelFunc: Q8 };
@@ -27554,7 +27441,7 @@ function sY(e) {
 var rY = { kernelName: og, backendName: "webgl", kernelFunc: sY };
 function aY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
-  tu([r, a], "avgPoolGrad");
+  nu([r, a], "avgPoolGrad");
   let { filterSize: o, strides: u, pad: l } = s, c = S.computePool2DInfo(i.shape, o, u, 1, l), p = new tY(c);
   return n.runWebGLProgram(p, [r], i.dtype);
 }
@@ -27689,7 +27576,7 @@ function gY(e, t, n, s) {
   let u = s.dataRefCount.get(i.slice.origDataId) || 1;
   return s.dataRefCount.set(i.slice.origDataId, u + 1), a;
 }
-function ou(e) {
+function uu(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
   if (wt.assertParamsValid(r, o, u), w.sizeFromShape(u) === 0)
     return n.makeTensorInfo(u, r.dtype, []);
@@ -27704,14 +27591,14 @@ function ou(e) {
   }
   return n.uploadToGPU(r.dataId), gY(r, o, u, n);
 }
-var bY = { kernelName: Oo, backendName: "webgl", kernelFunc: ou };
+var bY = { kernelName: Po, backendName: "webgl", kernelFunc: uu };
 var yY = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
   w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
-  let o = a.reduce((y, v) => y * v), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = [], f = he({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = qt({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = ou({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
+  let o = a.reduce((y, v) => y * v), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = [], f = he({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = qt({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = uu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
   return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
 };
-var vY = { kernelName: io, backendName: "webgl", kernelFunc: yY };
+var vY = { kernelName: oo, backendName: "webgl", kernelFunc: yY };
 function xY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.readSync(r.dataId), u = n.readSync(a.dataId), l = R1(o, u, a.dtype, a.shape, i);
   return n.makeTensorInfo([i], a.dtype, l);
@@ -27724,7 +27611,7 @@ function kY(e) {
 var IY = { kernelName: lg, backendName: "webgl", kernelFunc: kY };
 var SY = "return float(a != b);";
 var X1 = jt({ opSnippet: SY, cpuKernelImpl: tX, dtype: "bool" });
-var CY = { kernelName: Io, backendName: "webgl", kernelFunc: X1 };
+var CY = { kernelName: So, backendName: "webgl", kernelFunc: X1 };
 function Jl(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
   return kn({ inputs: { x: r.complexTensorInfos.real }, backend: n });
@@ -27958,7 +27845,7 @@ function Y1(e) {
   let u = o.map((l) => l.shape);
   return S.assertParamsConsistent(u, a), Gi(o, a, n);
 }
-var UY = { kernelName: oo, backendName: "webgl", kernelFunc: Y1 };
+var UY = { kernelName: uo, backendName: "webgl", kernelFunc: Y1 };
 var Q1 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.outputShape = e.outShape;
@@ -28258,7 +28145,7 @@ function Z1({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluAct
   let u = e.shape, l = s.texData.get(e.dataId), c = n.inChannels, p = u[0] * u[1] * u[2], d = n.outChannels, h = n.dataFormat === "channelsLast", f = false, m = false, g, b = [];
   if (!((p === 1 || d === 1) && c > H1) && l.isPacked && h && l.texture != null && u[2] % 2 !== 0 && w.arraysEqual(l.shape.slice(-3), u.slice(-3))) {
     let x = u[0] * u[1] * (u[2] + 1), k = { dataId: e.dataId, shape: [1, x, n.inChannels], dtype: e.dtype }, T = l.shape;
-    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(Ju(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
+    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(el(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
     let N = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
     b.push(N);
     let E = zd({ a: k, b: N, backend: s, transposeA: f, transposeB: m, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), A = s.texData.get(E.dataId);
@@ -28548,7 +28435,7 @@ function i9(e) {
   return n.runWebGLProgram(c, [r, a], "float32");
 }
 var o9 = { kernelName: pg, backendName: "webgl", kernelFunc: i9 };
-var u9 = iu + `
+var u9 = ou + `
   return cos(x);
 `;
 var l9 = Ke({ opSnippet: u9 });
@@ -28633,7 +28520,7 @@ var m9 = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new f9(r.shape, a.shape, o, u, l);
   return n.runWebGLProgram(c, [r, a, i], "float32");
 };
-var g9 = { kernelName: lo, backendName: "webgl", kernelFunc: m9 };
+var g9 = { kernelName: co, backendName: "webgl", kernelFunc: m9 };
 var bw = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }], this.outputShape = e;
@@ -28697,7 +28584,7 @@ function b9(e) {
   }
   return h;
 }
-var y9 = { kernelName: pl, backendName: "webgl", kernelFunc: b9 };
+var y9 = { kernelName: hl, backendName: "webgl", kernelFunc: b9 };
 var xw = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }], this.outputShape = e;
@@ -28761,7 +28648,7 @@ function v9(e) {
   }
   return h;
 }
-var x9 = { kernelName: uo, backendName: "webgl", kernelFunc: v9 };
+var x9 = { kernelName: lo, backendName: "webgl", kernelFunc: v9 };
 function w9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
   if (r.shape.length === 1) {
@@ -28817,7 +28704,7 @@ function S9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = new I9(f, a, i);
   return n.runWebGLProgram(m, [r], r.dtype);
 }
-var C9 = { kernelName: co, backendName: "webgl", kernelFunc: S9 };
+var C9 = { kernelName: po, backendName: "webgl", kernelFunc: S9 };
 var e2 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = Sn(this.outputShape.length);
@@ -29315,7 +29202,7 @@ var j9 = `
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
 `;
 var K9 = (e) => {
-  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(j9, s.shape, r.shape) : new so(q9, s.shape, r.shape);
+  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(j9, s.shape, r.shape) : new ro(q9, s.shape, r.shape);
   return n.runWebGLProgram(a, [s, r], s.dtype);
 };
 var X9 = { kernelName: bg, backendName: "webgl", kernelFunc: K9 };
@@ -29324,7 +29211,7 @@ var Y9 = `
 `;
 var Q9 = "return float(a == b);";
 var Z9 = jt({ opSnippet: Q9, packedOpSnippet: Y9, dtype: "bool", cpuKernelImpl: MK });
-var J9 = { kernelName: po, backendName: "webgl", kernelFunc: Z9 };
+var J9 = { kernelName: ho, backendName: "webgl", kernelFunc: Z9 };
 var eQ = `
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
@@ -29342,8 +29229,8 @@ var eQ = `
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
 `;
 var tQ = Ke({ opSnippet: eQ });
-var nQ = { kernelName: hl, backendName: "webgl", kernelFunc: tQ };
-var sQ = iu + `
+var nQ = { kernelName: fl, backendName: "webgl", kernelFunc: tQ };
+var sQ = ou + `
   return exp(x);
 `;
 var rQ = `
@@ -29362,10 +29249,10 @@ function qm(e) {
   let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
   return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), he({ inputs: { x: a }, backend: s, attrs: { shape: o } });
 }
-var iQ = { kernelName: ho, backendName: "webgl", kernelFunc: qm };
+var iQ = { kernelName: fo, backendName: "webgl", kernelFunc: qm };
 var Iw = "return exp(x) - 1.0;";
 var oQ = Ke({ opSnippet: Iw, packedOpSnippet: Iw, cpuKernelImpl: BK });
-var uQ = { kernelName: fo, backendName: "webgl", kernelFunc: oQ };
+var uQ = { kernelName: mo, backendName: "webgl", kernelFunc: oQ };
 var Sw = class {
   constructor(e, t, n) {
     this.variableNames = ["real", "imag"];
@@ -29445,7 +29332,7 @@ function ec(e) {
     return t.runWebGLProgram(i, [], a, o);
   }
 }
-var pQ = { kernelName: fl, backendName: "webgl", kernelFunc: ec };
+var pQ = { kernelName: ml, backendName: "webgl", kernelFunc: ec };
 var hQ = class {
   constructor(e) {
     this.variableNames = ["Image"], this.outputShape = [];
@@ -29467,7 +29354,7 @@ var hQ = class {
     `;
   }
 };
-var fQ = { kernelName: mo, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
+var fQ = { kernelName: go, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
   let { image: n } = e, s = t, r = new hQ(n.shape);
   return s.runWebGLProgram(r, [n], n.dtype);
 } };
@@ -29648,7 +29535,7 @@ function AQ(e) {
   let f = new _Q(i, p, [l, c]), m = n.runWebGLProgram(f, [h, d], h.dtype), g = he({ inputs: { x: m }, backend: n, attrs: { shape: u } });
   return n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(m), g;
 }
-var EQ = { kernelName: bo, backendName: "webgl", kernelFunc: AQ };
+var EQ = { kernelName: yo, backendName: "webgl", kernelFunc: AQ };
 var RQ = class {
   constructor(e, t) {
     this.variableNames = ["A", "indices"], this.outputShape = t, this.rank = t.length;
@@ -29690,13 +29577,13 @@ function r2(e) {
   let b = he({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
   return p.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
 }
-var FQ = { kernelName: go, backendName: "webgl", kernelFunc: r2 };
+var FQ = { kernelName: bo, backendName: "webgl", kernelFunc: r2 };
 var OQ = "return float(a > b);";
 var PQ = `
   return vec4(greaterThan(a, b));
 `;
 var zQ = jt({ opSnippet: OQ, packedOpSnippet: PQ, cpuKernelImpl: GK, dtype: "bool" });
-var MQ = { kernelName: yo, backendName: "webgl", kernelFunc: zQ };
+var MQ = { kernelName: vo, backendName: "webgl", kernelFunc: zQ };
 var LQ = "return float(a >= b);";
 var BQ = `
   return vec4(greaterThanEqual(a, b));
@@ -29710,31 +29597,31 @@ function UQ(e) {
 var GQ = { kernelName: vg, backendName: "webgl", kernelFunc: UQ };
 var HQ = "return float(!isnan(x) && !isinf(x));";
 var qQ = Ke({ opSnippet: HQ, dtype: "bool" });
-var jQ = { kernelName: ml, backendName: "webgl", kernelFunc: qQ };
+var jQ = { kernelName: gl, backendName: "webgl", kernelFunc: qQ };
 var KQ = "return float(isinf(x));";
 var XQ = Ke({ opSnippet: KQ, dtype: "bool" });
-var YQ = { kernelName: gl, backendName: "webgl", kernelFunc: XQ };
+var YQ = { kernelName: bl, backendName: "webgl", kernelFunc: XQ };
 var QQ = "return float(isnan(x));";
 var ZQ = Ke({ opSnippet: QQ, dtype: "bool" });
-var JQ = { kernelName: bl, backendName: "webgl", kernelFunc: ZQ };
+var JQ = { kernelName: yl, backendName: "webgl", kernelFunc: ZQ };
 var eZ = "return float(a < b);";
 var tZ = `
   return vec4(lessThan(a, b));
 `;
 var nZ = jt({ opSnippet: eZ, packedOpSnippet: tZ, cpuKernelImpl: qK, dtype: "bool" });
-var sZ = { kernelName: vo, backendName: "webgl", kernelFunc: nZ };
+var sZ = { kernelName: xo, backendName: "webgl", kernelFunc: nZ };
 var rZ = "return float(a <= b);";
 var aZ = `
   return vec4(lessThanEqual(a, b));
 `;
 var iZ = jt({ opSnippet: rZ, packedOpSnippet: aZ, cpuKernelImpl: jK, dtype: "bool" });
-var oZ = { kernelName: xo, backendName: "webgl", kernelFunc: iZ };
+var oZ = { kernelName: wo, backendName: "webgl", kernelFunc: iZ };
 function uZ(e) {
   let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = KK(s, r, a);
   return t.makeTensorInfo([i.length], "float32", i);
 }
 var lZ = { kernelName: xg, backendName: "webgl", kernelFunc: uZ };
-var cZ = iu + `
+var cZ = ou + `
   return x < 0.0 ? 0./0. : log(x);
 `;
 var dZ = `
@@ -29748,11 +29635,11 @@ var dZ = `
 `;
 var pZ = Ke({ opSnippet: cZ, packedOpSnippet: dZ, cpuKernelImpl: XK });
 var hZ = { kernelName: Wa, backendName: "webgl", kernelFunc: pZ };
-var fZ = iu + `
+var fZ = ou + `
   return log(1.0 + x);
 `;
 var mZ = Ke({ opSnippet: fZ });
-var gZ = { kernelName: yl, backendName: "webgl", kernelFunc: mZ };
+var gZ = { kernelName: vl, backendName: "webgl", kernelFunc: mZ };
 var bZ = "return float(a >= 1.0 && b >= 1.0);";
 var yZ = `
   return vec4(
@@ -29760,10 +29647,10 @@ var yZ = `
     vec4(greaterThanEqual(b, vec4(1.0))));
 `;
 var vZ = jt({ opSnippet: bZ, packedOpSnippet: yZ, dtype: "bool" });
-var xZ = { kernelName: wo, backendName: "webgl", kernelFunc: vZ };
+var xZ = { kernelName: ko, backendName: "webgl", kernelFunc: vZ };
 var wZ = "return float(!(x >= 1.0));";
 var kZ = Ke({ opSnippet: wZ });
-var IZ = { kernelName: vl, backendName: "webgl", kernelFunc: kZ };
+var IZ = { kernelName: xl, backendName: "webgl", kernelFunc: kZ };
 var SZ = "return float(a >= 1.0 || b >= 1.0);";
 var CZ = `
   return min(
@@ -29988,13 +29875,13 @@ var LZ = jt({ opSnippet: zZ, packedOpSnippet: MZ, cpuKernelImpl: QK });
 var BZ = { kernelName: Ga, backendName: "webgl", kernelFunc: LZ };
 function VZ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
-  tu(r, "maxPool");
+  nu(r, "maxPool");
   let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
   w.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(r.shape, a, i, l, o, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     return kn({ inputs: { x: r }, backend: n });
-  let p = new el(c, "max", false);
+  let p = new tl(c, "max", false);
   return n.runWebGLProgram(p, [r], r.dtype);
 }
 var WZ = { kernelName: Ha, backendName: "webgl", kernelFunc: VZ };
@@ -30134,14 +30021,14 @@ function jZ(e) {
 var KZ = { kernelName: Ig, backendName: "webgl", kernelFunc: jZ };
 function XZ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
-  tu([a, i], "maxPoolGrad");
-  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = S.computePool2DInfo(o.shape, u, l, 1, c, p), h = true, f = new el(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new HZ(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
+  nu([a, i], "maxPoolGrad");
+  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = S.computePool2DInfo(o.shape, u, l, 1, c, p), h = true, f = new tl(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new HZ(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
   return n.disposeIntermediateTensorInfo(m), b;
 }
 var YZ = { kernelName: kg, backendName: "webgl", kernelFunc: XZ };
 function QZ(e, t, n, s) {
-  let r = new el(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
-  r = new el(n, "max", true, true, t);
+  let r = new tl(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
+  r = new tl(n, "max", true, true, t);
   let i = s.runWebGLProgram(r, [e], "float32");
   return [a, i];
 }
@@ -30327,7 +30214,7 @@ var p7 = `
   return result;
 `;
 var h7 = jt({ opSnippet: d7, packedOpSnippet: p7 });
-var f7 = { kernelName: xl, backendName: "webgl", kernelFunc: h7 };
+var f7 = { kernelName: wl, backendName: "webgl", kernelFunc: h7 };
 var m7 = class {
   constructor(e, t, n) {
     this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, n], this.userCode = `
@@ -30415,28 +30302,28 @@ function C7(e) {
   let r;
   return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Zr(s.shape, S7) : r = new Hs(s.shape, I7), n.runWebGLProgram(r, [s], s.dtype);
 }
-var N7 = { kernelName: ko, backendName: "webgl", kernelFunc: C7 };
+var N7 = { kernelName: Io, backendName: "webgl", kernelFunc: C7 };
 var T7 = xs.nonMaxSuppressionV3Impl;
 function $7(e) {
   S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = T7(l, c, i, o, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var _7 = { kernelName: So, backendName: "webgl", kernelFunc: $7 };
+var _7 = { kernelName: Co, backendName: "webgl", kernelFunc: $7 };
 var A7 = xs.nonMaxSuppressionV4Impl;
 function E7(e) {
   S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), { selectedIndices: d, validOutputs: h } = A7(c, p, i, o, u, l);
   return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
 }
-var R7 = { kernelName: wl, backendName: "webgl", kernelFunc: E7 };
+var R7 = { kernelName: kl, backendName: "webgl", kernelFunc: E7 };
 var D7 = xs.nonMaxSuppressionV5Impl;
 function F7(e) {
   S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = D7(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var O7 = { kernelName: Co, backendName: "webgl", kernelFunc: F7 };
+var O7 = { kernelName: No, backendName: "webgl", kernelFunc: F7 };
 var P7 = class {
   constructor(e, t, n, s) {
     this.variableNames = ["indices"], this.outputShape = [e, t], this.userCode = `
@@ -30455,7 +30342,7 @@ var z7 = (e) => {
   let d = [...r.shape, a], h = he({ inputs: { x: p }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(p), h;
 };
-var M7 = { kernelName: To, backendName: "webgl", kernelFunc: z7 };
+var M7 = { kernelName: $o, backendName: "webgl", kernelFunc: z7 };
 function Md(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "complex64") {
@@ -30464,7 +30351,7 @@ function Md(e) {
   } else
     return ec({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
 }
-var L7 = { kernelName: Go, backendName: "webgl", kernelFunc: Md };
+var L7 = { kernelName: Ho, backendName: "webgl", kernelFunc: Md };
 function l2(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
@@ -30475,7 +30362,7 @@ function l2(e) {
   } else
     return ec({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
 }
-var B7 = { kernelName: No, backendName: "webgl", kernelFunc: l2 };
+var B7 = { kernelName: To, backendName: "webgl", kernelFunc: l2 };
 function V7(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
@@ -30490,7 +30377,7 @@ function V7(e) {
   }), l = Y1({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
 }
-var W7 = { kernelName: $o, backendName: "webgl", kernelFunc: V7 };
+var W7 = { kernelName: _o, backendName: "webgl", kernelFunc: V7 };
 var U7 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t.map((u, l) => u[0] + e[l] + u[1]);
@@ -30617,15 +30504,15 @@ function Y7(e) {
   }
   return u.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
 }
-var Q7 = { kernelName: _o, backendName: "webgl", kernelFunc: Y7 };
+var Q7 = { kernelName: Ao, backendName: "webgl", kernelFunc: Y7 };
 var d2 = (e) => {
   let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = sX(s, r, a, i);
   return t.makeTensorInfo([o.length], i, o);
 };
-var Z7 = { kernelName: kl, backendName: "webgl", kernelFunc: d2 };
+var Z7 = { kernelName: Il, backendName: "webgl", kernelFunc: d2 };
 var J7 = "return 1.0 / x;";
 var eJ = Ke({ opSnippet: J7 });
-var tJ = { kernelName: Il, backendName: "webgl", kernelFunc: eJ };
+var tJ = { kernelName: Sl, backendName: "webgl", kernelFunc: eJ };
 var nJ = ss + `
   return (x < 0.0) ? 0.0 : x;
 `;
@@ -30969,7 +30856,7 @@ function vJ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new yJ(r.shape, u, l, a, i) : new bJ(r.shape, u, l, a, i);
   return n.runWebGLProgram(c, [r], r.dtype);
 }
-var xJ = { kernelName: Sl, backendName: "webgl", kernelFunc: vJ };
+var xJ = { kernelName: Cl, backendName: "webgl", kernelFunc: vJ };
 var wJ = class {
   constructor(e, t, n) {
     this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t;
@@ -31141,7 +31028,7 @@ function NJ(e) {
   let u = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new CJ(r.shape, o) : new SJ(r.shape, o);
   return n.runWebGLProgram(u, [r], r.dtype);
 }
-var TJ = { kernelName: Eo, backendName: "webgl", kernelFunc: NJ };
+var TJ = { kernelName: Ro, backendName: "webgl", kernelFunc: NJ };
 var $J = class {
   constructor(e, t) {
     this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }];
@@ -31170,7 +31057,7 @@ var $J = class {
     `;
   }
 };
-var _J = { kernelName: Ho, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var _J = { kernelName: qo, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new $J(s.shape, a), [l, c] = S.getImageCenter(i, s.shape[1], s.shape[2]), p = [[l, c, Math.sin(r), Math.cos(r)]];
   return o.runWebGLProgram(u, [s], s.dtype, p);
 } };
@@ -31191,7 +31078,7 @@ var AJ = `
   }
 `;
 var EJ = Ke({ opSnippet: AJ });
-var RJ = { kernelName: Ro, backendName: "webgl", kernelFunc: EJ };
+var RJ = { kernelName: Do, backendName: "webgl", kernelFunc: EJ };
 var DJ = "return inversesqrt(x);";
 var FJ = Ke({ opSnippet: DJ, cpuKernelImpl: rX });
 var OJ = { kernelName: si, backendName: "webgl", kernelFunc: FJ };
@@ -31233,7 +31120,7 @@ function PJ(e) {
   let h = he({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = he({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = n.makeTensorInfo([], "float32", new Float32Array([0])), g = new p2(u, o, h.shape.length, f.shape.length, c, d), b = n.runWebGLProgram(g, [f, h, m], f.dtype), y = he({ inputs: { x: b }, backend: n, attrs: { shape: i } });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(m), y;
 }
-var zJ = { kernelName: Do, backendName: "webgl", kernelFunc: PJ };
+var zJ = { kernelName: Fo, backendName: "webgl", kernelFunc: PJ };
 var MJ = class {
   constructor(e, t, n) {
     this.variableNames = ["c", "a", "b"], this.outputShape = t;
@@ -31266,7 +31153,7 @@ function LJ(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new MJ(s.shape.length, r.shape, r.shape.length);
   return n.runWebGLProgram(i, [s, r, a], cn(r.dtype, a.dtype));
 }
-var BJ = { kernelName: Fo, backendName: "webgl", kernelFunc: LJ };
+var BJ = { kernelName: Oo, backendName: "webgl", kernelFunc: LJ };
 var VJ = `
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
@@ -31275,8 +31162,8 @@ var VJ = `
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
 `;
 var WJ = Ke({ opSnippet: VJ });
-var UJ = { kernelName: Cl, backendName: "webgl", kernelFunc: WJ };
-var GJ = iu + `
+var UJ = { kernelName: Nl, backendName: "webgl", kernelFunc: WJ };
+var GJ = ou + `
   return 1.0 / (1.0 + exp(-1.0 * x));
 `;
 var HJ = `
@@ -31297,8 +31184,8 @@ var KJ = `
   return sign(x);
 `;
 var XJ = Ke({ opSnippet: KJ });
-var YJ = { kernelName: Nl, backendName: "webgl", kernelFunc: XJ };
-var QJ = iu + `
+var YJ = { kernelName: Tl, backendName: "webgl", kernelFunc: XJ };
+var QJ = ou + `
   return sin(x);
 `;
 var ZJ = Ke({ opSnippet: QJ });
@@ -31308,7 +31195,7 @@ var eee = `
   return (e2x - 1.0 / e2x) / 2.0;
 `;
 var tee = Ke({ opSnippet: eee });
-var nee = { kernelName: Po, backendName: "webgl", kernelFunc: tee };
+var nee = { kernelName: zo, backendName: "webgl", kernelFunc: tee };
 var see = `
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
@@ -31331,7 +31218,7 @@ var see = `
   return result;
 `;
 var ree = Ke({ opSnippet: see });
-var aee = { kernelName: Tl, backendName: "webgl", kernelFunc: ree };
+var aee = { kernelName: $l, backendName: "webgl", kernelFunc: ree };
 var iee = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
   w.assert(r.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
@@ -31342,7 +31229,7 @@ var iee = (e) => {
   let l = [], c = c2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = S.getReshaped(c.shape, a, o, false), d = S.getPermuted(p.length, a.length, false), h = S.getReshapedPermuted(c.shape, a, o, false), f = he({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = qt({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: h } });
   return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
 };
-var oee = { kernelName: zo, backendName: "webgl", kernelFunc: iee };
+var oee = { kernelName: Mo, backendName: "webgl", kernelFunc: iee };
 function uee(e) {
   let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
   if (a.shape.length !== 1)
@@ -31372,7 +31259,7 @@ function cee(e) {
   let i = Array.from(n.readSync(r.dataId)), o = n.readSync(s.dataId), u = Array.from(n.readSync(a.dataId)), [l, c, p] = uX(o, s.shape, s.dtype, i, u);
   return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
 }
-var dee = { kernelName: $l, backendName: "webgl", kernelFunc: cee };
+var dee = { kernelName: _l, backendName: "webgl", kernelFunc: cee };
 function pee(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
@@ -31411,17 +31298,17 @@ function yee(e) {
   return u.map((d) => {
     let h = [...p];
     h[o] = d;
-    let f = ou({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
+    let f = uu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
     return c[o] += d, f;
   });
 }
-var vee = { kernelName: Mo, backendName: "webgl", kernelFunc: yee };
+var vee = { kernelName: Lo, backendName: "webgl", kernelFunc: yee };
 var Tw = "return sqrt(x);";
 var xee = Ke({ opSnippet: Tw, packedOpSnippet: Tw, cpuKernelImpl: lX });
 var wee = { kernelName: ii, backendName: "webgl", kernelFunc: xee };
 var kee = "return x * x;";
 var Iee = Ke({ opSnippet: kee });
-var See = { kernelName: _l, backendName: "webgl", kernelFunc: Iee };
+var See = { kernelName: Al, backendName: "webgl", kernelFunc: Iee };
 var $w = "return (a - b) * (a - b);";
 var Cee = jt({ opSnippet: $w, packedOpSnippet: $w });
 var Nee = { kernelName: li, backendName: "webgl", kernelFunc: Cee };
@@ -31459,7 +31346,7 @@ function Aee(e) {
     k = he({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let N = wt.computeOutShape(y, v, x), E = ou({ inputs: { x: r }, backend: n, attrs: { begin: y, size: N } });
+    let N = wt.computeOutShape(y, v, x), E = uu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: N } });
     k = he({ inputs: { x: E }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo(E);
   } else if (n.shouldExecuteOnCPU([r])) {
     let E = n.readSync(r.dataId), A = De(r.shape, r.dtype, E), P = cX(h, A, x, y);
@@ -31471,7 +31358,7 @@ function Aee(e) {
   let T = he({ inputs: { x: k }, backend: n, attrs: { shape: f } });
   return n.disposeIntermediateTensorInfo(k), T;
 }
-var Eee = { kernelName: Lo, backendName: "webgl", kernelFunc: Aee };
+var Eee = { kernelName: Bo, backendName: "webgl", kernelFunc: Aee };
 function Ree(e) {
   let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = dX(d, h, r, a, i, o, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
@@ -31501,7 +31388,7 @@ function Pee(e) {
 var zee = { kernelName: _g, backendName: "webgl", kernelFunc: Pee };
 var Mee = "return tan(x);";
 var Lee = Ke({ opSnippet: Mee });
-var Bee = { kernelName: Bo, backendName: "webgl", kernelFunc: Lee };
+var Bee = { kernelName: Vo, backendName: "webgl", kernelFunc: Lee };
 var Vee = `
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
@@ -31667,7 +31554,7 @@ function Xee(e) {
       k(K, Y, v.shape);
   }
   let T = v;
-  v = ou({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Ur(n, T);
+  v = uu({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Ur(n, T);
   let N = r2({ inputs: { x: g, indices: v }, backend: n, attrs: { axis: 1, batchDims: 1 } });
   Ur(n, g);
   let E = l.slice(0, -1);
@@ -31675,7 +31562,7 @@ function Xee(e) {
   let A = N;
   return N = he({ inputs: { x: N }, attrs: { shape: E }, backend: n }), Ur(n, A), [N, v];
 }
-var Yee = { kernelName: Vo, backendName: "webgl", kernelFunc: Xee };
+var Yee = { kernelName: Wo, backendName: "webgl", kernelFunc: Xee };
 var Qee = class {
   constructor(e, t, n, s, r, a) {
     this.variableNames = ["Image", "Transforms"], this.outputShape = a;
@@ -31816,10 +31703,10 @@ function Zee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Qee(p, d, i, o, u, g);
   return n.runWebGLProgram(b, [r, a], "float32");
 }
-var Jee = { kernelName: Wo, backendName: "webgl", kernelFunc: Zee };
+var Jee = { kernelName: Uo, backendName: "webgl", kernelFunc: Zee };
 function ete(e) {
   let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
-  tu(a, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
+  nu(a, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
   let i = s.readSync(a.dataId), { outputValues: o, outputShape: u, indices: l } = bX(i, r, a.shape, a.dtype);
   return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
 }
@@ -31835,12 +31722,12 @@ function nte(e) {
   let f = new Array(u);
   for (let m = 0; m < f.length; m++) {
     d[a] = m;
-    let g = ou({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = he({ inputs: { x: g }, backend: n, attrs: { shape: l } });
+    let g = uu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = he({ inputs: { x: g }, backend: n, attrs: { shape: l } });
     f[m] = b, p.push(g);
   }
   return p.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
 }
-var ste = { kernelName: Uo, backendName: "webgl", kernelFunc: nte };
+var ste = { kernelName: Go, backendName: "webgl", kernelFunc: nte };
 var rte = class {
   constructor(e, t) {
     this.variableNames = ["x", "segmentIds"];
@@ -31980,7 +31867,7 @@ function ate(e) {
 var ite = { kernelName: up, backendName: "webgl", kernelFunc: ate };
 var ote = [d8, h8, g8, v8, w8, S8, N8, $8, R8, F8, z8, B8, U8, j8, Y8, Z8, eY, rY, iY, uY, pY, vY, wY, IY, _Y, EY, OY, qX, MY, UY, jY, JY, t9, s9, a9, o9, c9, h9, g9, y9, x9, k9, C9, T9, E9, D9, P9, L9, V9, H9, X9, J9, nQ, aQ, iQ, uQ, cQ, pQ, fQ, gQ, xQ, IQ, NQ, $Q, EQ, FQ, MQ, WQ, HX, GQ, VY, jQ, YQ, JQ, KX, sZ, oZ, lZ, hZ, gZ, xZ, IZ, TZ, EZ, FZ, PZ, BZ, WZ, GZ, KZ, YZ, ZZ, e7, n7, i7, c7, f7, k7, JX, N7, _7, R7, O7, CY, M7, B7, W7, H7, X7, YX, Q7, Z7, NY, y7, tJ, aJ, lJ, t8, hJ, gJ, xJ, IJ, TJ, _J, RJ, OJ, zJ, BJ, UJ, jJ, YJ, JJ, nee, bY, x7, aee, oee, lee, dee, hee, mee, bee, vee, wee, See, Nee, $ee, Eee, Dee, Oee, zee, v7, u8, Bee, Uee, qee, Yee, Jee, l8, tte, ste, ite, L7];
 for (let e of ote)
-  Al(e);
+  El(e);
 var zs = X();
 zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
 zs.registerFlag("WEBGPU_CPU_FORWARD", () => true);
@@ -32189,10 +32076,14 @@ var qte = `if (a < 0.0) { return 1.0/0.0; }
 var jte = "return f32(!(a >= 1.0));";
 var Kte = "return -a;";
 var Xte = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
-var Yte = "if(a < 0.0) { return 0.0; } return a;";
-var Qte = "return clamp(a, 0.0, 6.0);";
-var Zte = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
-var Jte = `
+var Yte = `
+  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
+  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
+`;
+var Qte = "if(a < 0.0) { return 0.0; } return a;";
+var Zte = "return clamp(a, 0.0, 6.0);";
+var Jte = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
+var ene = `
   var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
   let isNaN = isnanVec4(a);
 
@@ -32210,20 +32101,20 @@ var Jte = `
   }
   return resFloat;
 `;
-var ene = "return 1.0/sqrt(a);";
-var tne = "return 1.0 / (1.0 + exp(-1.0 * a));";
-var nne = "return sin(a);";
-var sne = `
+var tne = "return 1.0/sqrt(a);";
+var nne = "return 1.0 / (1.0 + exp(-1.0 * a));";
+var sne = "return sin(a);";
+var rne = `
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
 `;
-var rne = "return sqrt(a);";
-var ane = "return a * a;";
-var ine = `
+var ane = "return sqrt(a);";
+var ine = "return a * a;";
+var one = `
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
 `;
-var one = "return f32(i32((a)));";
+var une = "return f32(i32((a)));";
 function Hr(e, t) {
   switch (e) {
     case 0:
@@ -32251,27 +32142,27 @@ function Hr(e, t) {
     case 11:
       return Kte;
     case 14:
-      return Xte;
+      return t ? Yte : Xte;
     case 12:
-      return t ? Jte : Yte;
+      return t ? ene : Qte;
     case 13:
-      return t ? Zte : Qte;
+      return t ? Jte : Zte;
     case 15:
-      return ene;
-    case 18:
       return tne;
-    case 16:
+    case 18:
       return nne;
-    case 17:
+    case 16:
       return sne;
-    case 19:
+    case 17:
       return rne;
-    case 20:
+    case 19:
       return ane;
-    case 21:
+    case 20:
       return ine;
-    case 22:
+    case 21:
       return one;
+    case 22:
+      return une;
     default:
       throw new Error(`BinaryType ${e} is not implemented!`);
   }
@@ -32290,12 +32181,12 @@ function Zs(e, t = false) {
   if (e === "prelu")
     return tc(14, t);
   if (e === "sigmoid")
-    return Hr(18);
+    return Hr(18, t);
   if (e === "leakyrelu")
-    return Hr(14);
+    return Hr(14, t);
   throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);
 }
-function une(e, t) {
+function lne(e, t) {
   if (Math.max(...e) > 3)
     throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");
   let n = e.length, s = e.map((a) => `${t}[${a}]`), r = new Array(n - 1);
@@ -32340,7 +32231,7 @@ function Ue() {
       let index = getGlobalIndex();
 `;
 }
-function lne(e, t, n, s = false) {
+function cne(e, t, n, s = false) {
   let r = [];
   if (r.push(`
     let workGroupSizeX = ${n.workGroupSize[0]}u;
@@ -32398,10 +32289,10 @@ function lne(e, t, n, s = false) {
   }), a !== "" && r.push(`
     @group(0) @binding(${1 + n.variableNames.length}) var<uniform> uniforms: Uniforms;
     `);
-  let [o, u] = mne(t.shape, n.dispatchLayout), l = [Ew, r.join(`
-`), Rw(t.shape), o, cne(t.shape.length)];
-  if (n.atomic || l.push(dne(t.shape, t.dtype, n.isVec4)), u === t.shape.length) {
-    let p = e.map((d) => pne(d, t.shape, n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
+  let [o, u] = gne(t.shape, n.dispatchLayout), l = [Ew, r.join(`
+`), Rw(t.shape), o, dne(t.shape.length)];
+  if (n.atomic || l.push(pne(t.shape, t.dtype, n.isVec4)), u === t.shape.length) {
+    let p = e.map((d) => hne(d, t.shape, n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
 `);
     l.push(p);
   }
@@ -32456,7 +32347,7 @@ var Ew = `
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
 `;
-function cne(e) {
+function dne(e) {
   let t = "";
   switch (e) {
     case 0:
@@ -32495,7 +32386,7 @@ function cne(e) {
   }
   return t;
 }
-function dne(e, t, n) {
+function pne(e, t, n) {
   let s = e.length, r = cd(t, n), a;
   if (n ? a = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${r}(value);
@@ -32531,11 +32422,11 @@ function dne(e, t, n) {
   }
   return a;
 }
-function pne(e, t, n, s) {
-  let r = hne(e, n);
-  return e.shape.length <= t.length && (r += fne(e, t, n, s)), r;
+function hne(e, t, n, s) {
+  let r = fne(e, n);
+  return e.shape.length <= t.length && (r += mne(e, t, n, s)), r;
 }
-function hne(e, t) {
+function fne(e, t) {
   let n = e.name, s = e.shape.length, r = Wt(s), a = "get" + n.charAt(0).toUpperCase() + n.slice(1), i = ["d0", "d1", "d2", "d3"].slice(0, s), o = i.map((c) => `${c} : i32`).join(", ");
   if (s < 1)
     return t ? `
@@ -32560,7 +32451,7 @@ function hne(e, t) {
     }
    `;
 }
-function fne(e, t, n, s) {
+function mne(e, t, n, s) {
   let r = e.name, a = r.charAt(0).toUpperCase() + r.slice(1), i = "get" + a + "ByOutput", o = e.shape.length, u = t.length, l = Wt(u);
   if (w.arraysEqual(e.shape, t) && s)
     return n ? `
@@ -32636,7 +32527,7 @@ function fne(e, t, n, s) {
     }
   `;
 }
-function mne(e, t) {
+function gne(e, t) {
   let { x: n, y: s = [], z: r = [] } = t, a = e.length;
   if (n.length === a)
     return [`fn getOutputCoords() -> ${Wt(a)}{
@@ -32651,7 +32542,7 @@ function mne(e, t) {
       if (u += h.length, h.length === 1)
         i += `let d${h[0]} = i32(globalId[${d}]);`;
       else {
-        let f = une(h, "uniforms.outShape");
+        let f = lne(h, "uniforms.outShape");
         i += `var index${d} = i32(globalId[${d}]);`;
         for (let m = 0; m < f.length; m++)
           i += `let d${h[m]} = index${d} / ${f[m]};`, m === f.length - 1 ? i += `let d${h[m + 1]} = index${d} - d${h[m]} * ${f[m]};` : i += `index${d} = index${d} - d${h[m]} * ${f[m]};`;
@@ -32808,7 +32699,7 @@ function y2(e, t, n, s) {
     }
   }`;
 }
-var gne = class {
+var bne = class {
   constructor(e, t, n, s = null, r = null, a = null) {
     this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] }, t[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
     let i = s != null, o = a != null;
@@ -32954,7 +32845,7 @@ function $v(e, t) {
     }
   `;
 }
-function bne(e) {
+function yne(e) {
   return `
     let TileSize = ${e[0] * 4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
@@ -33069,11 +32960,11 @@ var v2 = class {
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${this.outputShape[1] > 1 ? $v([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : bne(this.workGroupSize)}
+      ${this.outputShape[1] > 1 ? $v([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : yne(this.workGroupSize)}
     `;
   }
 };
-function yne() {
+function vne() {
   return `
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
     ${Dr()}
@@ -33107,7 +32998,7 @@ function yne() {
     }
   `;
 }
-var vne = class {
+var xne = class {
   constructor(e, t = false, n = false, s = null, r = null, a = null) {
     this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize);
     let i = s != null, o = a != null;
@@ -33151,11 +33042,11 @@ var vne = class {
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${yne()}
+      ${vne()}
     `;
   }
 };
-function xne(e) {
+function wne(e) {
   let t = e[1] / 2, n = e[0], s = t > n ? t : n;
   return `
   var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
@@ -33246,7 +33137,7 @@ function xne(e) {
   }
   `;
 }
-var wne = class {
+var kne = class {
   constructor(e, t, n, s = null, r = null, a = null) {
     this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 16, 1], w.assert(e[1] <= 16 || t[2] <= 16, () => "This program can be only used when A width or B Height are small"), this.outputShape = n, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(n[2] / this.workGroupSize[0]), Math.ceil(n[1] * 2 / this.workGroupSize[1]), n[0]];
     let i = s != null;
@@ -33295,7 +33186,7 @@ var wne = class {
           setOutputAtCoords(batch, row, col, value);
         }
       }
-      ${xne(this.workGroupSize)}
+      ${wne(this.workGroupSize)}
     `;
   }
 };
@@ -33303,12 +33194,12 @@ function Me(e) {
   let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a), o = w.sizeFromShape(i);
   return w.assert(a === o, () => `The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
 }
-var kne = { kernelName: Ao, backendName: "webgpu", kernelFunc: Me };
+var Ine = { kernelName: Eo, backendName: "webgpu", kernelFunc: Me };
 function _v({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
-  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = qo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
+  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = jo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
   w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
   let k = n ? [b, p, h] : [b, h, p], T = s ? [y, f, d] : [y, d, f], N = Me({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = Me({ inputs: { x: t }, backend: r, attrs: { shape: T } }), A = [N, E], P = Math.max(b, y), R = p % 4 === 0 && f % 4 === 0 && !n && !s && f >= 32, F;
-  h * f <= 32 ? F = new vne([P, h, f], n, s, a, u, i) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? F = new wne(k, T, [P, h, f], a, u, i) : R ? F = new gne(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), a, u, i) : F = new v2(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), n, s, a, u, i);
+  h * f <= 32 ? F = new xne([P, h, f], n, s, a, u, i) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? F = new kne(k, T, [P, h, f], a, u, i) : R ? F = new bne(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), a, u, i) : F = new v2(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), n, s, a, u, i);
   let $ = [N, E];
   a && $.push(a), i && $.push(i);
   let z = [{ type: "int32", data: [h] }, { type: "int32", data: [f] }, { type: "int32", data: [p] }];
@@ -33319,11 +33210,11 @@ function _v({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = nu
     r.disposeData(K.dataId);
   return q;
 }
-function Ine(e) {
+function Sne(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
   return _v({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
 }
-var Sne = { kernelName: ra, backendName: "webgpu", kernelFunc: Ine };
+var Cne = { kernelName: ra, backendName: "webgpu", kernelFunc: Sne };
 var Dw = class {
   constructor(e, t, n) {
     this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = S.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
@@ -33347,7 +33238,7 @@ var Dw = class {
     `;
   }
 };
-var Cne = class {
+var Nne = class {
   constructor(e, t, n, s) {
     this.variableNames = ["A", "B"], this.size = true;
     let r = 256;
@@ -33385,7 +33276,7 @@ var Cne = class {
         `;
   }
 };
-var Nne = class {
+var Tne = class {
   constructor(e, t, n) {
     this.variableNames = ["A", "B"], this.workPerThread = 4, this.isVec4 = true, this.size = true;
     let s = 128;
@@ -33429,20 +33320,20 @@ var x2 = class {
 };
 function Fw(e, t, n) {
   if (w.arraysEqual(t, n) && w.sizeFromShape(t) % 4 === 0)
-    return new Nne(e, t, n);
+    return new Tne(e, t, n);
   let r = t.length === 1 && n.length > 1 && t[0] < 1024, a = n.length === 1 && t.length > 1 && n[0] < 1024;
-  return r || a ? new Cne(e, t, n, a) : new x2(e, t, n);
+  return r || a ? new Nne(e, t, n, a) : new x2(e, t, n);
 }
 function es(e) {
   let { inputs: t } = e, { x: n } = t;
   return e.backend.incRef(n.dataId), { dataId: n.dataId, shape: n.shape, dtype: n.dtype };
 }
-var Tne = { kernelName: Ba, backendName: "webgpu", kernelFunc: es };
-function uu(e) {
+var $ne = { kernelName: Ba, backendName: "webgpu", kernelFunc: es };
+function lu(e) {
   let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.tensorMap.get(a.dataId), o = es({ inputs: { x: s }, backend: n }), u = es({ inputs: { x: r }, backend: n });
   return i.complexTensorInfos = { real: o, imag: u }, a;
 }
-var $ne = { kernelName: jd, backendName: "webgpu", kernelFunc: uu };
+var _ne = { kernelName: jd, backendName: "webgpu", kernelFunc: lu };
 var nc = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.size = true;
@@ -33488,7 +33379,7 @@ function mn({ opSnippet: e, cpuKernelImpl: t, supportsComplex: n = false, dtype:
         let g = new Dw(17, i.shape, o.shape), b = new Dw(18, i.shape, o.shape), y = [{ dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: i.shape }, { dataId: d.complexTensorInfos.real.dataId, dtype: d.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: d.complexTensorInfos.imag.dataId, dtype: d.complexTensorInfos.imag.dtype, shape: o.shape }];
         h = u.runWebGPUProgram(g, y, "float32"), f = u.runWebGPUProgram(b, y, "float32");
       }
-      let m = uu({ inputs: { real: h, imag: f }, backend: u });
+      let m = lu({ inputs: { real: h, imag: f }, backend: u });
       return u.disposeData(h.dataId), u.disposeData(f.dataId), m;
     }
     let l = s || cn(i.dtype, o.dtype);
@@ -33500,12 +33391,12 @@ function mn({ opSnippet: e, cpuKernelImpl: t, supportsComplex: n = false, dtype:
     return u.runWebGPUProgram(c, [i, o], l);
   };
 }
-var { addImpl: _ne, ceilImpl: Ane, concatImpl: Ene, equalImpl: Rne, expImpl: Dne, expm1Impl: Fne, floorImpl: One, gatherNdImpl: Pne, gatherV2Impl: zne, greaterEqualImpl: Mne, greaterImpl: Lne, lessEqualImpl: Bne, lessImpl: Vne, logImpl: Wne, maxImpl: Une, maximumImpl: Gne, minimumImpl: Hne, multiplyImpl: qne, negImpl: jne, notEqualImpl: Kne, prodImpl: Xne, rangeImpl: Yne, rsqrtImpl: Qne, simpleAbsImpl: Zne, sliceImpl: Jne, stridedSliceImpl: ese, stringNGramsImpl: tse, subImpl: nse, tileImpl: sse, topKImpl: rse, transposeImpl: ase, uniqueImpl: zpe } = Zy;
-var ise = Kt({ opType: 0, cpuKernelImpl: Zne });
-var ose = { kernelName: ao, backendName: "webgpu", kernelFunc: ise };
-var use = mn({ opSnippet: 1, cpuKernelImpl: _ne, supportsComplex: true });
-var lse = { kernelName: Ir, backendName: "webgpu", kernelFunc: use };
-var cse = class {
+var { addImpl: Ane, ceilImpl: Ene, concatImpl: Rne, equalImpl: Dne, expImpl: Fne, expm1Impl: One, floorImpl: Pne, gatherNdImpl: zne, gatherV2Impl: Mne, greaterEqualImpl: Lne, greaterImpl: Bne, lessEqualImpl: Vne, lessImpl: Wne, logImpl: Une, maxImpl: Gne, maximumImpl: Hne, minimumImpl: qne, multiplyImpl: jne, negImpl: Kne, notEqualImpl: Xne, prodImpl: Yne, rangeImpl: Qne, rsqrtImpl: Zne, simpleAbsImpl: Jne, sliceImpl: ese, stridedSliceImpl: tse, stringNGramsImpl: nse, subImpl: sse, tileImpl: rse, topKImpl: ase, transposeImpl: ise, uniqueImpl: Mpe } = Zy;
+var ose = Kt({ opType: 0, cpuKernelImpl: Jne });
+var use = { kernelName: io, backendName: "webgpu", kernelFunc: ose };
+var lse = mn({ opSnippet: 1, cpuKernelImpl: Ane, supportsComplex: true });
+var cse = { kernelName: Ir, backendName: "webgpu", kernelFunc: lse };
+var dse = class {
   constructor(e) {
     this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN";
   }
@@ -33530,14 +33421,14 @@ var cse = class {
     `;
   }
 };
-function dse(e) {
+function pse(e) {
   let { inputs: t, backend: n } = e, s = t;
   if (s.length === 1)
     return es({ inputs: { x: s[0] }, backend: n });
-  let r = s.map((o) => o.dtype).reduce((o, u) => cn(o, u)), a = s.map((o) => o.shape), i = new cse(a);
+  let r = s.map((o) => o.dtype).reduce((o, u) => cn(o, u)), a = s.map((o) => o.shape), i = new dse(a);
   return n.runWebGPUProgram(i, s, r);
 }
-var pse = { kernelName: ka, backendName: "webgpu", kernelFunc: dse };
+var hse = { kernelName: ka, backendName: "webgpu", kernelFunc: pse };
 var w2 = class {
   constructor(e, t, n) {
     this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "axis : i32, infinityValue : f32,", this.size = true;
@@ -33631,7 +33522,7 @@ var w2 = class {
     `;
   }
 };
-var hse = class {
+var fse = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.workGroupSize = [16, 16, 1];
     let n = new Array(e.length);
@@ -33665,7 +33556,7 @@ var hse = class {
     `;
   }
 };
-var fse = class {
+var mse = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true;
     let n = new Array(e.length);
@@ -33674,7 +33565,7 @@ var fse = class {
     this.outputShape = n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.newDim = t, this.shaderKey = `transpose_${t}`;
   }
   getUserCode() {
-    let e = Wt(this.outputShape.length), t = mse(this.newDim);
+    let e = Wt(this.outputShape.length), t = gse(this.newDim);
     return `
       ${Ue()}
 
@@ -33690,7 +33581,7 @@ var fse = class {
     `;
   }
 };
-function mse(e) {
+function gse(e) {
   let t = e.length;
   if (t > 4)
     throw Error(`Transpose for rank ${t} is not yet supported`);
@@ -33704,31 +33595,31 @@ function wi(e) {
   for (let c = 0; c < u.length; c++)
     u[c] = r.shape[a[c]];
   if (n.shouldExecuteOnCPU([r])) {
-    let p = i.tensorMap.get(r.dataId).values, d = ase(p, r.shape, r.dtype, a, u);
+    let p = i.tensorMap.get(r.dataId).values, d = ise(p, r.shape, r.dtype, a, u);
     return n.makeTensorInfo(u, r.dtype, d);
   }
   if (r.shape.length === 2 && w.arraysEqual(a, [1, 0])) {
-    let c = new hse(r.shape, a);
+    let c = new fse(r.shape, a);
     return i.runWebGPUProgram(c, [r], r.dtype);
   }
-  let l = new fse(r.shape, a);
+  let l = new mse(r.shape, a);
   return i.runWebGPUProgram(l, [r], r.dtype);
 }
-var gse = { kernelName: pi, backendName: "webgpu", kernelFunc: wi };
-function bse(e) {
+var bse = { kernelName: pi, backendName: "webgpu", kernelFunc: wi };
+function yse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = S.getAxesPermutation(i, r.shape.length), u = r, l = [];
   o != null && (u = wi({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = S.getInnerMostAxes(i.length, u.shape.length)), S.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
   let c = new w2(u.shape, i[0], "max"), p = [{ type: "int32", data: [i[0]] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
   return l.forEach((h) => n.disposeData(h.dataId)), d;
 }
-var yse = { kernelName: Ia, backendName: "webgpu", kernelFunc: bse };
-function vse(e) {
+var vse = { kernelName: Ia, backendName: "webgpu", kernelFunc: yse };
+function xse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = S.getAxesPermutation(i, r.shape.length), u = r, l = [];
   o != null && (u = wi({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = S.getInnerMostAxes(i.length, u.shape.length)), S.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
   let c = new w2(u.shape, i[0], "min"), p = [{ type: "int32", data: [i[0]] }, { type: "float32", data: [Number.POSITIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
   return l.forEach((h) => n.disposeData(h.dataId)), d;
 }
-var xse = { kernelName: il, backendName: "webgpu", kernelFunc: vse };
+var wse = { kernelName: ol, backendName: "webgpu", kernelFunc: xse };
 var k2 = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,", this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `pool2D_${t}`, this.poolType = t;
@@ -33796,25 +33687,25 @@ var I2 = class {
     `;
   }
 };
-function wse(e) {
+function kse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = S.computePool2DInfo(r.shape, a, i, l, o, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     return es({ inputs: { x: r }, backend: n });
   let p, d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }];
   return c.filterHeight === 1 && c.filterWidth === 1 ? p = new I2(c) : (p = new k2(c, "avg"), d.push({ type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] })), n.runWebGPUProgram(p, [r], r.dtype, d);
 }
-var kse = { kernelName: Sa, backendName: "webgpu", kernelFunc: wse };
-function Ise(e) {
+var Ise = { kernelName: Sa, backendName: "webgpu", kernelFunc: kse };
+function Sse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
   return _v({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
 }
-var Sse = { kernelName: Ca, backendName: "webgpu", kernelFunc: Ise };
-var Cse = class {
+var Cse = { kernelName: Ca, backendName: "webgpu", kernelFunc: Sse };
+var Nse = class {
   constructor(e, t) {
     this.variableNames = ["source"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.rank = t.length, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Wt(e.length)}, `, this.shaderKey = "slice";
   }
   getUserCode() {
-    let e = Wt(this.rank), t = Nse(this.rank), n;
+    let e = Wt(this.rank), t = Tse(this.rank), n;
     return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${Km[a]} = uniforms.start[${a}] + coords.${Km[a]};`), `
       ${Ue()}
         if (index < uniforms.size) {
@@ -33829,40 +33720,40 @@ var Cse = class {
   }
 };
 var Km = ["x", "y", "z", "w", "u", "v"];
-function Nse(e) {
+function Tse(e) {
   if (e === 1)
     return "sourceLoc";
   if (e <= 6)
     return Km.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
   throw Error(`Slicing for rank ${e} is not yet supported`);
 }
-function lu(e) {
+function cu(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
   if (wt.assertParamsValid(r, o, u), n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
-    let p = n.tensorMap.get(r.dataId), d = Jne(p.values, o, u, r.shape, r.dtype);
+    let p = n.tensorMap.get(r.dataId), d = ese(p.values, o, u, r.shape, r.dtype);
     return n.makeTensorInfo(u, r.dtype, d);
   }
   if (w.sizeFromShape(u) === 0)
     return n.makeTensorInfo(u, r.dtype, []);
-  let l = new Cse(o, u), c = [{ type: "int32", data: o }];
+  let l = new Nse(o, u), c = [{ type: "int32", data: o }];
   return n.runWebGPUProgram(l, [r], r.dtype, c);
 }
-var Tse = { kernelName: Oo, backendName: "webgpu", kernelFunc: lu };
-var $se = (e) => {
+var $se = { kernelName: Po, backendName: "webgpu", kernelFunc: cu };
+var _se = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
   w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");
-  let o = a.reduce((y, v) => y * v), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = [], f = Me({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = wi({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = lu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
+  let o = a.reduce((y, v) => y * v), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = [], f = Me({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = wi({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = cu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
   return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeData(y.dataId)), b;
 };
-var _se = { kernelName: io, backendName: "webgpu", kernelFunc: $se };
-var S2 = mn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: Kne });
-var Ase = { kernelName: Io, backendName: "webgpu", kernelFunc: S2 };
+var Ase = { kernelName: oo, backendName: "webgpu", kernelFunc: _se };
+var S2 = mn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: Xne });
+var Ese = { kernelName: So, backendName: "webgpu", kernelFunc: S2 };
 function sc(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
   return es({ inputs: { x: r.complexTensorInfos.real }, backend: n });
 }
-var Ese = { kernelName: np, backendName: "webgpu", kernelFunc: sc };
-function Rse(e, t) {
+var Rse = { kernelName: np, backendName: "webgpu", kernelFunc: sc };
+function Dse(e, t) {
   let n = new nc(e.shape, 22), s = t.runWebGPUProgram(n, [e], "int32");
   return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
@@ -33871,7 +33762,7 @@ function Xm(e) {
   if (a === "complex64") {
     if (r.dtype === "complex64")
       return es({ inputs: { x: r }, backend: n });
-    let i = $t(r.shape), o = Xm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = uu({ inputs: { real: o, imag: i }, backend: n });
+    let i = $t(r.shape), o = Xm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = lu({ inputs: { real: o, imag: i }, backend: n });
     return i.dispose(), n.disposeData(o.dataId), u;
   }
   if (r.dtype === "complex64") {
@@ -33883,17 +33774,17 @@ function Xm(e) {
     return { dataId: i.dataId, shape: i.shape, dtype: a };
   }
   if (a === "int32")
-    return Rse(r, n);
+    return Dse(r, n);
   if (a === "bool") {
     let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = S2({ inputs: { a: r, b: i }, backend: n });
     return n.disposeData(i.dataId), u;
   }
   throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
 }
-var Dse = { kernelName: Na, backendName: "webgpu", kernelFunc: Xm };
-var Fse = Kt({ opType: 1, cpuKernelImpl: Ane });
-var Ose = { kernelName: Ta, backendName: "webgpu", kernelFunc: Fse };
-var Pse = class {
+var Fse = { kernelName: Na, backendName: "webgpu", kernelFunc: Xm };
+var Ose = Kt({ opType: 1, cpuKernelImpl: Ene });
+var Pse = { kernelName: Ta, backendName: "webgpu", kernelFunc: Ose };
+var zse = class {
   constructor(e) {
     this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.isVec4 = true, this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4";
   }
@@ -33917,7 +33808,7 @@ var Pse = class {
     `;
   }
 };
-var zse = class {
+var Mse = class {
   constructor(e) {
     this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "clip";
   }
@@ -33936,12 +33827,12 @@ var zse = class {
     `;
   }
 };
-function Mse(e) {
+function Lse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o, u = [{ type: "float32", data: [a] }, { type: "float32", data: [i] }];
-  return w.sizeFromShape(r.shape) % 4 === 0 ? o = new Pse(r.shape) : o = new zse(r.shape), n.runWebGPUProgram(o, [r], r.dtype, u);
+  return w.sizeFromShape(r.shape) % 4 === 0 ? o = new zse(r.shape) : o = new Mse(r.shape), n.runWebGPUProgram(o, [r], r.dtype, u);
 }
-var Lse = { kernelName: Sr, backendName: "webgpu", kernelFunc: Mse };
-var Bse = class {
+var Bse = { kernelName: Sr, backendName: "webgpu", kernelFunc: Lse };
+var Vse = class {
   constructor(e) {
     this.uniforms = "", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = S.computeOutShape(e, 1), this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1;
     for (let t = 0; t < this.offsetLength; t++)
@@ -33979,11 +33870,11 @@ function Zp(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
   return es({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
 }
-var Vse = { kernelName: Zd, backendName: "webgpu", kernelFunc: Zp };
+var Wse = { kernelName: Zd, backendName: "webgpu", kernelFunc: Zp };
 function Ym(e, t, n) {
   let s = e[0].dtype;
   if (s === "complex64") {
-    let h = e.map((y) => sc({ inputs: { input: y }, backend: n })), f = e.map((y) => Zp({ inputs: { input: y }, backend: n })), m = Ym(h, t, n), g = Ym(f, t, n), b = uu({ inputs: { real: m, imag: g }, backend: n });
+    let h = e.map((y) => sc({ inputs: { input: y }, backend: n })), f = e.map((y) => Zp({ inputs: { input: y }, backend: n })), m = Ym(h, t, n), g = Ym(f, t, n), b = lu({ inputs: { real: m, imag: g }, backend: n });
     return h.forEach((y) => n.disposeData(y.dataId)), f.forEach((y) => n.disposeData(y.dataId)), n.disposeData(m.dataId), n.disposeData(g.dataId), b;
   }
   let r = n.shouldExecuteOnCPU(e);
@@ -33991,10 +33882,10 @@ function Ym(e, t, n) {
     let h = e.map((x) => {
       let k = w.sizeFromShape(x.shape.slice(t));
       return Me({ inputs: { x }, backend: n, attrs: { shape: [-1, k] } });
-    }), f = h.map((x) => ({ vals: n.readSync(x.dataId), shape: x.shape })), m = S.computeOutShape(h.map((x) => x.shape), 1), g = h[0].shape[0] === 1, b = Ene(f, m, s, g), y = S.computeOutShape(e.map((x) => x.shape), t), v = n.makeTensorInfo(y, s, b);
+    }), f = h.map((x) => ({ vals: n.readSync(x.dataId), shape: x.shape })), m = S.computeOutShape(h.map((x) => x.shape), 1), g = h[0].shape[0] === 1, b = Rne(f, m, s, g), y = S.computeOutShape(e.map((x) => x.shape), t), v = n.makeTensorInfo(y, s, b);
     return h.forEach((x) => n.disposeData(x.dataId)), v;
   }
-  let { tensors2D: a, outShape: i } = Wse(e, t, n), o = a.map((h) => h.shape), u = new Bse(o), l = [], c = new Array(o.length - 1);
+  let { tensors2D: a, outShape: i } = Use(e, t, n), o = a.map((h) => h.shape), u = new Vse(o), l = [], c = new Array(o.length - 1);
   if (c.length > 0) {
     c[0] = o[0][1], l.push({ type: "int32", data: [c[0]] });
     for (let h = 1; h < c.length; h++)
@@ -34005,7 +33896,7 @@ function Ym(e, t, n) {
   let d = Me({ inputs: { x: p }, backend: n, attrs: { shape: i } });
   return n.disposeData(p.dataId), d;
 }
-function Wse(e, t, n) {
+function Use(e, t, n) {
   let s = S.computeOutShape(e.map((a) => a.shape), t);
   return { tensors2D: e.map((a) => Me({ inputs: { x: a }, backend: n, attrs: { shape: [w.sizeFromShape(a.shape.slice(0, t)), w.sizeFromShape(a.shape.slice(t))] } })), outShape: s };
 }
@@ -34019,8 +33910,8 @@ function C2(e) {
   let u = o.map((l) => l.shape);
   return S.assertParamsConsistent(u, a), Ym(o, a, n);
 }
-var Use = { kernelName: oo, backendName: "webgpu", kernelFunc: C2 };
-var Gse = class {
+var Gse = { kernelName: uo, backendName: "webgpu", kernelFunc: C2 };
+var Hse = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       dimAOuter : i32, dimBOuter : i32, dimInner : i32,`, this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.outputShape[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, this.hasLeakyreluAlpha = r, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.hasLeakyreluAlpha && this.variableNames.push("leakyreluAlpha"), this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.tileBOuter, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`;
@@ -34147,7 +34038,7 @@ var Gse = class {
       `;
   }
 };
-var Hse = class {
+var qse = class {
   constructor(e, t = false, n = null, s = false) {
     this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Cv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Nv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`;
   }
@@ -34222,7 +34113,7 @@ var Hse = class {
   `;
   }
 };
-var qse = class {
+var jse = class {
   constructor(e, t = false, n = null, s = false) {
     this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,", this.workGroupSize = [128, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, this.shaderKey = `conv2DNaive_${this.activation}`;
   }
@@ -34291,7 +34182,7 @@ var qse = class {
     `;
   }
 };
-var jse = class {
+var Kse = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
       inChannels : i32,`, this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.isChannelsLast = t, this.shaderKey = `im2col_${this.isChannelsLast}`;
@@ -34330,7 +34221,7 @@ var jse = class {
   `;
   }
 };
-function Kse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function Xse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
   let u = e.shape, l = n.dataFormat === "channelsLast", c = false, p = false, d = n.filterHeight === n.inHeight && n.filterWidth === n.inWidth && n.padInfo.type === "VALID", h, f;
   if (d) {
     let b = n.inHeight * n.inWidth * n.inChannels;
@@ -34342,10 +34233,10 @@ function Kse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluAc
   let m = _v({ a: h, b: f, transposeA: c, transposeB: p, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), g = Me({ inputs: { x: m }, backend: s, attrs: { shape: n.outShape } });
   return s.disposeData(h.dataId), s.disposeData(f.dataId), s.disposeData(m.dataId), g;
 }
-function Xse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function Yse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
   let { filterWidth: u, filterHeight: l, inChannels: c, strideWidth: p, strideHeight: d, padInfo: h, outWidth: f, outHeight: m, dilationWidth: g, dilationHeight: b, dataFormat: y } = n, v = y === "channelsLast", x = u * l * c, k = m * f, T = [k, x], N = false, E = false, A = [], P = Me({ inputs: { x: e }, backend: s, attrs: { shape: e.shape.slice(1) } }), R = Me({ inputs: { x: t }, backend: s, attrs: { shape: [1, x, -1] } });
   A.push(P), A.push(R);
-  let F = new jse(T, v), $ = [{ type: "int32", data: [h.left, h.top] }, { type: "int32", data: [p, d] }, { type: "int32", data: [g, b] }, { type: "int32", data: [f] }, { type: "int32", data: [c * u] }, { type: "int32", data: [c] }], z = s.runWebGPUProgram(F, [P], P.dtype, $), W = Me({ inputs: { x: z }, backend: s, attrs: { shape: [1, T[0], T[1]] } });
+  let F = new Kse(T, v), $ = [{ type: "int32", data: [h.left, h.top] }, { type: "int32", data: [p, d] }, { type: "int32", data: [g, b] }, { type: "int32", data: [f] }, { type: "int32", data: [c * u] }, { type: "int32", data: [c] }], z = s.runWebGPUProgram(F, [P], P.dtype, $), W = Me({ inputs: { x: z }, backend: s, attrs: { shape: [1, T[0], T[1]] } });
   A.push(z), A.push(W);
   let q = [1, T[0], T[1]], K = new v2(q, [1, k, n.outChannels], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), N, E, r, o, a), Y = q[1], Z = q[2], te = n.outChannels, ee = [{ type: "int32", data: [Y] }, { type: "int32", data: [te] }, { type: "int32", data: [Z] }], se = [W, R];
   r && se.push(r), a && se.push(a), o === "leakyrelu" && ($.push({ type: "float32", data: [i] }), K.uniforms += " alpha : f32,");
@@ -34358,26 +34249,26 @@ function Xse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluAc
 function N2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
   let u = r != null, l = a != null, c;
   if (n.filterHeight === n.inHeight && n.filterWidth === n.inWidth && n.padInfo.type === "VALID" || n.filterHeight === 1 && n.filterWidth === 1 && n.dilationHeight === 1 && n.dilationWidth === 1 && n.strideHeight === 1 && n.strideWidth === 1 && (n.padInfo.type === "SAME" || n.padInfo.type === "VALID"))
-    return Kse({ x: e, filter: t, convInfo: n, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
+    return Xse({ x: e, filter: t, convInfo: n, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
   if (X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") && e.shape[0] === 1)
-    return Xse({ x: e, filter: t, convInfo: n, backend: s, bias: r, preluActivationWeights: a, leakyreluAlpha: i, activation: o });
-  let d = X().getBool("WEBGPU_USE_NAIVE_CONV2D"), h = (n.inChannels % 4 === 0 || n.inChannels === 3 && n.padInfo.type === "VALID") && n.outChannels % 4 === 0 && n.outChannels >= 32, f = [n.padInfo.top, n.padInfo.left], m = [{ type: "int32", data: [n.filterHeight, n.filterWidth] }, { type: "int32", data: [...f] }, { type: "int32", data: [n.strideHeight, n.strideWidth] }, { type: "int32", data: [n.dilationHeight, n.dilationWidth] }];
+    return Yse({ x: e, filter: t, convInfo: n, backend: s, bias: r, preluActivationWeights: a, leakyreluAlpha: i, activation: o });
+  let d = X().getBool("WEBGPU_USE_NAIVE_CONV2D"), h = (n.inChannels % 4 === 0 || n.inChannels === 3 && n.padInfo.type === "VALID") && n.outChannels % 4 === 0, f = [n.padInfo.top, n.padInfo.left], m = [{ type: "int32", data: [n.filterHeight, n.filterWidth] }, { type: "int32", data: [...f] }, { type: "int32", data: [n.strideHeight, n.strideWidth] }, { type: "int32", data: [n.dilationHeight, n.dilationWidth] }];
   if (d)
-    c = new qse(n, u, o, l);
+    c = new jse(n, u, o, l);
   else {
-    h ? c = new Gse(n, u, o, l) : c = new Hse(n, u, o, l);
+    h ? c = new Hse(n, u, o, l) : c = new qse(n, u, o, l);
     let b = n.outShape[1] * n.outShape[2], y = n.outShape[3], v = n.filterHeight * n.filterWidth * n.inShape[3];
     m.push({ type: "int32", data: [b] }, { type: "int32", data: [y] }, { type: "int32", data: [v] });
   }
   let g = [e, t];
   return u && g.push(r), l && g.push(a), o === "leakyrelu" && (m.push({ type: "float32", data: [i] }), c.uniforms += " alpha : f32,"), s.runWebGPUProgram(c, g, e.dtype, m);
 }
-function Yse(e) {
+function Qse(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = n, p = S.convertConv2DDataFormat(u), d = S.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p);
   return N2({ x: r, filter: a, convInfo: d, backend: s });
 }
-var Qse = { kernelName: $a, backendName: "webgpu", kernelFunc: Yse };
-var Zse = class {
+var Zse = { kernelName: $a, backendName: "webgpu", kernelFunc: Qse };
+var Jse = class {
   constructor(e) {
     this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Cv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Nv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
   }
@@ -34439,7 +34330,7 @@ var Zse = class {
   `;
   }
 };
-var Jse = class {
+var ere = class {
   constructor(e) {
     this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;
   }
@@ -34497,23 +34388,23 @@ var Jse = class {
   `;
   }
 };
-function ere(e) {
+function tre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = S.convertConv2DDataFormat(l), d = S.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = [{ type: "int32", data: [d.filterHeight, d.filterWidth] }, { type: "int32", data: [d.filterHeight - 1 - d.padInfo.top, d.filterWidth - 1 - d.padInfo.left] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.batchSize, d.outHeight, d.outWidth, d.outChannels] }], f;
   if (X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))
-    f = new Jse(d);
+    f = new ere(d);
   else {
-    f = new Zse(d);
+    f = new Jse(d);
     let m = d.inShape[1] * d.inShape[2], g = d.inShape[3], b = d.filterHeight * d.filterWidth * d.outChannels;
     h.push({ type: "uint32", data: [m] }, { type: "uint32", data: [g] }, { type: "uint32", data: [b] });
   }
   return n.runWebGPUProgram(f, [r, a], "float32", h);
 }
-var tre = { kernelName: _a, backendName: "webgpu", kernelFunc: ere };
-var nre = Kt({ opType: 2 });
-var sre = { kernelName: Aa, backendName: "webgpu", kernelFunc: nre };
-var rre = Kt({ opType: 3 });
-var are = { kernelName: Ea, backendName: "webgpu", kernelFunc: rre };
-var ire = class {
+var nre = { kernelName: _a, backendName: "webgpu", kernelFunc: tre };
+var sre = Kt({ opType: 2 });
+var rre = { kernelName: Aa, backendName: "webgpu", kernelFunc: sre };
+var are = Kt({ opType: 3 });
+var ire = { kernelName: Ea, backendName: "webgpu", kernelFunc: are };
+var ore = class {
   constructor(e, t, n, s) {
     this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true;
     let [r] = t;
@@ -34580,12 +34471,12 @@ var ire = class {
     `;
   }
 };
-var ore = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new ire(r.shape[3], a.shape, o, u), p = [{ type: "float32", data: [l] }];
+var ure = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new ore(r.shape[3], a.shape, o, u), p = [{ type: "float32", data: [l] }];
   return n.runWebGPUProgram(c, [r, a, i], "float32", p);
 };
-var ure = { kernelName: lo, backendName: "webgpu", kernelFunc: ore };
-var lre = class {
+var lre = { kernelName: co, backendName: "webgpu", kernelFunc: ure };
+var cre = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `depthToSpace_${t}`, this.dataFormat = t;
   }
@@ -34628,11 +34519,11 @@ var lre = class {
     return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
   }
 };
-function cre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = [{ type: "int32", data: [a] }], g = new lre(f, i);
+function dre(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = [{ type: "int32", data: [a] }], g = new cre(f, i);
   return n.runWebGPUProgram(g, [r], r.dtype, m);
 }
-var dre = { kernelName: co, backendName: "webgpu", kernelFunc: cre };
+var pre = { kernelName: po, backendName: "webgpu", kernelFunc: dre };
 var T2 = class {
   constructor(e, t = false, n = null, s = false) {
     this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,", this.workGroupSize = [4, 4, 4], this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [0, 1], y: [2], z: [3] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise3x3_${n}`;
@@ -34815,16 +34706,16 @@ var $2 = class {
     `;
   }
 };
-function pre(e) {
+function hre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
   c == null && (c = [1, 1]);
   let p = S.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d = [{ type: "int32", data: [p.padInfo.top, p.padInfo.left] }, { type: "int32", data: [p.strideHeight, p.strideWidth] }, { type: "int32", data: [p.dilationHeight, p.dilationWidth] }, { type: "int32", data: [p.inHeight, p.inWidth] }], h;
   return p.batchSize === 1 && p.inHeight === p.outHeight && p.inWidth === p.outWidth && p.strideHeight === 1 && p.strideWidth === 1 && p.filterHeight === p.filterWidth && p.inChannels === p.outChannels && p.dilationHeight === 1 && p.dilationWidth === 1 && p.filterHeight === 3 && p.inChannels % 4 === 0 ? h = new T2(p) : (h = new $2(p), d.push({ type: "int32", data: [p.filterHeight] }, { type: "int32", data: [p.filterWidth] }, { type: "int32", data: [p.outChannels / p.inChannels] })), n.runWebGPUProgram(h, [r, a], r.dtype, d);
 }
-var hre = { kernelName: Ra, backendName: "webgpu", kernelFunc: pre };
-var _2 = mn({ opSnippet: 0, cpuKernelImpl: qne, supportsComplex: true });
-var fre = { kernelName: Ya, backendName: "webgpu", kernelFunc: _2 };
-var mre = class {
+var fre = { kernelName: Ra, backendName: "webgpu", kernelFunc: hre };
+var _2 = mn({ opSnippet: 0, cpuKernelImpl: jne, supportsComplex: true });
+var mre = { kernelName: Ya, backendName: "webgpu", kernelFunc: _2 };
+var gre = class {
   constructor(e, t) {
     this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize];
     let [n] = S.computeOutAndReduceShapes(this.inputShape, [1]);
@@ -34895,18 +34786,18 @@ function rc(e, t, n, s, r) {
     let m = r.tensorMap.get(c.dataId).values;
     switch (s) {
       case "max":
-        let g = Une(m, w.sizeFromShape(d), h, e.dtype);
+        let g = Gne(m, w.sizeFromShape(d), h, e.dtype);
         f = r.makeTensorInfo(h, e.dtype, g);
         break;
       case "prod":
-        let { outVals: b, outShape: y, outDtype: v } = Xne(c.shape, c.dtype, m, u);
+        let { outVals: b, outShape: y, outDtype: v } = Yne(c.shape, c.dtype, m, u);
         f = r.makeTensorInfo(y, v, b);
         break;
       default:
         throw new Error(`${s} CPU implementation is not yet supported.`);
     }
   } else {
-    let m = w.sizeFromShape(d), b = w.sizeFromShape(c.shape) / m, y = { windowSize: m, inSize: m, batchSize: b, outSize: 1 }, v = s === "mean" ? "float32" : cp(e.dtype), x = [{ type: "int32", data: [m] }], k = new mre(y, s), T = r.runWebGPUProgram(k, [c], v, x);
+    let m = w.sizeFromShape(d), b = w.sizeFromShape(c.shape) / m, y = { windowSize: m, inSize: m, batchSize: b, outSize: 1 }, v = s === "mean" ? "float32" : cp(e.dtype), x = [{ type: "int32", data: [m] }], k = new gre(y, s), T = r.runWebGPUProgram(k, [c], v, x);
     i.push(T), f = Me({ inputs: { x: T }, attrs: { shape: h }, backend: r });
   }
   return i.forEach((m) => r.disposeData(m.dataId)), f;
@@ -34915,8 +34806,8 @@ function Av(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   return rc(r, a, i, "sum", n);
 }
-var gre = { kernelName: oi, backendName: "webgpu", kernelFunc: Av };
-function bre(e) {
+var bre = { kernelName: oi, backendName: "webgpu", kernelFunc: Av };
+function yre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = S.decodeEinsumEquation(r, a.length);
   S.checkEinsumDimSizes(i.length, u, a);
   let { path: l, steps: c } = S.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
@@ -34935,21 +34826,21 @@ function bre(e) {
     m !== d && n.disposeData(m.dataId);
   return d;
 }
-var yre = { kernelName: Qd, backendName: "webgpu", kernelFunc: bre };
-var vre = Kt({ opType: 4 });
-var xre = { kernelName: Fa, backendName: "webgpu", kernelFunc: vre };
-var wre = mn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: Rne });
-var kre = { kernelName: po, backendName: "webgpu", kernelFunc: wre };
-var A2 = Kt({ opType: 5, cpuKernelImpl: Dne, dtype: "float32" });
-var Ire = { kernelName: Oa, backendName: "webgpu", kernelFunc: A2 };
+var vre = { kernelName: Qd, backendName: "webgpu", kernelFunc: yre };
+var xre = Kt({ opType: 4 });
+var wre = { kernelName: Fa, backendName: "webgpu", kernelFunc: xre };
+var kre = mn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: Dne });
+var Ire = { kernelName: ho, backendName: "webgpu", kernelFunc: kre };
+var A2 = Kt({ opType: 5, cpuKernelImpl: Fne, dtype: "float32" });
+var Sre = { kernelName: Oa, backendName: "webgpu", kernelFunc: A2 };
 function Qm(e) {
   let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
   return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), Me({ inputs: { x: a }, backend: s, attrs: { shape: o } });
 }
-var Sre = { kernelName: ho, backendName: "webgpu", kernelFunc: Qm };
-var Cre = Kt({ opType: 6, cpuKernelImpl: Fne });
-var Nre = { kernelName: fo, backendName: "webgpu", kernelFunc: Cre };
-var Tre = class {
+var Cre = { kernelName: fo, backendName: "webgpu", kernelFunc: Qm };
+var Nre = Kt({ opType: 6, cpuKernelImpl: One });
+var Tre = { kernelName: mo, backendName: "webgpu", kernelFunc: Nre };
+var $re = class {
   constructor(e) {
     this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "fill";
   }
@@ -34963,18 +34854,18 @@ var Tre = class {
   `;
   }
 };
-function cu(e) {
+function du(e) {
   let { backend: t, attrs: n } = e, { shape: s, value: r } = n, { dtype: a } = n;
   if (a = a || w.inferDtype(r), a === "string") {
     let i = w.getArrayFromDType(a, w.sizeFromShape(s));
     return i.fill(r), t.makeTensorInfo(s, a, i);
   } else {
-    let i = new Tre(s), o = [{ type: "float32", data: [r] }];
+    let i = new $re(s), o = [{ type: "float32", data: [r] }];
     return t.runWebGPUProgram(i, [], a, o);
   }
 }
-var $re = { kernelName: fl, backendName: "webgpu", kernelFunc: cu };
-var _re = class {
+var _re = { kernelName: ml, backendName: "webgpu", kernelFunc: du };
+var Are = class {
   constructor(e) {
     this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "flipLeftRight";
   }
@@ -34991,20 +34882,20 @@ var _re = class {
     `;
   }
 };
-var Are = { kernelName: mo, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
-  let { image: n } = e, s = t, r = new _re(n.shape);
+var Ere = { kernelName: go, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
+  let { image: n } = e, s = t, r = new Are(n.shape);
   return s.runWebGPUProgram(r, [n], n.dtype);
 } };
-var Ere = Kt({ opType: 7, cpuKernelImpl: One });
-var Rre = { kernelName: Pa, backendName: "webgpu", kernelFunc: Ere };
-var Dre = mn({ opSnippet: 12, dtype: "int32" });
-var Fre = { kernelName: za, backendName: "webgpu", kernelFunc: Dre };
-var Ore = (e, t, n, s, r) => {
+var Rre = Kt({ opType: 7, cpuKernelImpl: Pne });
+var Dre = { kernelName: Pa, backendName: "webgpu", kernelFunc: Rre };
+var Fre = mn({ opSnippet: 12, dtype: "int32" });
+var Ore = { kernelName: za, backendName: "webgpu", kernelFunc: Fre };
+var Pre = (e, t, n, s, r) => {
   let a = [s, ...n];
   return r && a.push(r), e.createBindGroup({ layout: t, entries: a.map((i, o) => ({ binding: o, resource: i })) });
 };
 var E2 = (e, t, n, s, r, a = false) => {
-  let i = { dtype: r.dtype, shape: r.shape }, o = lne(s, i, t, a), u = e.createShaderModule({ code: o, label: t.constructor.name });
+  let i = { dtype: r.dtype, shape: r.shape }, o = cne(s, i, t, a), u = e.createShaderModule({ code: o, label: t.constructor.name });
   return e.createComputePipeline({ layout: n, compute: { module: u, entryPoint: "main" }, label: t.constructor.name });
 };
 function R2(e, t, n, s = "", r = "") {
@@ -35027,9 +34918,9 @@ function Ow(e) {
     y = c.inputTexture.createView();
   return n.runFromPixelsProgram(c, g.bufferInfo.buffer, f, y, l.dataId), l;
 }
-var Pre = { kernelName: fd, backendName: "webgpu", kernelFunc: zre };
+var zre = { kernelName: fd, backendName: "webgpu", kernelFunc: Mre };
 var Bi;
-function zre(e) {
+function Mre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s;
   if (r == null)
     throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
@@ -35048,7 +34939,7 @@ function zre(e) {
   let m = n.makeTensorInfo(d, "int32"), g = n.tensorMap.get(m.dataId);
   return g.values = new Int32Array(f), n.maybeReleaseBuffer(m.dataId), n.uploadToGPU(m.dataId), m;
 }
-var Mre = class {
+var Lre = class {
   constructor(e, t, n, s, r) {
     this.uniforms = "varianceEpsilon : f32,", this.workGroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t), S.assertAndGetBroadcastShape(e, n), this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), s != null && (S.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset")), r != null && (S.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale")), this.offsetShape = s, this.scaleShape = r, this.shaderKey = "batchNorm";
   }
@@ -35072,20 +34963,20 @@ var Mre = class {
   `;
   }
 };
-var Lre = { kernelName: Ma, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var Bre = { kernelName: Ma, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { x: s, scale: r, offset: a, mean: i, variance: o } = e, { varianceEpsilon: u } = t, l = n, c = [s, i, o], p = null;
   a != null && (p = a.shape, c.push(a));
   let d = null;
   r != null && (d = r.shape, c.push(r));
-  let h = new Mre(s.shape, i.shape, o.shape, p, d), f = [{ type: "float32", data: [u] }];
+  let h = new Lre(s.shape, i.shape, o.shape, p, d), f = [{ type: "float32", data: [u] }];
   return l.runWebGPUProgram(h, c, s.dtype, f);
 } };
-function Bre(e) {
+function Vre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = S.convertConv2DDataFormat(c), g = S.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m);
   return N2({ x: r, filter: a, convInfo: g, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: f, activation: h });
 }
-var Vre = { kernelName: aa, backendName: "webgpu", kernelFunc: Bre };
-function Wre(e) {
+var Wre = { kernelName: aa, backendName: "webgpu", kernelFunc: Vre };
+function Ure(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = c;
   f == null && (f = [1, 1]), w.assert(S.eitherStridesOrDilationsAreOne(u, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);
   let m = S.computeConv2DInfo(r.shape, a.shape, u, f, l, p, true), g = [r, a], b = i != null, y = o != null;
@@ -35093,8 +34984,8 @@ function Wre(e) {
   let v = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.inHeight, m.inWidth] }], x;
   return m.batchSize === 1 && m.inHeight === m.outHeight && m.inWidth === m.outWidth && m.strideHeight === 1 && m.strideWidth === 1 && m.filterHeight === m.filterWidth && m.inChannels === m.outChannels && m.dilationHeight === 1 && m.dilationWidth === 1 && m.filterHeight === 3 && m.inChannels % 4 === 0 ? x = new T2(m, b, d, y) : (x = new $2(m, b, d, y), v.push({ type: "int32", data: [m.filterHeight] }, { type: "int32", data: [m.filterWidth] }, { type: "int32", data: [m.outChannels / m.inChannels] })), d === "leakyrelu" && (v.push({ type: "float32", data: [h] }), x.uniforms += " alpha : f32,"), n.runWebGPUProgram(x, g, "float32", v);
 }
-var Ure = { kernelName: ia, backendName: "webgpu", kernelFunc: Wre };
-var Gre = class {
+var Gre = { kernelName: ia, backendName: "webgpu", kernelFunc: Ure };
+var Hre = class {
   constructor(e, t) {
     this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Wt(e)},`;
   }
@@ -35117,22 +35008,22 @@ var Gre = class {
       `;
   }
 };
-function Hre(e) {
+function qre(e) {
   let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, i = a[a.length - 1], o = w.sizeFromShape(s.shape), [u, l, c, p] = S.prepareAndValidate(s, r), d = Me({ inputs: { x: r }, backend: n, attrs: { shape: [l, i] } }), h = Me({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
   if (n.shouldExecuteOnCPU([s, r]) || s.dtype === "string") {
-    let y = n.readSync(r.dataId), v = n.bufferSync(s), x = Pne(y, v, s.dtype, l, i, c, p, s.shape, o);
+    let y = n.readSync(r.dataId), v = n.bufferSync(s), x = zne(y, v, s.dtype, l, i, c, p, s.shape, o);
     return n.makeTensorInfo(u, s.dtype, x.values);
   }
-  let f = new Gre(i, [l, c]), m = [{ type: "int32", data: [i] }, { type: "int32", data: p }], g = n.runWebGPUProgram(f, [h, d], h.dtype, m), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: u } });
+  let f = new Hre(i, [l, c]), m = [{ type: "int32", data: [i] }, { type: "int32", data: p }], g = n.runWebGPUProgram(f, [h, d], h.dtype, m), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: u } });
   return n.disposeData(d.dataId), n.disposeData(h.dataId), n.disposeData(g.dataId), b;
 }
-var qre = { kernelName: bo, backendName: "webgpu", kernelFunc: Hre };
-var jre = class {
+var jre = { kernelName: yo, backendName: "webgpu", kernelFunc: qre };
+var Kre = class {
   constructor(e, t) {
     this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "gather";
   }
   getUserCode() {
-    let e = Kre(this.aShape);
+    let e = Xre(this.aShape);
     return `
       ${Ue()}
         if (index < uniforms.size) {
@@ -35145,7 +35036,7 @@ var jre = class {
     `;
   }
 };
-function Kre(e) {
+function Xre(e) {
   let t = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], n = [];
   for (let s = 0; s < e.length; s++)
     s === 2 ? n.push("indexZ") : n.push(`${t[s]}`);
@@ -35156,42 +35047,42 @@ function D2(e) {
   p.push(d), p.push(h);
   let f = [l.batchSize, l.outerSize, c / l.batchSize, l.sliceSize];
   if (n.shouldExecuteOnCPU([r, a])) {
-    let v = n.tensorMap.get(h.dataId).values, x = De(h.shape, h.dtype, v), T = n.tensorMap.get(d.dataId).values, N = De(d.shape, d.dtype, T), E = zne(N, x, f);
+    let v = n.tensorMap.get(h.dataId).values, x = De(h.shape, h.dtype, v), T = n.tensorMap.get(d.dataId).values, N = De(d.shape, d.dtype, T), E = Mne(N, x, f);
     return p.forEach((A) => n.disposeData(A.dataId)), n.makeTensorInfo(l.outputShape, E.dtype, E.values);
   }
-  let m = new jre(d.shape, f), g = n.runWebGPUProgram(m, [d, h], d.dtype);
+  let m = new Kre(d.shape, f), g = n.runWebGPUProgram(m, [d, h], d.dtype);
   p.push(g);
   let b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
   return p.forEach((y) => n.disposeData(y.dataId)), b;
 }
-var Xre = { kernelName: go, backendName: "webgpu", kernelFunc: D2 };
-var Yre = mn({ opSnippet: 5, cpuKernelImpl: Lne, dtype: "bool" });
-var Qre = { kernelName: yo, backendName: "webgpu", kernelFunc: Yre };
-var Zre = mn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: Mne });
-var Jre = { kernelName: La, backendName: "webgpu", kernelFunc: Zre };
-function eae(e) {
+var Yre = { kernelName: bo, backendName: "webgpu", kernelFunc: D2 };
+var Qre = mn({ opSnippet: 5, cpuKernelImpl: Bne, dtype: "bool" });
+var Zre = { kernelName: vo, backendName: "webgpu", kernelFunc: Qre };
+var Jre = mn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: Lne });
+var eae = { kernelName: La, backendName: "webgpu", kernelFunc: Jre };
+function tae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = [{ type: "float32", data: [a] }], o = new nc(r.shape, 14);
   return o.uniforms = "alpha : f32,", n.runWebGPUProgram(o, [r], "float32", i);
 }
-var tae = { kernelName: Va, backendName: "webgpu", kernelFunc: eae };
-var nae = mn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: Vne });
-var sae = { kernelName: vo, backendName: "webgpu", kernelFunc: nae };
-var rae = mn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: Bne });
-var aae = { kernelName: xo, backendName: "webgpu", kernelFunc: rae };
-var iae = Kt({ opType: 9, cpuKernelImpl: Wne });
-var oae = { kernelName: Wa, backendName: "webgpu", kernelFunc: iae };
-var uae = mn({ opSnippet: 9, dtype: "bool" });
-var lae = { kernelName: wo, backendName: "webgpu", kernelFunc: uae };
-var cae = Kt({ opType: 10 });
-var dae = { kernelName: vl, backendName: "webgpu", kernelFunc: cae };
+var nae = { kernelName: Va, backendName: "webgpu", kernelFunc: tae };
+var sae = mn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: Wne });
+var rae = { kernelName: xo, backendName: "webgpu", kernelFunc: sae };
+var aae = mn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: Vne });
+var iae = { kernelName: wo, backendName: "webgpu", kernelFunc: aae };
+var oae = Kt({ opType: 9, cpuKernelImpl: Une });
+var uae = { kernelName: Wa, backendName: "webgpu", kernelFunc: oae };
+var lae = mn({ opSnippet: 9, dtype: "bool" });
+var cae = { kernelName: ko, backendName: "webgpu", kernelFunc: lae };
+var dae = Kt({ opType: 10 });
+var pae = { kernelName: xl, backendName: "webgpu", kernelFunc: dae };
 function F2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s;
   return rc(r, a, i, "max", n);
 }
-var pae = { kernelName: Ua, backendName: "webgpu", kernelFunc: F2 };
-var hae = mn({ opSnippet: 15, cpuKernelImpl: Gne });
-var fae = { kernelName: Ga, backendName: "webgpu", kernelFunc: hae };
-function mae(e) {
+var hae = { kernelName: Ua, backendName: "webgpu", kernelFunc: F2 };
+var fae = mn({ opSnippet: 15, cpuKernelImpl: Hne });
+var mae = { kernelName: Ga, backendName: "webgpu", kernelFunc: fae };
+function gae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = S.computePool2DInfo(r.shape, a, i, l, o, u), p, d = [];
   if (c.filterHeight === 1 && c.filterWidth === 1) {
     if (w.arraysEqual(c.inShape, c.outShape))
@@ -35201,20 +35092,20 @@ function mae(e) {
     p = new k2(c, "max"), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] });
   return n.runWebGPUProgram(p, [r], r.dtype, d);
 }
-var gae = { kernelName: Ha, backendName: "webgpu", kernelFunc: mae };
-function bae(e) {
+var bae = { kernelName: Ha, backendName: "webgpu", kernelFunc: gae };
+function yae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { keepDims: a, axis: i } = s;
   return rc(r, i, a, "mean", n);
 }
-var yae = { kernelName: qa, backendName: "webgpu", kernelFunc: bae };
-function vae(e) {
+var vae = { kernelName: qa, backendName: "webgpu", kernelFunc: yae };
+function xae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   return rc(r, a, i, "min", n);
 }
-var xae = { kernelName: ja, backendName: "webgpu", kernelFunc: vae };
-var wae = mn({ opSnippet: 16, cpuKernelImpl: Hne });
-var kae = { kernelName: Ka, backendName: "webgpu", kernelFunc: wae };
-var Iae = class {
+var wae = { kernelName: ja, backendName: "webgpu", kernelFunc: xae };
+var kae = mn({ opSnippet: 16, cpuKernelImpl: qne });
+var Iae = { kernelName: Ka, backendName: "webgpu", kernelFunc: kae };
+var Sae = class {
   constructor(e, t, n) {
     this.uniforms = "", this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((s, r) => s[0] + e[r] + s[1]), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.xShape = e, t.map((s, r) => {
       this.uniforms += ` pad${r} : vec2<i32>,`;
@@ -35242,53 +35133,53 @@ var Iae = class {
     `;
   }
 };
-var Sae = { kernelName: Xa, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { x: s } = e, { paddings: r, mode: a } = t, i = n, o = r.map((c) => ({ type: "int32", data: [c[0], c[1]] })), u = new Iae(s.shape, r, a);
+var Cae = { kernelName: Xa, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { x: s } = e, { paddings: r, mode: a } = t, i = n, o = r.map((c) => ({ type: "int32", data: [c[0], c[1]] })), u = new Sae(s.shape, r, a);
   return i.runWebGPUProgram(u, [s], s.dtype, o);
 } };
-function Cae(e) {
+function Nae(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (n.shouldExecuteOnCPU([s])) {
-    let a = n.tensorMap.get(s.dataId), [i, o] = jne(a.values, s.shape, s.dtype);
+    let a = n.tensorMap.get(s.dataId), [i, o] = Kne(a.values, s.shape, s.dtype);
     return n.makeTensorInfo(o, s.dtype, i);
   }
   let r = new nc(s.shape, 11);
   return n.runWebGPUProgram(r, [s], s.dtype);
 }
-var Nae = { kernelName: ko, backendName: "webgpu", kernelFunc: Cae };
-function Tae(e) {
+var Tae = { kernelName: Io, backendName: "webgpu", kernelFunc: Nae };
+function $ae(e) {
   console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = xs.nonMaxSuppressionV3Impl(l, c, i, o, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var $ae = { kernelName: So, backendName: "webgpu", kernelFunc: Tae };
-function _ae(e) {
+var _ae = { kernelName: Co, backendName: "webgpu", kernelFunc: $ae };
+function Aae(e) {
   console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = xs.nonMaxSuppressionV5Impl(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var Aae = { kernelName: Co, backendName: "webgpu", kernelFunc: _ae };
+var Eae = { kernelName: No, backendName: "webgpu", kernelFunc: Aae };
 function Ld(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "complex64") {
-    let r = sc({ inputs: { input: s }, backend: n }), a = Ld({ inputs: { x: r }, backend: n }), i = Zp({ inputs: { input: s }, backend: n }), o = Ld({ inputs: { x: i }, backend: n }), u = uu({ inputs: { real: a, imag: o }, backend: n });
+    let r = sc({ inputs: { input: s }, backend: n }), a = Ld({ inputs: { x: r }, backend: n }), i = Zp({ inputs: { input: s }, backend: n }), o = Ld({ inputs: { x: i }, backend: n }), u = lu({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
   } else
-    return cu({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
+    return du({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
 }
-var Eae = { kernelName: Go, backendName: "webgpu", kernelFunc: Ld };
+var Rae = { kernelName: Ho, backendName: "webgpu", kernelFunc: Ld };
 function O2(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported under string dtype");
   if (s.dtype === "complex64") {
-    let r = sc({ inputs: { input: s }, backend: n }), a = O2({ inputs: { x: r }, backend: n }), i = Zp({ inputs: { input: s }, backend: n }), o = Ld({ inputs: { x: i }, backend: n }), u = uu({ inputs: { real: a, imag: o }, backend: n });
+    let r = sc({ inputs: { input: s }, backend: n }), a = O2({ inputs: { x: r }, backend: n }), i = Zp({ inputs: { input: s }, backend: n }), o = Ld({ inputs: { x: i }, backend: n }), u = lu({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
   } else
-    return cu({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
+    return du({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
 }
-var Rae = { kernelName: No, backendName: "webgpu", kernelFunc: O2 };
-function Dae(e) {
+var Dae = { kernelName: To, backendName: "webgpu", kernelFunc: O2 };
+function Fae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
     return Qm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
@@ -35302,8 +35193,8 @@ function Dae(e) {
   }), l = C2({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeData(c.dataId)), l;
 }
-var Fae = { kernelName: $o, backendName: "webgpu", kernelFunc: Dae };
-var Oae = class {
+var Oae = { kernelName: _o, backendName: "webgpu", kernelFunc: Fae };
+var Pae = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), t.map((n, s) => {
       this.uniforms += ` pad${s} : vec2<i32>,`;
@@ -35335,38 +35226,38 @@ var P2 = (e) => {
     return es({ inputs: { x: r }, backend: n });
   if (w.sizeFromShape(r.shape) === 0) {
     let l = a.map((c, p) => c[0] + r.shape[p] + c[1]);
-    return cu({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
+    return du({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
   }
   let o = [{ type: "float32", data: [i] }];
   a.map((l) => o.push({ type: "int32", data: [l[0], l[1]] }));
-  let u = new Oae(r.shape, a);
+  let u = new Pae(r.shape, a);
   return n.runWebGPUProgram(u, [r], r.dtype, o);
 };
-var Pae = { kernelName: Qa, backendName: "webgpu", kernelFunc: P2 };
-var zae = mn({ opSnippet: 13 });
-var Mae = { kernelName: Za, backendName: "webgpu", kernelFunc: zae };
-function Lae(e) {
+var zae = { kernelName: Qa, backendName: "webgpu", kernelFunc: P2 };
+var Mae = mn({ opSnippet: 13 });
+var Lae = { kernelName: Za, backendName: "webgpu", kernelFunc: Mae };
+function Bae(e) {
   let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = new x2(14, s.shape, r.shape);
   return n.runWebGPUProgram(a, [s, r], "float32");
 }
-var Bae = { kernelName: Ja, backendName: "webgpu", kernelFunc: Lae };
-function Vae(e) {
+var Vae = { kernelName: Ja, backendName: "webgpu", kernelFunc: Bae };
+function Wae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   return rc(r, a, i, "prod", n);
 }
-var Wae = { kernelName: _o, backendName: "webgpu", kernelFunc: Vae };
-var Uae = (e) => {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = Yne(s, r, a, i);
+var Uae = { kernelName: Ao, backendName: "webgpu", kernelFunc: Wae };
+var Gae = (e) => {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = Qne(s, r, a, i);
   return t.makeTensorInfo([o.length], i, o);
 };
-var Gae = { kernelName: kl, backendName: "webgpu", kernelFunc: Uae };
+var Hae = { kernelName: Il, backendName: "webgpu", kernelFunc: Gae };
 var z2 = mn({ opSnippet: 3 });
-var Hae = { kernelName: Da, backendName: "webgpu", kernelFunc: z2 };
-var qae = Kt({ opType: 12 });
-var jae = { kernelName: ei, backendName: "webgpu", kernelFunc: qae };
-var Kae = Kt({ opType: 13 });
-var Xae = { kernelName: ni, backendName: "webgpu", kernelFunc: Kae };
-var Yae = class {
+var qae = { kernelName: Da, backendName: "webgpu", kernelFunc: z2 };
+var jae = Kt({ opType: 12 });
+var Kae = { kernelName: ei, backendName: "webgpu", kernelFunc: jae };
+var Xae = Kt({ opType: 13 });
+var Yae = { kernelName: ni, backendName: "webgpu", kernelFunc: Xae };
+var Qae = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "resizeBilinear";
   }
@@ -35417,12 +35308,12 @@ var Yae = class {
     `;
   }
 };
-function Qae(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, size: i, halfPixelCenters: o } = s, [u, l] = i, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [o ? 0.5 : 0] }], f = new Yae(r.shape, u, l);
+function Zae(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, size: i, halfPixelCenters: o } = s, [u, l] = i, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [o ? 0.5 : 0] }], f = new Qae(r.shape, u, l);
   return n.runWebGPUProgram(f, [r], "float32", h);
 }
-var Zae = { kernelName: ti, backendName: "webgpu", kernelFunc: Qae };
-var Jae = class {
+var Jae = { kernelName: ti, backendName: "webgpu", kernelFunc: Zae };
+var eie = class {
   constructor(e, t, n, s) {
     this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.halfPixelCenters = s, this.shaderKey = `resizeNearest_${s}`;
   }
@@ -35462,12 +35353,12 @@ var Jae = class {
     `;
   }
 };
-function eie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [a ? 0.5 : 0] }], f = new Jae(r.shape, u, l, i);
+function tie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [a ? 0.5 : 0] }], f = new eie(r.shape, u, l, i);
   return n.runWebGPUProgram(f, [r], r.dtype, h);
 }
-var tie = { kernelName: Sl, backendName: "webgpu", kernelFunc: eie };
-var nie = class {
+var nie = { kernelName: Cl, backendName: "webgpu", kernelFunc: tie };
+var sie = class {
   constructor(e, t) {
     this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3<f32>,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3");
@@ -35497,13 +35388,13 @@ var nie = class {
       `;
   }
 };
-var sie = { kernelName: Ho, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new nie(s.shape, a), [l, c] = S.getImageCenter(i, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
+var rie = { kernelName: qo, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new sie(s.shape, a), [l, c] = S.getImageCenter(i, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
   return typeof a == "number" ? p.push({ type: "float32", data: [Number.parseFloat(a.toFixed(2))] }) : p.push({ type: "float32", data: a }), o.runWebGPUProgram(u, [s], s.dtype, p);
 } };
-var rie = Kt({ opType: 15, cpuKernelImpl: Qne });
-var aie = { kernelName: si, backendName: "webgpu", kernelFunc: rie };
-var iie = class {
+var aie = Kt({ opType: 15, cpuKernelImpl: Zne });
+var iie = { kernelName: si, backendName: "webgpu", kernelFunc: aie };
+var oie = class {
   constructor(e, t, n, s, r, a, i) {
     this.variableNames = ["updates", "indices"], this.workGroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.dispatchLayout = Be(e), this.dispatch = _e(this.dispatchLayout, e, this.workGroupSize), this.sliceDimGreaterThanOne = t > 1, this.shaderKey = `scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;
     let o = Wt(r.length);
@@ -35555,15 +35446,15 @@ var iie = class {
       }`;
   }
 };
-function oie(e) {
+function uie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = S.calculateShapes(a, r, i), d = [p / l, l];
   if (p === 0)
     return n.makeTensorInfo(i, r.dtype);
-  let h = Me({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = Me({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = cu({ backend: n, attrs: { shape: d, value: 0, dtype: m } }), b = w.sizeFromShape(f.shape), y = [{ type: "int32", data: [o] }, { type: "int32", data: c }, { type: "int32", data: [b] }], v = new iie(f.shape, o, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = Me({ inputs: { x }, backend: n, attrs: { shape: i } });
+  let h = Me({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = Me({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = du({ backend: n, attrs: { shape: d, value: 0, dtype: m } }), b = w.sizeFromShape(f.shape), y = [{ type: "int32", data: [o] }, { type: "int32", data: c }, { type: "int32", data: [b] }], v = new oie(f.shape, o, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = Me({ inputs: { x }, backend: n, attrs: { shape: i } });
   return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(x.dataId), k;
 }
-var uie = { kernelName: Do, backendName: "webgpu", kernelFunc: oie };
-var lie = class {
+var lie = { kernelName: Fo, backendName: "webgpu", kernelFunc: uie };
+var cie = class {
   constructor(e, t, n) {
     this.variableNames = ["c", "a", "b"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.cRank = e, this.rank = n, this.shaderKey = "select";
   }
@@ -35594,25 +35485,25 @@ var lie = class {
     `;
   }
 };
-function cie(e) {
-  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new lie(s.shape.length, r.shape, r.shape.length);
+function die(e) {
+  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new cie(s.shape.length, r.shape, r.shape.length);
   return n.runWebGPUProgram(i, [s, r, a], cn(r.dtype, a.dtype));
 }
-var die = { kernelName: Fo, backendName: "webgpu", kernelFunc: cie };
-var pie = Kt({ opType: 18 });
-var hie = { kernelName: ai, backendName: "webgpu", kernelFunc: pie };
-var fie = Kt({ opType: 16 });
-var mie = { kernelName: ri, backendName: "webgpu", kernelFunc: fie };
-var gie = Kt({ opType: 17 });
-var bie = { kernelName: Po, backendName: "webgpu", kernelFunc: gie };
-var M2 = mn({ opSnippet: 2, cpuKernelImpl: nse, supportsComplex: true });
-var yie = { kernelName: ci, backendName: "webgpu", kernelFunc: M2 };
-function vie(e) {
+var pie = { kernelName: Oo, backendName: "webgpu", kernelFunc: die };
+var hie = Kt({ opType: 18 });
+var fie = { kernelName: ai, backendName: "webgpu", kernelFunc: hie };
+var mie = Kt({ opType: 16 });
+var gie = { kernelName: ri, backendName: "webgpu", kernelFunc: mie };
+var bie = Kt({ opType: 17 });
+var yie = { kernelName: zo, backendName: "webgpu", kernelFunc: bie };
+var M2 = mn({ opSnippet: 2, cpuKernelImpl: sse, supportsComplex: true });
+var vie = { kernelName: ci, backendName: "webgpu", kernelFunc: M2 };
+function xie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = F2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = S.expandShapeToKeepDim(o.shape, i), l = Me({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = M2({ inputs: { a: r, b: l }, backend: n }), p = A2({ inputs: { x: c }, backend: n }), d = Av({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = Me({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = z2({ inputs: { a: p, b: h }, backend: n });
   return n.disposeData(o.dataId), n.disposeData(l.dataId), n.disposeData(c.dataId), n.disposeData(p.dataId), n.disposeData(d.dataId), n.disposeData(h.dataId), f;
 }
-var xie = { kernelName: ui, backendName: "webgpu", kernelFunc: vie };
-var wie = (e) => {
+var wie = { kernelName: ui, backendName: "webgpu", kernelFunc: xie };
+var kie = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
   w.assert(r.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");
   let o = a.reduce((b, y) => b * y), u = [[0, 0]];
@@ -35622,8 +35513,8 @@ var wie = (e) => {
   let l = [], c = P2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = S.getReshaped(c.shape, a, o, false), d = S.getPermuted(p.length, a.length, false), h = S.getReshapedPermuted(c.shape, a, o, false), f = Me({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = wi({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: h } });
   return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeData(b.dataId)), g;
 };
-var kie = { kernelName: zo, backendName: "webgpu", kernelFunc: wie };
-var Iie = class {
+var Iie = { kernelName: Mo, backendName: "webgpu", kernelFunc: kie };
+var Sie = class {
   constructor(e, t, n, s, r, a, i = true) {
     this.variableNames = ["updates", "indices", "defaultValue"], this.workGroupSize = [64, 1, 1], this.workPerThread = 4, this.size = true, this.outputShape = a, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
     let o = t > 1;
@@ -35669,30 +35560,30 @@ var Iie = class {
       }`;
   }
 };
-function Sie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = S.calculateShapes(a, r, o), d = false, h = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: c }], f = new Iie(l, u, r.shape.length, a.shape.length, c, [p, 1], d), m = n.runWebGPUProgram(f, [a, r, i], a.dtype, h), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: o } });
+function Cie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = S.calculateShapes(a, r, o), d = false, h = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: c }], f = new Sie(l, u, r.shape.length, a.shape.length, c, [p, 1], d), m = n.runWebGPUProgram(f, [a, r, i], a.dtype, h), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: o } });
   return n.disposeData(m.dataId), g;
 }
-var Cie = { kernelName: ip, backendName: "webgpu", kernelFunc: Sie };
-function Nie(e) {
+var Nie = { kernelName: ip, backendName: "webgpu", kernelFunc: Cie };
+function Tie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = S.prepareSplitSize(r, a, o), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
   return u.map((d) => {
     let h = [...p];
     h[o] = d;
-    let f = lu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
+    let f = cu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
     return c[o] += d, f;
   });
 }
-var Tie = { kernelName: Mo, backendName: "webgpu", kernelFunc: Nie };
-var $ie = Kt({ opType: 19 });
-var _ie = { kernelName: ii, backendName: "webgpu", kernelFunc: $ie };
-var Aie = { kernelName: _l, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
+var $ie = { kernelName: Lo, backendName: "webgpu", kernelFunc: Tie };
+var _ie = Kt({ opType: 19 });
+var Aie = { kernelName: ii, backendName: "webgpu", kernelFunc: _ie };
+var Eie = { kernelName: Al, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
   let { x: n } = e, s = t, r = new nc(n.shape, 20);
   return s.runWebGPUProgram(r, [n], n.dtype);
 } };
-var Eie = mn({ opSnippet: 11 });
-var Rie = { kernelName: li, backendName: "webgpu", kernelFunc: Eie };
-var Die = class {
+var Rie = mn({ opSnippet: 11 });
+var Die = { kernelName: li, backendName: "webgpu", kernelFunc: Rie };
+var Fie = class {
   constructor(e) {
     this.variableNames = ["x"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
     let t = Wt(this.outputShape.length);
@@ -35716,32 +35607,32 @@ var Die = class {
      `;
   }
 };
-function Fie(e) {
+function Oie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
   if (m)
     k = Me({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let T = wt.computeOutShape(y, v, x), N = lu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: T } });
+    let T = wt.computeOutShape(y, v, x), N = cu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: T } });
     k = Me({ inputs: { x: N }, backend: n, attrs: { shape: f } }), n.disposeData(N.dataId);
   } else if (n.shouldExecuteOnCPU([r])) {
-    let N = n.readSync(r.dataId), E = De(r.shape, r.dtype, N), A = ese(h, E, x, y);
+    let N = n.readSync(r.dataId), E = De(r.shape, r.dtype, N), A = tse(h, E, x, y);
     k = n.makeTensorInfo(f, r.dtype, A.values);
   } else {
-    let N = new Die(h), E = [{ type: "int32", data: y }, { type: "int32", data: x }], A = n.runWebGPUProgram(N, [r], r.dtype, E);
+    let N = new Fie(h), E = [{ type: "int32", data: y }, { type: "int32", data: x }], A = n.runWebGPUProgram(N, [r], r.dtype, E);
     k = Me({ inputs: { x: A }, backend: n, attrs: { shape: f } }), n.disposeData(A.dataId);
   }
   return k;
 }
-var Oie = { kernelName: Lo, backendName: "webgpu", kernelFunc: Fie };
-function Pie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = tse(d, h, r, a, i, o, u, l);
+var Pie = { kernelName: Bo, backendName: "webgpu", kernelFunc: Oie };
+function zie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = nse(d, h, r, a, i, o, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var zie = { kernelName: op, backendName: "webgpu", kernelFunc: Pie };
-var Mie = Kt({ opType: 21 });
-var Lie = { kernelName: di, backendName: "webgpu", kernelFunc: Mie };
-var Bie = class {
+var Mie = { kernelName: op, backendName: "webgpu", kernelFunc: zie };
+var Lie = Kt({ opType: 21 });
+var Bie = { kernelName: di, backendName: "webgpu", kernelFunc: Lie };
+var Vie = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.workGroupSize = [64, 1, 1], this.size = true;
     let n = new Array(e.length);
@@ -35750,7 +35641,7 @@ var Bie = class {
     this.outputShape = n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile";
   }
   getUserCode() {
-    let e = Vie(this.rank, "uniforms.");
+    let e = Wie(this.rank, "uniforms.");
     return `
       ${Ue()}
         if (index < uniforms.size) {
@@ -35761,7 +35652,7 @@ var Bie = class {
     `;
   }
 };
-function Vie(e, t = "") {
+function Wie(e, t = "") {
   if (e >= 5)
     throw Error(`Tile for rank ${e} is not yet supported`);
   if (e === 1)
@@ -35771,17 +35662,17 @@ function Vie(e, t = "") {
     s.push(`(${n[r]} % ${t}aShape[${r}])`);
   return s.join();
 }
-function Wie(e) {
+function Uie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
   if (n.shouldExecuteOnCPU([r]) || r.dtype === "string" || r.shape.length >= 5) {
-    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = sse(c, a);
+    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = rse(c, a);
     return n.makeTensorInfo(p.shape, p.dtype, p.values);
   }
-  let i = new Bie(r.shape, a);
+  let i = new Vie(r.shape, a);
   return n.runWebGPUProgram(i, [r], r.dtype);
 }
-var Uie = { kernelName: Cr, backendName: "webgpu", kernelFunc: Wie };
-var Gie = class {
+var Gie = { kernelName: Cr, backendName: "webgpu", kernelFunc: Uie };
+var Hie = class {
   constructor(e) {
     this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`, this.shaderKey = "swap";
@@ -35857,7 +35748,7 @@ var Gie = class {
       `;
   }
 };
-var Hie = class {
+var qie = class {
   constructor(e) {
     this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge";
   }
@@ -35933,18 +35824,18 @@ function Pw(e) {
     t *= 2;
   return t;
 }
-function qie(e) {
+function jie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s, o = r.shape, u = o[o.length - 1];
   if (n.shouldExecuteOnCPU([r])) {
-    let k = n.readSync(r.dataId), [T, N] = rse(k, o, r.dtype, a, i);
+    let k = n.readSync(r.dataId), [T, N] = ase(k, o, r.dtype, a, i);
     return [n.makeTensorInfo(T.shape, T.dtype, T.values), n.makeTensorInfo(N.shape, N.dtype, N.values)];
   }
   if (a === 0)
     return o[o.length - 1] = 0, [n.makeTensorInfo(o, r.dtype, []), n.makeTensorInfo(o, "int32", [])];
   if (u === 1)
-    return [r, cu({ attrs: { shape: o, dtype: "int32", value: 0 }, backend: n })];
+    return [r, du({ attrs: { shape: o, dtype: "int32", value: 0 }, backend: n })];
   let c = w.sizeFromShape(o) / u, p = Me({ inputs: { x: r }, attrs: { shape: [c, u] }, backend: n }), d = Pw(a), h = Pw(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, T, N) => {
-    let E = m(), A = new Gie(N), R = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [T] }], F = f;
+    let E = m(), A = new Hie(N), R = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [T] }], F = f;
     f = n.runWebGPUProgram(A, E, "int32", R), Vi(n, F);
   };
   for (let k = 1; k < d; k *= 2) {
@@ -35953,14 +35844,14 @@ function qie(e) {
       g(T, N, [c, h]);
   }
   for (let k = h; k > d; k /= 2) {
-    let T = m(), N = new Hie([c, k / 2]), A = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
+    let T = m(), N = new qie([c, k / 2]), A = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
     f = n.runWebGPUProgram(N, T, "int32", A), Vi(n, P);
     let R = d / 2, F = R * 2;
     for (let $ = R; $ >= 1; $ /= 2)
       g(F, $, f.shape);
   }
   let b = f;
-  f = lu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Vi(n, b);
+  f = cu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Vi(n, b);
   let y = D2({ inputs: { x: p, indices: f }, backend: n, attrs: { axis: 1, batchDims: 1 } });
   Vi(n, p);
   let v = o.slice(0, -1);
@@ -35968,8 +35859,8 @@ function qie(e) {
   let x = y;
   return y = Me({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Vi(n, x), [y, f];
 }
-var jie = { kernelName: Vo, backendName: "webgpu", kernelFunc: qie };
-var Kie = class {
+var Kie = { kernelName: Wo, backendName: "webgpu", kernelFunc: jie };
+var Xie = class {
   constructor(e) {
     this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "transform";
   }
@@ -36093,8 +35984,8 @@ var Kie = class {
         `;
   }
 };
-function Xie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Kie(g), y = i === "nearest" ? 1 : 2, v;
+function Yie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Xie(g), y = i === "nearest" ? 1 : 2, v;
   switch (o) {
     case "constant":
       v = 1;
@@ -36115,8 +36006,8 @@ function Xie(e) {
   let x = [{ type: "int32", data: [y] }, { type: "int32", data: [v] }, { type: "float32", data: [u] }];
   return n.runWebGPUProgram(b, [r, a], "float32", x);
 }
-var Yie = { kernelName: Wo, backendName: "webgpu", kernelFunc: Xie };
-function Qie(e) {
+var Qie = { kernelName: Uo, backendName: "webgpu", kernelFunc: Yie };
+function Zie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
   let i = r, o = i.shape.length, u = r.shape[a], l = new Array(o - 1), c = 0;
@@ -36127,16 +36018,16 @@ function Qie(e) {
   let f = new Array(u);
   for (let m = 0; m < f.length; m++) {
     d[a] = m;
-    let g = lu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l } });
+    let g = cu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l } });
     f[m] = b, p.push(g);
   }
   return p.forEach((m) => n.disposeData(m.dataId)), f;
 }
-var Zie = { kernelName: Uo, backendName: "webgpu", kernelFunc: Qie };
-var Jie = [Sne, ose, lse, pse, yse, xse, kse, Sse, _se, Dse, Ose, Lse, $ne, Use, Qse, tre, sre, are, ure, dre, hre, yre, xre, kre, Ire, Sre, Nre, $re, Are, Pre, Rre, Fre, Lre, Vre, Ure, qre, Xre, Qre, Jre, Tne, Vse, tae, sae, aae, oae, lae, dae, pae, fae, gae, yae, xae, kae, Sae, fre, Nae, $ae, Aae, Ase, Rae, Fae, Pae, Mae, Bae, Wae, Gae, Ese, Hae, jae, Xae, kne, Zae, tie, sie, aie, uie, die, hie, mie, bie, Tse, Oie, zie, xie, kie, Cie, Tie, _ie, Aie, Rie, yie, gre, Lie, Uie, jie, Yie, gse, Zie, Eae];
-for (let e of Jie)
-  Al(e);
-var eoe = class {
+var Jie = { kernelName: Go, backendName: "webgpu", kernelFunc: Zie };
+var eoe = [Cne, use, cse, hse, vse, wse, Ise, Cse, Ase, Fse, Pse, Bse, _ne, Gse, Zse, nre, rre, ire, lre, pre, fre, vre, wre, Ire, Sre, Cre, Tre, _re, Ere, zre, Dre, Ore, Bre, Wre, Gre, jre, Yre, Zre, eae, $ne, Wse, nae, rae, iae, uae, cae, pae, hae, mae, bae, vae, wae, Iae, Cae, mre, Tae, _ae, Eae, Ese, Dae, Oae, zae, Lae, Vae, Uae, Hae, Rse, qae, Kae, Yae, Ine, Jae, nie, rie, iie, lie, pie, fie, gie, yie, $se, Pie, Mie, wie, Iie, Nie, $ie, Aie, Eie, Die, vie, bre, Bie, Gie, Kie, Qie, bse, Jie, Rae];
+for (let e of eoe)
+  El(e);
+var toe = class {
   constructor(e) {
     this.device = e, this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
   }
@@ -36245,10 +36136,9 @@ var L2 = class {
     return { bindGroupLayout: n, pipelineLayout: s };
   }
 };
-var toe = class extends L2 {
+var noe = class extends L2 {
   constructor() {
-    super(...arguments);
-    this.layout = null, this.useImport = true;
+    super(...arguments), this.layout = null, this.useImport = true;
   }
   getUserCode() {
     return this.makeFromPixelsSource();
@@ -36263,7 +36153,7 @@ var toe = class extends L2 {
     return { bindGroupLayout: n, pipelineLayout: s };
   }
 };
-var noe = X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
+var soe = X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
 var Mw = (e, t) => {
   let n = e.limits.maxComputeWorkgroupsPerDimension, s = t.dispatchLayout, r = t.dispatch;
   if (r.every((i) => i <= n))
@@ -36272,12 +36162,11 @@ var Mw = (e, t) => {
   let a = Math.ceil(Math.sqrt(r[0]));
   return a > n ? (a = Math.ceil(Math.cbrt(r[0])), w.assert(a <= n, () => "Total dispatch size exceeds WebGPU maximum."), [a, a, a]) : [a, a, 1];
 };
-var B2 = class extends tl {
+var B2 = class extends nl {
   constructor(e, t = false) {
-    super();
-    if (this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, !Tv())
+    if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, !Tv())
       throw new Error("WebGPU is not supported on this device");
-    this.layoutCache = {}, this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = t, this.bufferManager = new eoe(this.device), this.tensorMap = new Ud(this, Ss()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), X().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas));
+    this.layoutCache = {}, this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = t, this.bufferManager = new toe(this.device), this.tensorMap = new Ud(this, Ss()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), X().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas));
   }
   nextDataId() {
     return B2.nextDataId++;
@@ -36355,7 +36244,7 @@ var B2 = class extends tl {
       case "copyExternal":
         return this.fromPixelProgram || (this.fromPixelProgram = new L2()), this.fromPixelProgram;
       case "import":
-        return this.fromPixelImportProgram || (this.fromPixelImportProgram = new toe()), this.fromPixelImportProgram;
+        return this.fromPixelImportProgram || (this.fromPixelImportProgram = new noe()), this.fromPixelImportProgram;
       default:
         w.assert(false, () => "Unsupported fromPixels shape");
         return;
@@ -36518,7 +36407,7 @@ var B2 = class extends tl {
       if (N.dtype === "complex64")
         throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");
       return this.uploadToGPU(N.dataId), { dtype: this.tensorMap.get(N.dataId).dtype, shape: N.shape, name: e.variableNames[E] };
-    }), p = c.map((N) => N.dtype).concat(r.dtype), d = c.map((N) => S.getBroadcastDims(N.shape, r.shape)), h = c.map((N) => w.arraysEqual(N.shape, r.shape)).join("_"), f = d.map((N) => N.join("_")).join(";"), m = R2(e, i, p, f, h), { bindGroupLayout: g, pipelineLayout: b } = this.getCachedOrCreateLayout(e.variableNames.length), y = this.getAndSavePipeline(m, () => E2(this.device, e, b, c, r)), v = this.activeTimers != null, x = Ore(this.device, g, t.map((N) => this.tensorToBinding(N)), this.tensorToBinding(r), l);
+    }), p = c.map((N) => N.dtype).concat(r.dtype), d = c.map((N) => S.getBroadcastDims(N.shape, r.shape)), h = c.map((N) => w.arraysEqual(N.shape, r.shape)).join("_"), f = d.map((N) => N.join("_")).join(";"), m = R2(e, i, p, f, h), { bindGroupLayout: g, pipelineLayout: b } = this.getCachedOrCreateLayout(e.variableNames.length), y = this.getAndSavePipeline(m, () => E2(this.device, e, b, c, r)), v = this.activeTimers != null, x = Pre(this.device, g, t.map((N) => this.tensorToBinding(N)), this.tensorToBinding(r), l);
     this.ensureCommandEncoderReady();
     let k = this.getComputePass();
     v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 0), k.setPipeline(y), k.setBindGroup(0, x), k.dispatch(e.dispatch[0], e.dispatch[1], e.dispatch[2]), v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t.forEach((N) => {
@@ -36540,7 +36429,7 @@ var B2 = class extends tl {
     let s = new BigUint64Array(n.getMappedRange()), r = Number(s[1] - s[0]);
     return n.unmap(), this.bufferManager.releaseBuffer(n, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), r / 1e6;
   }
-  shouldExecuteOnCPU(e, t = noe) {
+  shouldExecuteOnCPU(e, t = soe) {
     return X().getBool("WEBGPU_CPU_FORWARD") && e.every((n) => this.tensorMap.get(n.dataId).bufferInfo.buffer == null && w.sizeFromShape(n.shape) < t);
   }
   numDataIds() {
@@ -36552,8 +36441,8 @@ var B2 = class extends tl {
 };
 var Ev = B2;
 Ev.nextDataId = 0;
-var soe = {};
-Ae(soe, { WebGPUBackend: () => Ev, webgpu_util: () => m2 });
+var roe = {};
+Ae(roe, { WebGPUBackend: () => Ev, webgpu_util: () => m2 });
 Tv() && pp("webgpu", async () => {
   X().set("CHECK_COMPUTATION_FOR_ERRORS", false);
   let e = { powerPreference: X().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, t = await navigator.gpu.requestAdapter(e), n = t.limits, s = {}, r = t.features.has("timestamp-query");
@@ -36564,10 +36453,10 @@ Tv() && pp("webgpu", async () => {
 var It = ((e) => (e[e.float32 = 0] = "float32", e[e.int32 = 1] = "int32", e[e.bool = 2] = "bool", e[e.string = 3] = "string", e[e.complex64 = 4] = "complex64", e))(It || {});
 var Jp = ((e) => (e[e.linear = 0] = "linear", e[e.relu = 1] = "relu", e[e.relu6 = 2] = "relu6", e[e.prelu = 3] = "prelu", e[e.leakyrelu = 4] = "leakyrelu", e[e.sigmoid = 5] = "sigmoid", e[e.elu = 6] = "elu", e))(Jp || {});
 var V2;
-function roe(e) {
+function aoe(e) {
   V2 = e.wasm.cwrap(ra, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function aoe(e) {
+function ioe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t;
   if (r.dtype !== "float32" || a.dtype !== "float32")
     throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");
@@ -36581,10 +36470,10 @@ function aoe(e) {
   let m = o == null ? 0 : n.dataIdMap.get(o.dataId).id, g = Jp[c];
   if (g == null)
     throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);
-  let b = u ? r.shape[2] : r.shape[1], y = l ? a.shape[1] : a.shape[2], v = qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)), x = n.makeOutput([...v, b, y], r.dtype), k = n.dataIdMap.get(x.dataId).id, T = new Uint8Array(new Int32Array(r.shape).buffer), N = new Uint8Array(new Int32Array(a.shape).buffer);
+  let b = u ? r.shape[2] : r.shape[1], y = l ? a.shape[1] : a.shape[2], v = jo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)), x = n.makeOutput([...v, b, y], r.dtype), k = n.dataIdMap.get(x.dataId).id, T = new Uint8Array(new Int32Array(r.shape).buffer), N = new Uint8Array(new Int32Array(a.shape).buffer);
   return V2(d, T, r.shape.length, h, N, a.shape.length, u, l, g, f, m, p || 0, k), x;
 }
-var ioe = { kernelName: ra, backendName: "wasm", setupFunc: roe, kernelFunc: aoe };
+var ooe = { kernelName: ra, backendName: "wasm", setupFunc: aoe, kernelFunc: ioe };
 function Xt(e, t) {
   let n;
   function s(a) {
@@ -36596,7 +36485,7 @@ function Xt(e, t) {
   }
   return { kernelName: e, backendName: "wasm", setupFunc: s, kernelFunc: r };
 }
-var ooe = Xt(ao);
+var uoe = Xt(io);
 function gn(e, t, n) {
   let s;
   function r(i) {
@@ -36611,34 +36500,34 @@ function gn(e, t, n) {
   }
   return { kernelName: e, backendName: "wasm", setupFunc: r, kernelFunc: a };
 }
-var uoe = true;
-var loe = gn(Ir, uoe);
+var loe = true;
+var coe = gn(Ir, loe);
 var W2;
-function coe(e) {
+function doe(e) {
   W2 = e.wasm.cwrap(ka, null, ["array", "number", "number", "number"]);
 }
-function doe(e) {
+function poe(e) {
   let { inputs: t, backend: n } = e, s = n.makeOutput(t[0].shape, t[0].dtype);
   if (w.sizeFromShape(s.shape) === 0)
     return s;
   let r = t.map((o) => n.dataIdMap.get(o.dataId).id), a = new Uint8Array(new Int32Array(r).buffer), i = n.dataIdMap.get(s.dataId).id;
   return W2(a, r.length, It[s.dtype], i), s;
 }
-var poe = { kernelName: ka, backendName: "wasm", setupFunc: coe, kernelFunc: doe };
+var hoe = { kernelName: ka, backendName: "wasm", setupFunc: doe, kernelFunc: poe };
 function eh(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype), r = n.typedArrayFromHeap(t);
   return n.typedArrayFromHeap(s).set(r), s;
 }
-var hoe = { kernelName: Ba, backendName: "wasm", kernelFunc: eh };
+var foe = { kernelName: Ba, backendName: "wasm", kernelFunc: eh };
 var U2;
-function foe(e) {
+function moe(e) {
   U2 = e.wasm.cwrap(pi, null, ["number", "array", "number", "number", "number", "array", "number"]);
 }
 function wr(e) {
-  let { inputs: t, backend: n, attrs: s } = e, [r, a] = goe(t.x.shape, s.perm), i = true;
+  let { inputs: t, backend: n, attrs: s } = e, [r, a] = boe(t.x.shape, s.perm), i = true;
   for (let f = 0; f < a.length; f++)
     a[f] !== f && (i = false);
-  let o = moe(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
+  let o = goe(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
   if (i) {
     let f = eh({ inputs: t, backend: n });
     return f.shape = o, f;
@@ -36646,13 +36535,13 @@ function wr(e) {
   let l = n.makeOutput(o, u.dtype), c = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(l.dataId).id, d = new Uint8Array(new Int32Array(a).buffer), h = new Uint8Array(new Int32Array(u.shape).buffer);
   return U2(c, h, u.shape.length, It[u.dtype], p, d, a.length), l;
 }
-function moe(e, t) {
+function goe(e, t) {
   let n = new Array(e.length);
   for (let s = 0; s < n.length; s++)
     n[s] = e[t[s]];
   return n;
 }
-function goe(e, t) {
+function boe(e, t) {
   let n = [], s = [];
   for (let r = 0; r < e.length; ++r)
     e[r] !== 1 && n.push(e[r]), e[t[r]] !== 1 && s.push(t[r]);
@@ -36664,7 +36553,7 @@ function goe(e, t) {
   }
   return [n, s];
 }
-var boe = { kernelName: pi, backendName: "wasm", kernelFunc: wr, setupFunc: foe };
+var yoe = { kernelName: pi, backendName: "wasm", kernelFunc: wr, setupFunc: moe };
 function Fr(e, t, n) {
   let s = e.shape, r = e.shape.length, a = w.parseAxisParam(t, s), i = a, o = S.getAxesPermutation(i, r), u = null, l = false;
   if (o != null) {
@@ -36678,10 +36567,10 @@ function Fr(e, t, n) {
   return { transposed: u, originalAxes: a, axes: i, inputWasTransposed: l };
 }
 var G2;
-function yoe(e) {
-  G2 = e.wasm.cwrap(rl, null, ["number, number, number"]);
-}
 function voe(e) {
+  G2 = e.wasm.cwrap(al, null, ["number, number, number"]);
+}
+function xoe(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
@@ -36700,12 +36589,12 @@ function voe(e) {
   }
   return y;
 }
-var xoe = { kernelName: rl, backendName: "wasm", setupFunc: yoe, kernelFunc: voe };
+var woe = { kernelName: al, backendName: "wasm", setupFunc: voe, kernelFunc: xoe };
 var H2;
-function woe(e) {
-  H2 = e.wasm.cwrap(al, null, ["number, number, number"]);
-}
 function koe(e) {
+  H2 = e.wasm.cwrap(il, null, ["number, number, number"]);
+}
+function Ioe(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
@@ -36724,12 +36613,12 @@ function koe(e) {
   }
   return y;
 }
-var Ioe = { kernelName: al, backendName: "wasm", setupFunc: woe, kernelFunc: koe };
+var Soe = { kernelName: il, backendName: "wasm", setupFunc: koe, kernelFunc: Ioe };
 var q2;
-function Soe(e) {
+function Coe(e) {
   q2 = e.wasm.cwrap(Ia, null, ["number", "number", "number", "number", "number"]);
 }
-function Coe(e) {
+function Noe(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r } = s, { x: a } = n, i = t.dataIdMap.get(a.dataId).id, o = i, u = a, { transposed: l, axes: c, inputWasTransposed: p } = Fr(a, r, t);
   if (p) {
     let b = t.dataIdMap.get(l.dataId).id;
@@ -36738,12 +36627,12 @@ function Coe(e) {
   let d = u.shape.slice(0, -1), h = t.makeOutput(d, "int32"), f = t.dataIdMap.get(h.dataId).id, m = w.sizeFromShape(h.shape), g = u.shape[c[0]];
   return q2(o, It[u.dtype], m, g, f), p && t.disposeData(l.dataId), h;
 }
-var Noe = { kernelName: Ia, backendName: "wasm", kernelFunc: Coe, setupFunc: Soe };
+var Toe = { kernelName: Ia, backendName: "wasm", kernelFunc: Noe, setupFunc: Coe };
 var j2;
-function Toe(e) {
+function $oe(e) {
   j2 = e.wasm.cwrap(Sa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function $oe(e) {
+function _oe(e) {
   let { inputs: t, attrs: n, backend: s } = e, r = t.x, a = s.dataIdMap.get(r.dataId).id, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = n, c = S.computePool2DInfo(r.shape, i, o, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.strideHeight, y = c.strideWidth, v = c.inChannels;
   if (c.dataFormat !== "channelsLast")
     throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
@@ -36752,26 +36641,26 @@ function $oe(e) {
   let x = s.makeOutput(c.outShape, "float32"), k = s.dataIdMap.get(x.dataId).id;
   return j2(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, k), x;
 }
-var _oe = { kernelName: Sa, backendName: "wasm", setupFunc: Toe, kernelFunc: $oe };
+var Aoe = { kernelName: Sa, backendName: "wasm", setupFunc: $oe, kernelFunc: _oe };
 function yn(e) {
   let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a);
   return w.assert(a === w.sizeFromShape(i), () => `new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
 }
-var Aoe = { kernelName: Ao, backendName: "wasm", kernelFunc: yn };
+var Eoe = { kernelName: Eo, backendName: "wasm", kernelFunc: yn };
 var K2;
-function Eoe(e) {
+function Roe(e) {
   K2 = e.wasm.cwrap(Ca, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
 }
-function Roe(e) {
+function Doe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
   if (r.dtype !== "float32" || a.dtype !== "float32")
     throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
-  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
+  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = jo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
   w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
   let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], T = yn({ inputs: { x: r }, backend: n, attrs: { shape: x } }), N = yn({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = n.dataIdMap.get(T.dataId).id, A = n.dataIdMap.get(N.dataId).id, P = i ? T.shape[2] : T.shape[1], R = o ? N.shape[1] : N.shape[2], F = Math.max(g, b), $ = n.makeOutput([F, P, R], T.dtype), z = n.dataIdMap.get($.dataId).id, W = new Uint8Array(new Int32Array(T.shape).buffer), q = new Uint8Array(new Int32Array(N.shape).buffer);
   return K2(E, W, T.shape.length, A, q, N.shape.length, i, o, z), n.disposeData(T.dataId), n.disposeData(N.dataId), $.shape = v, $;
 }
-var Doe = { kernelName: Ca, backendName: "wasm", setupFunc: Eoe, kernelFunc: Roe };
+var Foe = { kernelName: Ca, backendName: "wasm", setupFunc: Roe, kernelFunc: Doe };
 function va(e) {
   let { inputs: { x: t }, attrs: { begin: n, size: s }, backend: r } = e, [a, i] = wt.parseSliceParams(t, n, s), o = wt.isSliceContinous(t.shape, a, i), u = r.readSync(t.dataId), l = r.makeOutput(i, t.dtype), c = w.computeStrides(t.shape), p = r.dataIdMap.get(l.dataId);
   if (o) {
@@ -36784,25 +36673,25 @@ function va(e) {
   }
   let d = r.typedArrayFromHeap(l), h = t.shape.length;
   if (h === 2)
-    Foe(u, c[0], d, a, i);
+    Ooe(u, c[0], d, a, i);
   else if (h === 3)
-    Ooe(u, c[0], c[1], d, a, i);
+    Poe(u, c[0], c[1], d, a, i);
   else if (h === 4)
-    Poe(u, c[0], c[1], c[2], d, a, i);
+    zoe(u, c[0], c[1], c[2], d, a, i);
   else {
     let f = Fd(u, a, i, t.shape, t.dtype);
     d.set(f);
   }
   return l;
 }
-function Foe(e, t, n, s, r) {
+function Ooe(e, t, n, s, r) {
   let a = 0, i = s[0], o = s[1], u = i + r[0];
   for (let l = i; l < u; l++) {
     let c = l * t + o;
     n.set(e.subarray(c, c + r[1]), a), a += r[1];
   }
 }
-function Ooe(e, t, n, s, r, a) {
+function Poe(e, t, n, s, r, a) {
   let i = 0, o = r[0], u = r[1], l = r[2], c = o + a[0], p = u + a[1];
   for (let d = o; d < c; d++)
     for (let h = u; h < p; h++) {
@@ -36810,7 +36699,7 @@ function Ooe(e, t, n, s, r, a) {
       s.set(e.subarray(f, f + a[2]), i), i += a[2];
     }
 }
-function Poe(e, t, n, s, r, a, i) {
+function zoe(e, t, n, s, r, a, i) {
   let o = 0, u = a[0], l = a[1], c = a[2], p = u + i[0], d = l + i[1], h = c + i[2], f = a[3];
   for (let m = u; m < p; m++)
     for (let g = l; g < d; g++)
@@ -36819,27 +36708,27 @@ function Poe(e, t, n, s, r, a, i) {
         r.set(e.subarray(y, y + i[3]), o), o += i[3];
       }
 }
-var zoe = { kernelName: Oo, backendName: "wasm", kernelFunc: va };
-function Moe(e) {
+var Moe = { kernelName: Po, backendName: "wasm", kernelFunc: va };
+function Loe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s, o = a.reduce((b, y) => b * y), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = yn({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wr({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = yn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = va({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
   return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(h.dataId), g;
 }
-var Loe = { kernelName: io, backendName: "wasm", kernelFunc: Moe };
+var Boe = { kernelName: oo, backendName: "wasm", kernelFunc: Loe };
 function ac(e) {
   let { inputs: { x: t }, attrs: { dtype: n }, backend: s } = e, r = s.makeOutput(t.shape, n), a = s.typedArrayFromHeap(t);
   return s.typedArrayFromHeap(r).set(a), r;
 }
-var Boe = { kernelName: Na, backendName: "wasm", kernelFunc: ac };
-var Voe = Xt(Ta);
+var Voe = { kernelName: Na, backendName: "wasm", kernelFunc: ac };
+var Woe = Xt(Ta);
 var X2;
-function Woe(e) {
+function Uoe(e) {
   X2 = e.wasm.cwrap(Sr, null, ["number", "number", "number", "number"]);
 }
-function Uoe(e) {
+function Goe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o = n.dataIdMap.get(r.dataId).id, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(u.dataId).id;
   return X2(o, a, i, l), u;
 }
-var Goe = { kernelName: Sr, backendName: "wasm", setupFunc: Woe, kernelFunc: Uoe };
+var Hoe = { kernelName: Sr, backendName: "wasm", setupFunc: Uoe, kernelFunc: Goe };
 function Y2(e) {
   let { inputs: t, backend: n } = e, s = w.parseAxisParam(e.attrs.axis, t[0].shape)[0], r = S.computeOutShape(t.map((h) => h.shape), s), a = t.filter((h) => w.sizeFromShape(h.shape) > 0);
   if (a.length === 1)
@@ -36872,47 +36761,47 @@ function Y2(e) {
   }
   return i;
 }
-var Hoe = { kernelName: oo, backendName: "wasm", kernelFunc: Y2 };
+var qoe = { kernelName: uo, backendName: "wasm", kernelFunc: Y2 };
 var Q2;
-function qoe(e) {
+function joe(e) {
   Q2 = e.wasm.cwrap($a, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function joe(e) {
+function Koe(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, i = s.dataIdMap.get(r.dataId).id, o = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p, dataFormat: d } = n, h = S.convertConv2DDataFormat(d), f = S.computeConv2DInfo(r.shape, a.shape, u, l, c, p, false, h), m = f.filterHeight, g = f.filterWidth, b = f.padInfo.top, y = f.padInfo.right, v = f.padInfo.bottom, x = f.padInfo.left, k = f.dilationHeight, T = f.dilationWidth, N = f.strideHeight, E = f.strideWidth, A = f.inChannels, P = f.outChannels, R = f.padInfo.type === "SAME" ? 1 : 0;
   if (f.dataFormat !== "channelsLast")
     throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);
   let F = s.makeOutput(f.outShape, "float32"), $ = s.dataIdMap.get(F.dataId).id;
   return Q2(i, r.shape[0], r.shape[1], r.shape[2], o, m, g, b, y, v, x, R, k, T, N, E, A, P, $), F;
 }
-var Koe = { kernelName: $a, backendName: "wasm", setupFunc: qoe, kernelFunc: joe };
+var Xoe = { kernelName: $a, backendName: "wasm", setupFunc: joe, kernelFunc: Koe };
 var Z2;
-function Xoe(e) {
+function Yoe(e) {
   Z2 = e.wasm.cwrap(_a, 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 Yoe(e) {
+function Qoe(e) {
   let { backend: t, inputs: n, attrs: s } = e, { dy: r, filter: a } = n, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, inputShape: c } = s, p = 1, d = S.convertConv2DDataFormat(u), h = S.computeConv2DInfo(c, a.shape, i, p, o, l, false, d), { batchSize: f, filterHeight: m, filterWidth: g, inChannels: b, inHeight: y, inWidth: v, outChannels: x, outHeight: k, outWidth: T, strideHeight: N, strideWidth: E } = h, A = m - 1 - h.padInfo.top, P = g - 1 - h.padInfo.left, R = h.dataFormat === "channelsLast", F = w.computeStrides(h.inShape), $ = w.computeStrides(r.shape), [z, W, q] = w.computeStrides(a.shape), K = F[0], Y = R ? F[1] : F[2], Z = R ? F[2] : 1, te = R ? 1 : F[1], ee = $[0], se = R ? $[1] : $[2], ne = R ? $[2] : 1, oe = R ? 1 : $[1], re = t.makeOutput(h.inShape, "float32"), le = t.dataIdMap.get(re.dataId).id, me = t.dataIdMap.get(r.dataId).id, ke = t.dataIdMap.get(a.dataId).id;
   return Z2(me, ke, f, m, g, y, v, b, k, T, x, N, E, A, P, z, W, q, K, Y, Z, te, ee, se, ne, oe, le), re;
 }
-var Qoe = { kernelName: _a, backendName: "wasm", setupFunc: Xoe, kernelFunc: Yoe };
-var Zoe = Xt(Aa);
-var Joe = Xt(Ea);
+var Zoe = { kernelName: _a, backendName: "wasm", setupFunc: Yoe, kernelFunc: Qoe };
+var Joe = Xt(Aa);
+var eue = Xt(Ea);
 var J2 = ((e) => (e[e.bilinear = 0] = "bilinear", e[e.nearest = 1] = "nearest", e))(J2 || {});
 var eN;
-function eue(e) {
-  eN = e.wasm.cwrap(lo, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
-}
 function tue(e) {
+  eN = e.wasm.cwrap(co, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
+}
+function nue(e) {
   let { backend: t, inputs: n, attrs: s } = e, { method: r, extrapolationValue: a, cropSize: i } = s, { image: o, boxes: u, boxInd: l } = n, c = u.shape[0], [p, d] = i, h = [c, p, d, o.shape[3]], f = t.dataIdMap.get(o.dataId), m;
   o.dtype !== "float32" && (m = ac({ backend: t, inputs: { x: o }, attrs: { dtype: "float32" } }), f = t.dataIdMap.get(m.dataId));
   let g = f.id, b = t.dataIdMap.get(u.dataId).id, y = t.dataIdMap.get(l.dataId).id, v = t.makeOutput(h, "float32"), x = t.dataIdMap.get(v.dataId).id, k = new Uint8Array(new Int32Array(o.shape).buffer);
   return eN(g, b, y, c, k, p, d, J2[r], a, x), m != null && t.disposeData(m.dataId), v;
 }
-var nue = { kernelName: lo, backendName: "wasm", setupFunc: eue, kernelFunc: tue };
+var sue = { kernelName: co, backendName: "wasm", setupFunc: tue, kernelFunc: nue };
 var tN;
-function sue(e) {
-  tN = e.wasm.cwrap(pl, null, ["number", "number", "number", "number", "number", "number"]);
-}
 function rue(e) {
+  tN = e.wasm.cwrap(hl, null, ["number", "number", "number", "number", "number", "number"]);
+}
+function aue(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length;
   w.assert(r.dtype === "float32" || r.dtype === "int32", () => `cumprod does not support ${r.dtype} tensors in the WASM backend`);
   let l = S.getAxesPermutation([a], u), c = r;
@@ -36928,12 +36817,12 @@ function rue(e) {
   }
   return g;
 }
-var aue = { kernelName: pl, backendName: "wasm", setupFunc: sue, kernelFunc: rue };
+var iue = { kernelName: hl, backendName: "wasm", setupFunc: rue, kernelFunc: aue };
 var nN;
-function iue(e) {
-  nN = e.wasm.cwrap(uo, null, ["number", "number", "number", "number", "number", "number"]);
-}
 function oue(e) {
+  nN = e.wasm.cwrap(lo, null, ["number", "number", "number", "number", "number", "number"]);
+}
+function uue(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length;
   w.assert(r.dtype === "float32" || r.dtype === "int32", () => `cumsum does not support ${r.dtype} tensors in the WASM backend`);
   let l = S.getAxesPermutation([a], u), c = r;
@@ -36949,71 +36838,71 @@ function oue(e) {
   }
   return g;
 }
-var uue = { kernelName: uo, backendName: "wasm", setupFunc: iue, kernelFunc: oue };
+var lue = { kernelName: lo, backendName: "wasm", setupFunc: oue, kernelFunc: uue };
 var sN;
-function lue(e) {
-  sN = e.wasm.cwrap(co, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
-}
 function cue(e) {
+  sN = e.wasm.cwrap(po, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
+}
+function due(e) {
   let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = t.makeOutput(f, "float32"), b = t.dataIdMap.get(r.dataId).id, y = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), v = new Uint8Array(new Int32Array(f).buffer), x = new Uint8Array(new Int32Array(w.computeStrides(f)).buffer), k = t.dataIdMap.get(m.dataId).id;
   return sN(b, a, i === "NHWC" ? 1 : 0, y, r.shape.length - 1, v, x, f.length, k), m;
 }
-var due = { kernelName: co, backendName: "wasm", setupFunc: lue, kernelFunc: cue };
+var pue = { kernelName: po, backendName: "wasm", setupFunc: cue, kernelFunc: due };
 var rN;
-function pue(e) {
+function hue(e) {
   rN = e.wasm.cwrap(Ra, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function hue(e) {
+function fue(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, i = s.dataIdMap.get(r.dataId).id, o = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p } = n, d = l == null ? [1, 1] : l, h = S.computeConv2DInfo(r.shape, a.shape, u, d, c, p, true), f = h.filterHeight, m = h.filterWidth, g = h.padInfo.top, b = h.padInfo.right, y = h.padInfo.bottom, v = h.padInfo.left, x = h.dilationHeight, k = h.dilationWidth, T = h.strideHeight, N = h.strideWidth, E = h.inChannels, A = h.outChannels, P = h.padInfo.type === "SAME" ? 1 : 0;
   if (h.dataFormat !== "channelsLast")
     throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);
   let R = s.makeOutput(h.outShape, "float32"), F = s.dataIdMap.get(R.dataId).id;
   return rN(i, r.shape[0], r.shape[1], r.shape[2], o, f, m, g, b, y, v, P, x, k, T, N, E, A, F), R;
 }
-var fue = { kernelName: Ra, backendName: "wasm", setupFunc: pue, kernelFunc: hue };
-var mue = Xt(Fa);
-var gue = false;
-var bue = gn(po, gue, "bool");
-var yue = Xt(Oa, "float32");
+var mue = { kernelName: Ra, backendName: "wasm", setupFunc: hue, kernelFunc: fue };
+var gue = Xt(Fa);
+var bue = false;
+var yue = gn(ho, bue, "bool");
+var vue = Xt(Oa, "float32");
 function Zm(e) {
   let { inputs: t, attrs: n, backend: s } = e, { input: r } = t, { dim: a } = n, i = r.shape.length, o = r.shape.slice(), u = a;
   return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), yn({ inputs: { x: r }, backend: s, attrs: { shape: o } });
 }
-var vue = { kernelName: ho, backendName: "wasm", kernelFunc: Zm };
+var xue = { kernelName: fo, backendName: "wasm", kernelFunc: Zm };
 function aN(e) {
   let { attrs: { shape: t, value: n, dtype: s }, backend: r } = e, a = r.makeOutput(t, s);
   return r.typedArrayFromHeap(a).fill(n), a;
 }
-var xue = { kernelName: fl, backendName: "wasm", kernelFunc: aN };
+var wue = { kernelName: ml, backendName: "wasm", kernelFunc: aN };
 var iN;
-function wue(e) {
-  iN = e.wasm.cwrap(mo, null, ["number", "number", "number", "number", "number", "number"]);
-}
 function kue(e) {
+  iN = e.wasm.cwrap(go, null, ["number", "number", "number", "number", "number", "number"]);
+}
+function Iue(e) {
   let { inputs: t, backend: n } = e, { image: s } = t, r = n.makeOutput(s.shape, s.dtype), a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, [o, u, l, c] = s.shape;
   return iN(a, o, u, l, c, i), r;
 }
-var Iue = { kernelName: mo, backendName: "wasm", kernelFunc: kue, setupFunc: wue };
-var Sue = Xt(Pa);
-var Cue = false;
-var Nue = gn(za, Cue);
+var Sue = { kernelName: go, backendName: "wasm", kernelFunc: Iue, setupFunc: kue };
+var Cue = Xt(Pa);
+var Nue = false;
+var Tue = gn(za, Nue);
 var oN;
-function Tue(e) {
+function $ue(e) {
   oN = e.wasm.cwrap(Ma, null, ["number", "number", "number", "number", "number", "number", "number"]);
 }
-function $ue(e) {
+function _ue(e) {
   let { backend: t, inputs: n, attrs: s } = e, { varianceEpsilon: r } = s, { x: a, mean: i, variance: o, offset: u, scale: l } = n, c = t.dataIdMap.get(a.dataId).id, p = t.dataIdMap.get(i.dataId).id, d = t.dataIdMap.get(o.dataId).id, h = u != null ? t.dataIdMap.get(u.dataId).id : 0, f = l != null ? t.dataIdMap.get(l.dataId).id : 0, m = t.makeOutput(a.shape, a.dtype);
   if (w.sizeFromShape(a.shape) === 0)
     return m;
   let g = t.dataIdMap.get(m.dataId).id;
   return oN(c, p, d, h, f, r, g), m;
 }
-var _ue = { kernelName: Ma, backendName: "wasm", setupFunc: Tue, kernelFunc: $ue };
+var Aue = { kernelName: Ma, backendName: "wasm", setupFunc: $ue, kernelFunc: _ue };
 var uN;
-function Aue(e) {
+function Eue(e) {
   uN = e.wasm.cwrap(aa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Eue(e) {
+function Rue(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = S.computeConv2DInfo(r.shape, a.shape, u, c, l, d), g = Jp[h];
   if (g == null)
     throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);
@@ -37032,12 +36921,12 @@ function Eue(e) {
   let te = s.makeOutput(m.outShape, "float32"), ee = s.dataIdMap.get(te.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
   return uN(b, K, Y, Z, y, k, T, x, N, E, A, P, q, R, F, $, z, W, v, g, se, f || 0, ee), te;
 }
-var Rue = { kernelName: aa, backendName: "wasm", setupFunc: Aue, kernelFunc: Eue };
+var Due = { kernelName: aa, backendName: "wasm", setupFunc: Eue, kernelFunc: Rue };
 var lN;
-function Due(e) {
+function Fue(e) {
   lN = e.wasm.cwrap(ia, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Fue(e) {
+function Oue(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = S.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = Jp[h];
   if (g == null)
     throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
@@ -37056,24 +36945,24 @@ function Fue(e) {
   let te = s.makeOutput(m.outShape, "float32"), ee = s.dataIdMap.get(te.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
   return lN(b, K, Y, Z, y, k, T, x, N, E, A, P, q, R, F, $, z, W, v, g, se, f || 0, ee), te;
 }
-var Oue = { kernelName: ia, backendName: "wasm", setupFunc: Due, kernelFunc: Fue };
+var Pue = { kernelName: ia, backendName: "wasm", setupFunc: Fue, kernelFunc: Oue };
 var cN;
-function Pue(e) {
-  cN = e.wasm.cwrap(bo, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
-}
 function zue(e) {
+  cN = e.wasm.cwrap(yo, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
+}
+function Mue(e) {
   let { backend: t, inputs: n } = e, { params: s, indices: r } = n, [a, i, o, u] = $k.prepareAndValidate(s, r), l = t.makeOutput(a, s.dtype);
   if (i === 0)
     return l;
   let c = r.shape, p = c[c.length - 1], h = t.dataIdMap.get(s.dataId).id, m = t.dataIdMap.get(r.dataId).id, g = new Uint8Array(new Int32Array(u).buffer), b = t.dataIdMap.get(l.dataId).id;
   return cN(h, It[s.dtype], m, i, p, o, g, b), l;
 }
-var Mue = { kernelName: bo, backendName: "wasm", setupFunc: Pue, kernelFunc: zue };
+var Lue = { kernelName: yo, backendName: "wasm", setupFunc: zue, kernelFunc: Mue };
 var dN;
-function Lue(e) {
+function Bue(e) {
   dN = e.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
 }
-function Bue(e) {
+function Vue(e) {
   let { backend: t, inputs: n, attrs: s } = e, { x: r, indices: a } = n, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0], l = t.readSync(a.dataId), c = r.shape[u];
   for (let A = 0; A < l.length; ++A) {
     let P = l[A];
@@ -37085,16 +36974,16 @@ function Bue(e) {
   let b = d.shape.length - 1, v = t.dataIdMap.get(d.dataId).id, k = t.dataIdMap.get(f.dataId).id, T = t.dataIdMap.get(g.dataId).id, N = new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer), E = new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);
   return dN(v, It[r.dtype], N, b, k, p.batchSize, E, T), t.disposeData(d.dataId), t.disposeData(f.dataId), g.shape = p.outputShape, g;
 }
-var Vue = { kernelName: go, backendName: "wasm", setupFunc: Lue, kernelFunc: Bue };
-var Wue = false;
-var Uue = gn(yo, Wue, "bool");
-var Gue = false;
-var Hue = gn(La, Gue, "bool");
+var Wue = { kernelName: bo, backendName: "wasm", setupFunc: Bue, kernelFunc: Vue };
+var Uue = false;
+var Gue = gn(vo, Uue, "bool");
+var Hue = false;
+var que = gn(La, Hue, "bool");
 var pN;
-function que(e) {
+function jue(e) {
   pN = e.wasm.cwrap(Va, null, ["number", "number", "number", "number"]);
 }
-function jue(e) {
+function Kue(e) {
   let { inputs: { x: t }, attrs: { alpha: n }, backend: s } = e, r = s.dataIdMap.get(t.dataId).id, a = s.makeOutput(t.shape, "float32");
   if (w.sizeFromShape(t.shape) !== 0) {
     let i = s.dataIdMap.get(a.dataId).id;
@@ -37102,19 +36991,19 @@ function jue(e) {
   }
   return a;
 }
-var Kue = { kernelName: Va, backendName: "wasm", setupFunc: que, kernelFunc: jue };
-var Xue = false;
-var Yue = gn(vo, Xue, "bool");
-var Que = false;
-var Zue = gn(xo, Que, "bool");
-var Jue = Xt(Wa);
-var ele = false;
-var tle = gn(wo, ele, "bool");
+var Xue = { kernelName: Va, backendName: "wasm", setupFunc: jue, kernelFunc: Kue };
+var Yue = false;
+var Que = gn(xo, Yue, "bool");
+var Zue = false;
+var Jue = gn(wo, Zue, "bool");
+var ele = Xt(Wa);
+var tle = false;
+var nle = gn(ko, tle, "bool");
 var hN;
-function nle(e) {
+function sle(e) {
   hN = e.wasm.cwrap(Ua, null, ["number", "number", "number", "number"]);
 }
-function sle(e) {
+function rle(e) {
   let { backend: t, inputs: n, attrs: s } = e, { reductionIndices: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
@@ -37133,14 +37022,14 @@ function sle(e) {
   }
   return y;
 }
-var rle = { kernelName: Ua, backendName: "wasm", setupFunc: nle, kernelFunc: sle };
-var ale = false;
-var ile = gn(Ga, ale);
+var ale = { kernelName: Ua, backendName: "wasm", setupFunc: sle, kernelFunc: rle };
+var ile = false;
+var ole = gn(Ga, ile);
 var fN;
-function ole(e) {
+function ule(e) {
   fN = e.wasm.cwrap(Ha, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function ule(e) {
+function lle(e) {
   let { inputs: t, attrs: n, backend: s } = e, r = t.x, a = s.dataIdMap.get(r.dataId).id;
   w.assert(r.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);
   let { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = n, c = S.computePool2DInfo(r.shape, i, o, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.dilationHeight, y = c.dilationWidth, v = c.strideHeight, x = c.strideWidth, k = c.inChannels, T = c.outChannels;
@@ -37149,12 +37038,12 @@ function ule(e) {
   let N = s.makeOutput(c.outShape, "float32"), E = s.dataIdMap.get(N.dataId).id;
   return fN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, x, k, T, E), N;
 }
-var lle = { kernelName: Ha, backendName: "wasm", setupFunc: ole, kernelFunc: ule };
+var cle = { kernelName: Ha, backendName: "wasm", setupFunc: ule, kernelFunc: lle };
 var mN;
-function cle(e) {
+function dle(e) {
   mN = e.wasm.cwrap(qa, null, ["number, number, number"]);
 }
-function dle(e) {
+function ple(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t), f = p;
   if (h) {
     let x = t.dataIdMap.get(c.dataId).id;
@@ -37174,12 +37063,12 @@ function dle(e) {
   }
   return l.dtype !== "float32" && t.disposeData(y.dataId), v;
 }
-var ple = { kernelName: qa, backendName: "wasm", setupFunc: cle, kernelFunc: dle };
+var hle = { kernelName: qa, backendName: "wasm", setupFunc: dle, kernelFunc: ple };
 var gN;
-function hle(e) {
+function fle(e) {
   gN = e.wasm.cwrap(ja, null, ["number", "number", "number", "number"]);
 }
-function fle(e) {
+function mle(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
@@ -37198,74 +37087,74 @@ function fle(e) {
   }
   return y;
 }
-var mle = { kernelName: ja, backendName: "wasm", setupFunc: hle, kernelFunc: fle };
-var gle = false;
-var ble = gn(Ka, gle);
+var gle = { kernelName: ja, backendName: "wasm", setupFunc: fle, kernelFunc: mle };
+var ble = false;
+var yle = gn(Ka, ble);
 var bN = ((e) => (e[e.reflect = 0] = "reflect", e[e.symmetric = 1] = "symmetric", e))(bN || {});
 var yN;
-function yle(e) {
+function vle(e) {
   yN = e.wasm.cwrap(Xa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
 }
-function vle(e) {
+function xle(e) {
   let { inputs: { x: t }, backend: n, attrs: { paddings: s, mode: r } } = e, a = s.map((f, m) => f[0] + t.shape[m] + f[1]), i = n.dataIdMap.get(t.dataId).id, o = n.makeOutput(a, t.dtype), u = n.dataIdMap.get(o.dataId).id, l = new Uint8Array(new Int32Array(t.shape).buffer), c = s.map((f) => f[0]), p = s.map((f) => f[1]), d = new Uint8Array(new Int32Array(c).buffer), h = new Uint8Array(new Int32Array(p).buffer);
   return yN(i, l, t.shape.length, It[t.dtype], d, h, bN[r], u), o;
 }
-var xle = { kernelName: Xa, backendName: "wasm", kernelFunc: vle, setupFunc: yle };
-var wle = true;
-var kle = gn(Ya, wle);
-var Ile = Xt(ko);
+var wle = { kernelName: Xa, backendName: "wasm", kernelFunc: xle, setupFunc: vle };
+var kle = true;
+var Ile = gn(Ya, kle);
+var Sle = Xt(Io);
 function Rv(e, t) {
   let n = new Int32Array(e.wasm.HEAPU8.buffer, t, 4), s = n[0], r = n[1], a = n[2], i = n[3];
   return e.wasm._free(t), { pSelectedIndices: s, selectedSize: r, pSelectedScores: a, pValidOutputs: i };
 }
 var vN;
-function Sle(e) {
-  vN = e.wasm.cwrap(So, "number", ["number", "number", "number", "number", "number"]);
-}
 function Cle(e) {
+  vN = e.wasm.cwrap(Co, "number", ["number", "number", "number", "number", "number"]);
+}
+function Nle(e) {
   let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i } = s, { boxes: o, scores: u } = n, l = t.dataIdMap.get(o.dataId).id, c = t.dataIdMap.get(u.dataId).id, p = vN(l, c, a, r, i), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = Rv(t, p);
   return t.wasm._free(f), t.wasm._free(m), t.makeOutput([h], "int32", d);
 }
-var Nle = { kernelName: So, backendName: "wasm", setupFunc: Sle, kernelFunc: Cle };
+var Tle = { kernelName: Co, backendName: "wasm", setupFunc: Cle, kernelFunc: Nle };
 var xN;
-function Tle(e) {
-  xN = e.wasm.cwrap(wl, "number", ["number", "number", "number", "number", "number", "bool"]);
-}
 function $le(e) {
+  xN = e.wasm.cwrap(kl, "number", ["number", "number", "number", "number", "number", "bool"]);
+}
+function _le(e) {
   let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, padToMaxOutputSize: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = xN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Rv(t, d);
   t.wasm._free(m);
   let b = t.makeOutput([f], "int32", h), y = t.makeOutput([], "int32", g);
   return [b, y];
 }
-var _le = { kernelName: wl, backendName: "wasm", setupFunc: Tle, kernelFunc: $le };
+var Ale = { kernelName: kl, backendName: "wasm", setupFunc: $le, kernelFunc: _le };
 var wN;
-function Ale(e) {
-  wN = e.wasm.cwrap(Co, "number", ["number", "number", "number", "number", "number", "number"]);
-}
 function Ele(e) {
+  wN = e.wasm.cwrap(No, "number", ["number", "number", "number", "number", "number", "number"]);
+}
+function Rle(e) {
   let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, softNmsSigma: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = wN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Rv(t, d);
   t.wasm._free(g);
   let b = t.makeOutput([f], "int32", h), y = t.makeOutput([f], "float32", m);
   return [b, y];
 }
-var Rle = { kernelName: Co, backendName: "wasm", setupFunc: Ale, kernelFunc: Ele };
-var Dle = false;
-var Fle = gn(Io, Dle, "bool");
+var Dle = { kernelName: No, backendName: "wasm", setupFunc: Ele, kernelFunc: Rle };
+var Fle = false;
+var Ole = gn(So, Fle, "bool");
 var kN;
-function Ole(e) {
-  kN = e.wasm.cwrap(To, null, ["number", "number", "number", "number", "number"]);
-}
 function Ple(e) {
+  kN = e.wasm.cwrap($o, null, ["number", "number", "number", "number", "number"]);
+}
+function zle(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = n.makeOutput([...r.shape, a], "int32"), l = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(r.dataId).id;
   return kN(p, a, i, o, l), u;
 }
-var zle = { kernelName: To, backendName: "wasm", setupFunc: Ole, kernelFunc: Ple };
-function Mle(e) {
+var Mle = { kernelName: $o, backendName: "wasm", setupFunc: Ple, kernelFunc: zle };
+function Lle(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
   return n.typedArrayFromHeap(s).fill(1), s;
 }
-var Lle = { kernelName: No, backendName: "wasm", kernelFunc: Mle };
-function Ble(e) {
+var Ble = { kernelName: To, backendName: "wasm", kernelFunc: Lle };
+function Vle(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
     return Zm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
@@ -37279,37 +37168,37 @@ function Ble(e) {
   }), l = Y2({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeData(c.dataId)), l;
 }
-var Vle = { kernelName: $o, backendName: "wasm", kernelFunc: Ble };
+var Wle = { kernelName: _o, backendName: "wasm", kernelFunc: Vle };
 var IN;
-function Wle(e) {
+function Ule(e) {
   IN = e.wasm.cwrap(Qa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
 }
-function Ule(e) {
+function Gle(e) {
   let { inputs: { x: t }, backend: n, attrs: { paddings: s, constantValue: r } } = e, a = s.map((m, g) => m[0] + t.shape[g] + m[1]);
   if (w.sizeFromShape(t.shape) === 0)
     return aN({ backend: n, attrs: { shape: a, value: r, dtype: t.dtype } });
   let i = n.dataIdMap.get(t.dataId).id, o = n.makeOutput(a, t.dtype), l = n.dataIdMap.get(o.dataId).id, c = new Uint8Array(new Int32Array(t.shape).buffer), p = s.map((m) => m[0]), d = s.map((m) => m[1]), h = new Uint8Array(new Int32Array(p).buffer), f = new Uint8Array(new Int32Array(d).buffer);
   return IN(i, c, t.shape.length, It[t.dtype], h, f, r, l), o;
 }
-var SN = { kernelName: Qa, backendName: "wasm", kernelFunc: Ule, setupFunc: Wle };
-var Gle = false;
-var Hle = gn(Za, Gle);
+var SN = { kernelName: Qa, backendName: "wasm", kernelFunc: Gle, setupFunc: Ule };
+var Hle = false;
+var qle = gn(Za, Hle);
 var CN;
-function qle(e) {
+function jle(e) {
   CN = e.wasm.cwrap(Ja, null, ["number", "number", "number"]);
 }
-function jle(e) {
+function Kle(e) {
   let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, o = a, u = s, l = u;
   u.dtype !== "float32" && (l = ac({ backend: n, inputs: { x: s }, attrs: { dtype: "float32" } }), o = n.dataIdMap.get(l.dataId).id);
   let c = n.makeOutput(s.shape, "float32"), p = n.dataIdMap.get(c.dataId).id;
   return CN(o, i, p), u.dtype !== "float32" && n.disposeData(l.dataId), c;
 }
-var Kle = { kernelName: Ja, backendName: "wasm", setupFunc: qle, kernelFunc: jle };
+var Xle = { kernelName: Ja, backendName: "wasm", setupFunc: jle, kernelFunc: Kle };
 var NN;
-function Xle(e) {
-  NN = e.wasm.cwrap(_o, null, ["number", "number", "number", "number"]);
-}
 function Yle(e) {
+  NN = e.wasm.cwrap(Ao, null, ["number", "number", "number", "number"]);
+}
+function Qle(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t), f = p;
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
@@ -37327,21 +37216,21 @@ function Yle(e) {
   }
   return y;
 }
-var Qle = { kernelName: _o, backendName: "wasm", setupFunc: Xle, kernelFunc: Yle };
-var Zle = (e) => {
+var Zle = { kernelName: Ao, backendName: "wasm", setupFunc: Yle, kernelFunc: Qle };
+var Jle = (e) => {
   let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = rv(s, r, a, i), u = t.makeOutput([o.length], i);
   return t.typedArrayFromHeap(u).set(o), u;
 };
-var Jle = { kernelName: kl, backendName: "wasm", kernelFunc: Zle };
-var ece = true;
-var tce = gn(Da, ece);
-var nce = Xt(ei);
-var sce = Xt(ni);
+var ece = { kernelName: Il, backendName: "wasm", kernelFunc: Jle };
+var tce = true;
+var nce = gn(Da, tce);
+var sce = Xt(ei);
+var rce = Xt(ni);
 var TN;
-function rce(e) {
+function ace(e) {
   TN = e.wasm.cwrap(ti, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function ace(e) {
+function ice(e) {
   let { backend: t, inputs: n, attrs: s } = e, { images: r } = n, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, [c, p, d, h] = r.shape, f = [c, u, l, h], m = t.dataIdMap.get(r.dataId), g;
   m.dtype !== "float32" && (g = ac({ backend: t, inputs: { x: r }, attrs: { dtype: "float32" } }), m = t.dataIdMap.get(g.dataId));
   let b = m.id, y = t.makeOutput(f, "float32");
@@ -37350,12 +37239,12 @@ function ace(e) {
   let v = t.dataIdMap.get(y.dataId).id;
   return TN(b, c, p, d, h, u, l, a ? 1 : 0, i ? 1 : 0, v), g != null && t.disposeData(g.dataId), y;
 }
-var ice = { kernelName: ti, backendName: "wasm", setupFunc: rce, kernelFunc: ace };
+var oce = { kernelName: ti, backendName: "wasm", setupFunc: ace, kernelFunc: ice };
 var $N;
-function oce(e) {
-  $N = e.wasm.cwrap(Eo, null, ["number", "array", "number", "array", "number", "number"]);
-}
 function uce(e) {
+  $N = e.wasm.cwrap(Ro, null, ["number", "array", "number", "array", "number", "number"]);
+}
+function lce(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, i = w.parseAxisParam(a, r.shape);
   if (r.shape.length === 0)
     return eh({ inputs: { x: r }, backend: n });
@@ -37364,59 +37253,59 @@ function uce(e) {
   let d = yn({ inputs: { x: o }, attrs: { shape: r.shape }, backend: n });
   return n.disposeData(o.dataId), d;
 }
-var lce = { kernelName: Eo, backendName: "wasm", kernelFunc: uce, setupFunc: oce };
+var cce = { kernelName: Ro, backendName: "wasm", kernelFunc: lce, setupFunc: uce };
 var _N;
-function cce(e) {
-  _N = e.wasm.cwrap(Ho, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
-}
 function dce(e) {
+  _N = e.wasm.cwrap(qo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
+}
+function pce(e) {
   let { inputs: t, backend: n, attrs: s } = e, { image: r } = t, { radians: a, fillValue: i, center: o } = s, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(r.dataId).id, c = n.dataIdMap.get(u.dataId).id, [p, d, h, f] = r.shape, [m, g] = S.getImageCenter(o, d, h), b = i === 0, y = 255, v = typeof i == "number" ? [i, i, i, b ? 0 : y] : [...i, y], x = new Uint8Array(new Int32Array(v).buffer);
   return _N(l, p, d, h, f, a, m, g, x, v.length, c), u;
 }
-var pce = { kernelName: Ho, backendName: "wasm", kernelFunc: dce, setupFunc: cce };
-var hce = Xt(Ro);
-var fce = Xt(si);
+var hce = { kernelName: qo, backendName: "wasm", kernelFunc: pce, setupFunc: dce };
+var fce = Xt(Do);
+var mce = Xt(si);
 var AN;
-function mce(e) {
-  AN = e.wasm.cwrap(Do, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
-}
 function gce(e) {
+  AN = e.wasm.cwrap(Fo, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
+}
+function bce(e) {
   let { backend: t, inputs: n, attrs: s } = e, { indices: r, updates: a } = n, { shape: i } = s, o = t.makeOutput(i, a.dtype);
   if (w.sizeFromShape(i) === 0)
     return o;
   let { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = Ak.calculateShapes(a, r, i), f = t.dataIdMap.get(r.dataId).id, g = t.dataIdMap.get(a.dataId).id, b = new Uint8Array(new Int32Array(p).buffer), y = t.dataIdMap.get(o.dataId).id;
   return AN(f, g, It[a.dtype], u, l, c, b, d, y), o;
 }
-var bce = { kernelName: Do, backendName: "wasm", setupFunc: mce, kernelFunc: gce };
+var yce = { kernelName: Fo, backendName: "wasm", setupFunc: gce, kernelFunc: bce };
 var EN;
-function yce(e) {
+function vce(e) {
   EN = e.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
 }
-function vce(e) {
+function xce(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = n.dataIdMap.get(s.dataId).id, o = n.dataIdMap.get(r.dataId).id, u = n.dataIdMap.get(a.dataId).id, l = n.makeOutput(r.shape, r.dtype), c = n.dataIdMap.get(l.dataId).id, p = s.shape.length, d = r.shape.length, h = p === 0 || p > 1 || d === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
   return EN(i, o, u, h, c), l;
 }
-var xce = { kernelName: Fo, backendName: "wasm", kernelFunc: vce, setupFunc: yce };
+var wce = { kernelName: Oo, backendName: "wasm", kernelFunc: xce, setupFunc: vce };
 var RN;
-function wce(e) {
+function kce(e) {
   RN = e.wasm.cwrap(ai, null, ["number", "number"]);
 }
-function kce(e) {
+function Ice(e) {
   let { backend: t, inputs: { x: n } } = e, s = t.dataIdMap.get(n.dataId).id, r = t.makeOutput(n.shape, n.dtype), a = t.dataIdMap.get(r.dataId).id;
   return w.sizeFromShape(r.shape) === 0 || RN(s, a), r;
 }
-var Ice = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: wce, kernelFunc: kce };
-var Sce = Xt(ri);
+var Sce = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: kce, kernelFunc: Ice };
+var Cce = Xt(ri);
 var DN;
-function Cce(e) {
+function Nce(e) {
   DN = e.wasm.cwrap(ui, null, ["number", "number", "number", "number"]);
 }
-function Nce(e) {
+function Tce(e) {
   let { backend: t, inputs: { logits: n }, attrs: { dim: s } } = e, r = t.dataIdMap.get(n.dataId).id, a = t.makeOutput(n.shape, n.dtype), i = t.dataIdMap.get(a.dataId).id, o = n.shape[s], u = w.sizeFromShape(n.shape) / o;
   return w.sizeFromShape(a.shape) === 0 || DN(r, i, o, u), a;
 }
-var Tce = { kernelName: ui, backendName: "wasm", setupFunc: Cce, kernelFunc: Nce };
-function $ce(e) {
+var $ce = { kernelName: ui, backendName: "wasm", setupFunc: Nce, kernelFunc: Tce };
+function _ce(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s, o = w.sizeFromShape(a), u = [[0, 0]];
   u.push(...i);
   for (let T = 1 + a.length; T < r.shape.length; ++T)
@@ -37424,12 +37313,12 @@ function $ce(e) {
   let l = SN.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = S.getReshaped(l.shape, a, o, false), p = S.getPermuted(c.length, a.length, false), d = S.getReshapedPermuted(l.shape, a, o, false), m = yn({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wr({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = yn({ inputs: { x: y }, backend: n, attrs: { shape: d } });
   return n.disposeData(l.dataId), n.disposeData(m.dataId), n.disposeData(y.dataId), k;
 }
-var _ce = { kernelName: zo, backendName: "wasm", kernelFunc: $ce };
+var Ace = { kernelName: Mo, backendName: "wasm", kernelFunc: _ce };
 var FN;
-function Ace(e) {
+function Ece(e) {
   FN = e.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Ece(e) {
+function Rce(e) {
   let { backend: t, inputs: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = n, o = s.shape[0], u = s.shape[1], l = t.readSync(a.dataId)[0], c = [o + l, u], p = t.dataIdMap.get(s.dataId).id, d = t.dataIdMap.get(r.dataId).id, h = t.dataIdMap.get(i.dataId).id, f = t.makeOutput(c, s.dtype), m = t.dataIdMap.get(f.dataId).id, g = t.makeOutput(c.slice(0, 1), r.dtype), b = t.dataIdMap.get(g.dataId).id, y = t.makeOutput([l], "bool"), v = t.dataIdMap.get(y.dataId).id, x = t.makeOutput([o], s.dtype), k = t.dataIdMap.get(x.dataId).id, T = t.makeOutput([4], "int32"), N = t.dataIdMap.get(T.dataId).id, E = FN(p, d, It[r.dtype], o, l, u, h, m, b, v, k, N), A = t.readSync(T.dataId), P;
   switch (A[0]) {
     case 1: {
@@ -37451,12 +37340,12 @@ function Ece(e) {
   let R = f, F = g;
   return E !== c[0] && (R = va({ inputs: { x: f }, attrs: { begin: 0, size: [E, u] }, backend: t }), F = va({ inputs: { x: g }, attrs: { begin: 0, size: E }, backend: t }), t.disposeData(f.dataId), t.disposeData(g.dataId)), [R, F, y, x];
 }
-var Rce = { kernelName: sp, backendName: "wasm", setupFunc: Ace, kernelFunc: Ece };
+var Dce = { kernelName: sp, backendName: "wasm", setupFunc: Ece, kernelFunc: Rce };
 var ON;
-function Dce(e) {
-  ON = e.wasm.cwrap($l, null, ["number", "number", "number", "number", "number", "number", "number"]);
-}
 function Fce(e) {
+  ON = e.wasm.cwrap(_l, null, ["number", "number", "number", "number", "number", "number", "number"]);
+}
+function Oce(e) {
   let { backend: t, inputs: n } = e, { inputIndices: s, inputShape: r, newShape: a } = n;
   if (s.shape.length !== 2)
     throw new Error(`Input indices should be a matrix but received shape
@@ -37498,7 +37387,7 @@ function Fce(e) {
     throw t.disposeData(p.dataId), t.disposeData(h.dataId), new Error(y);
   return [p, h];
 }
-var Oce = { kernelName: $l, backendName: "wasm", setupFunc: Dce, kernelFunc: Fce };
+var Pce = { kernelName: _l, backendName: "wasm", setupFunc: Fce, kernelFunc: Oce };
 var PN;
 function zN(e) {
   PN = e.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
@@ -37534,15 +37423,15 @@ function MN(e, t) {
     throw n.disposeData(m.dataId), new Error(x);
   return m;
 }
-function Pce(e) {
+function zce(e) {
   return MN(e, true);
 }
-var zce = { kernelName: rp, backendName: "wasm", setupFunc: zN, kernelFunc: Pce };
-function Mce(e) {
+var Mce = { kernelName: rp, backendName: "wasm", setupFunc: zN, kernelFunc: zce };
+function Lce(e) {
   return MN(e, false);
 }
-var Lce = { kernelName: ap, backendName: "wasm", setupFunc: zN, kernelFunc: Mce };
-function Bce(e) {
+var Bce = { kernelName: ap, backendName: "wasm", setupFunc: zN, kernelFunc: Lce };
+function Vce(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = n, o = w.parseAxisParam(i, r.shape)[0], u = S.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
   return u.map((p) => {
     let d = [...c];
@@ -37551,25 +37440,25 @@ function Bce(e) {
     return l[o] += p, h;
   });
 }
-var Vce = { kernelName: Mo, backendName: "wasm", kernelFunc: Bce };
-var Wce = Xt(ii);
-var Uce = Xt(_l);
-var Gce = true;
-var Hce = gn(li, Gce);
+var Wce = { kernelName: Lo, backendName: "wasm", kernelFunc: Vce };
+var Uce = Xt(ii);
+var Gce = Xt(Al);
+var Hce = true;
+var qce = gn(li, Hce);
 var LN;
-function qce(e) {
+function jce(e) {
   LN = e.wasm.cwrap(hi, null, ["number", "number", "number", "number"]);
 }
-function jce(e) {
+function Kce(e) {
   let { backend: t, inputs: n, attrs: s } = e, { alpha: r } = s, { x: a } = n, i = t.dataIdMap.get(a.dataId).id, o = t.makeOutput(a.shape, a.dtype), u = t.dataIdMap.get(o.dataId).id;
   return LN(i, r, It[a.dtype], u), o;
 }
-var Kce = { kernelName: hi, backendName: "wasm", setupFunc: qce, kernelFunc: jce };
+var Xce = { kernelName: hi, backendName: "wasm", setupFunc: jce, kernelFunc: Kce };
 var BN;
-function Xce(e) {
-  BN = e.wasm.cwrap(Lo, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
-}
 function Yce(e) {
+  BN = e.wasm.cwrap(Bo, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
+}
+function Qce(e) {
   let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
   if (m)
     k = yn({ inputs: { x: r }, backend: t, attrs: { shape: f } });
@@ -37583,14 +37472,14 @@ function Yce(e) {
   }
   return k;
 }
-var Qce = { kernelName: Lo, backendName: "wasm", setupFunc: Xce, kernelFunc: Yce };
-var Zce = true;
-var Jce = gn(ci, Zce);
+var Zce = { kernelName: Bo, backendName: "wasm", setupFunc: Yce, kernelFunc: Qce };
+var Jce = true;
+var ede = gn(ci, Jce);
 var VN;
-function ede(e) {
+function tde(e) {
   VN = e.wasm.cwrap(oi, null, ["number", "number", "number", "number"]);
 }
-function tde(e) {
+function nde(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t), f = p;
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
@@ -37608,37 +37497,37 @@ function tde(e) {
   }
   return y;
 }
-var nde = { kernelName: oi, backendName: "wasm", setupFunc: ede, kernelFunc: tde };
-var sde = Xt(Bo);
-var rde = Xt(di);
+var sde = { kernelName: oi, backendName: "wasm", setupFunc: tde, kernelFunc: nde };
+var rde = Xt(Vo);
+var ade = Xt(di);
 var WN;
-function ade(e) {
+function ide(e) {
   WN = e.wasm.cwrap(Cr, null, ["number", "array", "number", "array", "number", "number"]);
 }
-function ide(e) {
+function ode(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, a = n.dataIdMap.get(r.dataId).id, { reps: i } = s, o = new Array(r.shape.length);
   for (let d = 0; d < o.length; d++)
     o[d] = r.shape[d] * i[d];
   let u = new Uint8Array(new Int32Array(r.shape).buffer), l = new Uint8Array(new Int32Array(o).buffer), c = n.makeOutput(o, r.dtype), p = n.dataIdMap.get(c.dataId).id;
   return WN(a, u, r.shape.length, l, o.length, It[c.dtype], p), c;
 }
-var ode = { kernelName: Cr, backendName: "wasm", setupFunc: ade, kernelFunc: ide };
+var ude = { kernelName: Cr, backendName: "wasm", setupFunc: ide, kernelFunc: ode };
 var UN;
-function ude(e) {
-  UN = e.wasm.cwrap(Vo, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
+function lde(e) {
+  UN = e.wasm.cwrap(Wo, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
 }
-var lde = ({ inputs: e, backend: t, attrs: n }) => {
+var cde = ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s } = e, { k: r, sorted: a } = n, i = t.dataIdMap.get(s.dataId).id, o = new Uint8Array(new Int32Array(s.shape).buffer), u = s.shape.slice();
   u[u.length - 1] = r;
   let l = t.makeOutput(u, s.dtype), c = t.dataIdMap.get(l.dataId).id, p = t.makeOutput(u, "int32"), d = t.dataIdMap.get(p.dataId).id;
   return UN(i, o, s.shape.length, It[s.dtype], r, a, c, d), [l, p];
 };
-var cde = { kernelName: Vo, backendName: "wasm", setupFunc: ude, kernelFunc: lde };
+var dde = { kernelName: Wo, backendName: "wasm", setupFunc: lde, kernelFunc: cde };
 var GN;
-function dde(e) {
-  GN = e.wasm.cwrap(Wo, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
-}
 function pde(e) {
+  GN = e.wasm.cwrap(Uo, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
+}
+function hde(e) {
   let { backend: t, inputs: n, attrs: s } = e, { image: r, transforms: a } = n, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), y = t.makeOutput(g, r.dtype), v = t.dataIdMap.get(y.dataId).id, k = t.dataIdMap.get(r.dataId).id, N = t.dataIdMap.get(a.dataId).id, E = i === "nearest" ? 1 : 2, A;
   switch (o) {
     case "constant":
@@ -37659,8 +37548,8 @@ function pde(e) {
   }
   return GN(k, N, a.shape[0] > 1, c, f, m, h, d, p, b, r.shape.length - 1, E, A, u, v), y;
 }
-var hde = { kernelName: Wo, backendName: "wasm", setupFunc: dde, kernelFunc: pde };
-function fde(e) {
+var fde = { kernelName: Uo, backendName: "wasm", setupFunc: pde, kernelFunc: hde };
+function mde(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
   let i = r.shape[a], o = r.shape.length, u = new Array(o - 1), l = 0;
@@ -37672,15 +37561,15 @@ function fde(e) {
     p[a] = h, c[h] = va({ inputs: { x: r }, attrs: { begin: p, size: d }, backend: n });
   return c.map(({ dataId: h, dtype: f }) => ({ dataId: h, dtype: f, shape: u }));
 }
-var mde = { kernelName: Uo, backendName: "wasm", kernelFunc: fde };
-function gde(e) {
+var gde = { kernelName: Go, backendName: "wasm", kernelFunc: mde };
+function bde(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
   return n.typedArrayFromHeap(s).fill(0), s;
 }
-var bde = { kernelName: Go, backendName: "wasm", kernelFunc: gde };
-var yde = [ioe, ooe, loe, poe, xoe, Ioe, Noe, _oe, Doe, Loe, Boe, Voe, Goe, Hoe, Koe, Qoe, Zoe, Joe, nue, aue, uue, due, fue, mue, bue, yue, vue, xue, Iue, Sue, Nue, _ue, Rue, Oue, Mue, Vue, Uue, Hue, hoe, Kue, Yue, Zue, Jue, tle, rle, ile, lle, ple, mle, ble, xle, kle, Ile, Nle, _le, Rle, Fle, zle, Lle, Vle, SN, Hle, Kle, Qle, Jle, tce, nce, sce, Aoe, ice, lce, pce, hce, fce, bce, xce, Ice, Sce, zoe, Tce, _ce, Rce, Oce, zce, Lce, Vce, Wce, Uce, Hce, Kce, Qce, Jce, nde, sde, rde, ode, cde, hde, boe, mde, bde];
-for (let e of yde)
-  Al(e);
+var yde = { kernelName: Ho, backendName: "wasm", kernelFunc: bde };
+var vde = [ooe, uoe, coe, hoe, woe, Soe, Toe, Aoe, Foe, Boe, Voe, Woe, Hoe, qoe, Xoe, Zoe, Joe, eue, sue, iue, lue, pue, mue, gue, yue, vue, xue, wue, Sue, Cue, Tue, Aue, Due, Pue, Lue, Wue, Gue, que, foe, Xue, Que, Jue, ele, nle, ale, ole, cle, hle, gle, yle, wle, Ile, Sle, Tle, Ale, Dle, Ole, Mle, Ble, Wle, SN, qle, Xle, Zle, ece, nce, sce, rce, Eoe, oce, cce, hce, fce, mce, yce, wce, Sce, Cce, Moe, $ce, Ace, Dce, Pce, Mce, Bce, Wce, Uce, Gce, qce, Xce, Zce, ede, sde, rde, ade, ude, dde, fde, yoe, gde, yde];
+for (let e of vde)
+  El(e);
 var Jm = X();
 Jm.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])));
 Jm.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
@@ -37693,13 +37582,12 @@ Jm.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
   }
 });
 var Lw = xa(a$());
-var vde = `"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",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
+var xde = `"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",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}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.onmessage=(e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["__emscripten_thread_exit"](result)}}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==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else if(e.data.cmd==="processProxyingQueue"){if(Module["_pthread_self"]()){Module["_emscripten_proxy_execute_queue"](e.data.queue)}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}});`;
-var xde = xa(i$());
-var wde = class extends tl {
+var wde = xa(i$());
+var kde = class extends nl {
   constructor(e) {
-    super();
-    this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(HN), eg = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Ud(this, Ss());
+    super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(HN), eg = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Ud(this, Ss());
   }
   write(e, t, n) {
     let s = { id: this.dataIdNextNumber++ };
@@ -37731,7 +37619,7 @@ var wde = class extends tl {
       return (t == null || t === 0) && (n == null || n >= i.length) ? i : i.slice(t, n);
     t = t || 0, n = n || w.sizeFromShape(a);
     let o = w.bytesPerElement(r), u = this.wasm.HEAPU8.slice(s + t * o, s + n * o);
-    return Sde(u.buffer, r);
+    return Cde(u.buffer, r);
   }
   disposeData(e, t = false) {
     if (this.dataIdMap.has(e)) {
@@ -37787,7 +37675,7 @@ var wde = class extends tl {
     }
   }
 };
-function kde(e) {
+function Ide(e) {
   return (t, n) => (w.fetch(e, { credentials: "same-origin" }).then((s) => {
     s.ok || t.env.a(`failed to load wasm binary file at '${e}'`), s.arrayBuffer().then((r) => {
       WebAssembly.instantiate(r, t).then((a) => {
@@ -37800,34 +37688,34 @@ function Bw(e, t, n) {
   if (Bd != null)
     return Bd;
   let s = "tfjs-backend-wasm.wasm";
-  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Mu != null && Mu[s] != null ? Mu[s] : n + s;
+  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Lu != null && Lu[s] != null ? Lu[s] : n + s;
 }
-async function Ide() {
+async function Sde() {
   let [e, t] = await Promise.all([X().getAsync("WASM_HAS_SIMD_SUPPORT"), X().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
   return new Promise((n, s) => {
     let r = {};
     r.locateFile = (o, u) => {
       if (o.endsWith(".worker.js")) {
-        let l = vde.replace(/\n/g, "\\n"), c = new Blob([l], { type: "application/javascript" });
+        let l = xde.replace(/\n/g, "\\n"), c = new Blob([l], { type: "application/javascript" });
         return URL.createObjectURL(c);
       }
-      return o.endsWith(".wasm") ? Bw(e, t, Ou != null ? Ou : u) : u + o;
-    }, Dv && (r.instantiateWasm = kde(Bw(e, t, Ou != null ? Ou : "")));
+      return o.endsWith(".wasm") ? Bw(e, t, Pu != null ? Pu : u) : u + o;
+    }, Dv && (r.instantiateWasm = Ide(Bw(e, t, Pu != null ? Pu : "")));
     let a = false;
     r.onAbort = () => {
-      if (a || Lu)
+      if (a || Bu)
         return;
-      Lu = true, s({ 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" });
+      Bu = true, s({ 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 && e && Bd == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + Lw.default.toString()], { type: "text/javascript" }), i = (0, Lw.default)(r)) : i = (0, xde.default)(r), i.then((o) => {
-      a = true, Lu = false;
+    t && e && Bd == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + Lw.default.toString()], { type: "text/javascript" }), i = (0, Lw.default)(r)) : i = (0, wde.default)(r), i.then((o) => {
+      a = true, Bu = false;
       let u = null;
       o.tfjs = { init: o.cwrap("init", null, []), initWithThreadsCount: o.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: o.cwrap("get_threads_count", "number", []), registerTensor: o.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: o.cwrap("dispose_data", u, ["number"]), dispose: o.cwrap("dispose", u, []) }, n({ wasm: o });
     });
   });
 }
-function Sde(e, t) {
+function Cde(e, t) {
   switch (t) {
     case "float32":
       return new Float32Array(e);
@@ -37839,25 +37727,25 @@ function Sde(e, t) {
       throw new Error(`Unknown dtype ${t}`);
   }
 }
-var Cde = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
+var Nde = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
 var Bd = null;
-var Ou = null;
-var Mu = {};
-var Lu = false;
+var Pu = null;
+var Lu = {};
+var Bu = false;
 var Dv = false;
-function Mpe(e, t = false) {
-  if (Gk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Lu)
+function Lpe(e, t = false) {
+  if (Gk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Bu)
     throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
   Bd = e, Dv = t;
 }
-function Lpe(e, t = false) {
-  if (Lu)
+function Bpe(e, t = false) {
+  if (Bu)
     throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
   if (typeof e == "string")
-    Ou = e;
+    Pu = e;
   else {
-    Mu = e;
-    let n = Cde.filter((s) => Mu[s] == null);
+    Lu = e;
+    let n = Nde.filter((s) => Lu[s] == null);
     if (n.length > 0)
       throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
   }
@@ -37865,22 +37753,22 @@ function Lpe(e, t = false) {
 }
 var HN = -1;
 var eg = -1;
-function Bpe(e) {
+function Vpe(e) {
   HN = e;
 }
-function Vpe() {
+function Wpe() {
   if (eg === -1)
     throw new Error("WASM backend not initialized.");
   return eg;
 }
-var Wpe = "0.0.0";
-var Nde = 2;
+var Upe = "0.0.0";
+var Tde = 2;
 pp("wasm", async () => {
-  let { wasm: e } = await Ide();
-  return new wde(e);
-}, Nde);
-var sr = "3.15.0-20220401";
-var Upe = { tfjs: sr, "tfjs-core": sr, "tfjs-data": sr, "tfjs-layers": sr, "tfjs-converter": sr, "tfjs-backend-cpu": sr, "tfjs-backend-webgl": sr, "tfjs-backend-wasm": sr };
+  let { wasm: e } = await Sde();
+  return new kde(e);
+}, Tde);
+var sr = "3.15.0-20220405";
+var Gpe = { tfjs: sr, "tfjs-core": sr, "tfjs-data": sr, "tfjs-layers": sr, "tfjs-converter": sr, "tfjs-backend-cpu": sr, "tfjs-backend-webgl": sr, "tfjs-backend-wasm": sr };
 
 // src/image/imagefxshaders.ts
 var vertexIdentity = `
@@ -38913,7 +38801,7 @@ var Env = class {
     __publicField(this, "ImageData");
     this.browser = typeof navigator !== "undefined";
     this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
-    this.tfjs = { version: Upe["tfjs-core"] };
+    this.tfjs = { version: Gpe["tfjs-core"] };
     this.offscreen = typeof OffscreenCanvas !== "undefined";
     this.initial = true;
     this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
@@ -38936,7 +38824,7 @@ var Env = class {
     this.backends = Object.keys(Ss().registryFactory);
     this.wasm.supported = typeof WebAssembly !== "undefined";
     this.wasm.backend = this.backends.includes("wasm");
-    if (this.wasm.supported && this.wasm.backend && Bde() === "wasm") {
+    if (this.wasm.supported && this.wasm.backend && Vde() === "wasm") {
       this.wasm.simd = await X().getAsync("WASM_HAS_SIMD_SUPPORT");
       this.wasm.multithread = await X().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
     }
@@ -38944,7 +38832,7 @@ var Env = class {
     const ctx = c ? c.getContext("webgl2") : void 0;
     this.webgl.supported = typeof ctx !== "undefined";
     this.webgl.backend = this.backends.includes("webgl");
-    if (this.webgl.supported && this.webgl.backend && (Bde() === "webgl" || Bde() === "humangl")) {
+    if (this.webgl.supported && this.webgl.backend && (Vde() === "webgl" || Vde() === "humangl")) {
       const gl2 = zA().gpgpu !== "undefined" ? await zA().getGPGPUContext().gl : null;
       if (gl2) {
         this.webgl.version = gl2.getParameter(gl2.VERSION);
@@ -38960,7 +38848,7 @@ var Env = class {
       this.webgpu.supported = false;
     }
     try {
-      this.kernels = Jf(Bde()).map((kernel) => kernel.kernelName.toLowerCase());
+      this.kernels = Jf(Vde()).map((kernel) => kernel.kernelName.toLowerCase());
     } catch (e) {
     }
   }
@@ -43308,7 +43196,7 @@ async function max2d(inputs, minScore) {
   const newScore = (await max.data())[0];
   Re([reshaped, max]);
   if (newScore > minScore) {
-    const coordinates = Gu(reshaped, 0);
+    const coordinates = Hu(reshaped, 0);
     const mod = kD(coordinates, width);
     const x = (await mod.data())[0];
     const div = xe(coordinates, we(width, "int32"));
@@ -43781,7 +43669,7 @@ async function predict11(image, config3, idx, count2) {
         obj.gender = gender[0] <= 0.5 ? "female" : "male";
         obj.genderScore = Math.min(0.99, confidence);
       }
-      const argmax = Gu(resT.find((t) => t.shape[1] === 100), 1);
+      const argmax = Hu(resT.find((t) => t.shape[1] === 100), 1);
       const age = (await argmax.data())[0];
       Re(argmax);
       const ageT = resT.find((t) => t.shape[1] === 100);
@@ -47647,7 +47535,7 @@ async function detectHands(input, config3) {
   t.filtered = Jn(classScores, 1);
   Re(classScores);
   t.max = As(t.filtered, 1);
-  t.argmax = Gu(t.filtered, 1);
+  t.argmax = Hu(t.filtered, 1);
   let id2 = 0;
   t.nms = await qn.nonMaxSuppressionAsync(t.boxes, t.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
   const nms = await t.nms.data();
@@ -48746,7 +48634,7 @@ async function register(instance) {
     log("error: humangl backend invalid context");
     reset(instance);
   }
-  if (!Wde(config2.name)) {
+  if (!Ude(config2.name)) {
     try {
       config2.canvas = await canvas(100, 100);
     } catch (err) {
@@ -48796,7 +48684,7 @@ async function register(instance) {
       const kernels = Jf("webgl");
       kernels.forEach((kernelConfig) => {
         const newKernelConfig = { ...kernelConfig, backendName: config2.name };
-        Al(newKernelConfig);
+        El(newKernelConfig);
       });
     } catch (err) {
       log("error: cannot update WebGL backend registration:", err);
@@ -48825,25 +48713,25 @@ function registerCustomOps() {
   if (!env.kernels.includes("mod")) {
     const kernelMod = {
       kernelName: "Mod",
-      backendName: Bde(),
+      backendName: Vde(),
       kernelFunc: (op2) => j(() => ge(op2.inputs.a, V(xe(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
     };
-    Al(kernelMod);
+    El(kernelMod);
     env.kernels.push("mod");
   }
   if (!env.kernels.includes("floormod")) {
     const kernelMod = {
       kernelName: "FloorMod",
-      backendName: Bde(),
+      backendName: Vde(),
       kernelFunc: (op2) => j(() => Hk(op2.inputs.a / op2.inputs.b) * op2.inputs.b + kD(op2.inputs.a, op2.inputs.b))
     };
-    Al(kernelMod);
+    El(kernelMod);
     env.kernels.push("floormod");
   }
 }
 async function check(instance, force = false) {
   instance.state = "backend";
-  if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Bde() !== instance.config.backend) {
+  if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Vde() !== instance.config.backend) {
     const timeStamp = now();
     if (instance.config.backend && instance.config.backend.length > 0) {
       if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) {
@@ -48887,7 +48775,7 @@ async function check(instance, force = false) {
         if (instance.config.debug)
           log("wasm path:", instance.config.wasmPath);
         if (typeof (tfjs_esm_exports == null ? void 0 : tfjs_esm_exports.setWasmPaths) !== "undefined")
-          await Lpe(instance.config.wasmPath, instance.config.wasmPlatformFetch);
+          await Bpe(instance.config.wasmPath, instance.config.wasmPlatformFetch);
         else
           throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
         const simd = await X().getAsync("WASM_HAS_SIMD_SUPPORT");
@@ -48898,15 +48786,15 @@ async function check(instance, force = false) {
           log("warning: wasm simd support is not enabled");
       }
       try {
-        await Mde(instance.config.backend);
-        await Lde();
+        await Lde(instance.config.backend);
+        await Bde();
         init();
       } catch (err) {
         log("error: cannot set backend:", instance.config.backend, err);
         return false;
       }
     }
-    if (Bde() === "humangl") {
+    if (Vde() === "humangl") {
       Jw.set("CHECK_COMPUTATION_FOR_ERRORS", false);
       Jw.set("WEBGL_CPU_FORWARD", true);
       Jw.set("WEBGL_USE_SHAPES_UNIFORMS", true);
@@ -48921,12 +48809,12 @@ async function check(instance, force = false) {
           log(`gl version:${gl2.getParameter(gl2.VERSION)} renderer:${gl2.getParameter(gl2.RENDERER)}`);
       }
     }
-    if (Bde() === "webgpu") {
+    if (Vde() === "webgpu") {
     }
-    Rde();
-    await Lde();
+    Dde();
+    await Bde();
     instance.performance.initBackend = Math.trunc(now() - timeStamp);
-    instance.config.backend = Bde();
+    instance.config.backend = Vde();
     await env.updateBackend();
     registerCustomOps();
   }
@@ -48942,9 +48830,9 @@ function fakeOps(kernelNames, config3) {
           log("kernelFunc", kernelName, config3.backend);
       }
     };
-    Al(kernelConfig);
+    El(kernelConfig);
   }
-  env.kernels = Jf(Bde()).map((kernel) => kernel.kernelName.toLowerCase());
+  env.kernels = Jf(Vde()).map((kernel) => kernel.kernelName.toLowerCase());
 }
 
 // src/util/draw.ts
@@ -51005,7 +50893,7 @@ var Human = class {
         (_a2 = this.events) == null ? void 0 : _a2.dispatchEvent(new Event(event));
     });
     this.env = env;
-    config.wasmPath = Upe["tfjs-core"].includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Ede}/dist/`;
+    config.wasmPath = Gpe["tfjs-core"].includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Rde}/dist/`;
     config.modelBasePath = env.browser ? "../models/" : "file://models/";
     config.backend = env.browser ? "humangl" : "tensorflow";
     this.version = version;
@@ -51082,7 +50970,7 @@ var Human = class {
         log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
       if (!await check(this))
         log("error: backend check failed");
-      await Lde();
+      await Bde();
       if (this.env.browser) {
         if (this.config.debug)
           log("configuration:", this.config);
diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map
index bdffc527..493bc08d 100644
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/tfjs/load.ts", "../src/models.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/iris.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/util/draw.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
-  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, 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for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n 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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(ar(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new W$(this.backendInstance),!0}setupRegisteredKernels(){Jf(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Jf(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof tl)&&typeof n.then==\"function\"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error(\"No backend found in registry.\");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!=\"function\")throw new Error(\"Please provide a function to tidy()\");t=e}else{if(typeof e!=\"string\"&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(typeof t!=\"function\")throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return tm.nextTensorId++}nextVariableId(){return tm.nextVariableId++}clone(e){let t=M.runKernel(Ba,{x:e}),n={x:e},s=a=>({x:()=>{let i=\"float32\",o={x:a},u={dtype:i};return M.runKernel(Na,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Zf(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Of(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Of(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Zf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:T,dtype:N}=x;return this.makeTensorFromDataId(k,T,N)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Of(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=nx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",s=s||this.backend;let r=e;n===\"string\"&&ir(e[0])&&(r=e.map(o=>Rl(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n===\"string\"){let o=this.state.tensorInfo.get(a),u=Xw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||\"float32\";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new gd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let n=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(n=e.size*Xf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof gd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let n=e.size*Xf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=nx(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=Hd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Rg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let 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Hg=L({batchToSpaceND_:SE});function CE(e){let t;return e.rank===0||e.rank===1?t=G(e,[1,1,1,e.size]):e.rank===2?t=G(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=G(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function NE(e,t,n,s,r,a){a==null&&(a=.001);let i=_(e,\"x\",\"batchNorm\"),o=_(t,\"mean\",\"batchNorm\"),u=_(n,\"variance\",\"batchNorm\"),l;r!=null&&(l=_(r,\"scale\",\"batchNorm\"));let c;s!=null&&(c=_(s,\"offset\",\"batchNorm\")),O(o.rank===u.rank,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),O(c==null||o.rank===c.rank,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),O(l==null||o.rank===l.rank,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let d={x:CE(i),scale:l,offset:c,mean:o,variance:u},h={varianceEpsilon:a},f=M.runKernel(Ma,d,h);return G(f,i.shape)}var qu=L({batchNorm_:NE});function TE(e,t,n,s,r,a){let 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4 or rank 1 but got rank ${o.rank}.`),O(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),qu(i,o,u,c,l,a)}var RE=L({batchNorm4d_:EE});function DE(e,t,n){let s=_(e,\"x\",\"bincount\"),r=_(t,\"weights\",\"bincount\");O(s.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},i={size:n};return M.runKernel(ug,a,i)}var Qk=L({bincount_:DE});function FE(e,t){let n=_(e,\"s0\",\"broadcastArgs\",\"int32\"),s=_(t,\"s1\",\"broadcastArgs\",\"int32\");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};gb.className=\"Adam\";Tr(gb);var bb=class extends _r{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=$r(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};bb.className=\"Adamax\";Tr(bb);var Ip=class extends _r{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};yb.className=\"Momentum\";Tr(yb);var vb=class extends _r{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),dn(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),dn(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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adagrad(e,t=.1){return new mb(e,t)}},Pi={sgd:Wr.sgd,momentum:Wr.momentum,adadelta:Wr.adadelta,adagrad:Wr.adagrad,rmsprop:Wr.rmsprop,adamax:Wr.adamax,adam:Wr.adam},WO=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:e=>e())();function OI(){return new Promise(e=>WO(()=>e()))}var 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UO(e,t){let n=e[0].length;e.forEach((r,a)=>{O(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),O(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let i=0;i<n;i++)O(i===t||r[i]===s[i],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function GO(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var xb=30;function HO(e){return e<=xb?e:hd(e,Math.floor(Math.sqrt(e)))}function qO(e,t,n){let s=n*(typeof e==\"number\"?e:e[0]),r=t*(typeof e==\"number\"?e:e[1]);return[s,r]}function jO(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let i=0;i<a;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(a+1))}return r}function KO(e,t,n=!0){let s=[];if(n){s.push(t);for(let 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},DL=0,Ge=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=DL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+\"_\"+Dp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s=\"float32\"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+\"_ib-\"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new hs(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return bn(this.getNodeAtIndex(e,\"input\").inputTensors)}getOutputAt(e){return bn(this.getNodeAtIndex(e,\"output\").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer input\" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");return ea(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return a}else{let a=FL(e),i=this.computeOutputShape(a),o,u=OL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function mB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Dp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(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 y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,\"input layer has >1 nodes\"),Cs(x===0,\"input layer has >1 tensors\"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Yo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.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 t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,T)=>{(x==null||k==null||T==null)&&(x=b.sourceLayer,k=b.nodeIndex,T=b.tensorIndex);let N=x.inboundNodes[k];if(v.indexOf(N)!==-1)throw new hs(`The tensor ${b.name} at layer \"${x.name}\" is part of a cycle.`);if(y.indexOf(N)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(N)===-1&&v.push(N);let E=N.inboundLayers.length;for(let A=0;A<E;A++){let P=N.inputTensors[A],R=N.inboundLayers[A],F=N.nodeIndices[A],$=N.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(N);v.indexOf(N)>=0;)v.splice(v.indexOf(N),1);i.push(N)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],T=b.nodeIndices[x],N=k.inboundNodes[T],E=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(y+1,E),n[N.id]=N}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Uc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],T=a[x.id];return k<T?-1:k>T?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Uc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new hs(`Graph disconnected: cannot obtain value for tensor ${x} at layer \"${v.name}\". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new hs(`The name \"${b}\" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split(\"/\");i=o.slice(0,-2).concat([o[o.length-1]]).join(\"/\")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Pb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${uS}`,t.backend=\"TensorFlow.js\",t}toJSON(e,t=!0){let n=wm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Qr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return _u(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=fa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+\"_0_0\";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=fa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U(\"Provide either a layer name or layer index\");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let N=r[x];if(N.inboundNodes.length<=k){i(m,g);return}let E=N.inboundNodes[k];b.push(E.outputTensors[T])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=ms(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!BM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e==\"object\"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e==\"object\"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]==\"object\"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function lS(e,t){return gB(e,t,\"classWeight\")}async function cS(e,t,n,s){if(t!=null||s!=null)throw new Error(\"Support sampleWeight is not implemented yet\");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Gu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Zt(i,\"float32\")}else return null}function bB(e,t){return V(e,t)}var yB=32;function dS(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=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 a=Px(\"input\",e.inputNames,n),i=Px(\"output\",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  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Use LayersModel.compile(modelCompileArgs).\");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Td?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Lx(e,this.feedInputNames,this.feedInputShapes,!1,\"input\"),t=Lx(t,this.feedOutputNames,r,!1,\"target\"),TB(e,t,null),$B(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[i,o]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error(\"sample weight is not supported yet.\");let u=null;if(s!=null){let l=lS(s,this.outputNames);u=[];for(let c=0;c<l.length;++c)u.push(await cS(o[c],null,l[c]))}return[i,o,u]}testLoop(e,t,n,s=0,r){return j(()=>{let a=this.checkNumSamples(t,n,r,\"steps\"),i=[];if(s>0)throw new Fe(\"Verbose mode is not implemented yet.\");if(r!=null)throw new Fe(\"steps mode in testLoop() is not implemented yet\");{let o=Im(a,n),u=Zt(ys(0,a));for(let l=0;l<o.length;++l){let c=o[l][0],p=o[l][1],d=ta(u,c,p-c),h=Wb(t,d),f=e(h);if(l===0)for(let m=0;m<f.length;++m)i.push(we(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=ie(i[m],V(p-c,g))}}for(let l=0;l<i.length;++l)i[l]=xe(i[l],a)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;Sx(e,s)>1&&(r+=`_${Sx(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let p=new Qr(c),d=_u(this.outputs,p,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],d[f]);r[f]!=null&&(g=bB(g,r[f]));let b=St(g);t.push(b),f===0?h=g:h=ie(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],b=this.metricsTensors[f][1];m=St(g(s[b],d[b]))}Ht(m),a.push(m)}return h=St(h),this.calculateLosses().forEach(f=>{h=ie(h,f)}),h},o=this.collectedTrainableWeights.map(c=>c.read()),u=!0;return[this.optimizer_.minimize(i,u,o)].concat(a)}}makeTestFunction(){this.testFunction=e=>j(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Qr(a),o=_u(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=St(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=St(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return CB(this,e,t,n)}async fitDataset(e,t){return xB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return Re(i),ds(n[0],e),ds(n[1],t),bn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=dm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-dm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss==\"string\")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!=\"string\")throw new Error(\"Serialization of non-string loss is not supported.\");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]==\"string\")e[s]=Ws(n[s]);else throw new Error(\"Serialization of non-string loss is not supported.\")}return e}getMetricIdentifiers(){if(typeof this.metrics==\"string\"||typeof this.metrics==\"function\")return[Ws(qc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(qc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(qc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error(\"Loading weight_metrics is not supported yet.\");if(e.loss_weights!=null)throw new Error(\"Loading loss_weights is not supported yet.\");if(e.sample_weight_mode!=null)throw new Error(\"Loading sample_weight_mode is not supported yet.\");let t=Qu(e.optimizer_config),n=ms(t),s;if(typeof e.loss==\"string\")s=Kr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>Kr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=Kr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>Kr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Kr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e==\"string\"){let u=En.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");let n=await En.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:AB,generatedBy:`TensorFlow.js tfjs-layers v${uS}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u=\"optimizer\",{data:l,specs:c}=await En.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=En.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Dx(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Dx(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className=\"Model\";ae.registerClass(pr);var hS=class extends pr{};hS.className=\"Functional\";ae.registerClass(hS);async function EB(e,t){\"modelTopology\"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Qu(n),r=ms(s,t);if(e.weightsManifest!=null){let a=await En.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function RB(e,t){if(t==null&&(t={}),typeof e==\"string\"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return DB(e,void 0,t)}async function DB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=ms(Qu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U(\"LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.\");let{modelWeights:l,optimizerWeights:c}=FB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function FB(e,t){let n=En.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group===\"optimizer\"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Cm=class extends pr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp(\"sequential_\"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Cm||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U(\"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 U(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");let s=ZI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+\"_input\"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=QI(this.outputs[0])}this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:fa(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError(\"There are no layers in the model.\");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError(\"Sequential model cannot be built: model is empty. Add some layers first.\");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+\"_model\"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className===\"Merge\")throw new U(\"Legacy serialization format not supported yet.\");r=t}else w.assert(t.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Cm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=ms(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Ub=Cm;Ub.className=\"Sequential\";ae.registerClass(Ub);function Cpe(e){return new pr(e)}function Npe(e){return new Ub(e)}function Tpe(e,t){return t==null&&(t={}),RB(e,t)}function OB(e){return ZI(e)}function $pe(e,t){zb.registerCallbackConstructor(e,t)}var In=class extends ae.Serializable{getConfig(){return{}}},fS=class extends In{apply(e,t=1){return aL(e,t)}};fS.className=\"elu\";ae.registerClass(fS);var mS=class extends In{apply(e){return gI(e)}};mS.className=\"selu\";ae.registerClass(mS);var gS=class extends In{apply(e){return Xs(e)}};gS.className=\"relu\";ae.registerClass(gS);var bS=class extends In{apply(e){return j(()=>bp(6,Xs(e)))}};bS.className=\"relu6\";ae.registerClass(bS);var yS=class extends In{apply(e){return e}};yS.className=\"linear\";ae.registerClass(yS);var vS=class extends In{apply(e){return qs(e)}};vS.className=\"sigmoid\";ae.registerClass(vS);var xS=class extends In{apply(e){return oL(e)}};xS.className=\"hardSigmoid\";ae.registerClass(xS);var wS=class extends In{apply(e){return Pl(e)}};wS.className=\"softplus\";ae.registerClass(wS);var kS=class extends In{apply(e){return iL(e)}};kS.className=\"softsign\";ae.registerClass(kS);var IS=class extends In{apply(e){return Hu(e)}};IS.className=\"tanh\";ae.registerClass(IS);var Gb=class extends In{apply(e,t=-1){return ub(e,t)}};Gb.className=\"softmax\";ae.registerClass(Gb);var SS=class extends In{apply(e,t=-1){return iI(e,t)}};SS.className=\"logSoftmax\";ae.registerClass(SS);var CS=class extends In{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};CS.className=\"swish\";ae.registerClass(CS);var NS=class extends In{apply(e){return j(()=>V(e,Hu(Pl(e))))}};NS.className=\"mish\";ae.registerClass(NS);function br(e){return e.getClassName()}function Gf(e,t={}){return Ml(e,ae.SerializationMap.getMap().classNameMap,t,\"activation\")}function yr(e){if(e==null){let t={};return t.className=\"linear\",t.config={},Gf(t)}if(typeof e==\"string\"){let t={};return t.className=e,t.config={},Gf(t)}else return e instanceof In?e:Gf(e)}function Hb(e){if(e!=null&&typeof e!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var TS=class extends ae.Serializable{},Ul=class extends TS{constructor(e){super();Hb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Vl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ul.className=\"L1L2\";ae.registerClass(Ul);function PB(e){return Hb(e),new Ul({l1:e!=null?e.l1:null,l2:0})}function zB(e){return Hb(e),new Ul({l2:e!=null?e.l2:null,l1:0})}var Vx={l1l2:\"L1L2\"};function it(e){return wb(e)}function Wx(e,t={}){return Ml(e,ae.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function ft(e){if(e==null)return null;if(typeof e==\"string\"){let n={className:e in Vx?Vx[e]:e,config:{}};return Wx(n)}else return e instanceof TS?e:Wx(e)}var qb=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Vn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};qb.className=\"ReLU\";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Xg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jb.className=\"LeakyReLU\";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes==\"number\")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight(\"alpha\",t,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),sb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:it(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Kb.className=\"PReLU\";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return fp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Xb.className=\"ELU\";ae.registerClass(Xb);var Yb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),\"float32\"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Yb.className=\"ThresholdedReLU\";ae.registerClass(Yb);var Qb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Qb.className=\"Softmax\";ae.registerClass(Qb);function Xi(e,t,n){if(typeof e==\"number\")return fa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!tL(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function gs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n===\"same\"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s===\"valid\")e=e*t+gr([n-t,0]);else if(s===\"same\")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function Zb(e,t){return j(()=>(Ct(t),t===\"channelsFirst\"?qe(e,[0,2,3,1]):e))}function $S(e,t){return j(()=>(Ct(t),t===\"channelsFirst\"?qe(e,[0,2,3,4,1]):e))}function MB(e,t,n,s=1,r=\"valid\",a,i=1){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a===\"channelsFirst\"&&(e=qe(e,[0,2,1])),r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let o=Zk(e,t,s,r===\"same\"?\"same\":\"valid\",\"NWC\",i);return n!=null&&(o=ws(o,n)),o})}function Ux(e,t,n,s=[1,1],r=\"valid\",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Zb(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=ha.conv2d({x:u,filter:t,strides:s,pad:r===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:n,activation:o}),a===\"channelsFirst\"&&(u=qe(u,[0,3,1,2])),u})}function LB(e,t,n,s=[1,1,1],r=\"valid\",a,i){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=$S(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return o=eI(o,t,s,r===\"same\"?\"same\":\"valid\",\"NDHWC\",i),n!=null&&(o=ws(o,n)),a===\"channelsFirst\"&&(o=qe(o,[0,4,1,2,3])),o})}var Jb=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",Jb.verifyArgs(t),this.rank=e,Bt(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xi(t.kernelSize,e,\"kernelSize\"),this.strides=Xi(t.strides==null?1:t.strides,e,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Ct(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Xi(t.dilationRate==null?1:t.dilationRate,e,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`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 U(`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 U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs(\"kernelSize\"in e,\"required key 'kernelSize' not in config\"),typeof e.kernelSize!=\"number\"&&!kb(e.kernelSize,\"number\",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Gl=class extends Jb{constructor(e,t){super(e,t);this.kernel=null,Gl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,\"filters\"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=UI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=MB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=LB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat===\"channelsLast\"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate==\"number\"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat===\"channelsLast\"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!(\"filters\"in e)||typeof e.filters!=\"number\"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},_S=class extends Gl{constructor(e){super(2,e);_S.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!kb(e.kernelSize,\"number\",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},zp=_S;zp.className=\"Conv2D\";ae.registerClass(zp);var AS=class extends Gl{constructor(e){super(3,e);AS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Mp=AS;Mp.className=\"Conv3D\";ae.registerClass(Mp);var ey=class extends zp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U(\"Input should have rank 4; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat===\"channelsFirst\"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,1]));let g=Jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ey.className=\"Conv2DTranspose\";ae.registerClass(ey);var ty=class extends Mp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U(\"Input should have rank 5; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat===\"channelsFirst\"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,4,1]));let k=tR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ty.className=\"Conv3DTranspose\";ae.registerClass(ty);var ES=class extends Gl{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(t.padding!=null&&t.padding!==\"same\"&&t.padding!==\"valid\")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,\"float32\",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight(\"pointwise_kernel\",r,\"float32\",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(e=qe(e,[0,2,3,1])),n=c3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};ES.className=\"SeparableConv\";var ny=class extends ES{constructor(e){super(2,e)}};ny.className=\"SeparableConv2D\";ae.registerClass(ny);var RS=class extends Gl{constructor(e){super(1,e);RS.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!kb(e.kernelSize,\"number\",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},sy=RS;sy.className=\"Conv1D\";ae.registerClass(sy);var ry=class extends Ge{constructor(e){super(e);typeof e.cropping==\"number\"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]==\"number\"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?\"channelsLast\":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat===\"channelsFirst\"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat===\"channelsLast\"){let n=Gc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ry.className=\"Cropping2D\";ae.registerClass(ry);var ay=class extends Ge{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?\"nearest\":e.interpolation,ZM(this.interpolation)}computeOutputShape(e){if(this.dataFormat===\"channelsFirst\"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat===\"channelsFirst\"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation===\"nearest\"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation===\"nearest\"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ay.className=\"UpSampling2D\";ae.registerClass(ay);function BB(e,t,n=[1,1],s=\"valid\",r,a){return j(()=>{r==null&&(r=bs()),Ct(r);let i=Zb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=hp(i,t,n,s===\"same\"?\"same\":\"valid\",\"NHWC\",a),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}var iy=class extends Jb{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat===\"channelsFirst\"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=BB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=gs(t,this.kernelSize[0],this.padding,this.strides[0]),a=gs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};iy.className=\"DepthwiseConv2D\";ae.registerClass(iy);function DS(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U(\"When inputs is an array, neither initialState or constants should be provided\");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function FS(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe(\"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.\"),r!=null&&(r=ce(ce(r,\"bool\"),\"float32\"),r.rank===u-1&&(r=Pn(r,-1)),r=qe(r,l)),s&&(t=Zn(t,0),r!=null&&(r=Zn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],T=ge(Qn(k),k),N=ie(V(v[0],k),V(d[0],T)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,T)));return{output:N,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Jn(c,1)),[p,g,d]})}var OS=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(e.cell)?t=new Vp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ym(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe(\"Constants support is not implemented in RNN yet.\");ym(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=DS(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let i={training:s},u=FS((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Bl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?gm(t,[1,n]):t):this.cell.stateSize>1?[gm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===OS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=ms(s,n);return new e(Object.assign(t,{cell:r}))}},Ar=OS;Ar.className=\"RNN\";ae.registerClass(Ar);var Hl=class extends Ge{},Lp=class extends Hl{constructor(e){super(e);this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight(\"kernel\",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Lp.className=\"SimpleRNNCell\";ae.registerClass(Lp);var oy=class extends Ar{constructor(e){e.cell=new Lp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};oy.className=\"SimpleRNN\";ae.registerClass(oy);var Bp=class extends Hl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.resetAfter)throw new U(\"GRUCell does not support reset_after parameter set to true.\");this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Bp.className=\"GRUCell\";ae.registerClass(Bp);var uy=class extends Ar{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Bp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};uy.className=\"GRU\";ae.registerClass(uy);var ql=class extends Hl{constructor(e){super(e);this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new Np().apply([a]),c=r.apply([a*2]);return $x($x(u,l),c)}},t.className=\"CustomInit\",t)}else s=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};ql.className=\"LSTMCell\";ae.registerClass(ql);var ly=class extends Ar{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new ql(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ly.className=\"LSTM\";ae.registerClass(ly);var Vp=class extends Hl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){ym(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ea(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(ms(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return vm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Pb(t)}};Vp.className=\"StackedRNNCells\";ae.registerClass(Vp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):XI(t(),n),o=()=>Wl(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var PS=class extends Ar{constructor(e){if(e.unroll)throw new Fe(\"Unrolling is not possible with convolutional RNNs.\");if(Array.isArray(e.cell))throw new Fe(\"It is not possible at the moment to stack convolutional cells.\");super(e);this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U(\"ConvRNN2D cell does not support constants\");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t===\"channelsFirst\",u=e[o?3:2],l=e[o?4:3],c=gs(u,s[0],r,a[0],i[0]),p=gs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};PS.className=\"ConvRNN2D\";var Wp=class extends ql{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t});this.filters=t,Bt(this.filters,\"filters\"),this.kernelSize=Xi(n,2,\"kernelSize\"),this.kernelSize.forEach(o=>Bt(o,\"kernelSize\")),this.strides=Xi(s||1,2,\"strides\"),this.strides.forEach(o=>Bt(o,\"strides\")),this.padding=r||\"valid\",Un(this.padding),this.dataFormat=a||\"channelsLast\",Ct(this.dataFormat),this.dilationRate=Xi(i||1,2,\"dilationRate\"),this.dilationRate.forEach(o=>Bt(o,\"dilationRate\"))}build(e){var t;e=nt(e);let n=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight(\"kernel\",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return $b([d,h,f])}},t.className=\"CustomInit\",t)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,T]=Bn(this.kernel.read(),i,y),[N,E,A,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,N,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,T,P,this.padding);let[R,F,$,z]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ca(e,t,this.strides,s||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ca(e,t,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Wp.className=\"ConvLSTM2DCell\";ae.registerClass(Wp);var cy=class extends PS{constructor(e){let t=new Wp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};cy.className=\"ConvLSTM2D\";ae.registerClass(cy);var Up=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Wl(()=>XI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Up.className=\"Dropout\";ae.registerClass(Up);var dy=class extends Up{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};dy.className=\"SpatialDropout1D\";ae.registerClass(dy);var py=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=UI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};py.className=\"Dense\";ae.registerClass(py);var hy=class extends Ge{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to \"Flatten\" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return rL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};hy.className=\"Flatten\";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};fy.className=\"Activation\";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),nL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};my.className=\"RepeatVector\";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n=\"Total size of new array must be unchanged.\",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U(\"Can only specifiy one unknown dimension.\");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};gy.className=\"Reshape\";ae.registerClass(gy);var by=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(e.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};by.className=\"Permute\";ae.registerClass(by);var yy=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return pm(Ku(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=pm(Ku(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};yy.className=\"Masking\";ae.registerClass(yy);var vy=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,\"inputDim\"),this.outputDim=e.outputDim,Bt(this.outputDim,\"outputDim\"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Ku(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!==\"int32\"&&(n=Sp(n,\"int32\"));let s=KI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};vy.className=\"Embedding\";ae.registerClass(vy);var bi=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U(\"Operands could not be broadcast together with shapes \"+JSON.stringify(e)+\" \"+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Bl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an Array\");if(!Array.isArray(e))throw new U(\"`inputs` should be an Array\");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},xy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};xy.className=\"Add\";ae.registerClass(xy);var wy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};wy.className=\"Multiply\";ae.registerClass(wy);var ky=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};ky.className=\"Average\";ae.registerClass(ky);var Iy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$r(t,e[n]);return t})}};Iy.className=\"Maximum\";ae.registerClass(Iy);var Sy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=bp(t,e[n]);return t})}};Sy.className=\"Minimum\";ae.registerClass(Sy);var Cy=class extends bi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \"+JSON.stringify(e))}mergeFunction(e){return j(()=>$b(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U(\"A `Concatenate` layer should be called on a list of inputs.\");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an array for Concatenate\");if(!Array.isArray(e))throw new U(\"`inputs` should be an array for Concatenate\");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Qn(e[a]),\"bool\")):t[a].rank<e[a].rank?s.push(Pn(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return qk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cy.className=\"Concatenate\";ae.registerClass(Cy);function Su(e,t){for(;e<0;)e+=t;return e}function VB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n==\"number\"&&(n=[n,n]),e.dtype===\"complex64\"||t.dtype===\"complex64\")throw new Fe(\"batchDot is not implemented for complex64-type Tensors yet.\");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=Pn(o,1)),o})}var Ny=class extends bi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Su(r,e[a].shape.length)):s=[Su(this.axes,t.shape.length),Su(this.axes,n.shape.length)],this.normalize&&(t=Nd(t,s[0]),n=Nd(n,s[1])),VB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Su(this.axes,e.length),Su(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ny.className=\"Dot\";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Wl(()=>ie(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Ty.className=\"GaussianNoise\";ae.registerClass(Ty);var $y=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Wl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};$y.className=\"GaussianDropout\";ae.registerClass($y);var _y=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Wl(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=jo(zl(n),this.rate);u=Sp(u,\"float32\");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};_y.className=\"AlphaDropout\";ae.registerClass(_y);function Zu(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=$E(e,t,n,s,r,a);else if(e.rank===3)i=AE(e,t,n,s,r,a);else if(e.rank===4)i=RE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function WB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance;return[Zu(e,i,o,n,t,r),i,o]})}function UB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Zu(e,l,c,d,p,r),i,o]})}function GB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?WB(e,t,n,s,r):UB(e,t,n,s,r)}var Ay=class extends Ge{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.movingMeanInitializer=ht(e.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||\"ones\"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=fa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Zu(s,b,y,v,x,this.epsilon)}else return Zu(s,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 p();let[d,h,f]=GB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),T=V(ge(k,y),x);b.write(ge(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ay.className=\"BatchNormalization\";ae.registerClass(Ay);var Ey=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=tb(n,this.axis,!0),u=fa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=ps(i,d),o=ps(o,d),c=ps(c,h),p=ps(p,h),Zu(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ey.className=\"LayerNormalization\";ae.registerClass(Ey);function HB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(n==null&&(n=bs()),n!==\"channelsLast\"&&n!==\"channelsFirst\")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n===\"channelsFirst\"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],fi(e,s)})}var Ry=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding==\"number\")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]==\"number\")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat===\"channelsFirst\"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>HB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ry.className=\"ZeroPadding2D\";ae.registerClass(Ry);function Gp(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=Zb(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=eb(e,t,n,o):i=Gg(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}function zS(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=$S(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=dI(e,t,n,o):i=Yk(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,4,1,2,3])),i})}var MS=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize==\"number\")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]==\"number\")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,\"poolSize\"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides==\"number\")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]==\"number\")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=gs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Bl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Dy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"max\")}};Dy.className=\"MaxPooling1D\";ae.registerClass(Dy);var Fy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"avg\")}};Fy.className=\"AveragePooling1D\";ae.registerClass(Fy);var LS=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"max\")}};Oy.className=\"MaxPooling2D\";ae.registerClass(Oy);var Py=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"avg\")}};Py.className=\"AveragePooling2D\";ae.registerClass(Py);var BS=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[4]:e[3];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),s=gs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,\"max\")}};zy.className=\"MaxPooling3D\";ae.registerClass(zy);var My=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,\"avg\")}};My.className=\"AveragePooling3D\";ae.registerClass(My);var VS=class extends Ge{constructor(e){super(e);this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Ly=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return St(n,1)})}};Ly.className=\"GlobalAveragePooling1D\";ae.registerClass(Ly);var By=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};By.className=\"GlobalMaxPooling1D\";ae.registerClass(By);var WS=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat===\"channelsLast\"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Vy=class extends WS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?St(n,[1,2]):St(n,[2,3])})}};Vy.className=\"GlobalAveragePooling2D\";ae.registerClass(Vy);var Wy=class extends 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r4=async(e,t,n)=>{switch(e.op){case\"If\":case\"StatelessIf\":{let s=I(\"thenBranch\",e,t,n),r=I(\"elseBranch\",e,t,n),a=I(\"cond\",e,t,n),i=I(\"args\",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case\"While\":case\"StatelessWhile\":{let s=I(\"body\",e,t,n),r=I(\"cond\",e,t,n),a=I(\"args\",e,t,n),i=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),o=a.map(c=>c.id),u=await i[0].data();i.forEach(c=>{!c.kept&&o.indexOf(c.id)===-1&&c.dispose()});let l=a;for(;u[0];){let c=l;l=await n.functionMap[s].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);let p=l.map(h=>h.id);c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);u=await 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s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case\"TensorArrayReadV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n);return[n.getTensorArray(s.id).read(r)]}case\"TensorArrayGatherV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"dtype\",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case\"TensorArrayScatterV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case\"TensorArrayConcatV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id),a=I(\"dtype\",e,t,n);return[r.concat(a)]}case\"TensorArraySplitV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"lengths\",e,t,n),i=n.getTensorArray(s.id);return i.split(a,r),[i.idTensor]}case\"TensorArraySizeV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return[we(r.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case\"TensorListSetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorList(s.id);return i.setItem(r,a),[i.idTensor]}case\"TensorListGetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).getItem(r,a,i)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let s=I(\"indices\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"numElements\",e,t,n),o=n4(r,s,a,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let s=I(\"elementShape\",e,t,n),r=I(\"elementDType\",e,t,n),a;e.op===\"TensorListReserve\"?a=\"numElements\":a=\"maxNumElements\";let i=I(a,e,t,n),o=t4(s,r,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListGather\":{let s=I(\"tensorListId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).gather(r,i,a)]}case\"TensorListStack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=I(\"numElements\",e,t,n);return[n.getTensorList(s.id).stack(r,a,i)]}case\"TensorListFromTensor\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=e4(s,r,a);return n.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":{let s=I(\"tensorListId\",e,t,n),r=n.getTensorList(s.id),a=I(\"dtype\",e,t,n),i=I(\"elementShape\",e,t,n);return[r.concat(a,i)]}case\"TensorListPushBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"tensor\",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case\"TensorListPopBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case\"TensorListSplit\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"lengths\",e,t,n),i=s4(s,a,r);return n.addTensorList(i),[i.idTensor]}case\"TensorListLength\":{let s=I(\"tensorListId\",e,t,n),r=n.getTensorList(s.id);return[we(r.size(),\"int32\")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Xx(e,t,n){let[s,r]=I(\"fusedOps\",e,t,n),a=s===\"biasadd\",i=!a,o=r===\"prelu\",u=s===\"fusedbatchnorm\",l=I(\"numArgs\",e,t,n);if(a){if(o&&l!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!o&&a&&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=I(\"strides\",e,t,n),p=sd(e,t,n),d=I(\"dataFormat\",e,t,n).toUpperCase(),h=I(\"dilations\",e,t,n),[f,m]=I(\"args\",e,t,n);i&&(m=f,f=void 0);let g=I(\"leakyreluAlpha\",e,t,n);return{stride:c,pad:p,dataFormat:d,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var a4=(e,t,n)=>{switch(e.op){case\"Conv1D\":{let s=I(\"stride\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilation\",e,t,n);return[Zk(I(\"x\",e,t,n),I(\"filter\",e,t,n),s,r,a,i)]}case\"Conv2D\":{let s=I(\"strides\",e,t,n),r=sd(e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilations\",e,t,n);return[ca(I(\"x\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2]],r,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=Xx(e,t,n);return[ha.conv2d({x:I(\"x\",e,t,n),filter:I(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"FusedDepthwiseConv2dNative\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=Xx(e,t,n);return[ha.depthwiseConv2d({x:I(\"x\",e,t,n),filter:I(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let s=I(\"outputShape\",e,t,n),r=I(\"strides\",e,t,n),a=sd(e,t,n);return[Jk(I(\"x\",e,t,n),I(\"filter\",e,t,n),s,[r[1],r[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let s=I(\"strides\",e,t,n),r=sd(e,t,n),a=I(\"dilations\",e,t,n),i=I(\"dataFormat\",e,t,n).toUpperCase();return[hp(I(\"input\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2]],r,i,[a[1],a[2]])]}case\"Conv3D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilations\",e,t,n);return[eI(I(\"x\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2],s[3]],r,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[Gg(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPool\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[eb(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPoolWithArgmax\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n),i=I(\"includeBatchInIndex\",e,t,n),{result:o,indexes:u}=fD(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r,i);return[o,u]}case\"AvgPool3D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[Yk(I(\"x\",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case\"MaxPool3D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[dI(I(\"x\",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case\"Dilation2D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dilations\",e,t,n),i=s[1],o=s[2],u=a[1],l=a[2];return[fR(I(\"x\",e,t,n),I(\"filter\",e,t,n),[i,o],r,[u,l],\"NHWC\")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},i4=(e,t,n)=>{switch(e.op){case\"Fill\":{let s=I(\"shape\",e,t,n),r=I(\"dtype\",e,t,n),a=I(\"value\",e,t,n);return[Ol(s,a,r)]}case\"LinSpace\":{let s=I(\"start\",e,t,n),r=I(\"stop\",e,t,n),a=I(\"num\",e,t,n);return[GR(s,r,a)]}case\"Multinomial\":{let 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r=I(\"keyDType\",e,t,n),a=I(\"valueDType\",e,t,n),i=new c4(r,a);return s.addHashTable(e.name,i),[i.handle]}case\"LookupTableImport\":case\"LookupTableImportV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"values\",e,t,n);return[await s.getHashTableById(r.id).import(a,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"defaultValue\",e,t,n);return[await s.getHashTableById(r.id).find(a,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let r=I(\"tableHandle\",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},p4=(e,t,n)=>{switch(e.op){case\"ResizeBilinear\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[qn.resizeBilinear(s,[r[0],r[1]],a,i)]}case\"ResizeNearestNeighbor\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[qn.resizeNearestNeighbor(s,[r[0],r[1]],a,i)]}case\"CropAndResize\":{let s=I(\"image\",e,t,n),r=I(\"boxes\",e,t,n),a=I(\"boxInd\",e,t,n),i=I(\"cropSize\",e,t,n),o=I(\"method\",e,t,n),u=I(\"extrapolationValue\",e,t,n);return[qn.cropAndResize(s,r,a,i,o,u)]}case\"ImageProjectiveTransformV3\":{let s=I(\"images\",e,t,n),r=I(\"transforms\",e,t,n),a=I(\"outputShape\",e,t,n),i=I(\"fillValue\",e,t,n),o=I(\"interpolation\",e,t,n),u=I(\"fillMode\",e,t,n);return[qn.transform(s,r,o.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},h4=(e,t,n)=>{switch(e.op){case\"Equal\":return[Kn(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"NotEqual\":return[Ku(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"Greater\":return[Wn(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"GreaterEqual\":return[jo(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"Less\":return[aI(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"LessEqual\":return[Ko(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"LogicalAnd\":return[Ds(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"LogicalNot\":return[Jg(I(\"a\",e,t,n))];case\"LogicalOr\":return[cI(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"Select\":case\"SelectV2\":return[vn(I(\"condition\",e,t,n),I(\"a\",e,t,n),I(\"b\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},f4=(e,t,n)=>{switch(e.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[We(I(\"a\",e,t,n),I(\"b\",e,t,n),I(\"transposeA\",e,t,n),I(\"transposeB\",e,t,n))];case\"Einsum\":return[kR(I(\"equation\",e,t,n),...I(\"tensors\",e,t,n))];case\"Transpose\":return[qe(I(\"x\",e,t,n),I(\"perm\",e,t,n))];case\"_FusedMatMul\":let[s,r]=I(\"fusedOps\",e,t,n),a=s===\"biasadd\",i=r===\"prelu\",o=I(\"numArgs\",e,t,n),u=I(\"leakyreluAlpha\",e,t,n);if(a){if(i&&o!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&o!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[l,c]=I(\"args\",e,t,n);return[ha.matMul({a:I(\"a\",e,t,n),b:I(\"b\",e,t,n),transposeA:I(\"transposeA\",e,t,n),transposeB:I(\"transposeB\",e,t,n),bias:l,activation:r,preluActivationWeights:c,leakyreluAlpha:u})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},m4=(e,t,n)=>{switch(e.op){case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[qu(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"FusedBatchNormV3\":return[qu(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"LRN\":return[qR(I(\"x\",e,t,n),I(\"radius\",e,t,n),I(\"bias\",e,t,n),I(\"alpha\",e,t,n),I(\"beta\",e,t,n))];case\"Softmax\":return[ub(I(\"x\",e,t,n))];case\"LogSoftmax\":return[iI(I(\"x\",e,t,n))];case\"SparseToDense\":return[NI(I(\"sparseIndices\",e,t,n),I(\"outputShape\",e,t,n),I(\"sparseValues\",e,t,n),I(\"defaultValue\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},g4=(e,t,n)=>{switch(e.op){case\"Max\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[As(I(\"x\",e,t,n),i,o)]}case\"Mean\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[St(I(\"x\",e,t,n),i,o)]}case\"Min\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[fm(I(\"x\",e,t,n),i,o)]}case\"Sum\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[ve(I(\"x\",e,t,n),i,o)]}case\"All\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[qk(I(\"x\",e,t,n),i,o)]}case\"Any\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[pm(I(\"x\",e,t,n),i,o)]}case\"ArgMax\":{let i=I(\"axis\",e,t,n);return[Gu(I(\"x\",e,t,n),i)]}case\"ArgMin\":{let i=I(\"axis\",e,t,n);return[JA(I(\"x\",e,t,n),i)]}case\"Prod\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[pI(I(\"x\",e,t,n),i,o)]}case\"Cumprod\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[aR(I(\"x\",e,t,n),i,o,u)]}case\"Cumsum\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[sI(I(\"x\",e,t,n),i,o,u)]}case\"Bincount\":let s=I(\"x\",e,t,n),r=I(\"weights\",e,t,n),a=I(\"size\",e,t,n);return[Qk(s,r,a)];case\"DenseBincount\":{let i=I(\"x\",e,t,n),o=I(\"weights\",e,t,n),u=I(\"size\",e,t,n),l=I(\"binaryOutput\",e,t,n);return[uR(i,o,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},b4=(e,t,n)=>{switch(e.op){case\"ConcatV2\":case\"Concat\":{let s=I(\"n\",e,t,n),r=I(\"axis\",e,t,n),a=I(\"tensors\",e,t,n);return a=a.slice(0,s),[Ft(a,r)]}case\"Gather\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[ju(s,ce(r,\"int32\"),0)]}case\"GatherV2\":{let s=I(\"axis\",e,t,n),r=I(\"batchDims\",e,t,n),a=I(\"x\",e,t,n),i=I(\"indices\",e,t,n);return[ju(a,ce(i,\"int32\"),s,r)]}case\"Reverse\":{let s=I(\"dims\",e,t,n),r=[];for(let i=0;i<s.length;i++)s[i]&&r.push(i);let a=I(\"x\",e,t,n);return[Zn(a,r)]}case\"ReverseV2\":{let s=I(\"axis\",e,t,n),r=I(\"x\",e,t,n);return[Zn(r,s)]}case\"Slice\":{let s=I(\"begin\",e,t,n),r=I(\"size\",e,t,n);return[He(I(\"x\",e,t,n),s,r)]}case\"StridedSlice\":{let s=I(\"begin\",e,t,n),r=I(\"end\",e,t,n),a=I(\"strides\",e,t,n),i=I(\"beginMask\",e,t,n),o=I(\"endMask\",e,t,n),u=I(\"ellipsisMask\",e,t,n),l=I(\"newAxisMask\",e,t,n),c=I(\"shrinkAxisMask\",e,t,n),p=I(\"x\",e,t,n);return[D3(p,s,r,a,i,o,u,l,c)]}case\"Pack\":return j(()=>{let s=I(\"axis\",e,t,n),r=I(\"tensors\",e,t,n),a=r[0].shape,i=mr(r[0]).shape,o=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(mr(u).shape,i))throw new Error(\"the input tensors shape does not match\");return l?u:G(u,a)});return[Jn(o,s)]});case\"Unpack\":{let s=I(\"axis\",e,t,n),r=I(\"tensor\",e,t,n);return Fs(r,s)}case\"Tile\":{let s=I(\"reps\",e,t,n);return[ps(I(\"x\",e,t,n),s)]}case\"Split\":case\"SplitV\":{let s=I(\"axis\",e,t,n),r=I(\"numOrSizeSplits\",e,t,n),a=I(\"x\",e,t,n);return Bn(a,r,s)}case\"ScatterNd\":{let s=I(\"indices\",e,t,n),r=I(\"values\",e,t,n),a=I(\"shape\",e,t,n);return[X3(s,r,a)]}case\"GatherNd\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[J3(s,r)]}case\"SparseToDense\":{let s=I(\"sparseIndices\",e,t,n),r=I(\"outputShape\",e,t,n),a=I(\"sparseValues\",e,t,n),i=I(\"defaultValue\",e,t,n);return[NI(s,a,r,a.dtype===i.dtype?i:ce(i,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},y4=(e,t,n)=>{switch(e.op){case\"SparseFillEmptyRows\":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Wc.sparseFillEmptyRows(I(\"indices\",e,t,n),I(\"values\",e,t,n),I(\"denseShape\",e,t,n),I(\"defaultValue\",e,t,n));return[s,r,a,i]}case\"SparseReshape\":{let{outputIndices:s,outputShape:r}=Wc.sparseReshape(I(\"inputIndices\",e,t,n),I(\"inputShape\",e,t,n),I(\"newShape\",e,t,n));return[s,r]}case\"SparseSegmentMean\":return[Wc.sparseSegmentMean(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];case\"SparseSegmentSum\":return[Wc.sparseSegmentSum(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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j(()=>QW(a,i,o));case\"basic_math\":return j(()=>ZW(a,i,o));case\"control\":return r4(a,i,o);case\"convolution\":return j(()=>a4(a,i,o));case\"creation\":return j(()=>i4(a,i,o));case\"dynamic\":return o4(a,i,o);case\"evaluation\":return j(()=>u4(a,i,o));case\"image\":return j(()=>p4(a,i,o));case\"graph\":return j(()=>l4(a,i,o));case\"logical\":return j(()=>h4(a,i,o));case\"matrices\":return j(()=>f4(a,i,o));case\"normalization\":return j(()=>m4(a,i,o));case\"reduction\":return j(()=>g4(a,i,o));case\"slice_join\":return j(()=>b4(a,i,o));case\"sparse\":return j(()=>y4(a,i,o));case\"spectral\":return j(()=>v4(a,i,o));case\"string\":return j(()=>x4(a,i,o));case\"transformation\":return j(()=>w4(a,i,o));case\"hash_table\":return d4(a,i,o,s);case\"custom\":let u=XS(a.op);if(u&&u.customExecutor)return u.customExecutor(new YW(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Zx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>An(d)[0]),c=[];s!=null&&(c=s.map(d=>An(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((b0(d)||N4(d)||T4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function k4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>An(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var I4=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],S4=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],C4=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function b0(e){return I4.indexOf(e.op)>=0}function N4(e){return S4.indexOf(e.op)>=0}function T4(e){return C4.indexOf(e.op)>=0}var zm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new zm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+\"--\"+s.join(this.SEPERATOR)}compile(e,t){let n=Zx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. 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Missing the following inputs: [${s}]`)}return k4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[An(c)[0]]),r=t.map(c=>An(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return j(()=>{let c=new Qx(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=An(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=Yx(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. 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Kc:a=Xc;break;default:}break;default:}if(a===qf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},A0=class extends Ut{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(e={}){if(!X().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let t=new A0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),fs(n,t)}},E0=class extends Ut{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Ki([a,r,o,i],[1,4])}else this.cropBox=Ki([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(e,t={}){if(!X().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!e){if(e=document.createElement(\"video\"),!t.resizeWidth||!t.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new E0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Nk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=Pn(ce(e,\"float32\"),0),n;n=qn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}},R0=class{},D0=class extends Ut{split(e){return new aU(this,e)}},aU=class extends 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Vt(p.inShape,\"float32\"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,T]=l,N=n.data.get(a.dataId).values,[E,A,P,R]=c,{batchSize:F,filterDepth:$,filterHeight:z,filterWidth:W,inChannels:q,inDepth:K,inHeight:Y,inWidth:Z,outChannels:te,outDepth:ee,outHeight:se,outWidth:ne,strideDepth:oe,strideHeight:re,strideWidth:le}=p,me=$-1-p.padInfo.front,ke=z-1-p.padInfo.top,Se=W-1-p.padInfo.left;for(let Ee=0;Ee<F;++Ee)for(let Pe=0;Pe<q;++Pe)for(let Xe=0;Xe<K;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/oe)),tt=Math.min(ee,($+Je)/oe);for(let Ce=0;Ce<Y;++Ce){let ut=Ce-ke,at=Math.max(0,Math.ceil(ut/re)),Jt=Math.min(se,(z+ut)/re);for(let Nt=0;Nt<Z;++Nt){let Cn=Nt-Se,Et=Math.max(0,Math.ceil(Cn/le)),en=Math.min(ne,(W+Cn)/le),Nn=0;for(let Tn=Ye;Tn<tt;++Tn){let Yt=Tn*oe-Je;for(let Dn=at;Dn<Jt;++Dn){let tn=Dn*re-ut;for(let Ms=Et;Ms<en;++Ms){let Ls=Ms*le-Cn,ki=v*Ee+x*Tn+k*Dn+T*Ms,Js=E*($-1-Yt)+A*(z-1-tn)+P*(W-1-Ls)+R*Pe;for(let Bs=0;Bs<te;++Bs){let 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MH={kernelName:lo,backendName:\"cpu\",kernelFunc:zH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumprod\");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,\"int32\"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?1:h[v];else{let x=m(b,y-1);d[v]=i?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var BH={kernelName:pl,backendName:\"cpu\",kernelFunc:LH};function VH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumsum\");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,\"int32\"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var WH={kernelName:uo,backendName:\"cpu\",kernelFunc:VH};function UH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=ev(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=W0(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var GH={kernelName:hg,backendName:\"cpu\",kernelFunc:UH};function HH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],u=r.shape[1],l=r.shape[2],c=r.shape[3],p=u*a,d=l*a,h=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(o*p*d*h),g=0;for(let b=0;b<o;++b)for(let y=0;y<p;++y){let v=Math.floor(y/a),x=y%a;for(let k=0;k<d;++k){let T=Math.floor(k/a),N=k%a,E=(x*a+N)*h;for(let A=0;A<h;++A){let R=A+E+c*(T+l*(v+u*b));m[g++]=f[R]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,m)}var qH={kernelName:co,backendName:\"cpu\",kernelFunc:HH};function DC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s;be([r,a],\"depthwiseConv2DNative\");let c=w.computeStrides(r.shape),p=w.computeStrides(a.shape),d=u;d==null&&(d=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=S.computeConv2DInfo(r.shape,a.shape,i,d,o,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,T=new Vt(h.outShape,r.dtype),N=n.data.get(r.dataId).values,E=n.data.get(a.dataId).values,A=T.values;for(let P=0;P<h.batchSize;++P){let R=P*c[0],F=P*T.strides[0];for(let $=0;$<h.outHeight;++$){let z=F+$*T.strides[1],W=$*h.strideHeight-x;for(let q=0;q<f;++q){let K=W+q*g;if(K<0||K>=h.inHeight)continue;let Y=q*p[0],Z=R+K*c[1];for(let te=0;te<h.outWidth;++te){let ee=z+te*T.strides[2],se=te*h.strideWidth-v;for(let ne=0;ne<m;++ne){let oe=se+ne*b;if(oe<0||oe>=h.inWidth)continue;let re=Y+ne*p[1],le=Z+oe*h.inChannels,me=ee,ke=re;for(let Se=0;Se<h.inChannels;++Se){let Ee=N[le+Se];for(let Pe=0;Pe<k;++Pe)A[me+Pe]+=Ee*E[ke+Pe];me+=k,ke+=k}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var jH={kernelName:Ra,backendName:\"cpu\",kernelFunc:DC};function KH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],\"depthwiseConv2dNativeBackpropFilter\");let p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,\"float32\"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),T=n.data.get(a.dataId).values,N=new Vt(a.shape,a.dtype,T);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*N.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var XH={kernelName:fg,backendName:\"cpu\",kernelFunc:KH};function YH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],\"depthwiseConv2DNativeBackpropInput\");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,\"float32\"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,T]=p,N=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let ke=me-se,Se=Math.max(0,Math.ceil(ke/te)),Ee=Math.min(Y,(F+ke)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-ke;for(let at=Je;at<Ye;++at){let Jt=at*ee-Xe,Nt=x*re+k*Ce+T*at,Cn=E*(F-1-ut)+A*($-1-Jt)+P*le;for(let Et=0;Et<oe;++Et){let en=le*oe+Et,Nn=v[Nt+en],Tn=N[Cn+Et];tt+=Nn*Tn}}}m[g*re+b*me+y*Pe+le]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var QH={kernelName:mg,backendName:\"cpu\",kernelFunc:YH};function ZH(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,i=De([r,r],s.dtype),o=i.values;for(let l=0;l<a.length;l++)o[l*r+l]=a[l];let u=[...s.shape,...s.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var JH={kernelName:gg,backendName:\"cpu\",kernelFunc:ZH},e6={kernelName:Yd,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:i,dilations:o}=n,u=t,l=u.data.get(s.dataId).values,c=s.shape.length,p=u.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:T,filterWidth:N,dilationHeight:E,dilationWidth:A,outShape:P}=S.computeDilation2DInfo(s.shape,r.shape,a,i,\"NHWC\",o),R=w.sizeFromShape(P),F=P.length,$=w.getArrayFromDType(s.dtype,R);for(let W=0;W<h;++W)for(let q=0;q<b;++q){let K=q*x-v.top;for(let Y=0;Y<y;++Y){let Z=Y*k-v.left;for(let te=0;te<g;++te){let ee=Number.MIN_SAFE_INTEGER;for(let ne=0;ne<T;++ne){let oe=K+ne*E;if(oe>=0&&oe<f)for(let re=0;re<N;++re){let le=Z+re*A;if(le>=0&&le<m){let me=w.locToIndex([W,oe,le,te],c,w.computeStrides(s.shape)),ke=w.locToIndex([ne,re,te],d,w.computeStrides(r.shape)),Se=l[me]+p[ke];Se>ee&&(ee=Se)}}}let 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e=Ne.getNumber(\"WEBGL_VERSION\");return e===0?0:o1(e)});Ne.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>Ne.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!dp.isMobile());Ne.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>u1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>Ne.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:Ne.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));Ne.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>l1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>c1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>Ne.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);Ne.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>dp.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Ne.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);Ne.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);Ne.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);Ne.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);function fn(){let e,t,n,s,r,a,i,o,u,l;return X().getNumber(\"WEBGL_VERSION\")===2?(e=\"#version 300 es\",t=\"in\",n=\"out\",s=\"in\",r=\"texture\",a=\"outputColor\",i=\"out vec4 outputColor;\",o=`\n      bool isnan_custom(float val) {\n        uint floatToUint = floatBitsToUint(val);\n        return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `,u=\"\",l=`\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `):(e=\"\",t=\"attribute\",n=\"varying\",s=\"varying\",r=\"texture2D\",a=\"gl_FragColor\",i=\"\",o=`\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `,u=`\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `,l=`\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function yi(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join(\"\")}function qp(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join(\"\")}function L5(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function B5(e,t,n=\"index\"){let s=e.map((a,i)=>i),r=L5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join(\"\")}function hv(e){let t=w.computeStrides(e).map(n=>n.toString());return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n  }\n`}function fv(){return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`}var d1=`\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`,{getBroadcastDims:p1}=S;function V5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:\"\"};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=mv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push(\"uniform int outShape;\");break;case 2:s.push(\"uniform ivec2 outShape;\"),s.push(\"uniform int outShapeStrides;\");break;case 3:s.push(\"uniform ivec3 outShape;\"),s.push(\"uniform ivec2 outShapeStrides;\");break;case 4:s.push(\"uniform ivec4 outShape;\"),s.push(\"uniform ivec3 outShapeStrides;\");break;default:break}s.push(\"uniform ivec2 outTexShape;\")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:\"\"};`)});let r=s.join(`\n`),a=e.map(h=>W5(h,t,n.packedInputs,n.enableShapeUniforms)).join(`\n`),i=t.texShape,o=fn(),u=H5(o),l,c,p=K5(o);return t.isPacked?(l=U5(t.logicalShape,i,n.enableShapeUniforms),c=j5(o)):(l=G5(t.logicalShape,i,n.enableShapeUniforms),c=q5(o)),n.packedInputs&&(p+=Z5),[p,u,c,r,l,a,n.userCode].join(`\n`)}function nu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return cK(e,t);case 1:return pK(e,t);case 2:return fK(e,t);case 3:return gK(e,t);case 4:return yK(e,t);case 5:return vK(e);case 6:return xK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function h1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return lK(e);case 1:return dK(e,t);case 2:return hK(e,t);case 3:return mK(e,t);default:return bK(e,t)}}function W5(e,t,n=!1,s){let r=\"\";n?r+=h1(e,s):r+=nu(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=wK(e,t):r+=kK(e,t)),r}function U5(e,t,n){switch(e.length){case 0:return f1();case 1:return J5(e,t,n);case 2:return oK(e,t,n);case 3:return tK(e,t,n);default:return sK(e,t,n)}}function G5(e,t,n){switch(e.length){case 0:return f1();case 1:return eK(e,t,n);case 2:return uK(e,t,n);case 3:return nK(e,t,n);case 4:return rK(e,t,n);case 5:return aK(e,t);case 6:return iK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function H5(e){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${e.texture2D}(textureSampler, uv).r;\n    }\n  `}function q5(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}function j5(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}function K5(e){return`${e.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${e.varyingFs} vec2 resultUV;\n    ${e.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${e.defineSpecialNaN}\n    ${e.defineSpecialInf}\n    ${e.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${X5}\n    ${Y5}\n    ${Q5}\n  `}var X5=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Y5=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Q5=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,Z5=`\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;function f1(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function J5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${s[1]}.0);\n      }\n    `:s[1]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${s[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);\n    }\n  `}function eK(e,t,n){return t[0]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `:t[1]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}function tK(e,t,n){if(n)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function nK(e,t,n){if(n)return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${qp([\"r\",\"c\",\"d\"],e)}\n    return ivec3(r, c, d);\n  }\n`;let s=yi([\"r\",\"c\",\"d\"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec3(r, c, d);\n    }\n  `}function sK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o=\"\",u=\"b, r, c\";for(let l=2;l<e.length-1;l++)i*=e[e.length-l-1],o=`\n      int b${l} = index / ${i};\n      index -= b${l} * ${i};\n    `+o,u=`b${l}, `+u;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      ${o}\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec${e.length}(${u});\n    }\n  `}function rK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${qp([\"r\",\"c\",\"d\",\"d2\"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let s=yi([\"r\",\"c\",\"d\",\"d2\"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec4(r, c, d, d2);\n    }\n  `}function aK(e,t){let n=yi([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function iK(e,t){let n=yi([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function oK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));\n      }\n    `;let r=Math.ceil(e[1]/2);return n?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function uK(e,t,n){return w.arraysEqual(e,t)?n?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `:e[1]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:e[0]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:n?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}function vi(e){return`offset${e}`}function lK(e){let t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),s=fn();return`\n    vec4 ${n}() {\n      return ${s.texture2D}(${t}, halfCR);\n    }\n  `}function cK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`\n      float ${s}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let i=vi(n);if(t)return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;let[o,u]=e.shapeInfo.texShape;return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${o}, ${u}, ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}function dK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=fn();if(t)return`\n    vec4 ${s}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`\n    vec4 ${s}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${i[0]}, ${i[1]}, index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}function pK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${s}(int index) {\n        ${su(e)}\n      }\n    `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`\n      float ${s}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let o=vi(n);return i===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `:a===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:t?`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}function hK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=fn();if(a!=null&&w.arraysEqual(n,a))return t?`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `:`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `;if(t)return`\n    vec4 ${r}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${r}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `}function fK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`\n      float ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `;let d=a[0],h=a[1];return`\n    float ${r}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=ru(e,u),h=[\"row\",\"col\"];return`\n      ${nu(d,t)}\n      float ${r}(int row, int col) {\n        return ${r}(${au(h,o)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${su(e)}\n      }\n    `;let l=a[0],c=a[1],p=vi(s);return c===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `:l===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${s}, uv);\n    }\n  `:t?`\n      float ${r}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s}Shape[1] + col + ${p};\n        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n  float ${r}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${p};\n    vec2 uv = uvFromFlat(${l}, ${c}, index);\n    return sampleTexture(${s}, uv);\n  }\n`}function mK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=ru(e,d),m=[\"b\",\"row\",\"col\"];return`\n        ${h1(f,t)}\n        vec4 ${r}(int b, int row, int col) {\n          return ${r}(${au(m,h)});\n        }\n      `}let o=fn();if(t)return`\n    vec4 ${r}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `;let u=i[0],l=i[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`\n    vec4 ${r}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${l}, ${p}, ${c}, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `}function gK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=ru(e,l),g=[\"row\",\"col\",\"depth\"];return`\n        ${nu(m,t)}\n        float ${r}(int row, int col, int depth) {\n          return ${r}(${au(g,u)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${i}, 1)));\n        ${su(e)}\n      }\n    `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        int stride1 = ${s}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n        float ${r}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${i}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${d}.0, ${p}.0);\n          return sampleTexture(${s}, uv);\n        }\n      `;if(d===i&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;let f=vi(s);return t?`\n    float ${r}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${s}Shape[1] * ${s}Shape[2];\n      int stride1 = ${s}Shape[2];\n      int index = row * ${a} + col * ${i} + depth + ${f};\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n      return sampleTexture(${s}, uv);\n    }\n    `:`\n      float ${r}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${i} + depth + ${f};\n        vec2 uv = uvFromFlat(${p}, ${d}, index);\n        return sampleTexture(${s}, uv);\n      }\n  `}function bK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=fn();if(t)return`\n    vec4 ${s}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);\n    }\n  `;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[i-1]/2),d=p*Math.ceil(a[i-2]/2),h=\"int b, int row, int col\",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=a[i-m-1],f=`b${m} * ${d} + `+f;return`\n    vec4 ${s}(${h}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});\n      return ${r.texture2D}(${n}, uv);\n    }\n  `}function yK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=ru(e,u),v=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${nu(y,t)}\n      float ${r}(int row, int col, int depth, int depth2) {\n        return ${r}(${au(v,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${o}, ${i}, ${a}, 1)));\n        ${su(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${i}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;if(h===a&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;let b=vi(s);return t?`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `:`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `}function vK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=ru(e,u),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${nu(m)}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${au(g,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${o}, ${i}, ${a}, ${r})) +\n          depth3;\n        ${su(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${i}, ${a}, ${r}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(h===r&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let f=vi(n);return`\n    float ${s}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} + depth * ${a} +\n          depth2 * ${r} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${d}, ${h}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function xK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=ru(e,r),b=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${nu(g)}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${au(b,a)});\n      }\n    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                int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let m=vi(n);return`\n    float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${l} + depth * ${u} +\n          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${h}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function su(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function wK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=p1(e.shapeInfo.logicalShape,t.logicalShape),u=rt(i),l=i-a,c,p=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?c=\"\":i<2&&o.length>=1?c=\"coords = 0;\":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`\n`);let d=\"\";i<2&&a>0?d=\"coords\":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(\", \");let h=\"return outputValue;\",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(m&&!b)i===1?h=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:h=`\n        return vec4(outputValue.x);\n      `;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h=\"return vec4(outputValue.x);\":o.indexOf(y)>-1?h=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":o.indexOf(v)>-1&&(h=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n    vec4 ${r}() {\n      ${u} coords = getOutputCoords();\n      ${c}\n      vec4 outputValue = get${s}(${d});\n      ${h}\n    }\n  `}function kK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`\n      float ${r}() {\n        return sampleTexture(${n}, resultUV);\n      }\n    `;let l=rt(u),c=p1(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,d,h=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];o===0?d=\"\":u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`\n`);let f=\"\";return u<2&&o>0?f=\"coords\":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(\", \"),`\n    float ${r}() {\n      ${l} coords = getOutputCoords();\n      ${d}\n      return get${s}(${f});\n    }\n  `}function rt(e){if(e<=1)return\"int\";if(e===2)return\"ivec2\";if(e===3)return\"ivec3\";if(e===4)return\"ivec4\";if(e===5)return\"ivec5\";if(e===6)return\"ivec6\";throw Error(`GPU for rank ${e} is not yet supported`)}function mv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function ru(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function au(e,t){return t.map(n=>e[n]).join(\", \")}function IK(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=V5(r,i,t),u=GC(e.gl,o),l=e.createProgram(u);return X().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...m1(e,t,l)}}function m1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,\"NAN\",!1),X().getNumber(\"WEBGL_VERSION\")===1&&(c=e.getUniformLocation(n,\"INFINITY\",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,\"outShape\",d),l=e.getUniformLocation(n,\"outShapeStrides\",d),u=e.getUniformLocation(n,\"outTexShape\",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function rw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function SK(e,t,n,s,r){t.program.enableShapeUniforms||(rw(t.inShapeInfos,n),rw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=mv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new 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4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type===\"float\")e.gl.uniform1fv(c,p);else if(u.type===\"vec2\")e.gl.uniform2fv(c,p);else if(u.type===\"vec3\")e.gl.uniform3fv(c,p);else if(u.type===\"vec4\")e.gl.uniform4fv(c,p);else if(u.type===\"int\")e.gl.uniform1iv(c,p);else if(u.type===\"ivec2\")e.gl.uniform2iv(c,p);else if(u.type===\"ivec3\")e.gl.uniform3iv(c,p);else if(u.type===\"ivec4\")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function CK(e,t,n){let s=\"\";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=mv(e.packedInputs,i.shape,u),d=\"\",h=\"\",f=\"\";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=S.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:\"\"}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+=\"_\"+s+\"_\"+r+`${X().getNumber(\"WEBGL_VERSION\")}`,a}function Sn(e){return X().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&e<=4}var NK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?qp([\"r\",\"c\",\"d\"],e):yi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${t.output} = result;\n      }\n    `}},TK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?qp([\"r\",\"c\",\"d\"],e):yi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${t.output} = result;\n      }\n    `}},$K=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${d1}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}},_K=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${d1}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${t.output} = encode_float(x);\n      }\n    `}},AK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\"),this.userCode=`\n      ${this.enableShapeUniforms?fv():hv(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${n.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}},EK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"\",r=\"result\";t&&(r=\"floor(result * 255. + 0.5)\");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`\n          localCoords = coords;\n          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?\"outShape[2]\":`${e[2]}`}) {\n          localCoords[2] += ${i};\n          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?\"outShape[1]\":`${e[1]}`}) {\n            localCoords[1] += ${a};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${o}] = values[0];\n            } else if (offset == 1) {\n              result[${o}] = values[1];\n            } else if (offset == 2) {\n              result[${o}] = values[2];\n            } else {\n       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t=X().getNumber(\"WEBGL_VERSION\");e!=null?(this.gl=e,S5(t,e)):this.gl=vs(t);let n=\"WEBGL_color_buffer_float\",s=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),X().getNumber(\"WEBGL_VERSION\")===1){let r=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Du(this.gl,r),Ln(this.gl,a))this.textureHalfFloatExtension=Du(this.gl,a);else if(X().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),Ln(this.gl,s))this.colorBufferHalfFloatExtension=Du(this.gl,s);else if(X().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\",Ln(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ln(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=b1(this.gl),this.indexBuffer=y1(this.gl),this.framebuffer=QC(this.gl),this.textureConfig=dv(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. 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e=DK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ad(this.gl,e,this.framebuffer),this.debug&&Fu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ad(this.gl,this.outputTexture,this.framebuffer),this.debug&&Fu(this.gl)):Vm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let 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vX=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=ln(\"rc\",this.rank),n=rt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${n} rc = getOutputCoords();\n\n          if(${s}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?\"r\":\"rp1\"}, ${s===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${s};\n    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}},P1=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n=\"\";for(let s=0;s<4;s++){let r=\"thisRC = rc;\";s%2===1&&(r+=\"thisRC.z += 1;\"),s>1&&(r+=\"thisRC.y += 1;\"),n+=`\n        ${r}\n        ${s>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${s}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${s>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${xX(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?fv():hv(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n        int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}};function xX(e,t){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t?B5([\"r\",\"c\",\"d\"],\"inputShape\"):yi([\"r\",\"c\",\"d\"],e)}\n      return ivec3(r, c, d);\n    }\n  `}var wX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=iw(t,n),r=ow(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=aw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=iw(n,s),a=ow(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=aw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function kX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function aw(e,t,n,s,r){let a=IX(t,s),i;if(r){let[u,l]=eu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=kX(n,a);return i*o}function IX(e,t){switch(e){case 3:return vv(t);case 4:return xv(t);case 1:return gv(t);case 0:return bv(t);case 2:return yv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SX(e){return X().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?e?3:1:e?4:0}function iw(e,t){if(e===1)return 3;if(e===0||e==null)return SX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function ow(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      float unaryOperation(float x) {\n        ${t}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},ss=\"if (isnan(x)) return x;\",CX=\"return x;\",uw=\"return abs(x);\",NX=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",TX=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,$X=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Mi=\"return x;\",_X=\"return 1.0 / (1.0 + exp(-1.0 * x));\",AX=\"return x;\",EX=`\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`,RX=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,DX=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,FX=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Zr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},OX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=ln(\"rc\",t),s=rt(t),r=yX(t,n),a=n.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${r});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}},PX=xs.whereImpl,zX=1e-7,MX=1e-4,Zc={};function LX(e){return e in Zc||(Zc[e]={}),Zc[e]}var BX=X().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),VX=600;function WX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*VX/1024/1024}var z1=class extends tl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Kf)t=e;else{let n=vs(X().getNumber(\"WEBGL_VERSION\"),e);t=new Kf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber(\"WEBGL_VERSION\"));t=new Kf(n),this.binaryCache=LX(X().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new wX(this.gpgpu),this.numMBBeforeWarning=WX(),this.texData=new Ud(this,Ss())}nextDataId(){return z1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||X().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Zr(i,Mi):p=new Hs(i,Mi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s===\"string\")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s===\"complex64\"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=S.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Zr(s,Mi):h=new Hs(s,Mi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool(\"DEBUG\")&&!X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&X().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,l;if(a!==\"complex64\"&&X().get(\"WEBGL_BUFFER_SUPPORTED\")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let c;if(a===\"complex64\"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=S.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;o?d=new Zr(r,Mi):d=new Hs(r,Mi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!VC(n))throw X().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool(\"WEBGL_PACK\")&&s===!0,i=a?id(t):t,o=a?new _K(i):new $K(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],\"float32\"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=BX){return X().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return PX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Zr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let s=D1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,uw,e.dtype);let t=new Hs(e.shape,uw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new OX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ba(e.shape),...ya(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ba(t),...ya(t)],a=new P1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=id(r),o;s?o=new TK(i):o=new NK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.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(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!Ju(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=CK(e,l,c),d=this.getAndSaveBinary(p,()=>IK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),X().get(\"ENGINE_COMPILE_ONLY\")||SK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get(\"WEBGL_FLUSH_THRESHOLD\");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool(\"WEBGL_LAZILY_UNPACK\")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=X().getBool(\"DEBUG\");X().set(\"DEBUG\",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(X().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zX:MX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=r1(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=eu(c[0],c[1])),o?d=new EK(p,m):d=new AK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),T=this.texData.get(k.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(k.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=UX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await OI(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(pv(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=m1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},M1=z1;M1.nextDataId=0;function UX(e,t){if(t===\"float32\"||t===\"complex64\")return e;if(t===\"int32\"||t===\"bool\"){let n=t===\"int32\"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ope=\"0.0.0\";function GX(){X().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}dp.isBrowser()&&pp(\"webgl\",()=>new M1,2);var Ppe={forceHalfFloat:GX},L1=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,so=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      float binaryOperation(float a, float b) {\n        ${e}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `}},jp=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`,Zl=class{constructor(e,t,n,s=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Sn(r);let a=\"\";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${rt(r)} coords = getOutputCoords();\n        `,r===1)this.enableShapeUniforms?a+=`\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `:a+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let o=ln(\"coords\",r);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= outShape[${r} - 2];\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= outShape[${r} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= ${this.outputShape[r-2]};\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= ${this.outputShape[r-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${a}\n\n        setOutput(result);\n      }\n    `}};function kn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var HX={kernelName:Ba,backendName:\"webgl\",kernelFunc:kn};function Rr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.texData.get(a.dataId),o=kn({inputs:{x:s},backend:n}),u=kn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var qX={kernelName:jd,backendName:\"webgl\",kernelFunc:Rr},B1=\"return (a < 0.) ? b * a : a;\",V1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function jX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],\"float32\",w.createScalarValue(a,\"float32\")),o=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(V1,r.shape,i.shape):new so(B1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],\"float32\");return n.disposeIntermediateTensorInfo(i),u}var KX={kernelName:Va,backendName:\"webgl\",kernelFunc:jX},W1=\"return (a < 0.) ? b * a : a;\",U1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function XX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(U1,s.shape,r.shape):new so(W1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],\"float32\")}var YX={kernelName:Ja,backendName:\"webgl\",kernelFunc:XX},iu=\"if (isnan(x)) return x;\",QX=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,ZX=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Zr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype===\"complex64\"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new so(e,u.shape,l.shape);return c.runWebGLProgram(E,[T,N],cn(x.dtype,k.dtype))}),y=Rr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(f):f,b=u.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new so(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Kp(e,t=!1){if(e===\"linear\")return t?AX:CX;if(e===\"relu\")return t?RX:TX;if(e===\"elu\")return t?EX:NX;if(e===\"relu6\")return t?DX:$X;if(e===\"prelu\")return t?U1:W1;if(e===\"leakyrelu\")return t?V1:B1;if(e===\"sigmoid\")return t?FX:_X;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var G1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?\"i * 2, rc.y\":\"rc.y, i * 2\",d=r?\"rc.z, i * 2\":\"i * 2, rc.z\",h=s?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],f=r?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],m=\"\",g=\"\";i&&(o?m=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:u?m=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:m=`vec4 activation(vec4 x) {\n          ${i}\n        }`,g=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let y=\"rc.x\",v=\"rc.x\";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${c}; i++) {\n          int batchA = ${y};\n          int batchB = ${v};\n          vec4 a = getMatrixA(batchA, ${p});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${h[0]} * ${f[0]});\n          result += (${h[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}},lw={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},cw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}},dw=\"return a * b;\";function kv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=S.upcastType(s.dtype,r.dtype);if(s.dtype===\"complex64\"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new cw(lw.REAL,s.shape,r.shape),c=new cw(lw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Rr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=JK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?i=new Zl(dw,s.shape,r.shape):i=new so(dw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var JX={kernelName:Ya,backendName:\"webgl\",kernelFunc:kv};function e8(e,t,n){let s=[ba(e.shape),...ya(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ba(t),...ya(t)],i=new P1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. 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coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${o===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${o===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${o===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}},n8=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=\"0.0\",o=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",o=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",o=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let l=Math.floor(n/4)*4,c=n%4,p=`\n      if (${t===\"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${t===\"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${o}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${o}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,d=\"vec4\";t===\"all\"?(i=\"1.0\",p=`\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",p=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${h}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${i});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function s8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=S.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function xi(e,t,n,s){let r=s8(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n===\"mean\"?c=i===0?new pw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new pw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new n8({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var r8=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=a8(t);this.userCode=`\n    void main() {\n      ${s} resRC = getOutputCoords();\n      setOutput(getA(${r}));\n    }\n    `}};function a8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var i8=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=rt(this.rank),r=O1(\"rc\",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${s} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${o}) {\n        result[1] = ${u};\n      }\n      --${r[this.rank-1]};\n      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${o}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function Xp(e,t,n){let s=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new i8(e.shape,t):new r8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function o8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=S.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Xp(e,u,s),o=S.getInnerMostAxes(o.length,a)),S.assertAxesAreInnerMostDims(\"sum\",o,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=S.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=cp(e.dtype),v=xi(b,y,\"sum\",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Yp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return o8(r,a,i,n)}var u8={kernelName:oi,backendName:\"webgl\",kernelFunc:Yp};function qt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=wv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Xp(r,a,i);return l}var l8={kernelName:pi,backendName:\"webgl\",kernelFunc:qt},H1=1e3;function zd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=n?N.shape[1]:N.shape[2],F=a!=null,$=i!=null,z=u===\"leakyrelu\",W=u!=null?Kp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>H1&&q===!1){let Z=N,te=E;n&&(Z=qt({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=qt({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=kv({inputs:{a:ne,b:re},backend:r});K=Yp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=cn(e.dtype,t.dtype),te=new G1(k,T,[P,h,f],n,s,F,W,$,z),ee=[N,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],\"float32\",w.createScalarValue(o,\"float32\"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function c8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return 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y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var N8={kernelName:rl,backendName:\"webgl\",kernelFunc:C8};function T8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims(\"any\",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,\"any\",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var $8={kernelName:al,backendName:\"webgl\",kernelFunc:T8},_8=class{constructor(e,t,n){this.variableNames=[\"A\"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[r,a];let i=t===\"max\"?\">\":\"<\",o=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${s};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${s}; i++) {\n          int inIdx = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}},A8=class{constructor(e,t,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,o=i.length,u=rt(o),l=ln(\"coords\",o),c,p;if(a===1){p=o+1;let N=rt(p);c=`\n        ${N} sourceLocR = ${N}(${l.join()}, 0);\n        ++${l[o-1]};\n        ${N} sourceLocG = ${N}(${l.join()}, 0);\n        ++${l[o-2]};\n        ${N} sourceLocA = ${N}(${l.join()}, 0);\n        --${l[o-1]};\n        ${N} sourceLocB = ${N}(${l.join()}, 0);\n        --${l[o-2]};`}else p=o,c=`\n        ${u} sourceLocR = coords;\n        ++${l[o-1]};\n        ${u} sourceLocG = coords;\n        ++${l[o-2]};\n        ${u} sourceLocA = coords;\n        --${l[o-1]};\n        ${u} sourceLocB = coords;\n        --${l[o-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,p),h=\".\"+d[p-1],f=d.map(N=>\"int \"+N),m=ln(\"sourceLocR\",p-1).concat(\"inIdx.r\"),g=ln(\"sourceLocG\",p-1).concat(\"inIdx.g\"),b=ln(\"sourceLocB\",p-1).concat(\"inIdx.b\"),y=ln(\"sourceLocA\",p-1).concat(\"inIdx.a\"),v=n===\"max\"?\"greaterThan\":\"lessThan\",x=s?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${y.join()})));`,k=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,T=s?\"\":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${T}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};\n        bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n          sourceLocB${h}, sourceLocA${h}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${x}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}};function q1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=S.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new _8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=q1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function j1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=S.computeOptimalWindowSize(a),o=new A8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,\"int32\");if(l.shape.length===t.shape.length){let c=j1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function K1(e,t,n,s){let r=[n];if(S.assertAxesAreInnerMostDims(\"arg\"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=q1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return j1(e,t,s)}function E8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=K1(n,u,i[0],\"max\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var R8={kernelName:Ia,backendName:\"webgl\",kernelFunc:E8};function D8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=K1(n,u,i[0],\"min\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var F8={kernelName:il,backendName:\"webgl\",kernelFunc:D8},O8=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,P8=Ke({opSnippet:O8}),z8={kernelName:ol,backendName:\"webgl\",kernelFunc:P8},M8=ss+\"return log(x + sqrt(x * x + 1.0));\",L8=Ke({opSnippet:M8}),B8={kernelName:ul,backendName:\"webgl\",kernelFunc:L8},V8=ss+`\n  return atan(x);\n`,W8=Ke({opSnippet:V8}),U8={kernelName:ll,backendName:\"webgl\",kernelFunc:W8},G8=QX+`\n  return atan(a, b);\n`,H8=`\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+ZX+`\n  return result;\n`,q8=jt({opSnippet:G8,packedOpSnippet:H8}),j8={kernelName:dl,backendName:\"webgl\",kernelFunc:q8},K8=ss+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelName:cl,backendName:\"webgl\",kernelFunc:X8},el=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t===\"avg\",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(f||(b=\"-1.0 / 1e-20\"),n){let N=\">=\";this.userCode=`\n        const ivec2 strides = ivec2(${i}, ${o});\n        const ivec2 pads = ivec2(${d}, ${h});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${u}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p};\n                wC += ${l}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${N} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let y=\"max\",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(v=\"avgValue / count\");let x=Math.floor(a/4)*4,k=a%4,T=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${y}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${o});\n      const ivec2 pads = ivec2(${d}, ${h});\n      const float initializationValue = ${b};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${b});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${u}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${l};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              getValue(batch, xR, xC + 3 * ${l}, d)\n            );\n\n            ${T}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${k===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${T}\n          } else if (${k===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${T}\n          } else if (${k===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              initializationValue\n            );\n\n            ${T}\n          }\n        }\n        setOutput(${v});\n      }\n    `}},Iv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t===\"avg\",v=\"0.0\";if(y||(v=\"-1.0 / 1e-20\"),n){let A=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${o}, ${u});\n        const ivec3 pads = ivec3(${m}, ${g}, ${b});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${d};\n              wD += ${l}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${h};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${p}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${A} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let x=\"max\",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(k=\"avgValue / count\");let T=Math.floor(a/4)*4,N=a%4,E=`\n      if (${y}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${x}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${o}, ${u});\n      const ivec3 pads = ivec3(${m}, ${g}, ${b});\n      const float initializationValue = ${v};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${v});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${l}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${T}; wC += 4) {\n              int xC = xCCorner + wC * ${p};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n              );\n\n              ${E}\n            }\n\n            int xC = xCCorner + ${T};\n            if (${N===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${N===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${N===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                initializationValue\n              );\n\n              ${E}\n            }\n          }\n          setOutput(${k});\n        }\n      }\n    `}};function Q8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tu(r,\"avgPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.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(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new el(c,\"avg\",!1);return n.runWebGLProgram(p,[r],\"float32\")}var Z8={kernelName:Sa,backendName:\"webgl\",kernelFunc:Q8};function J8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Iv(p,\"avg\",!1);return n.runWebGLProgram(d,[r],\"float32\")}var eY={kernelName:qd,backendName:\"webgl\",kernelFunc:J8},tY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${l}, ${c});\n      const float avgMultiplier = float(${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${o};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${u};\n            wC+= ${i}) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},nY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${h}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${o}) {\n          float dyD = float(dyDCorner + wD) / ${r}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${p};\n              wR += ${u}) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${d};\n                wC += ${l}) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function sY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new nY(d);return n.runWebGLProgram(h,[r],i.dtype)}var rY={kernelName:og,backendName:\"webgl\",kernelFunc:sY};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;tu([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=new tY(c);return n.runWebGLProgram(p,[r],i.dtype)}var iY={kernelName:ig,backendName:\"webgl\",kernelFunc:aY};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return 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o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=ou({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},vY={kernelName:io,backendName:\"webgl\",kernelFunc:yY};function xY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=R1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var wY={kernelName:ug,backendName:\"webgl\",kernelFunc:xY};function kY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],\"int32\",Int32Array.from(o))}var IY={kernelName:lg,backendName:\"webgl\",kernelFunc:kY},SY=\"return float(a != b);\",X1=jt({opSnippet:SY,cpuKernelImpl:tX,dtype:\"bool\"}),CY={kernelName:Io,backendName:\"webgl\",kernelFunc:X1};function Jl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.real},backend:n})}var NY={kernelName:np,backendName:\"webgl\",kernelFunc:Jl},TY=\"return float(int(x));\";function $Y(e,t){let n=new Hs(e.shape,TY),s=t.runWebGLProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Hm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return kn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Hm({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=Rr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype===\"complex64\"){let i=Jl({inputs:{input:r},backend:n}),o=Hm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=kn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return $Y(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=X1({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var _Y={kernelName:Na,backendName:\"webgl\",kernelFunc:Hm},mw=\"return ceil(x);\",AY=Ke({opSnippet:mw,packedOpSnippet:mw,cpuKernelImpl:PK}),EY={kernelName:Ta,backendName:\"webgl\",kernelFunc:AY},RY=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `}},DY=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `}};function FY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;X().getBool(\"WEBGL_PACK_CLIP\")?o=new DY(r.shape):o=new RY(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var OY={kernelName:Sr,backendName:\"webgl\",kernelFunc:FY},PY=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `}};function gw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function zY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new PY(s.shape),i=[gw(s,r.complexTensorInfos.real),gw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var MY={kernelName:Kd,backendName:\"webgl\",kernelFunc:zY},LY=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join(`\n        `)}\n      }\n    `}},BY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=rt(s),a=ln(\"coords\",s),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`\n        if (${u} < ${o[f]}  && ${u} >= ${o[f-1]}) {\n          return getChannel(\n            getT${f}(${Jc(i,u,m)}),\n            vec2(${Jc(l,u,m)}));\n        }`}let d=o.length,h=o[o.length-1];p+=`\n        return getChannel(\n          getT${d}(${Jc(i,u,h)}),\n          vec2(${Jc(l,u,h)}));`,this.userCode=`\n      float getValue(${i.map(f=>\"int \"+f)}) {\n        ${p}\n      }\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[s-1]} = ${a[s-1]} + 1;\n        if (${a[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[s-2]} = ${a[s-2]} + 1;\n        if (${a[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[s-1]} = ${a[s-1]} - 1;\n        if (${a[s-2]} < ${n[s-2]} &&\n            ${a[s-1]} < ${n[s-1]}) {\n          result.b = getValue(${a});\n        }\n        setOutput(result);\n      }\n    `}};function Jc(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function Qp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var VY={kernelName:Zd,backendName:\"webgl\",kernelFunc:Qp};function Gi(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let c=e.map(m=>Jl({inputs:{input:m},backend:n})),p=e.map(m=>Qp({inputs:{input:m},backend:n})),d=Gi(c,t,n),h=Gi(p,t,n),f=Rr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=S.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=zK(p,d,s,h),m=S.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let c=Math.floor(e.length/2),p=Gi(e.slice(0,c),t,n),d=Gi(e.slice(c),t,n),h=Gi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&e[0].shape.length>1){let c=new BY(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=WY(e,t,n),o=new LY(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function WY(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function Y1(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return kn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Gi(o,a,n)}var UY={kernelName:oo,backendName:\"webgl\",kernelFunc:Y1},Q1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat===\"channelsLast\",g=m?1:2,b=m?2:3,y=m?3:1,v=\"\",x=\"\";n&&(s?v=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?v=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:v=`\n          float activation(float x) {\n            ${n}\n          }\n        `,x=\"result = activation(result);\");let k=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${v}\n\n      const ivec2 strides = ivec2(${o}, ${u});\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${y}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${g}], coords[${b}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${p}; wR++) {\n          int xR = xRCorner + wR * ${l};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${d}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${h}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${f===1}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${h}) *\n                    getW(wR, wC, ${h}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${h}, xR, xC) *\n                    getW(wR, wC, ${h}, d2);\n              }\n\n            } else if (${f===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${f===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2),\n                getW(wR, wC, ${h} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1),\n                  getX(batch, xR, xC, ${h} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC),\n                  getX(batch, ${h} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${k}\n        ${x}\n        setOutput(result);\n      }\n    `}},GY=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${r}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${n}, ${s});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${o};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${p}; wR++) {\n            int xR = xRCorner + wR * ${u};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${d}; wC++) {\n              int xC = xCCorner + wC * ${l};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${h}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${f===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${h}) *\n                  getW(wF, wR, wC, ${h}, d2);\n              } else if (${f===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${f===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1),\n                  getX(batch, xF, xR, xC, ${h} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2),\n                  getW(wF, wR, wC, ${h} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},HY=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"},{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=e,this.enableShapeUniforms=Sn(this.outputShape.length);let{dataFormat:n}=t,s=fn(),r=n===\"channelsLast\",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?\"if(blockIndex < outShape[1] && pos < outShape[0]) {\":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u=\"\";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`\n          blockIndex = rc.y + ${c};\n          pos = rc.x + ${l};\n\n          ${o}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${a}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${r}) {\n                  innerDims = vec2(d1, ch);\n                  result[${l*2+c}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${l*2+c}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${u}\n\n        ${s.output} = result;\n      }\n    `}};function Z1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat===\"channelsLast\",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>H1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(Ju(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let N=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=zd({a:k,b:N,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=T,A.shape=n.outShape,g=kn({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=zd({a:k,b:T,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:N},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(N)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function J1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h===\"channelsLast\",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let N=new HY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(N,[k],\"float32\",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o===\"leakyrelu\",z=o?Kp(o,!0):null,W=new G1(P.shape,T.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,T];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],\"float32\",w.createScalarValue(i,\"float32\"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,\"float32\"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function qY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type===\"SAME\"||d.padInfo.type===\"VALID\"))h=Z1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)h=J1({x:r,filter:a,convInfo:d,backend:n});else{let m=new Q1(d);h=n.runWebGLProgram(m,[r,a],\"float32\")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var jY={kernelName:$a,backendName:\"webgl\",kernelFunc:qY},KY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${a}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},XY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${a}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},YY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${r};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${a};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${s} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},QY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${o}, ${u}, ${l});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${r}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${s}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${s} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function ZY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new KY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var JY={kernelName:cg,backendName:\"webgl\",kernelFunc:ZY};function e9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new XY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var t9={kernelName:_a,backendName:\"webgl\",kernelFunc:e9};function n9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new GY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var s9={kernelName:Xd,backendName:\"webgl\",kernelFunc:n9};function r9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=S.computeConv3DInfo(r.shape,u,i,1,o),c=new YY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var a9={kernelName:dg,backendName:\"webgl\",kernelFunc:r9};function i9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=S.computeConv3DInfo(u,a.shape,o,1,i),c=new QY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var o9={kernelName:pg,backendName:\"webgl\",kernelFunc:i9},u9=iu+`\n  return cos(x);\n`,l9=Ke({opSnippet:u9}),c9={kernelName:Aa,backendName:\"webgl\",kernelFunc:l9},d9=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,p9=Ke({opSnippet:d9}),h9={kernelName:Ea,backendName:\"webgl\",kernelFunc:p9},f9=class{constructor(e,t,n,s,r){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s===\"bilinear\"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,\"(y2-y1) * height_ratio\",`y1*${h} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,\"(x2-x1) * width_ratio\",`x1*${f} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${y});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${a}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${v};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${h} ) {\n          setOutput(float(${r}));\n          return;\n        }\n        float in_x = ${x};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${r}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${d} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}},m9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new f9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],\"float32\")},g9={kernelName:lo,backendName:\"webgl\",kernelFunc:m9},bw=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"1.0\":`getX(${yw(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${rt(s)} coords = getOutputCoords();\n        int end = ${vw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${vw(s,\"coords\")} = idx;\n          val *= getX(${yw(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function yw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function vw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function b9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new bw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new bw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var y9={kernelName:pl,backendName:\"webgl\",kernelFunc:b9},xw=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"0.0\":`getX(${ww(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${rt(s)} coords = getOutputCoords();\n        int end = ${kw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${kw(s,\"coords\")} = idx;\n          val += getX(${ww(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function ww(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function kw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function v9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new xw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new xw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var x9={kernelName:uo,backendName:\"webgl\",kernelFunc:v9};function w9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=R1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=OK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var k9={kernelName:hg,backendName:\"webgl\",kernelFunc:w9},I9=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}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 S9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=new I9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var C9={kernelName:co,backendName:\"webgl\",kernelFunc:S9},e2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u=\"\",l=\"\";n&&(s?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:u=`\n          float activation(float x) {\n            ${n}\n          }\n        `,l=\"result = activation(result);\");let c=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${u}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${o};\n        int q = d2 - d1 * ${o};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${a}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${i}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${l}\n        setOutput(result);\n      }\n    `}},t2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`\n          vec4 xTexelC${g*2};\n          int xTexelC${g*2}Ready;\n          vec4 xTexelC${g*2+1};\n          int xTexelC${g*2+1}Ready;\n          vec4 xC${g};`;d+=`\n    for (int r = 0; r < ${l}; r++) {\n      `;for(let g=0;g<c;g++)d+=`\n          xTexelC${g*2} = vec4(0.0);\n          xTexelC${g*2}Ready = 0;\n          xTexelC${g*2+1} = vec4(0.0);\n          xTexelC${g*2+1}Ready = 0;\n          xC${g} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,o===1){if(b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n              `,u===1&&b>0?d+=`\n                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n                `:d+=`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n                  } else {\n                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n                  }\n                  `):d+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xC${b} = xTexelC${b};\n                `,b+1<c)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${y};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${b+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${b+1}Ready = 1;\n                  }\n                  `,u>1&&(d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                      xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${b}Ready = 1;\n                    }\n                    `),d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                  `):y===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${y};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${b+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${b+1}Ready = 1;\n                    }\n\n                    xC${b+1} = xTexelC${b+1};\n                    `}}else b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n              `,b+1<c&&(d+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n                `)):(d+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(\n                  xTexelC${b}.xy, xTexelC${b+1}.xy);\n              `,b+1<c&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<c&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<c&&(d+=`\n              wTexel = getW(r, ${b+1}, d1, q);\n              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}d+=`\n    }\n  `,d+=`\n      }\n    `;let h=\"\",f=\"\";n&&(s?h=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?h=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:h=`vec4 activation(vec4 x) {\n          ${n}\n        }`,f=\"result = activation(result);\");let m=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${h}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${a};\n        int q = d2 - d1 * ${a};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${d}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${m}\n        ${f}\n        setOutput(result);\n      }\n    `}};function N9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.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(r.shape,a.shape,i,c,o,l,!0),d;X().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new t2(p):d=new e2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],\"float32\",h)}var T9={kernelName:Ra,backendName:\"webgl\",kernelFunc:N9},$9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${a} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},_9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function A9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new $9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var E9={kernelName:fg,backendName:\"webgl\",kernelFunc:A9};function R9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new _9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var D9={kernelName:mg,backendName:\"webgl\",kernelFunc:R9},F9=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function O9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new F9(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var P9={kernelName:gg,backendName:\"webgl\",kernelFunc:O9},z9=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`\n      const ivec2 strides = ivec2(${r}, ${a});\n      const ivec2 pads = ivec2(${c}, ${p});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${i}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${o}; w++) {\n              int wIn = wBeg + w * ${l};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}};function M9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeDilation2DInfo(r.shape,a.shape,i,o,\"NHWC\",u),c,p=new z9(l);c=n.runWebGLProgram(p,[r,a],\"float32\");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var L9={kernelName:Yd,backendName:\"webgl\",kernelFunc:M9};function B9(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=qt({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=kv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Yp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of 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cQ={kernelName:yg,backendName:\"webgl\",kernelFunc:lQ},dQ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `}};function ec(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new dQ(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var pQ={kernelName:fl,backendName:\"webgl\",kernelFunc:ec},hQ=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${t} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},fQ={kernelName:mo,backendName:\"webgl\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new hQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Cw=\"return floor(x);\",mQ=Ke({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:VK}),gQ={kernelName:Pa,backendName:\"webgl\",kernelFunc:mQ},bQ=`\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`,yQ=`\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division 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n.disposeData(d.dataId),f}function CQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];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\"))b=Z1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)b=J1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,T=h===\"leakyrelu\",N=h?Kp(h,!1):null,E=new Q1(g,x,N,k,T),A=[r,a];if(i&&A.push(i),o&&A.push(o),T){let 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    ${r} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${a};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `}};function AQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=WK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new _Q(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var EQ={kernelName:bo,backendName:\"webgl\",kernelFunc:AQ},RQ=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=DQ(e,2);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${s}));\n      }\n    `}};function DQ(e,t){let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(\"index\"):s.push(`${n[r]}`);return s.join()}function r2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get(\"DEBUG\")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype===\"string\"){let y=n.bufferSync(h),v=n.bufferSync(d),x=UK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new RQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var FQ={kernelName:go,backendName:\"webgl\",kernelFunc:r2},OQ=\"return float(a > b);\",PQ=`\n  return vec4(greaterThan(a, b));\n`,zQ=jt({opSnippet:OQ,packedOpSnippet:PQ,cpuKernelImpl:GK,dtype:\"bool\"}),MQ={kernelName:yo,backendName:\"webgl\",kernelFunc:zQ},LQ=\"return 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b));\n`,iZ=jt({opSnippet:rZ,packedOpSnippet:aZ,cpuKernelImpl:jK,dtype:\"bool\"}),oZ={kernelName:xo,backendName:\"webgl\",kernelFunc:iZ};function uZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=KK(s,r,a);return t.makeTensorInfo([i.length],\"float32\",i)}var lZ={kernelName:xg,backendName:\"webgl\",kernelFunc:uZ},cZ=iu+`\n  return x < 0.0 ? 0./0. : log(x);\n`,dZ=`\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`,pZ=Ke({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:XK}),hZ={kernelName:Wa,backendName:\"webgl\",kernelFunc:pZ},fZ=iu+`\n  return log(1.0 + x);\n`,mZ=Ke({opSnippet:fZ}),gZ={kernelName:yl,backendName:\"webgl\",kernelFunc:mZ},bZ=\"return float(a >= 1.0 && b >= 1.0);\",yZ=`\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`,vZ=jt({opSnippet:bZ,packedOpSnippet:yZ,dtype:\"bool\"}),xZ={kernelName:wo,backendName:\"webgl\",kernelFunc:vZ},wZ=\"return float(!(x >= 1.0));\",kZ=Ke({opSnippet:wZ}),IZ={kernelName:vl,backendName:\"webgl\",kernelFunc:kZ},SZ=\"return float(a >= 1.0 || b >= 1.0);\",CZ=`\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`,NZ=jt({opSnippet:SZ,packedOpSnippet:CZ,dtype:\"bool\"}),TZ={kernelName:Jd,backendName:\"webgl\",kernelFunc:NZ},$Z=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${a}; j <= ${a}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${i}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${o};\n        setOutput(val);\n      }\n    `}},_Z=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${a};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${a}; j <= ${a}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${o};\n        setOutput(result);\n      }\n    `}},AZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=X().getBool(\"WEBGL_PACK_NORMALIZATION\")?new _Z(r.shape,a,i,o,u):new $Z(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},EZ={kernelName:ep,backendName:\"webgl\",kernelFunc:AZ},RZ=class{constructor(e,t,n,s,r){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${s}) * norm + float(${n});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${s})\n                * float(${r})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${r});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}},DZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new RZ(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},FZ={kernelName:wg,backendName:\"webgl\",kernelFunc:DZ};function OZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=xi(o,e.dtype,\"max\",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function a2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N<x.length;N++)x[N]=r.shape[c[N]];let k=wv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let T=n.texData.get(h.dataId);T.values=k}else h=Xp(r,c,n);l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims(\"max\",l,o);let[f,m]=S.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=S.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=YK(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=OZ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var PZ={kernelName:Ua,backendName:\"webgl\",kernelFunc:a2},zZ=L1+`\n  return max(a, b);\n`,MZ=`\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+jp+`\n  return result;\n`,LZ=jt({opSnippet:zZ,packedOpSnippet:MZ,cpuKernelImpl:QK}),BZ={kernelName:Ga,backendName:\"webgl\",kernelFunc:LZ};function VZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tu(r,\"maxPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.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(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new el(c,\"max\",!1);return n.runWebGLProgram(p,[r],r.dtype)}var WZ={kernelName:Ha,backendName:\"webgl\",kernelFunc:VZ};function UZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Iv(p,\"max\",!1);return n.runWebGLProgram(d,[r],r.dtype)}var GZ={kernelName:tp,backendName:\"webgl\",kernelFunc:UZ},HZ=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${r};\n          wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${a}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},qZ=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${p}, ${d});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${o};\n           wD += ${r}) {\n          float dyD = float(dyDCorner + wD) / ${t}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${u};\n              wR += ${a}) {\n            float dyR = float(dyRCorner + wR) / ${n}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${l};\n                wC += ${i}) {\n              float dyC = float(dyCCorner + wC) / ${s}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${h} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${u} * ${l} +\n                  wR * ${l} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function jZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new Iv(d,\"max\",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new qZ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var KZ={kernelName:Ig,backendName:\"webgl\",kernelFunc:jZ};function XZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;tu([a,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=S.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new el(d,\"max\",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new HZ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var YZ={kernelName:kg,backendName:\"webgl\",kernelFunc:XZ};function QZ(e,t,n,s){let r=new el(n,\"max\",!1),a=s.runWebGLProgram(r,[e],\"float32\");r=new el(n,\"max\",!0,!0,t);let i=s.runWebGLProgram(r,[e],\"float32\");return[a,i]}var ZZ={kernelName:Sg,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(S.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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setOutput(value);\n        } else {\n          ${r} coords = outC - start;\n          setOutput(getX(${o}));\n        }\n      }\n    `}},G7=class{constructor(e,t,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=rt(s),a=t.map(f=>f[0]).join(\",\"),i=t.map((f,m)=>f[0]+e[m]).join(\",\"),o=ln(\"rc\",s),u=ln(\"source\",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?\"source\":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;\n       if(${l}) {\n      `,s===1?\"\":`}\n       rc = outputLoc;\n       ${o[s-2]} += 1;\n       if(${o[s-2]} < ${this.outputShape[s-2]}) {`,s===1?\"\":`  ${o[s-1]} += 1;\n         if(${l}) {`],d=s===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",h=\"\";for(let f=0,m=s===1?2:4;f<m;f++)h+=`\n        ${p[f]}\n        if (${d}) {\n          result[${f}] = 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sign(a) * pow(abs(a), b);\n`,j7=`\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  `+jp+`\n  return result;\n`,K7=jt({opSnippet:q7,packedOpSnippet:j7}),X7={kernelName:Za,backendName:\"webgl\",kernelFunc:K7};function Y7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=S.getAxesPermutation(c,o),d=r;p!=null&&(d=qt({inputs:{x:r},backend:n,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,o),u.push(d)),S.assertAxesAreInnerMostDims(\"prod\",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=nX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=S.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=cp(r.dtype),v=xi(b,y,\"prod\",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=S.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Q7={kernelName:_o,backendName:\"webgl\",kernelFunc:Y7},d2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=sX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Z7={kernelName:kl,backendName:\"webgl\",kernelFunc:d2},J7=\"return 1.0 / x;\",eJ=Ke({opSnippet:J7}),tJ={kernelName:Il,backendName:\"webgl\",kernelFunc:eJ},nJ=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,sJ=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,rJ=Ke({opSnippet:nJ,packedOpSnippet:sJ}),aJ={kernelName:ei,backendName:\"webgl\",kernelFunc:rJ},iJ=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,oJ=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,uJ=Ke({opSnippet:iJ,packedOpSnippet:oJ}),lJ={kernelName:ni,backendName:\"webgl\",kernelFunc:uJ},cJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":p=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `}},dJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":p=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function pJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new dJ(r.shape,u,l,a,i):new cJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],\"float32\")}var hJ={kernelName:ti,backendName:\"webgl\",kernelFunc:pJ},fJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function mJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new fJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var gJ={kernelName:Tg,backendName:\"webgl\",kernelFunc:mJ},bJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}},yJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function vJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new yJ(r.shape,u,l,a,i):new bJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var xJ={kernelName:Sl,backendName:\"webgl\",kernelFunc:vJ},wJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${o[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${s}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function kJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new wJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var IJ={kernelName:Ng,backendName:\"webgl\",kernelFunc:kJ},SJ=class{constructor(e,t){this.variableNames=[\"x\"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet 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u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=mX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Gee(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var qee={kernelName:Cr,backendName:\"webgl\",kernelFunc:h2},jee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     `}},Kee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     `}};function Ur(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function _w(e){let t=1;for(;t<e;)t*=2;return t}function Xee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),u=X().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=gX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,\"int32\",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:\"int32\",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Ur(n,h);let b=_w(a),y=_w(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new jee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,\"int32\",q),Ur(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Kee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,\"int32\",z),Ur(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let T=v;v=ou({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Ur(n,T);let N=r2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Ur(n,g);let E=l.slice(0,-1);E.push(a),T=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Ur(n,T);let A=N;return N=he({inputs:{x:N},attrs:{shape:E},backend:n}),Ur(n,A),[N,v]}var Yee={kernelName:Vo,backendName:\"webgl\",kernelFunc:Xee},Qee=class{constructor(e,t,n,s,r,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=n===\"nearest\"?1:2,o;switch(s){case\"constant\":o=1;break;case\"reflect\":o=2;break;case\"wrap\":o=3;break;case\"nearest\":o=4;break;default:o=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${o} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${r});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${r});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${i} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}};function Zee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Qee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],\"float32\")}var Jee={kernelName:Wo,backendName:\"webgl\",kernelFunc:Zee};function ete(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;tu(a,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=bX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],\"int32\",l)]}var tte={kernelName:Ag,backendName:\"webgl\",kernelFunc:ete};function nte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=ou({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var ste={kernelName:Uo,backendName:\"webgl\",kernelFunc:nte},rte=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o=\"0.0\",u=\"sumValue\",l=Math.floor(n/4)*4,c=n%4,p=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";r%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `);let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${o};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${h}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${a})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function ate(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=S.getAxesPermutation([l],o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,o)[0]);let d=S.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=cp(r.dtype),g=(x,k,T,N,E)=>{let A=x.shape[0],P=x.shape[1],R=S.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new rte(F,k),z=n.compileAndRun($,[x,T],N);if(u.push(z),z.shape[1]===E)return z;let W=d2({backend:n,attrs:{start:0,stop:E,step:1,dtype:\"float32\"}}),q=h2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,N,E)},b=g(f,\"unsortedSegmentSum\",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=S.getUndoAxesPermutation(c);v=qt({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var ite={kernelName:up,backendName:\"webgl\",kernelFunc:ate},ote=[d8,h8,g8,v8,w8,S8,N8,$8,R8,F8,z8,B8,U8,j8,Y8,Z8,eY,rY,iY,uY,pY,vY,wY,IY,_Y,EY,OY,qX,MY,UY,jY,JY,t9,s9,a9,o9,c9,h9,g9,y9,x9,k9,C9,T9,E9,D9,P9,L9,V9,H9,X9,J9,nQ,aQ,iQ,uQ,cQ,pQ,fQ,gQ,xQ,IQ,NQ,$Q,EQ,FQ,MQ,WQ,HX,GQ,VY,jQ,YQ,JQ,KX,sZ,oZ,lZ,hZ,gZ,xZ,IZ,TZ,EZ,FZ,PZ,BZ,WZ,GZ,KZ,YZ,ZZ,e7,n7,i7,c7,f7,k7,JX,N7,_7,R7,O7,CY,M7,B7,W7,H7,X7,YX,Q7,Z7,NY,y7,tJ,aJ,lJ,t8,hJ,gJ,xJ,IJ,TJ,_J,RJ,OJ,zJ,BJ,UJ,jJ,YJ,JJ,nee,bY,x7,aee,oee,lee,dee,hee,mee,bee,vee,wee,See,Nee,$ee,Eee,Dee,Oee,zee,v7,u8,Bee,Uee,qee,Yee,Jee,l8,tte,ste,ite,L7];for(let e of ote)Al(e);var zs=X();zs.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);zs.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);zs.registerFlag(\"WEBGPU_MATMUL_WORK_PER_THREAD\",()=>4);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D\",()=>!1);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);zs.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);zs.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);zs.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);zs.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);zs.registerFlag(\"WEBGPU_USE_IMPORT\",()=>!1);var ute=\"return a + b;\",lte=\"return areal * breal - aimag * bimag;\",cte=\"return areal * bimag + aimag * breal;\",dte=\"return a / b;\",pte=\"return a * b;\",hte=\"return (a - b) * (a - b);\",fte=\"return a - b;\",mte=\"return f32(a == b);\",gte=\"return vec4<f32>(a == b);\",bte=\"return f32(a > b);\",yte=\"return vec4<f32>(a > b);\",vte=\"return f32(a >= b);\",xte=\"return vec4<f32>(a >= b);\",wte=\"return f32(a < b);\",kte=\"return vec4<f32>(a < b);\",Ite=\"return f32(a <= b);\",Ste=\"return vec4<f32>(a <= b);\",Cte=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",Nte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,Tte=`\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `,f2=`\n  if (isNaN.r) {\n    resultTemp.r = uniforms.NAN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = uniforms.NAN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = uniforms.NAN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = uniforms.NAN;\n  }\n  `,$te=`\n  let s = sign(a) * sign(b);\n  let ia = i32(round(a));\n  let ib = i32(round(b));\n  return f32(idiv(ia, ib, s));\n  `,_te=`\n  let ia = vec4<i32>(round(a));\n  let ib = vec4<i32>(round(b));\n  let cond = ib != vec4<i32>(0);\n  var resultTemp = vec4<i32>(0);\n  let s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4<f32>(resultTemp);\n  `,Ate=\"return f32(a != b);\",Ete=\"return vec4<f32>(a != b);\",Rte=`\n  if(a < 0.0 && floor(b) < b) {\n    return uniforms.NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  if (round(abs(b) % 2.0) != 1.0) {\n    return pow(abs(a), b);\n  }\n  return sign(a) * pow(abs(a), b);\n  `,Dte=`\n  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);\n  let isModRound1 = vec4<f32>(isModRound1Bool);\n  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);\n  var resultTemp = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  let isExpZero = b == vec4<f32>(0.0);\n  if (isExpZero.r) {\n    resultTemp.r = 1.0;\n  }\n  if (isExpZero.g) {\n    resultTemp.g = 1.0;\n  }\n  if (isExpZero.b) {\n    resultTemp.b = 1.0;\n  }\n  if (isExpZero.a) {\n    resultTemp.a = 1.0;\n  }\n  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;\n  ${f2}\n  return resultTemp;\n  `,Fte=\"if (a < 0.0) { return b * a; }  return a;\",Ote=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;function Aw(e,t){let n=t?f2:Tte;return t?`\n    var resultTemp = vec4<f32>(${e}(a, b));\n    let isNaN = isnanVec4(a) | isnanVec4(b);\n    `+n+`\n    return resultTemp;\n  `:n+`\n    return ${e}(a, b);\n  `}function tc(e,t){switch(e){case 0:return pte;case 1:return ute;case 2:return fte;case 3:return dte;case 4:return t?gte:mte;case 5:return t?yte:bte;case 6:return t?xte:vte;case 7:return t?kte:wte;case 8:return t?Ste:Ite;case 9:return t?Nte:Cte;case 10:return t?Ete:Ate;case 11:return hte;case 12:return t?_te:$te;case 14:return t?Ote:Fte;case 15:return Aw(\"max\",t);case 16:return Aw(\"min\",t);case 13:return t?Dte:Rte;case 17:return lte;case 18:return cte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Pte=\"return abs(a);\",zte=\"return ceil(a);\",Mte=\"return cos(a);\",Lte=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,Bte=\"return exp(a) - 1.0;\",Vte=\"if (a >= 0.0) { return a; }  return (exp(a) - 1.0);\",Wte=`\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,Ute=\"return exp(a);\",Gte=\"return floor(a);\",Hte=\"return a;\",qte=`if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`,jte=\"return f32(!(a >= 1.0));\",Kte=\"return -a;\",Xte=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Yte=\"if(a < 0.0) { return 0.0; } return a;\",Qte=\"return clamp(a, 0.0, 6.0);\",Zte=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",Jte=`\n  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));\n  let isNaN = isnanVec4(a);\n\n  if (isNaN.r) {\n    resFloat.r = a.r;\n  }\n  if (isNaN.g) {\n    resFloat.g = a.g;\n  }\n  if (isNaN.b) {\n    resFloat.b = a.b;\n  }\n  if (isNaN.a) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,ene=\"return 1.0/sqrt(a);\",tne=\"return 1.0 / (1.0 + exp(-1.0 * a));\",nne=\"return sin(a);\",sne=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,rne=\"return sqrt(a);\",ane=\"return a * a;\",ine=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,one=\"return f32(i32((a)));\";function Hr(e,t){switch(e){case 0:return Pte;case 2:return Mte;case 3:return Lte;case 1:return zte;case 4:return t?Wte:Vte;case 5:return Ute;case 6:return Bte;case 7:return Gte;case 8:return Hte;case 9:return qte;case 10:return jte;case 11:return Kte;case 14:return Xte;case 12:return t?Jte:Yte;case 13:return t?Zte:Qte;case 15:return ene;case 18:return tne;case 16:return nne;case 17:return sne;case 19:return rne;case 20:return ane;case 21:return ine;case 22:return one;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e===\"linear\")return Hr(8);if(e===\"relu\")return Hr(12,t);if(e===\"elu\")return Hr(4,t);if(e===\"relu6\")return Hr(13,t);if(e===\"prelu\")return tc(14,t);if(e===\"sigmoid\")return Hr(18);if(e===\"leakyrelu\")return Hr(14);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function une(e,t){if(Math.max(...e)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return\"i32\";if(e===2)return\"vec2<i32>\";if(e===3)return\"vec3<i32>\";if(e===4)return\"vec4<i32>\";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e===\"float32\"?t?\"vec4<f32>\":\"f32\":e===\"int32\"||e===\"bool\"?t?\"vec4<i32>\":\"i32\":e}function Sv(){return`\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`}function Dr(){return`\n  ${Sv()}\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n`}function Ue(){return`\n    ${Dr()}\n      let index = getGlobalIndex();\n`}function lne(e,t,n,s=!1){let r=[];if(r.push(`\n    let workGroupSizeX = ${n.workGroupSize[0]}u;\n    let workGroupSizeY = ${n.workGroupSize[1]}u;\n    let workGroupSizeZ = ${n.workGroupSize[2]}u;\n\n    var<private> localId: vec3<u32>;\n    var<private> globalId: vec3<u32>;\n    var<private> numWorkgroups: vec3<u32>;\n\n    // Only used when the y/z dimension of workgroup size is 1.\n    fn getGlobalIndex() -> i32 {\n      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {\n        return i32(globalId.x);\n      }\n\n      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n          localId.y * workGroupSizeX + localId.x;\n      let workGroupID = (globalId - localId)/vec3<u32>(\n          workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n        workGroupID.y * numWorkgroups.x + workGroupID.x) *\n        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n        localInvocationIndex);\n    }\n  `),s===!0)return r.push(`\n      struct Uniform {\n        size            : i32,\n        numChannels     : i32,\n        outShapeStrides : vec2<i32>,\n        dispatchSize    : vec3<u32>,\n      };\n\n      @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype,n.isVec4)}>;\n      @group(0) @binding(2) var<uniform> uniforms: Uniform;\n    `),[Ew,r.join(`\n`),Rw(t.shape),n.getUserCode()].join(`\n`);let a=\"struct Uniforms { NAN : f32, \";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${Wt(e[d].shape.length)}, `}),a+=`outShape : ${Wt(t.shape.length)}, `;let i=t.shape.length-1;a+=`\n       outShapeStrides: ${Wt(i)}, `,n.size&&(a+=\"size : i32, \"),n.uniforms&&(a+=n.uniforms),a+=\"};\",r.push(a),n.atomic?r.push(`\n    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n  `):r.push(`\n    @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype,n.isVec4)}>;\n  `),n.variableNames.forEach((p,d)=>{r.push(`\n    @group(0) @binding(${1+d}) var<storage, read> ${p}: array<${cd(e[d].dtype,n.isVec4)}>;\n    `)}),a!==\"\"&&r.push(`\n    @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;\n    `);let[o,u]=mne(t.shape,n.dispatchLayout),l=[Ew,r.join(`\n`),Rw(t.shape),o,cne(t.shape.length)];if(n.atomic||l.push(dne(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>pne(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`\n`);l.push(p)}return l.push(n.getUserCode()),l.join(`\n`)}var Ew=`\n  // Checks whether coordinates lie within the bounds of the shape.\n  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {\n    return all(coord >= vec2<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {\n    return all(coord >= vec3<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {\n    return all(coord >= vec4<i32>(0)) && all(coord < shape);\n  }\n\n  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n    return coord;\n  }\n  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {\n    return dot(coords, vec2<i32>(shape.y, 1));\n  }\n  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {\n    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));\n  }\n  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n    return dot(coords, vec4<i32>(\n        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let mod: i32 = a % b;\n    if (sign < 0. && mod != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;function cne(e){let t=\"\";switch(e){case 0:case 1:t+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:t+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:t+=`\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;break;case 4:t+=`\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function dne(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${r}(value);\n    }`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${r}(value);\n    }`,s>=2){let i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=Wt(s);n?a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return a}function pne(e,t,n,s){let r=hne(e,n);return e.shape.length<=t.length&&(r+=fne(e,t,n,s)),r}function hne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=i.map(c=>`${c} : i32`).join(\", \");if(s<1)return t?`\n        fn ${a}() -> vec4<f32> {\n          return vec4<f32>(${n}[0]);\n        }\n      `:`\n      fn ${a}() ->f32 {\n        return f32(${n}[0]);\n      }\n    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l=\"1D\"),t?`\n      fn ${a}(${o}) -> vec4<f32> {\n        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n          ${u}) / 4]);\n      }\n      `:`\n    fn ${a}(${o}) -> f32 {\n      return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n        ${u})]);\n    }\n   `}function fne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i=\"get\"+a+\"ByOutput\",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`\n        fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n          return vec4<f32>(${r}[globalIndex]);\n        }\n\n        fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n          return vec4<f32>(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n        }\n        `:`\n      fn ${i}Index(globalIndex : i32) -> f32 {\n        return f32(${r}[globalIndex]);\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32 {\n        return f32(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n      }\n      `;let c=S.getBroadcastDims(e.shape,t),p=u-o,d=\"\";if(o===0)return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n        return get${a}();\n      }\n    `:`\n      fn ${i}Index(globalIndex : i32) -> f32{\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32{\n        return get${a}();\n      }\n    `;u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(g=>`coords[${g+p}] = 0;`).join(`\n`);let h=\"\";if(u<2&&o>0)h=\"coords\";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords[${v+p}]`).join(\", \");h=`${g}(${b})`}else h=\"coords\";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        var coords = getCoordsFromIndex(globalIndex);\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n\n      fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {\n        var coords = coordsIn;\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n    `:`\n    fn ${i}Index(globalIndex : i32) -> f32 {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n\n    fn ${i}Coords(coordsIn : ${l}) -> f32 {\n      var coords = coordsIn;\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n  `}function mne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{\n      let globalIndex = getGlobalIndex();\n      return getCoordsFromIndex(globalIndex);\n    }\n    `,a];let i=\"\",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=une(h,\"uniforms.outShape\");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {\n    ${i}\n  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(\",\")}); }`,[p,u]}function Rw(e){let t=e.length;if(t<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;let a=\"var index2 = index;\"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${l};`}).join(\"\");return`\n    fn getCoordsFromIndex(index : i32) -> ${s} {\n      ${a}\n      return ${s}(${r.join(\",\")});\n    }\n  `}var m2={};Ae(m2,{ArrayBufferToTypedArray:()=>b2,GPUBytesPerElement:()=>jm,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Cv,computeWorkGroupSizeForMatMul:()=>g2,computeWorkPerThreadForConv2d:()=>Nv,flatDispatchLayout:()=>Be,isWebGPUSupported:()=>Tv,tilesFitEvenlyIntoShape:()=>Ks});var na=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(na(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(na(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(na(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function Cv(e,t){let n=na(e.x.map(r=>t[r])),s=na(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function g2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Nv(e,t){let n=na(e.x.map(r=>t[r])),s=na(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Be(e){return{x:e.map((t,n)=>n)}}function jm(e){if(e===\"float32\"||e===\"int32\"||e===\"bool\"||e===\"string\")return 4;if(e===\"complex64\")return 8;throw new Error(`Unknown dtype ${e}`)}function b2(e,t){if(t===\"float32\")return new Float32Array(e);if(t===\"int32\")return new Int32Array(e);if(t===\"bool\"||t===\"string\")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Tv(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function y2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>\"tileInner must be divisible by 4. And ColPerThread must be 4\"),`\n  var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;\n\n  let RowPerThread = ${e[1]};\n  let ColPerThread = ${e[0]};\n  let TileInner = ${s};\n\n  ${Dr()}\n\n    let tileRow = ${t===1?\"0\":\"i32(localId.y) * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${t===1?\"0\":\"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n    var acc: array<vec4<f32>, RowPerThread>;\n    var ACached : vec4<f32>;\n    var BCached : array<vec4<f32>, 4>;\n\n    // Loop over shared dimension.\n    var globalColA = tileCol;\n    let RowPerThreadB = TileInner / i32(workGroupSizeY);\n    let tileRowB = i32(localId.y) * RowPerThreadB;\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);\n        }\n        globalColA = globalColA + TileInner / ColPerThread;\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {\n            BCached[0] = mm_Bsub[k * ColPerThread][tileCol];\n            BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];\n            BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];\n            BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];\n\n            for (var i = 0; i < RowPerThread; i = i + 1) {\n                ACached = mm_Asub[tileRow + i][k];\n                acc[i] = BCached[0] * ACached.x + acc[i];\n                acc[i] = BCached[1] * ACached.y + acc[i];\n                acc[i] = BCached[2] * ACached.z + acc[i];\n                acc[i] = BCached[3] * ACached.w + acc[i];\n            }\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(globalRow + innerRow,\n                 globalCol,\n                 acc[innerRow], globalId);\n    }\n  }`}var gne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?\"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]\":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];\n        }\n        return vec4<f32>(0.0)`,t=this.fitB?\"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];\n        }\n        return vec4<f32>(0.0)`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${i}\n                }`:n=`\n            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n              ${i}\n            }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${e};\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${t};\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {\n        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])\n        {\n          var value = valueIn;\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col * 4);\n          ${r}\n          ${s}\n          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);\n        }\n      }\n      ${y2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}\n    `}};function $v(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`\n    var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;\n    ${Dr()}\n      let tileRow = i32(localId.y) * ${e[1]};\n      let tileCol = i32(localId.x) * ${e[0]};\n\n      let globalRow = i32(globalId.y) * ${e[1]};\n      let globalCol = i32(globalId.x) * ${e[0]};\n\n      let numTiles = (uniforms.dimInner - 1) / ${r} + 1;\n\n      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;\n      var ACached : f32;\n      var BCached : array<f32, ${e[0]}>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let ColPerThreadA = ${r} / ${t[0]};\n      let tileColA = i32(localId.x) * ColPerThreadA;\n      let RowPerThreadB = ${r} / ${t[1]};\n      let tileRowB = i32(localId.y) * RowPerThreadB;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileColA + innerCol;\n\n            mm_Asub[inputRow][inputCol] = mm_readA(\n                globalRow + innerRow,\n                t * ${r} + inputCol, globalId);\n          }\n        }\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n\n            mm_Bsub[inputRow][inputCol] = mm_readB(\n              t * ${r} + inputRow,\n              globalCol + innerCol, globalId);\n          }\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${r}; k = k + 1) {\n          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n            ACached = mm_Asub[tileRow + innerRow][k];\n            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n\n          if ((globalCol + innerCol) < uniforms.dimBOuter &&\n              (globalRow + innerRow) < uniforms.dimAOuter) {\n            mm_write(globalRow + innerRow,\n                     globalCol + innerCol,\n                     acc[innerRow][innerCol], globalId);\n          }\n        }\n      }\n    }\n  `}function bne(e){return`\n    let TileSize = ${e[0]*4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;\n\n    ${Dr()}\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),\n                                mm_readA(globalRow, colA + 1, globalId),\n                                mm_readA(globalRow, colA + 2, globalId),\n                                mm_readA(globalRow, colA + 3, globalId));\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),\n                              mm_readB(rowB + 1, globalCol, globalId),\n                              mm_readB(rowB + 2, globalCol, globalId),\n                              mm_readB(rowB + 3, globalCol, globalId));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {\n        mm_write(globalRow, globalCol, acc, globalId);\n      }\n    }\n  `}var v2=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=g2(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=o!=null;l&&this.variableNames.push(\"bias\"),c&&this.variableNames.push(\"preluActivationWeights\"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,u]:[this.outputShape[0],u,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let r=[t,s],a=[s,n];return[Ks(r,this.aShape.slice(1)),Ks(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?\"return A[batch * batchASize + row * uniforms.dimInner + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch * batchASize + row * uniforms.dimInner + col];\n           }\n           return 0.0;`:e=this.fitA?\"return A[batch * batchASize + col * uniforms.dimAOuter + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch* batchASize + col * uniforms.dimAOuter + row];\n           }\n           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n           }\n           return 0.0;`:t=this.fitB?\"return B[batch * batchBSize + col * uniforms.dimInner + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + col * uniforms.dimInner + row];\n           }\n           return 0.0;`;let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        var value = valueIn;\n        let batch = i32(globalId.z);\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${this.outputShape[1]>1?$v([this.workPerThread,this.workPerThread,1],this.workGroupSize):bne(this.workGroupSize)}\n    `}};function yne(){return`\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${Dr()}\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var vne=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e=\"return A[batch * batchASize + row * uniforms.dimInner + col];\":e=\"return A[batch * batchASize + col * uniforms.dimAOuter + row];\";let t;this.transposeB===!1?t=\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":t=\"return B[batch * batchBSize + col * uniforms.dimInner + row];\";let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        ${e}\n      }\n\n      fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {\n        var value = valueIn;\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${yne()}\n    `}};function xne(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`\n  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;\n  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Introduces two shared memory buffers, some logical threads could handle\n  // arithmetic operations and others handle IO operations between barrier api,\n  // makes ALUs and load/store units work simultaneously, could improves\n  // the performance.\n  ${Dr()}\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${s} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = tileRow;\n    for (var t = 0; t < numTiles; t = t + 1) {\n      if (t == 0) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        }\n      } else {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n      if (t != 0) {\n        t = t + 1;\n      }\n\n      if (t < numTiles) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub2[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};\n    if (tileRow >= ${t} && writeCol >= 0) {\n      mm_write(writeCol, globalCol, acc, globalId);\n    }\n  }\n  `}var wne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>\"This program can be only used when A width or B Height are small\"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push(\"bias\");let o=a!=null;o&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n          return A[batch * batchASize + row * uniforms.dimInner + col];\n        }\n        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n         }\n         return 0.0;`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            let b = getPreluActivationWeightsByOutputCoords(outCoord);\n            ${i}\n            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            ${i}\n        }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col);\n          var value = valueIn;\n          ${r}\n          ${s}\n          setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${xne(this.workGroupSize)}\n    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var kne={kernelName:Ao,backendName:\"webgpu\",kernelFunc:Me};function _v({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new vne([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new wne(k,T,[P,h,f],a,u,i):R?F=new gne(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),a,u,i):F=new v2(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),n,s,a,u,i);let $=[N,E];a&&$.push(a),i&&$.push(i);let z=[{type:\"int32\",data:[h]},{type:\"int32\",data:[f]},{type:\"int32\",data:[p]}];u===\"leakyrelu\"&&(z.push({type:\"float32\",data:[o]}),F.uniforms+=\" alpha : f32,\");let W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function Ine(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return _v({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var Sne={kernelName:ra,backendName:\"webgpu\",kernelFunc:Ine},Dw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n\n      ${Ue()}\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}},Cne=class{constructor(e,t,n,s){this.variableNames=[\"A\",\"B\"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);\n         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];\n         let b = getBByOutputCoords(coords);`;return`\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${tc(this.op,!1)}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Ue()}\n\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${t}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `}},Nne=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`\n      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {\n        ${tc(this.op,this.isVec4)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n    `}},x2=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOperation(a : f32, b : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n      `}};function Fw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new Nne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Cne(e,t,n,a):new x2(e,t,n)}function es(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Tne={kernelName:Ba,backendName:\"webgpu\",kernelFunc:es};function uu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.tensorMap.get(a.dataId),o=es({inputs:{x:s},backend:n}),u=es({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var $ne={kernelName:jd,backendName:\"webgpu\",kernelFunc:uu},nc=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${Hr(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype===\"complex64\"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Dw(17,i.shape,o.shape),b=new Dw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,\"float32\"),f=u.runWebGPUProgram(b,y,\"float32\")}let m=uu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(i.dtype,o.dtype);if((i.dtype===\"string\"||o.dtype===\"string\"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype===\"string\"?S.fromUint8ToStringArray(p):p,f=i.dtype===\"string\"?S.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:_ne,ceilImpl:Ane,concatImpl:Ene,equalImpl:Rne,expImpl:Dne,expm1Impl:Fne,floorImpl:One,gatherNdImpl:Pne,gatherV2Impl:zne,greaterEqualImpl:Mne,greaterImpl:Lne,lessEqualImpl:Bne,lessImpl:Vne,logImpl:Wne,maxImpl:Une,maximumImpl:Gne,minimumImpl:Hne,multiplyImpl:qne,negImpl:jne,notEqualImpl:Kne,prodImpl:Xne,rangeImpl:Yne,rsqrtImpl:Qne,simpleAbsImpl:Zne,sliceImpl:Jne,stridedSliceImpl:ese,stringNGramsImpl:tse,subImpl:nse,tileImpl:sse,topKImpl:rse,transposeImpl:ase,uniqueImpl:zpe}=Zy,ise=Kt({opType:0,cpuKernelImpl:Zne}),ose={kernelName:ao,backendName:\"webgpu\",kernelFunc:ise},use=mn({opSnippet:1,cpuKernelImpl:_ne,supportsComplex:!0}),lse={kernelName:Ir,backendName:\"webgpu\",kernelFunc:use},cse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(\" + \");return`\n      ${Ue()}\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${e.join(`\n        `)}\n            setOutputAtIndex(flatIndex, ${t});\n          }\n        }\n      }\n    `}};function dse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return es({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>cn(o,u)),a=s.map(o=>o.shape),i=new cse(a);return n.runWebGPUProgram(i,s,r)}var pse={kernelName:ka,backendName:\"webgpu\",kernelFunc:dse},w2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"axis : i32, infinityValue : f32,\",this.size=!0;let s=[t];S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n===\"min\"?\"<\":\">\";let[r]=S.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape[${r}]`;return`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${e}\n\n      // In order to get a flattened index into the input tensor, we need to\n      // add back the index along the reduced dimension to |outputCoords|.\n      // This function outputs the offset to the first value along\n      // |axis| and the stride to get the next value of the input along |axis|.\n      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        var i = ${this.outputShape.length-1};\n\n        var stride = 1;\n        var inputStride = 1;\n        var offset = 0;\n\n        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {\n          let length = ${n(`${this.inputShape.length} - r`)};\n          if (${this.inputShape.length} - r == uniforms.axis) {\n            inputStride = stride;\n          } else {\n            offset = offset + ${t(\"outputCoords\",\"i\")} * stride;\n            i = i - 1;\n          }\n          stride = stride * length;\n        }\n\n        return vec2<i32>(offset, inputStride);\n      }\n\n      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{\n        return coordInfo[0] + coordInfo[1] * index;\n      }\n\n      ${Ue()}\n        let outputIndex = index / i32(workGroupSizeX);\n        let coordInfo = getInputCoordInfo(outputIndex);\n        let Length = ${n(\"uniforms.axis\")};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n\n        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = f32(x[getInputIndex(coordInfo, k)]);\n          if (!isnan(candidate) && candidate ${this.op} bestValue) {\n            bestValue = candidate;\n            bestIndex = k;\n          }\n        }\n        xBestValues[localId.x] = bestValue;\n        xBestIndices[localId.x] = bestIndex;\n        workgroupBarrier();\n\n        var reduceSize = min(u32(Length), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `}},hse=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){return`\n      let TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;\n      ${Sv()}\n      fn main(@builtin(local_invocation_id) localId : vec3<u32>,\n              @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] = A[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}},fse=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=mse(this.newDim);return`\n      ${Ue()}\n\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function mse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function wi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=ase(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new hse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new fse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var gse={kernelName:pi,backendName:\"webgpu\",kernelFunc:wi};function bse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],\"max\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var yse={kernelName:Ia,backendName:\"webgpu\",kernelFunc:bse};function vse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],\"min\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var xse={kernelName:il,backendName:\"webgpu\",kernelFunc:vse},k2=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,\",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${e}\n            }\n          }\n\n          setOutputAtIndex(index, ${t});\n        }\n      }\n    `}},I2=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>,\",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function wse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});let p,d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new I2(c):(p=new k2(c,\"avg\"),d.push({type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var kse={kernelName:Sa,backendName:\"webgpu\",kernelFunc:wse};function Ise(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return _v({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Sse={kernelName:Ca,backendName:\"webgpu\",kernelFunc:Ise},Cse=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Wt(this.rank),t=Nse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>\"sourceLoc = uniforms.start + coords;\"):n=this.outputShape.map((r,a)=>`sourceLoc.${Km[a]} = uniforms.start[${a}] + coords.${Km[a]};`),`\n      ${Ue()}\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${n.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},Km=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Nse(e){if(e===1)return\"sourceLoc\";if(e<=6)return Km.slice(0,e).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}function lu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.tensorMap.get(r.dataId),d=Jne(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new Cse(o,u),c=[{type:\"int32\",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var Tse={kernelName:Oo,backendName:\"webgpu\",kernelFunc:lu},$se=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=lu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},_se={kernelName:io,backendName:\"webgpu\",kernelFunc:$se},S2=mn({opSnippet:10,dtype:\"bool\",cpuKernelImpl:Kne}),Ase={kernelName:Io,backendName:\"webgpu\",kernelFunc:S2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.real},backend:n})}var Ese={kernelName:np,backendName:\"webgpu\",kernelFunc:sc};function Rse(e,t){let n=new nc(e.shape,22),s=t.runWebGPUProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Xm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return es({inputs:{x:r},backend:n});let i=$t(r.shape),o=Xm({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=uu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype===\"complex64\"){let i=sc({inputs:{input:r},backend:n}),o=Xm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=es({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return Rse(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=S2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Dse={kernelName:Na,backendName:\"webgpu\",kernelFunc:Xm},Fse=Kt({opType:1,cpuKernelImpl:Ane}),Ose={kernelName:Ta,backendName:\"webgpu\",kernelFunc:Fse},Pse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}},zse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function Mse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:\"float32\",data:[a]},{type:\"float32\",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new Pse(r.shape):o=new zse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Lse={kernelName:Sr,backendName:\"webgpu\",kernelFunc:Mse},Bse=class{constructor(e){this.uniforms=\"\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Ue()}\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${e.join(`\n        `)}\n          }\n        }\n      }\n    `}};function Zp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Vse={kernelName:Zd,backendName:\"webgpu\",kernelFunc:Zp};function Ym(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>Zp({inputs:{input:y},backend:n})),m=Ym(h,t,n),g=Ym(f,t,n),b=uu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=S.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=Ene(f,m,s,g),y=S.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Wse(e,t,n),o=a.map(h=>h.shape),u=new Bse(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:\"int32\",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:\"int32\",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Wse(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return es({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Ym(o,a,n)}var Use={kernelName:oo,backendName:\"webgpu\",kernelFunc:C2},Gse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.hasLeakyreluAlpha&&this.variableNames.push(\"leakyreluAlpha\"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);\n    let divBy4Remainder${e} = flatIndex${e} % 4;\n    let divBy4Index${e} = flatIndex${e} / 4;\n    let curData${e} = x[divBy4Index${e}];\n    if (divBy4Remainder${e} == 0) {\n      temp = curData${e};\n    } else {\n      // TODO: This could end up being a redundant load with another one in\n      // the same shader invocation. Perhaps there's an opportunity for\n      // optimization\n      let nextData${e} = x[divBy4Index${e} + 1];\n      if (divBy4Remainder${e} == 1) {\n        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);\n      } else if (divBy4Remainder${e} == 2) {\n        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);\n      } else if (divBy4Remainder${e} == 3) {\n        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);\n      }\n    }\n    `}getUserCode(){let e=y2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];\n        let outCol = r % uniforms.outShape[2];\n        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);\n        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];\n        let inChCoord = c % uniforms.xShape[3];\n        var coord = vec4<i32>(\n            batch,\n            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n            inChCoord);\n        var resData = vec4<f32>(0.0);\n        ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for\n          // the 'same' padding type.\n          if (coordsInBounds4D(coord, uniforms.xShape)) {\n            resData = x[getIndexFromCoords4D(coord, uniforms.xShape) / 4];\n          } else {\n            resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);\n          ${this.getSampleAWithRemainder(1)}\n          resData = temp;\n          if (WCol == (uniforms.filterDims[1] - 1)) {\n            coord = vec4<i32>(\n              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);\n              ${this.getSampleAWithRemainder(2)}\n            if (inChCoord == 0) {\n              resData = vec4<f32>(resData.xyz, temp.x);\n            } else if (inChCoord == 1) {\n              resData = vec4<f32>(resData.xy, temp.xy);\n            } else {\n              resData = vec4<f32>(resData.x, temp.xyz);\n            }\n          }\n          `}\n        return resData;`,r=this.fitA?`${s}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {\n          ${s}\n         }\n         return vec4<f32>(0.0);\n        `,a=this.fitB?\"return W[row * uniforms.dimBOuter / 4 + col];\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter / 4 + col];\n         }\n         return vec4<f32>(0.0);\n        `,i=\"\",o=\"\";if(this.activation){let c=Zs(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${c}\n        }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {\n          let b = getLeakyreluAlphaByOutputCoords(outCoord);\n          ${c}\n        }`,new Error(\"Leakyrelu is not supported.\");i=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          ${c}\n        }`}o=\"value = activation(value, outCoord);\"}let u=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n        ${i}\n        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          let r = row;\n          let c = col * 4;\n          var batch = i32(globalId.z);\n          ${r}\n        }\n\n        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          ${a}\n        }\n\n        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {\n          var batch = i32(globalId.z);\n          var value = valueInput;\n          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)\n          {\n            let outCoord = vec4<i32>(\n              batch,\n              row / uniforms.outShape[2],\n              row % uniforms.outShape[2],\n              col * 4);\n            ${u}\n            ${o}\n            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],\n              value);\n          }\n        }\n        ${e}\n      `}},Hse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=$v(this.elementsPerThread,this.workGroupSize),t=`\n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);\n    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];\n    let coord = vec4<i32>(\n        batch,\n        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n        col % uniforms.xShape[3]);\n    // The bounds checking is always needed since we use it to pad zero for the\n    // 'same' padding type.\n    if(coordsInBounds4D(coord, uniforms.xShape)) {\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${t}\n    }\n    return 0.0;\n    `,s=this.fitB?\"return W[row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter + col];\n\t }\n\t return 0.0;\n\t `,r=\"\",a=\"\";if(this.activation){let u=Zs(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${u}\n                }`:r=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n                    ${u}\n                  }\n                `,a=\"value = activation(value, outCoord);\"}let i=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n    ${r}\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${n}\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      ${s}\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      ${i}\n      ${a}\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n    ${e}\n  `}},qse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${r}\n             }`:e=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n                    ${r}\n                  }\n                `,t=\"value = activation(value, outCoord);\"}let n=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${e}\n      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if(coordsInBounds4D(coord, uniforms.xShape)) {\n          return getX(batch, row, col, chan);\n        }\n        return 0.0;\n      }\n\n      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n        let coord = vec4<i32>(row, col, xChannel, outChannel);\n        if(coordsInBounds4D(coord, uniforms.wShape)) {\n          return getW(row, col, xChannel, outChannel);\n        }\n        return 0.0;\n      }\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          ${n}\n          ${t}\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Dr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let outChannel = coords[3];\n\n        var acc = 0.0;\n\n        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {\n              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n              let v = readInp(batch, coordRow, coordCol, xChannel);\n              let f = readFilt(row, col, xChannel, outChannel);\n              acc = acc + v * f;\n            }\n          }\n        }\n\n        writeResult(batch, coords[1], coords[2], outChannel, acc);\n      }\n    `}},jse=class{constructor(e,t){this.variableNames=[\"A\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,\n      inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`\n    ${Ue()}\n\n      for(var i = 0; i<${this.workPerThread}; i = i + 1) {\n        let flatIndex = index * ${this.workPerThread} + i;\n\n        let rc = getCoordsFromIndex(flatIndex);\n\n        if(flatIndex < uniforms.size) {\n          let blockIndex = rc[0];\n          let pos = rc[1];\n\n          let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];\n          let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;\n          var value = 0.0;\n          if(d0 < uniforms.aShape[${e}] && d0 >= 0) {\n            let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -\n              uniforms.pad[0];\n            let d1 = offsetX + uniforms.dilation[0] * ((pos %\n              uniforms.itemsPerBlockRow) / uniforms.inChannels);\n            let ch = pos % uniforms.inChannels;\n            if(d1 < uniforms.aShape[${t}] && d1 >= 0) {\n              value = getA(d0, d1, ch);\n            }\n          }\n          setOutputAtIndex(flatIndex, value);\n        }\n      }\n    }\n  `}};function Kse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat===\"channelsLast\",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=_v({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Xse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y===\"channelsLast\",x=u*l*c,k=m*f,T=[k,x],N=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new jse(T,v),$=[{type:\"int32\",data:[h.left,h.top]},{type:\"int32\",data:[p,d]},{type:\"int32\",data:[g,b]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*u]},{type:\"int32\",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,T[0],T[1]]}});A.push(z),A.push(W);let q=[1,T[0],T[1]],K=new v2(q,[1,k,n.outChannels],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),N,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:\"int32\",data:[Y]},{type:\"int32\",data:[te]},{type:\"int32\",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a),o===\"leakyrelu\"&&($.push({type:\"float32\",data:[i]}),K.uniforms+=\" alpha : f32,\");let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function N2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type===\"SAME\"||n.padInfo.type===\"VALID\"))return Kse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")&&e.shape[0]===1)return Xse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool(\"WEBGPU_USE_NAIVE_CONV2D\"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type===\"VALID\")&&n.outChannels%4===0&&n.outChannels>=32,f=[n.padInfo.top,n.padInfo.left],m=[{type:\"int32\",data:[n.filterHeight,n.filterWidth]},{type:\"int32\",data:[...f]},{type:\"int32\",data:[n.strideHeight,n.strideWidth]},{type:\"int32\",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new qse(n,u,o,l);else{h?c=new Gse(n,u,o,l):c=new Hse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:\"int32\",data:[b]},{type:\"int32\",data:[y]},{type:\"int32\",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),o===\"leakyrelu\"&&(m.push({type:\"float32\",data:[i]}),c.uniforms+=\" alpha : f32,\"),s.runWebGPUProgram(c,g,e.dtype,m)}function Yse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return N2({x:r,filter:a,convInfo:d,backend:s})}var Qse={kernelName:$a,backendName:\"webgpu\",kernelFunc:Yse},Zse=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      \n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n      return 0.0;\n    }\n    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n      return 0.0;\n    }\n    let coord = vec4<i32>(\n        batch,\n        i32(xR),\n        i32(xC),\n        col % uniforms.outBackprop[3]);\n    return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      let coordX = uniforms.filterDims.x - 1 -\n          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let coordY = uniforms.filterDims.y - 1 -\n          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n          coordX >= 0 && coordY >= 0) {\n        let coord = vec4<i32>(coordX, coordY, col,\n            row % uniforms.outBackprop[3]);\n        return W[getIndexFromCoords4D(coord, uniforms.wShape)];\n      }\n      return 0.0;\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n\n    ${$v(this.elementsPerThread,this.workGroupSize)}\n  `}},Jse=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`\n    ${Ue()} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${n}];\n\n        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function ere(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:\"int32\",data:[d.filterHeight,d.filterWidth]},{type:\"int32\",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\"))f=new Jse(d);else{f=new Zse(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:\"uint32\",data:[m]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[b]})}return n.runWebGPUProgram(f,[r,a],\"float32\",h)}var tre={kernelName:_a,backendName:\"webgpu\",kernelFunc:ere},nre=Kt({opType:2}),sre={kernelName:Aa,backendName:\"webgpu\",kernelFunc:nre},rre=Kt({opType:3}),are={kernelName:Ea,backendName:\"webgpu\",kernelFunc:rre},ire=class{constructor(e,t,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${n});\n        let width_ratio = f32(${a});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${s};\n        let width_scale = ${i};\n        let in_y = ${r};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${o};\n        if( in_x < 0.0 || in_x > ${t} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `}},ore=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new ire(r.shape[3],a.shape,o,u),p=[{type:\"float32\",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],\"float32\",p)},ure={kernelName:lo,backendName:\"webgpu\",kernelFunc:ore},lre=class{constructor(e,t){this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function cre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=[{type:\"int32\",data:[a]}],g=new lre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var dre={kernelName:co,backendName:\"webgpu\",kernelFunc:cre},T2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,\",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n            ${r}\n          }\n        `,t=\"dotProd[i] = activation(dotProd[i], coords);\"}let n=this.addBias?\"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      ${Sv()}\n      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = 0;\n        let r = i32(globalId.x);\n        let c = i32(globalId.y) * 4;\n        let d2 = i32(globalId.z) * 4;\n        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;\n        let d1 = d2;\n        let q = 0;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n\n        var wVals : array<vec4<f32>, 9>;\n        wVals[0] = getW(0, 0, d1, q);\n        wVals[1] = getW(0, 1, d1, q);\n        wVals[2] = getW(0, 2, d1, q);\n        wVals[3] = getW(1, 0, d1, q);\n        wVals[4] = getW(1, 1, d1, q);\n        wVals[5] = getW(1, 2, d1, q);\n        wVals[6] = getW(2, 0, d1, q);\n        wVals[7] = getW(2, 1, d1, q);\n        wVals[8] = getW(2, 2, d1, q);\n\n        var xVals : array<array<vec4<f32>, 6>, 3>;\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          let xR = xRCorner + wR * uniforms.dilation[0];\n          for (var wC = 0; wC < 6; wC = wC + 1) {\n            let xC = xCCorner + wC * uniforms.dilation[1];\n            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {\n              xVals[wR][wC] = vec4<f32>(0.0);\n            } else {\n              xVals[wR][wC] = getX(batch, xR, xC, d1);\n            }\n          }\n        }\n\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          for (var wC = 0; wC < 3; wC = wC + 1) {\n            let indexW = wR * 3 + wC;\n            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];\n            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];\n            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];\n            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d2);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            ${n}\n            ${t}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n          }\n        }\n      }\n    `}},$2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,\n      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n            ${r}\n          }\n        `,t=\"dotProd = activation(dotProd, coords);\"}let n=this.addBias?\"dotProd = dotProd + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,\n          value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Dr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n        let d2 = coords[3];\n        let d1 = d2 / uniforms.channelMul;\n        let q = d2 - d1 * uniforms.channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            // Here using a constant value |this.convInfo.filterHeight| instead\n            // of uniform value is in order to loop unrolling.\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          }\n\n        ${n}\n        ${t}\n        writeResult(batch, coords[1], coords[2], d2, dotProd);\n      }\n    `}};function pre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:\"int32\",data:[p.padInfo.top,p.padInfo.left]},{type:\"int32\",data:[p.strideHeight,p.strideWidth]},{type:\"int32\",data:[p.dilationHeight,p.dilationWidth]},{type:\"int32\",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new T2(p):(h=new $2(p),d.push({type:\"int32\",data:[p.filterHeight]},{type:\"int32\",data:[p.filterWidth]},{type:\"int32\",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var hre={kernelName:Ra,backendName:\"webgpu\",kernelFunc:pre},_2=mn({opSnippet:0,cpuKernelImpl:qne,supportsComplex:!0}),fre={kernelName:Ya,backendName:\"webgpu\",kernelFunc:_2},mre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"reduceSize : i32,\",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\";this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n           {  bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"&&(e=\" bestValue = bestValue * candidate; \",t=\"1.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${`\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${Ue()}\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${e}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${n}\n        }\n       }\n     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=S.getAxesPermutation(u,a),c=e;l!=null&&(c=wi({inputs:{x:e},attrs:{perm:l},backend:r}),u=S.getInnerMostAxes(u.length,a),i.push(c)),S.assertAxesAreInnerMostDims(s,u,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=S.expandShapeToKeepDim(p,o));let f;if((s===\"max\"||s===\"prod\")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case\"max\":let g=Une(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case\"prod\":let{outVals:b,outShape:y,outDtype:v}=Xne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s===\"mean\"?\"float32\":cp(e.dtype),x=[{type:\"int32\",data:[m]}],k=new mre(y,s),T=r.runWebGPUProgram(k,[c],v,x);i.push(T),f=Me({inputs:{x:T},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Av(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"sum\",n)}var gre={kernelName:oi,backendName:\"webgpu\",kernelFunc:Av};function bre(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=wi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=_2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Av({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var yre={kernelName:Qd,backendName:\"webgpu\",kernelFunc:bre},vre=Kt({opType:4}),xre={kernelName:Fa,backendName:\"webgpu\",kernelFunc:vre},wre=mn({opSnippet:4,dtype:\"bool\",cpuKernelImpl:Rne}),kre={kernelName:po,backendName:\"webgpu\",kernelFunc:wre},A2=Kt({opType:5,cpuKernelImpl:Dne,dtype:\"float32\"}),Ire={kernelName:Oa,backendName:\"webgpu\",kernelFunc:A2};function Qm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var Sre={kernelName:ho,backendName:\"webgpu\",kernelFunc:Qm},Cre=Kt({opType:6,cpuKernelImpl:Fne}),Nre={kernelName:fo,backendName:\"webgpu\",kernelFunc:Cre},Tre=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Ue()}\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function cu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new Tre(s),o=[{type:\"float32\",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var $re={kernelName:fl,backendName:\"webgpu\",kernelFunc:cu},_re=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}},Are={kernelName:mo,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new _re(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Ere=Kt({opType:7,cpuKernelImpl:One}),Rre={kernelName:Pa,backendName:\"webgpu\",kernelFunc:Ere},Dre=mn({opSnippet:12,dtype:\"int32\"}),Fre={kernelName:za,backendName:\"webgpu\",kernelFunc:Dre},Ore=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},E2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=lne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:\"main\"},label:t.constructor.name})};function R2(e,t,n,s=\"\",r=\"\"){return e.shaderKey+\"_\"+(e.workGroupSize?e.workGroupSize.join(\",\"):\"\")+t.map(i=>i.length).join(\",\")+n.join(\",\")+e.variableNames.join(\",\")+s+r}function Ow(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,\"int32\"),c=n.getFromPixelsProgram(a?\"import\":\"copyExternal\");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?\"import\":\"copyExternal\"],h=R2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>E2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var Pre={kernelName:fd,backendName:\"webgpu\",kernelFunc:zre},Bi;function zre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let i=typeof HTMLVideoElement!=\"undefined\"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!=\"undefined\"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!=\"undefined\"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!=\"undefined\"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool(\"WEBGPU_USE_IMPORT\")&&i)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Bi==null&&(Bi=document.createElement(\"canvas\").getContext(\"2d\")),Bi.canvas.width=c,Bi.canvas.height=p,Bi.drawImage(r,0,0,c,p),r=Bi.canvas),l||u||i||o)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,\"int32\"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Mre=class{constructor(e,t,n,s,r){this.uniforms=\"varianceEpsilon : f32,\",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\")),r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\")),this.offsetShape=s,this.scaleShape=r,this.shaderKey=\"batchNorm\"}getUserCode(){let e=\"0.0\";this.offsetShape!=null&&(e=\"getOffsetByOutputIndex(index)\");let t=\"1.0\";return this.scaleShape!=null&&(t=\"getScaleByOutputIndex(index)\"),`\n      ${Ue()}\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${e};\n          let scaleValue = ${t};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `}},Lre={kernelName:Ma,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Mre(s.shape,i.shape,o.shape,p,d),f=[{type:\"float32\",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Bre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return N2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Vre={kernelName:aa,backendName:\"webgpu\",kernelFunc:Bre};function Wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=S.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=i!=null,y=o!=null;b&&g.push(i),y&&g.push(o);let v=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new T2(m,b,d,y):(x=new $2(m,b,d,y),v.push({type:\"int32\",data:[m.filterHeight]},{type:\"int32\",data:[m.filterWidth]},{type:\"int32\",data:[m.outChannels/m.inChannels]})),d===\"leakyrelu\"&&(v.push({type:\"float32\",data:[h]}),x.uniforms+=\" alpha : f32,\"),n.runWebGPUProgram(x,g,\"float32\",v)}var Ure={kernelName:ia,backendName:\"webgpu\",kernelFunc:Wre},Gre=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n        ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${e};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `}};function Hre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Pne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Gre(i,[l,c]),m=[{type:\"int32\",data:[i]},{type:\"int32\",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var qre={kernelName:bo,backendName:\"webgpu\",kernelFunc:Hre},jre=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Kre(this.aShape);return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${e}));\n        }\n      }\n    `}};function Kre(e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let s=0;s<e.length;s++)s===2?n.push(\"indexZ\"):n.push(`${t[s]}`);return n.join()}function D2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),T=n.tensorMap.get(d.dataId).values,N=De(d.shape,d.dtype,T),E=zne(N,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new jre(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Xre={kernelName:go,backendName:\"webgpu\",kernelFunc:D2},Yre=mn({opSnippet:5,cpuKernelImpl:Lne,dtype:\"bool\"}),Qre={kernelName:yo,backendName:\"webgpu\",kernelFunc:Yre},Zre=mn({opSnippet:6,dtype:\"bool\",cpuKernelImpl:Mne}),Jre={kernelName:La,backendName:\"webgpu\",kernelFunc:Zre};function eae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:\"float32\",data:[a]}],o=new nc(r.shape,14);return o.uniforms=\"alpha : f32,\",n.runWebGPUProgram(o,[r],\"float32\",i)}var tae={kernelName:Va,backendName:\"webgpu\",kernelFunc:eae},nae=mn({opSnippet:7,dtype:\"bool\",cpuKernelImpl:Vne}),sae={kernelName:vo,backendName:\"webgpu\",kernelFunc:nae},rae=mn({opSnippet:8,dtype:\"bool\",cpuKernelImpl:Bne}),aae={kernelName:xo,backendName:\"webgpu\",kernelFunc:rae},iae=Kt({opType:9,cpuKernelImpl:Wne}),oae={kernelName:Wa,backendName:\"webgpu\",kernelFunc:iae},uae=mn({opSnippet:9,dtype:\"bool\"}),lae={kernelName:wo,backendName:\"webgpu\",kernelFunc:uae},cae=Kt({opType:10}),dae={kernelName:vl,backendName:\"webgpu\",kernelFunc:cae};function F2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,\"max\",n)}var pae={kernelName:Ua,backendName:\"webgpu\",kernelFunc:F2},hae=mn({opSnippet:15,cpuKernelImpl:Gne}),fae={kernelName:Ga,backendName:\"webgpu\",kernelFunc:hae};function mae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});p=new I2(c),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]})}else p=new k2(c,\"max\"),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var gae={kernelName:Ha,backendName:\"webgpu\",kernelFunc:mae};function bae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,\"mean\",n)}var yae={kernelName:qa,backendName:\"webgpu\",kernelFunc:bae};function vae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"min\",n)}var xae={kernelName:ja,backendName:\"webgpu\",kernelFunc:vae},wae=mn({opSnippet:16,cpuKernelImpl:Hne}),kae={kernelName:Ka,backendName:\"webgpu\",kernelFunc:wae},Iae=class{constructor(e,t,n){this.uniforms=\"\",this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(\",\"),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:\"\"}`).join(\",\"),s=e===1?\"start\":\"start[i]\",r=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=Wt(e),o=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${i}(${t});\n          let end = ${i}(${n});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${e}; i = i + 1) {\n            if (${a} < ${s}) {\n              ${a} = ${s} * 2 - ${a} - ${this.offset};\n            } else if(${a} >= ${r}) {\n              ${a} = (${r} - 1) * 2 - ${a} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${o}));\n        }\n      }\n    `}},Sae={kernelName:Xa,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:\"int32\",data:[c[0],c[1]]})),u=new Iae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function Cae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=jne(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var Nae={kernelName:ko,backendName:\"webgpu\",kernelFunc:Cae};function Tae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var $ae={kernelName:So,backendName:\"webgpu\",kernelFunc:Tae};function _ae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),n.makeTensorInfo([b.length],\"float32\",new Float32Array(b))]}var Aae={kernelName:Co,backendName:\"webgpu\",kernelFunc:_ae};function Ld(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=Ld({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype===\"string\"?\"\":0},backend:n})}var Eae={kernelName:Go,backendName:\"webgpu\",kernelFunc:Ld};function O2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=O2({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Rae={kernelName:No,backendName:\"webgpu\",kernelFunc:O2};function Dae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let o=[],u=t.map(c=>{let p=Qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=C2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Fae={kernelName:$o,backendName:\"webgpu\",kernelFunc:Dae},Oae=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(\",\"),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:\"\"}`).join(\",\"),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?\"any(outC < start)\":\"outC < start\",o=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${r};\n          let end = ${a};\n          let outC = getCoordsFromIndex(index);\n\n          if (${i} || ${o}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${u}));\n          }\n        }\n      }\n    `}},P2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return es({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return cu({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:\"float32\",data:[i]}];a.map(l=>o.push({type:\"int32\",data:[l[0],l[1]]}));let u=new Oae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},Pae={kernelName:Qa,backendName:\"webgpu\",kernelFunc:P2},zae=mn({opSnippet:13}),Mae={kernelName:Za,backendName:\"webgpu\",kernelFunc:zae};function Lae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new x2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],\"float32\")}var Bae={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Lae};function Vae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"prod\",n)}var Wae={kernelName:_o,backendName:\"webgpu\",kernelFunc:Vae},Uae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Yne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Gae={kernelName:kl,backendName:\"webgpu\",kernelFunc:Uae},z2=mn({opSnippet:3}),Hae={kernelName:Da,backendName:\"webgpu\",kernelFunc:z2},qae=Kt({opType:12}),jae={kernelName:ei,backendName:\"webgpu\",kernelFunc:qae},Kae=Kt({opType:13}),Xae={kernelName:ni,backendName:\"webgpu\",kernelFunc:Kae},Yae=class{constructor(e,t,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Qae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[o?.5:0]}],f=new Yae(r.shape,u,l);return n.runWebGPUProgram(f,[r],\"float32\",h)}var Zae={kernelName:ti,backendName:\"webgpu\",kernelFunc:Qae},Jae=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, roundBase : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))\":e=\"vec2<f32>(rc) * effectiveInputOverOutputRatioRC\",`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${e};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function eie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[a?.5:0]}],f=new Jae(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var tie={kernelName:Sl,backendName:\"webgpu\",kernelFunc:eie},nie=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n          cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3<f32>,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n        ${Ue()}\n\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}},sie={kernelName:Ho,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new nie(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:\"float32\",data:[l]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(r)]},{type:\"float32\",data:[Math.cos(r)]}];return typeof a==\"number\"?p.push({type:\"float32\",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:\"float32\",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},rie=Kt({opType:15,cpuKernelImpl:Qne}),aie={kernelName:si,backendName:\"webgpu\",kernelFunc:rie},iie=class{constructor(e,t,n,s,r,a,i){this.variableNames=[\"updates\",\"indices\"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",s=\"\",r=\"\",a=\"\";this.updatesRank===1?(s=\"coords[0]\",r=\"flattenedIndex\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.updatesRank===2&&(s=\"coords[0], coords[1]\",r=\"vec2<i32>(flattenedIndex, coords[1])\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        let d0 = index / uniforms.updatesShape[1];\n        let d1 = index - d0 * uniforms.updatesShape[1];\n        return vec2<i32>(d0, d1);\n      }\n      `);let i=`getUpdates(${s})`,o=this.type===\"int32\"?\"atomicAdd(&(result[flatIndex]), i32(updateValue));\":`\n     var assumed = atomicLoad(&(result[flatIndex]));\n     var success = 0;\n     for (; success == 0;) {\n       let new = bitcast<f32>(assumed) + updateValue;\n       let newI32 = bitcast<i32>(new);\n       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);\n       assumed = resValue[0];\n       success = resValue[1];\n     }\n     `;return`\n    ${a}\n\n      ${Ue()}\n\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${n};\n          }\n          let updateValue = ${i};\n          let flatIndex = getOutputIndexFromCoords(${r});\n\n         ${o}\n        }\n      }`}};function oie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=cu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:\"int32\",data:[o]},{type:\"int32\",data:c},{type:\"int32\",data:[b]}],v=new iie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var uie={kernelName:Do,backendName:\"webgpu\",kernelFunc:oie},lie=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let s=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function cie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new lie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var die={kernelName:Fo,backendName:\"webgpu\",kernelFunc:cie},pie=Kt({opType:18}),hie={kernelName:ai,backendName:\"webgpu\",kernelFunc:pie},fie=Kt({opType:16}),mie={kernelName:ri,backendName:\"webgpu\",kernelFunc:fie},gie=Kt({opType:17}),bie={kernelName:Po,backendName:\"webgpu\",kernelFunc:gie},M2=mn({opSnippet:2,cpuKernelImpl:nse,supportsComplex:!0}),yie={kernelName:ci,backendName:\"webgpu\",kernelFunc:M2};function vie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=F2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=M2({inputs:{a:r,b:l},backend:n}),p=A2({inputs:{x:c},backend:n}),d=Av({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=z2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var xie={kernelName:ui,backendName:\"webgpu\",kernelFunc:vie},wie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=P2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},kie={kernelName:zo,backendName:\"webgpu\",kernelFunc:wie},Iie=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\"),this.indicesSnippet=`getIndices(${l})`;let c=\"\";s===1?c=\"i\":s===2&&(c=\"i, coords[1]\"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?\"uniforms.strides[j]\":\"uniforms.strides\"}getUserCode(){return`\n      ${Ue()}\n\n        let globalIndex = index * ${this.workPerThread};\n        if (globalIndex < uniforms.size) {\n          var sum = vec4<f32>(0.0);\n          var found = vec4<bool>(false);\n          for (var i = 0; i < uniforms.updateSize; i = i + 1) {\n            var flattenedIndex = 0;\n            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n              let indexInside = i32(round(${this.indicesSnippet}));\n              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};\n            }\n            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n              let curIndex = globalIndex + innerIndex;\n              let coords = getCoordsFromIndex(curIndex);\n              if (flattenedIndex == coords[0]) {\n                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};\n                found[innerIndex] = true;\n              }\n            }\n          }\n          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n            let curIndex = globalIndex + innerIndex;\n            if (curIndex < uniforms.size)\n            {\n              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));\n            }\n          }\n        }\n      }`}};function Sie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=[{type:\"int32\",data:[l]},{type:\"int32\",data:[u]},{type:\"int32\",data:c}],f=new Iie(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var Cie={kernelName:ip,backendName:\"webgpu\",kernelFunc:Sie};function Nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=lu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var Tie={kernelName:Mo,backendName:\"webgpu\",kernelFunc:Nie},$ie=Kt({opType:19}),_ie={kernelName:ii,backendName:\"webgpu\",kernelFunc:$ie},Aie={kernelName:_l,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},Eie=mn({opSnippet:11}),Rie={kernelName:li,backendName:\"webgpu\",kernelFunc:Eie},Die=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Ue()}\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),N=lu({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=Me({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeData(N.dataId)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),E=De(r.shape,r.dtype,N),A=ese(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let N=new Die(h),E=[{type:\"int32\",data:y},{type:\"int32\",data:x}],A=n.runWebGPUProgram(N,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Oie={kernelName:Lo,backendName:\"webgpu\",kernelFunc:Fie};function Pie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=tse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var zie={kernelName:op,backendName:\"webgpu\",kernelFunc:Pie},Mie=Kt({opType:21}),Lie={kernelName:di,backendName:\"webgpu\",kernelFunc:Mie},Bie=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=Vie(this.rank,\"uniforms.\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function Vie(e,t=\"\"){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Wie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=sse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Bie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var Uie={kernelName:Cr,backendName:\"webgpu\",kernelFunc:Wie},Gie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n        dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}},Hie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}};function Vi(e,t){t!==null&&e.disposeData(t.dataId)}function Pw(e){let t=1;for(;t<e;)t*=2;return t}function qie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[T,N]=rse(k,o,r.dtype,a,i);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(N.shape,N.dtype,N.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,\"int32\",[])];if(u===1)return[r,cu({attrs:{shape:o,dtype:\"int32\",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Pw(a),h=Pw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,T,N)=>{let E=m(),A=new Gie(N),R=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[T]}],F=f;f=n.runWebGPUProgram(A,E,\"int32\",R),Vi(n,F)};for(let k=1;k<d;k*=2){let T=k*2;for(let N=k;N>=1;N/=2)g(T,N,[c,h])}for(let k=h;k>d;k/=2){let T=m(),N=new Hie([c,k/2]),A=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[d]}],P=f;f=n.runWebGPUProgram(N,T,\"int32\",A),Vi(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=lu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Vi(n,b);let y=D2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Vi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Vi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Vi(n,x),[y,f]}var jie={kernelName:Vo,backendName:\"webgpu\",kernelFunc:qie},Kie=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${Ue()}\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `}};function Xie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Kie(g),y=i===\"nearest\"?1:2,v;switch(o){case\"constant\":v=1;break;case\"reflect\":v=2;break;case\"wrap\":v=3;break;case\"nearest\":v=4;break;default:v=1;break}let x=[{type:\"int32\",data:[y]},{type:\"int32\",data:[v]},{type:\"float32\",data:[u]}];return n.runWebGPUProgram(b,[r,a],\"float32\",x)}var Yie={kernelName:Wo,backendName:\"webgpu\",kernelFunc:Xie};function Qie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=lu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Zie={kernelName:Uo,backendName:\"webgpu\",kernelFunc:Qie},Jie=[Sne,ose,lse,pse,yse,xse,kse,Sse,_se,Dse,Ose,Lse,$ne,Use,Qse,tre,sre,are,ure,dre,hre,yre,xre,kre,Ire,Sre,Nre,$re,Are,Pre,Rre,Fre,Lre,Vre,Ure,qre,Xre,Qre,Jre,Tne,Vse,tae,sae,aae,oae,lae,dae,pae,fae,gae,yae,xae,kae,Sae,fre,Nae,$ae,Aae,Ase,Rae,Fae,Pae,Mae,Bae,Wae,Gae,Ese,Hae,jae,Xae,kne,Zae,tie,sie,aie,uie,die,hie,mie,bie,Tse,Oie,zie,xie,kie,Cie,Tie,_ie,Aie,Rie,yie,gre,Lie,Uie,jie,Yie,gse,Zie,Eae];for(let e of Jie)Al(e);var eoe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=zw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=zw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function zw(e,t){return`${e}_${t}`}var L2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey=\"fromPixels\",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.useImport?\"texture_external\":\"texture_2d<f32>\"};\n\n      ${Ue()}\n        let flatIndexBase = index * uniforms.numChannels;\n        for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n          let flatIndex = flatIndexBase + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndexBase);\n            let values = ${e};\n            result[flatIndex] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:\"rgba8unorm\",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},toe=class extends L2{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},noe=X().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Mw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[a,a,a]):[a,a,1]},B2=class extends tl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Tv())throw new Error(\"WebGPU is not supported on this device\");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new eoe(this.device),this.tensorMap=new Ud(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return B2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*jm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let a=w.sizeFromShape(n)*jm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case\"copyExternal\":return this.fromPixelProgram||(this.fromPixelProgram=new L2),this.fromPixelProgram;case\"import\":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new toe),this.fromPixelImportProgram;default:w.assert(!1,()=>\"Unsupported fromPixels shape\");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(w.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype===\"complex64\"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=S.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=b2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type===\"int32\"?new Int32Array(s,o,a.data.length).set(a.data):a.type===\"uint32\"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"read-only-storage\"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"uniform\"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let N=this.tensorMap.get(r.dataId);return N.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Mw(this.device,e);let a=[{type:\"float32\",data:[NaN]}],i=t.concat(r).map(N=>N.shape),o=\"int32\";i.map(N=>{a.push({type:o,data:N})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let N=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((N,E)=>{if(N.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[E]}}),p=c.map(N=>N.dtype).concat(r.dtype),d=c.map(N=>S.getBroadcastDims(N.shape,r.shape)),h=c.map(N=>w.arraysEqual(N.shape,r.shape)).join(\"_\"),f=d.map(N=>N.join(\"_\")).join(\";\"),m=R2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>E2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Ore(this.device,g,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let T={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(T),X().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Mw(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=noe){return X().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Ev=B2;Ev.nextDataId=0;var soe={};Ae(soe,{WebGPUBackend:()=>Ev,webgpu_util:()=>m2});Tv()&&pp(\"webgpu\",async()=>{X().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let e={powerPreference:X().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has(\"timestamp-query\");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=[\"timestamp-query\"]:console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.\");let a=await t.requestDevice(s);return new Ev(a,r)},3);var It=(e=>(e[e.float32=0]=\"float32\",e[e.int32=1]=\"int32\",e[e.bool=2]=\"bool\",e[e.string=3]=\"string\",e[e.complex64=4]=\"complex64\",e))(It||{}),Jp=(e=>(e[e.linear=0]=\"linear\",e[e.relu=1]=\"relu\",e[e.relu6=2]=\"relu6\",e[e.prelu=3]=\"prelu\",e[e.leakyrelu=4]=\"leakyrelu\",e[e.sigmoid=5]=\"sigmoid\",e[e.elu=6]=\"elu\",e))(Jp||{}),V2;function roe(e){V2=e.wasm.cwrap(ra,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function aoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Jp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=qo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return V2(d,T,r.shape.length,h,N,a.shape.length,u,l,g,f,m,p||0,k),x}var ioe={kernelName:ra,backendName:\"wasm\",setupFunc:roe,kernelFunc:aoe};function Xt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,[\"number\",\"number\",\"number\"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,It[o.dtype],c),l}return{kernelName:e,backendName:\"wasm\",setupFunc:s,kernelFunc:r}}var ooe=Xt(ao);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=S.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,It[l.dtype],y))(),m}return{kernelName:e,backendName:\"wasm\",setupFunc:r,kernelFunc:a}}var uoe=!0,loe=gn(Ir,uoe),W2;function coe(e){W2=e.wasm.cwrap(ka,null,[\"array\",\"number\",\"number\",\"number\"])}function doe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return W2(a,r.length,It[s.dtype],i),s}var poe={kernelName:ka,backendName:\"wasm\",setupFunc:coe,kernelFunc:doe};function eh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var hoe={kernelName:Ba,backendName:\"wasm\",kernelFunc:eh},U2;function foe(e){U2=e.wasm.cwrap(pi,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function wr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=goe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=moe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=eh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return U2(c,h,u.shape.length,It[u.dtype],p,d,a.length),l}function moe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function goe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var boe={kernelName:pi,backendName:\"wasm\",kernelFunc:wr,setupFunc:foe};function Fr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=S.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=S.getInnerMostAxes(i.length,r),u=wr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var G2;function yoe(e){G2=e.wasm.cwrap(rl,null,[\"number, number, number\"])}function voe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"all\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;G2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var xoe={kernelName:rl,backendName:\"wasm\",setupFunc:yoe,kernelFunc:voe},H2;function woe(e){H2=e.wasm.cwrap(al,null,[\"number, number, number\"])}function koe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"any\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;H2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ioe={kernelName:al,backendName:\"wasm\",setupFunc:woe,kernelFunc:koe},q2;function Soe(e){q2=e.wasm.cwrap(Ia,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Coe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Fr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,\"int32\"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return q2(o,It[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var Noe={kernelName:Ia,backendName:\"wasm\",kernelFunc:Coe,setupFunc:Soe},j2;function Toe(e){j2=e.wasm.cwrap(Sa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function $oe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${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}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,\"float32\"),k=s.dataIdMap.get(x.dataId).id;return j2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var _oe={kernelName:Sa,backendName:\"wasm\",setupFunc:Toe,kernelFunc:$oe};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Aoe={kernelName:Ao,backendName:\"wasm\",kernelFunc:yn},K2;function Eoe(e){K2=e.wasm.cwrap(Ca,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\"])}function Roe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"BatchMatMul for non non-float32 tensors not yet supported.\");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=qo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],T=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),N=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(T.dataId).id,A=n.dataIdMap.get(N.dataId).id,P=i?T.shape[2]:T.shape[1],R=o?N.shape[1]:N.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],T.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(T.shape).buffer),q=new Uint8Array(new Int32Array(N.shape).buffer);return K2(E,W,T.shape.length,A,q,N.shape.length,i,o,z),n.disposeData(T.dataId),n.disposeData(N.dataId),$.shape=v,$}var Doe={kernelName:Ca,backendName:\"wasm\",setupFunc:Eoe,kernelFunc:Roe};function va(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype===\"string\"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype===\"string\"){let f=Fd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Foe(u,c[0],d,a,i);else if(h===3)Ooe(u,c[0],c[1],d,a,i);else if(h===4)Poe(u,c[0],c[1],c[2],d,a,i);else{let f=Fd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function Foe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Ooe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function Poe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var zoe={kernelName:Oo,backendName:\"wasm\",kernelFunc:va};function Moe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=va({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Loe={kernelName:io,backendName:\"wasm\",kernelFunc:Moe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Boe={kernelName:Na,backendName:\"wasm\",kernelFunc:ac},Voe=Xt(Ta),X2;function Woe(e){X2=e.wasm.cwrap(Sr,null,[\"number\",\"number\",\"number\",\"number\"])}function Uoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return X2(o,a,i,l),u}var Goe={kernelName:Sr,backendName:\"wasm\",setupFunc:Woe,kernelFunc:Uoe};function Y2(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=S.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return eh({inputs:{x:a[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=a.map(h=>h.shape);if(S.assertParamsConsistent(o,s),a[0].dtype===\"string\"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return yn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=S.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=tv(f,r,t[0].dtype,m),b=S.computeOutShape(a.map(v=>v.shape),s);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=S.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var Hoe={kernelName:oo,backendName:\"wasm\",kernelFunc:Y2},Q2;function qoe(e){Q2=e.wasm.cwrap($a,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function joe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=S.convertConv2DDataFormat(d),f=S.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,T=f.dilationWidth,N=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Yoe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=S.convertConv2DDataFormat(u),h=S.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:T,strideHeight:N,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat===\"channelsLast\",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,\"float32\"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return Z2(me,ke,f,m,g,y,v,b,k,T,x,N,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var Qoe={kernelName:_a,backendName:\"wasm\",setupFunc:Xoe,kernelFunc:Yoe},Zoe=Xt(Aa),Joe=Xt(Ea),J2=(e=>(e[e.bilinear=0]=\"bilinear\",e[e.nearest=1]=\"nearest\",e))(J2||{}),eN;function eue(e){eN=e.wasm.cwrap(lo,null,[\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function tue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!==\"float32\"&&(m=ac({backend:t,inputs:{x:o},attrs:{dtype:\"float32\"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,\"float32\"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return eN(g,b,y,c,k,p,d,J2[r],a,x),m!=null&&t.disposeData(m.dataId),v}var nue={kernelName:lo,backendName:\"wasm\",setupFunc:eue,kernelFunc:tue},tN;function sue(e){tN=e.wasm.cwrap(pl,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function rue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumprod\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;tN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var aue={kernelName:pl,backendName:\"wasm\",setupFunc:sue,kernelFunc:rue},nN;function iue(e){nN=e.wasm.cwrap(uo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function oue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;nN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var uue={kernelName:uo,backendName:\"wasm\",setupFunc:iue,kernelFunc:oue},sN;function lue(e){sN=e.wasm.cwrap(co,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function cue(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,\"float32\"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return sN(b,a,i===\"NHWC\"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var due={kernelName:co,backendName:\"wasm\",setupFunc:lue,kernelFunc:cue},rN;function pue(e){rN=e.wasm.cwrap(Ra,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function hue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=S.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,T=h.strideHeight,N=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type===\"SAME\"?1:0;if(h.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(h.outShape,\"float32\"),F=s.dataIdMap.get(R.dataId).id;return rN(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,T,N,E,A,F),R}var fue={kernelName:Ra,backendName:\"wasm\",setupFunc:pue,kernelFunc:hue},mue=Xt(Fa),gue=!1,bue=gn(po,gue,\"bool\"),yue=Xt(Oa,\"float32\");function Zm(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:o}})}var vue={kernelName:ho,backendName:\"wasm\",kernelFunc:Zm};function aN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var xue={kernelName:fl,backendName:\"wasm\",kernelFunc:aN},iN;function wue(e){iN=e.wasm.cwrap(mo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function kue(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return iN(a,o,u,l,c,i),r}var Iue={kernelName:mo,backendName:\"wasm\",kernelFunc:kue,setupFunc:wue},Sue=Xt(Pa),Cue=!1,Nue=gn(za,Cue),oN;function Tue(e){oN=e.wasm.cwrap(Ma,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function $ue(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return oN(c,p,d,h,f,r,g),m}var _ue={kernelName:Ma,backendName:\"wasm\",setupFunc:Tue,kernelFunc:$ue},uN;function Aue(e){uN=e.wasm.cwrap(aa,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 Eue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return uN(b,K,Y,Z,y,k,T,x,N,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var Rue={kernelName:aa,backendName:\"wasm\",setupFunc:Aue,kernelFunc:Eue},lN;function Due(e){lN=e.wasm.cwrap(ia,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 Fue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return lN(b,K,Y,Z,y,k,T,x,N,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var Oue={kernelName:ia,backendName:\"wasm\",setupFunc:Due,kernelFunc:Fue},cN;function Pue(e){cN=e.wasm.cwrap(bo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function zue(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,i,o,u]=$k.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(i===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return cN(h,It[s.dtype],m,i,p,o,g,b),l}var Mue={kernelName:bo,backendName:\"wasm\",setupFunc:Pue,kernelFunc:zue},dN;function Lue(e){dN=e.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Bue(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let A=0;A<l.length;++A){let P=l[A];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,T=t.dataIdMap.get(g.dataId).id,N=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),E=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return dN(v,It[r.dtype],N,b,k,p.batchSize,E,T),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Vue={kernelName:go,backendName:\"wasm\",setupFunc:Lue,kernelFunc:Bue},Wue=!1,Uue=gn(yo,Wue,\"bool\"),Gue=!1,Hue=gn(La,Gue,\"bool\"),pN;function que(e){pN=e.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\"])}function jue(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,\"float32\");if(w.sizeFromShape(t.shape)!==0){let i=s.dataIdMap.get(a.dataId).id;pN(r,It[t.dtype],n,i)}return a}var Kue={kernelName:Va,backendName:\"wasm\",setupFunc:que,kernelFunc:jue},Xue=!1,Yue=gn(vo,Xue,\"bool\"),Que=!1,Zue=gn(xo,Que,\"bool\"),Jue=Xt(Wa),ele=!1,tle=gn(wo,ele,\"bool\"),hN;function nle(e){hN=e.wasm.cwrap(Ua,null,[\"number\",\"number\",\"number\",\"number\"])}function sle(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;hN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var rle={kernelName:Ua,backendName:\"wasm\",setupFunc:nle,kernelFunc:sle},ale=!1,ile=gn(Ga,ale),fN;function ole(e){fN=e.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ule(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,T=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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x=t.dataIdMap.get(v.dataId).id;mN(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=S.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!==\"float32\"&&t.disposeData(y.dataId),v}var ple={kernelName:qa,backendName:\"wasm\",setupFunc:cle,kernelFunc:dle},gN;function hle(e){gN=e.wasm.cwrap(ja,null,[\"number\",\"number\",\"number\",\"number\"])}function fle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"min\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;gN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var mle={kernelName:ja,backendName:\"wasm\",setupFunc:hle,kernelFunc:fle},gle=!1,ble=gn(Ka,gle),bN=(e=>(e[e.reflect=0]=\"reflect\",e[e.symmetric=1]=\"symmetric\",e))(bN||{}),yN;function yle(e){yN=e.wasm.cwrap(Xa,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function vle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return yN(i,l,t.shape.length,It[t.dtype],d,h,bN[r],u),o}var xle={kernelName:Xa,backendName:\"wasm\",kernelFunc:vle,setupFunc:yle},wle=!0,kle=gn(Ya,wle),Ile=Xt(ko);function Rv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var vN;function Sle(e){vN=e.wasm.cwrap(So,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Cle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=vN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Rv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],\"int32\",d)}var Nle={kernelName:So,backendName:\"wasm\",setupFunc:Sle,kernelFunc:Cle},xN;function Tle(e){xN=e.wasm.cwrap(wl,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"bool\"])}function $le(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=xN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(m);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([],\"int32\",g);return[b,y]}var _le={kernelName:wl,backendName:\"wasm\",setupFunc:Tle,kernelFunc:$le},wN;function Ale(e){wN=e.wasm.cwrap(Co,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ele(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=wN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(g);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([f],\"float32\",m);return[b,y]}var Rle={kernelName:Co,backendName:\"wasm\",setupFunc:Ale,kernelFunc:Ele},Dle=!1,Fle=gn(Io,Dle,\"bool\"),kN;function Ole(e){kN=e.wasm.cwrap(To,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ple(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],\"int32\"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return kN(p,a,i,o,l),u}var zle={kernelName:To,backendName:\"wasm\",setupFunc:Ole,kernelFunc:Ple};function Mle(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Lle={kernelName:No,backendName:\"wasm\",kernelFunc:Mle};function Ble(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Zm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack 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SN={kernelName:Qa,backendName:\"wasm\",kernelFunc:Ule,setupFunc:Wle},Gle=!1,Hle=gn(Za,Gle),CN;function qle(e){CN=e.wasm.cwrap(Ja,null,[\"number\",\"number\",\"number\"])}function jle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!==\"float32\"&&(l=ac({backend:n,inputs:{x:s},attrs:{dtype:\"float32\"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,\"float32\"),p=n.dataIdMap.get(c.dataId).id;return CN(o,i,p),u.dtype!==\"float32\"&&n.disposeData(l.dataId),c}var Kle={kernelName:Ja,backendName:\"wasm\",setupFunc:qle,kernelFunc:jle},NN;function Xle(e){NN=e.wasm.cwrap(_o,null,[\"number\",\"number\",\"number\",\"number\"])}function Yle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t),f=p;if(h){let 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");Bd=e,Dv=t}function Lpe(e,t=!1){if(Lu)throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");if(typeof e==\"string\")Ou=e;else{Mu=e;let n=Cde.filter(s=>Mu[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Dv=t}var HN=-1,eg=-1;function Bpe(e){HN=e}function Vpe(){if(eg===-1)throw new Error(\"WASM backend not initialized.\");return eg}var Wpe=\"0.0.0\",Nde=2;pp(\"wasm\",async()=>{let{wasm:e}=await Ide();return new wde(e)},Nde);var sr=\"3.15.0-20220401\",Upe={tfjs:sr,\"tfjs-core\":sr,\"tfjs-data\":sr,\"tfjs-layers\":sr,\"tfjs-converter\":sr,\"tfjs-backend-cpu\":sr,\"tfjs-backend-webgl\":sr,\"tfjs-backend-wasm\":sr};export{ao as Abs,nl as Acos,sl as Acosh,fb as AdadeltaOptimizer,mb as AdagradOptimizer,gb as AdamOptimizer,bb as AdamaxOptimizer,Ir as Add,ka as AddN,rl as All,al as Any,Ia as ArgMax,il as ArgMin,ol as Asin,ul as Asinh,ll as Atan,dl as Atan2,cl as Atanh,Sa as AvgPool,qd as AvgPool3D,og as AvgPool3DGrad,ig as AvgPoolGrad,wde as BackendWasm,Ca as BatchMatMul,io as BatchToSpaceND,ug as Bincount,lg as BroadcastArgs,A$ as BroadcastTo,CW as Callback,zL as CallbackList,Na as Cast,Ta as Ceil,Sr as ClipByValue,jd as Complex,Kd as ComplexAbs,oo as Concat,$a as Conv2D,cg as Conv2DBackpropFilter,_a as Conv2DBackpropInput,Xd as Conv3D,dg as Conv3DBackpropFilterV2,pg as Conv3DBackpropInputV2,Aa as Cos,Ea as Cosh,lo as CropAndResize,pl as Cumprod,uo as Cumsum,BL as CustomCallback,Ud as DataStorage,hg as DenseBincount,co as DepthToSpace,Ra as DepthwiseConv2dNative,fg as DepthwiseConv2dNativeBackpropFilter,mg as DepthwiseConv2dNativeBackpropInput,gg as Diag,Yd as Dilation2D,Qf as Dilation2DBackpropFilter,Yf as Dilation2DBackpropInput,Jw as ENV,NW as EarlyStopping,Qd as Einsum,Fa as Elu,bg as EluGrad,S$ as Environment,po as Equal,hl as Erf,Oa as Exp,ho as ExpandDims,fo as Expm1,yg as FFT,fl as Fill,mo as FlipLeftRight,Pa as Floor,za as FloorDiv,fd as FromPixels,Ma as FusedBatchNorm,aa as FusedConv2D,ia as FusedDepthwiseConv2D,Kf as GPGPUContext,bo as GatherNd,go as GatherV2,E4 as GraphModel,yo as Greater,La as GreaterEqual,LL as History,vg as IFFT,Ba as Identity,Zd as Imag,Dt as InputSpec,ml as IsFinite,gl as IsInf,bl as IsNan,tl as KernelBackend,ep as LRN,wg as LRNGrad,AL as LayerVariable,pr as LayersModel,Va as LeakyRelu,vo as Less,xo as LessEqual,xg as LinSpace,Wa as Log,yl as Log1p,E$ as LogSoftmax,wo as LogicalAnd,vl as LogicalNot,Jd as LogicalOr,L0 as MathBackendCPU,M1 as MathBackendWebGL,Ua as Max,Ha as MaxPool,tp as MaxPool3D,Ig as MaxPool3DGrad,kg as MaxPoolGrad,Sg as MaxPoolWithArgmax,Ga as Maximum,qa as Mean,ja as Min,Ka as Minimum,Xa as MirrorPad,xl as Mod,yb as MomentumOptimizer,Cg as Multinomial,Ya as Multiply,ko as Neg,So as NonMaxSuppressionV3,wl as NonMaxSuppressionV4,Co as NonMaxSuppressionV5,Io as NotEqual,l_ as OP_SCOPE_SUFFIX,To as OneHot,No as OnesLike,_r as Optimizer,Wr as OptimizerConstructors,$o as Pack,Qa as PadV2,Tde as Pool,Za as Pow,Ja as Prelu,_o as Prod,vb as RMSPropOptimizer,Ar as RNN,kl as Range,e_ as Rank,np as Real,Da as RealDiv,Il as Reciprocal,oO as Reduction,ei as Relu,ni as Relu6,Ao as Reshape,ti as ResizeBilinear,Tg as ResizeBilinearGrad,Sl as ResizeNearestNeighbor,Ng as ResizeNearestNeighborGrad,Eo as Reverse,Ho as RotateWithOffset,Ro as Round,si as Rsqrt,Ip as SGDOptimizer,Do as ScatterNd,Fo as Select,Cl as Selu,Ub as Sequential,ai as Sigmoid,Nl as Sign,ri as Sin,Po as Sinh,Oo as Slice,ui as Softmax,Tl as Softplus,zo as SpaceToBatchND,sp as SparseFillEmptyRows,$l as SparseReshape,rp as SparseSegmentMean,ap as SparseSegmentSum,ip as SparseToDense,Mo as SplitV,ii as Sqrt,_l as Square,li as SquaredDifference,hi as Step,Lo as StridedSlice,op as StringNGrams,$g as StringSplit,_g as StringToHashBucketFast,ci as Sub,oi as Sum,$s as SymbolicTensor,Bo as Tan,di as Tanh,et as Tensor,Vt as TensorBuffer,Cr as Tile,Vo as TopK,Wo as Transform,pi as Transpose,Ag as Unique,Uo as Unpack,up as UnsortedSegmentSum,gd as Variable,Go as ZerosLike,ra as _FusedMatMul,Mt as abs,GA as acos,qA as acosh,ie as add,KA as addN,qk as all,pm as any,Gu as argMax,JA as argMin,tE as asin,sE as asinh,aE as atan,oE as atan2,lE as atanh,Gg as avgPool,Yk as avgPool3d,zA as backend,S as backend_util,Hde as basicLSTMCell,qu as batchNorm,$E as batchNorm2d,AE as batchNorm3d,RE as batchNorm4d,Hg as batchToSpaceND,Qk as bincount,vpe as booleanMaskAsync,OE as broadcastArgs,nd as broadcastTo,qo as broadcast_util,Nk as browser,De as buffer,_pe as callbacks,ce as cast,ME as ceil,Vn as clipByValue,lr as clone,oa as complex,Ft as concat,VE as concat1d,UE as concat2d,HE as concat3d,jE as concat4d,MM as constraints,Zk as conv1d,ca as conv2d,Jk as conv2dTranspose,eI as conv3d,tR as conv3dTranspose,Ade as copyRegisteredKernels,jg as cos,nI as cosh,TI as cosineWindow,aR as cumprod,sI as cumsum,js as customGrad,R4 as data,uR as denseBincount,Gk as deprecationWarn,cR as depthToSpace,hp as depthwiseConv2d,Epe as deregisterOp,dp as device_util,qde as diag,fR as dilation2d,Fde as disableDeprecationWarnings,Re as dispose,Ode as disposeVariables,xe as div,vR as divNoNan,jde as dot,nF as dropout,kR as einsum,fp as elu,Dde as enableDebugMode,Rde as enableProdMode,sF as enclosingPowerOfTwo,Ss as engine,X as env,Kn as equal,CR as erf,Xn as exp,Pn as expandDims,_R as expm1,rI as eye,lb as fft,Ol as fill,Wde as findBackend,Ude as findBackendFactory,mp as floor,Hk as floorDiv,GX as forceHalfFloat,ha as fused,ju as gather,J3 as gatherND,$k as gather_util,Bde as getBackend,nx as getGradient,Zf as getKernel,Jf as getKernelsForBackend,Vpe as getThreadsCount,RK as gpgpu_util,Yde as grad,Qde as grads,Wn as greater,jo as greaterEqual,Id as ifft,Kg as imag,qn as image,wpe as inTopKAsync,qM as initializers,OB as input,En as io,xI as irfft,Kde as isFinite,Xde as isInf,BR as isNaN,Ht as keep,xs as kernel_impls,$L as layers,Xg as leakyRelu,aI as less,Ko as lessEqual,VO as linalg,GR as linspace,Rpe as loadGraphModel,Tpe as loadLayersModel,qR as localResponseNormalization,Yn as log,Yg as log1p,epe as logSigmoid,iI as logSoftmax,iD as logSumExp,Ds as logicalAnd,Jg as logicalNot,cI as logicalOr,tpe as logicalXor,Spe as losses,We as matMul,iA as math,As as max,eb as maxPool,dI as maxPool3d,fD as maxPoolWithArgmax,$r as maximum,St as mean,dm as memory,npe as meshgrid,rW as metrics,fm as min,bp as minimum,xD as mirrorPad,kD as mod,Cpe as model,xW as models,tb as moments,xpe as movingAverage,V as mul,spe as multiRNNCell,TD as multinomial,kt as neg,OI as nextFrame,CI as norm,Ku as notEqual,vd as oneHot,Mn as ones,Qn as onesLike,L as op,rpe as outerProduct,fi as pad,ape as pad1d,ipe as pad2d,ope as pad3d,upe as pad4d,lpe as pool,pa as pow,sb as prelu,U_ as print,pI as prod,Pde as profile,cpe as rand,dpe as randomGamma,KD as randomNormal,zl as randomUniform,Xu as range,Lde as ready,wd as real,ZD as reciprocal,pp as registerBackend,$pe as registerCallbackConstructor,D$ as registerGradient,Al as registerKernel,Ape as registerOp,wW as regularizers,Xs as relu,hI as relu6,Vde as removeBackend,G as reshape,Zn as reverse,ppe as reverse1d,hpe as reverse2d,fpe as reverse3d,mpe as reverse4d,cb as rfft,fI as round,mI as rsqrt,we as scalar,X3 as scatterND,Ak as scatter_util,gI as selu,c3 as separableConv2d,Npe as sequential,ae as serialization,Mde as setBackend,Gde as setPlatform,Bpe as setThreadsCount,Mpe as setWasmPath,Lpe as setWasmPaths,S5 as setWebGLContext,p3 as setdiff1dAsync,Zy as shared,qs as sigmoid,f3 as sign,Ipe as signal,bI as sin,yI as sinh,He as slice,ib as slice1d,vI as slice2d,ob as slice3d,kd as slice4d,wt as slice_util,ub as softmax,Pl as softplus,nb as spaceToBatchND,Wc as sparse,NI as sparseToDense,kpe as spectral,Bn as split,dn as sqrt,ct as square,wI as squaredDifference,mr as squeeze,Jn as stack,yp as step,D3 as stridedSlice,Mf as string,ge as sub,ve as sum,cp as sumOutType,O3 as tan,Hu as tanh,fs as tensor,Zt as tensor1d,Ki as tensor2d,pA as tensor3d,gpe as tensor4d,bpe as tensor5d,ype as tensor6d,_s as tensor_util,_A as test_util,j as tidy,ps as tile,zde as time,z3 as topk,Pi as train,qe as transpose,db as truncatedNormal,hx as unique,_de as unregisterGradient,$de as unregisterKernel,V3 as unsortedSegmentSum,Fs as unstack,cn as upcastType,w as util,Zde as valueAndGrad,Jde as valueAndGrads,U3 as variable,XR as variableGrads,Upe as version,Dpe as version_converter,Ede as version_core,Fpe as version_cpu,uS as version_layers,Wpe as version_wasm,Ope as version_webgl,Ppe as webgl,I5 as webgl_util,soe as webgpu,vn as where,II as whereAsync,$t as zeros,je as zerosLike};\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use backend file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n* @license\n* Copyright 2018 Google LLC. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n* =============================================================================\n*/\n/** @license See the LICENSE file. */\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        return { tensor: null, canvas: inCanvas };\n      }\n      fx.reset();\n      if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n      if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n      if (config.filter.negative) fx.add('negative');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.vintage) fx.add('brownie');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.kodachrome) fx.add('kodachrome');\n      if (config.filter.technicolor) fx.add('technicolor');\n      if (config.filter.polaroid) fx.add('polaroid');\n      if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n      if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  const modelUrl = join(options.modelBasePath, modelPath || '');\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  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0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.2;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else {\n    t.batch = tf.squeeze(res); // when using tfhub model\n  }\n  tf.dispose(res);\n  t.boxes = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\n// eslint-disable-next-line class-methods-use-this\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n// eslint-disable-next-line class-methods-use-this\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n    // So we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const [contours, confidence, contourCoords] = model.execute(face.tensor as Tensor) as Array<Tensor>; // first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = await confidence.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\n      tf.dispose([contourCoords, coordsReshaped, confidence, contours]);\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n      } else {\n        if (config.face.iris?.enabled) rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // augment results with iris\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.mesh?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 0.140625, y: 0.171875 },\n  { x: 0.140625, y: 0.171875 },\n  { x: 0.171875, y: 0.171875 },\n  { x: 0.171875, y: 0.171875 },\n  { x: 0.203125, y: 0.171875 },\n  { x: 0.203125, y: 0.171875 },\n  { x: 0.234375, y: 0.171875 },\n  { x: 0.234375, y: 0.171875 },\n  { x: 0.265625, y: 0.171875 },\n  { x: 0.265625, y: 0.171875 },\n  { x: 0.296875, y: 0.171875 },\n  { x: 0.296875, y: 0.171875 },\n  { x: 0.328125, y: 0.171875 },\n  { x: 0.328125, y: 0.171875 },\n  { x: 0.359375, y: 0.171875 },\n  { x: 0.359375, y: 0.171875 },\n  { x: 0.390625, y: 0.171875 },\n  { x: 0.390625, y: 0.171875 },\n  { x: 0.421875, y: 0.171875 },\n  { x: 0.421875, y: 0.171875 },\n  { x: 0.453125, y: 0.171875 },\n  { x: 0.453125, y: 0.171875 },\n  { x: 0.484375, y: 0.171875 },\n  { x: 0.484375, y: 0.171875 },\n  { x: 0.515625, y: 0.171875 },\n  { x: 0.515625, y: 0.171875 },\n  { x: 0.546875, y: 0.171875 },\n  { x: 0.546875, y: 0.171875 },\n  { x: 0.578125, y: 0.171875 },\n  { x: 0.578125, y: 0.171875 },\n  { x: 0.609375, 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x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep, now, log } from './util';\nimport { env } from './env';\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Point } from '../result';\nimport type { AnyCanvas } from '../exports';\n\n/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n\nlet drawTime = 0;\n\nconst getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nconst rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nfunction point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  z = z || 0;\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    const z = pt[2] || 0;\n    ctx.strokeStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], Math.round(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    if (localOptions.drawLabels) {\n      // silly hack since fillText does not suport new line\n      const labels:string[] = [];\n      labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n      if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n      if (f.age) labels.push(`age: ${f.age || ''}`);\n      if (f.iris) labels.push(`distance: ${f.iris}`);\n      if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n      if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n      if (f.emotion && f.emotion.length > 0) {\n        const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n        if (emotion.length > 3) emotion.length = 3;\n        labels.push(emotion.join(' '));\n      }\n      if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n        if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n        if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n      }\n      if (labels.length === 0) labels.push('face');\n      ctx.fillStyle = localOptions.color;\n      for (let i = labels.length - 1; i >= 0; i--) {\n        const x = Math.max(f.box[0], 0);\n        const y = i * localOptions.lineHeight + f.box[1];\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(labels[i], x + 5, y + 16);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(labels[i], x + 4, y + 15);\n      }\n    }\n    // ctx.lineWidth = localOptions.lineWidth;\n    ctx.lineWidth = 2;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n      }\n      if (localOptions.drawPolygons) {\n        if (f.mesh.length > 450) {\n          for (let i = 0; i < triangulation.length / 3; i++) {\n            const points = [\n              triangulation[i * 3 + 0],\n              triangulation[i * 3 + 1],\n              triangulation[i * 3 + 2],\n            ].map((index) => f.mesh[index]);\n            lines(ctx, points, localOptions);\n          }\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n          ctx.strokeStyle = 'pink';\n          const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n          const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n          const pathV = new Path2D(`\n            M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n            C\n              ${valX} ${f.box[1]},\n              ${valX} ${f.box[1] + f.box[3]},\n              ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n          `);\n          const pathH = new Path2D(`\n            M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n            C \n              ${f.box[0]} ${valY},\n              ${f.box[0] + f.box[2]} ${valY},\n              ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n          `);\n          ctx.stroke(pathH);\n          ctx.stroke(pathV);\n        }\n        if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = 'pink';\n          ctx.fillStyle = 'pink';\n          const leftGaze = [\n            f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n          const rightGaze = [\n            f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n        }\n      }\n    }\n  }\n}\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + (2 * (result[i].keypoints[pt].position[2] || 0))}, ${127.5 - (2 * (result[i].keypoints[pt].position[2] || 0))}, 255, 0.5)` : localOptions.color;\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + (2 * pt.position[2])}, ${127.5 - (2 * pt.position[2])}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * (pt[2] || 0))}, ${127.5 - (2 * (pt[2] || 0))}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || 0;\n        ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (i * z)}, ${127.5 - (i * z)}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor, GraphModel } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\n  if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n  else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n  else res = await warmupNode(instance);\n  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {\n    log('compile pass: skip');\n    return;\n  }\n  const models = Object.values(allModels).filter((m) => m !== null) as GraphModel[];\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  for (const model of models) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && model.inputs[0].dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    const res = await model.executeAsync(tensor);\n    if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n    else tf.dispose(res);\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass kernels:', kernels.length);\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    // await runCompile(instance.models);\n    const res = await runInference(instance);\n    const t1 = now();\n    if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './util/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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+  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(ar(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new W$(this.backendInstance),!0}setupRegisteredKernels(){Jf(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Jf(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof nl)&&typeof n.then==\"function\"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error(\"No backend found in registry.\");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!=\"function\")throw new Error(\"Please provide a function to tidy()\");t=e}else{if(typeof e!=\"string\"&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(typeof t!=\"function\")throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return tm.nextTensorId++}nextVariableId(){return tm.nextVariableId++}clone(e){let t=M.runKernel(Ba,{x:e}),n={x:e},s=a=>({x:()=>{let i=\"float32\",o={x:a},u={dtype:i};return M.runKernel(Na,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Zf(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Of(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Of(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Zf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:T,dtype:N}=x;return this.makeTensorFromDataId(k,T,N)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Of(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=nx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",s=s||this.backend;let r=e;n===\"string\"&&ir(e[0])&&(r=e.map(o=>Dl(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n===\"string\"){let o=this.state.tensorInfo.get(a),u=Xw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||\"float32\";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new gd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let n=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(n=e.size*Xf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof gd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let n=e.size*Xf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=nx(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=Hd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Rg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let 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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};gb.className=\"Adam\";Tr(gb);var bb=class extends _r{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=$r(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};bb.className=\"Adamax\";Tr(bb);var Ip=class extends _r{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};yb.className=\"Momentum\";Tr(yb);var vb=class extends _r{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),dn(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),dn(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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adagrad(e,t=.1){return new mb(e,t)}},Pi={sgd:Wr.sgd,momentum:Wr.momentum,adadelta:Wr.adadelta,adagrad:Wr.adagrad,rmsprop:Wr.rmsprop,adamax:Wr.adamax,adam:Wr.adam},WO=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:e=>e())();function OI(){return new Promise(e=>WO(()=>e()))}var 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},DL=0,Ge=class extends ae.Serializable{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=DL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+\"_\"+Dp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s=\"float32\"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+\"_ib-\"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new hs(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return bn(this.getNodeAtIndex(e,\"input\").inputTensors)}getOutputAt(e){return bn(this.getNodeAtIndex(e,\"output\").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer input\" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");return ea(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return a}else{let a=FL(e),i=this.computeOutputShape(a),o,u=OL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`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 hs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Cd(this.weights)}build(e){this.built=!0}getWeights(e=!1){return vm(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer \"${this.name}\" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function mB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Dp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(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 y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,\"input layer has >1 nodes\"),Cs(x===0,\"input layer has >1 tensors\"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Qo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.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 t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,T)=>{(x==null||k==null||T==null)&&(x=b.sourceLayer,k=b.nodeIndex,T=b.tensorIndex);let N=x.inboundNodes[k];if(v.indexOf(N)!==-1)throw new hs(`The tensor ${b.name} at layer \"${x.name}\" is part of a cycle.`);if(y.indexOf(N)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(N)===-1&&v.push(N);let E=N.inboundLayers.length;for(let A=0;A<E;A++){let P=N.inputTensors[A],R=N.inboundLayers[A],F=N.nodeIndices[A],$=N.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(N);v.indexOf(N)>=0;)v.splice(v.indexOf(N),1);i.push(N)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],T=b.nodeIndices[x],N=k.inboundNodes[T],E=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(y+1,E),n[N.id]=N}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Uc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],T=a[x.id];return k<T?-1:k>T?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Uc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new hs(`Graph disconnected: cannot obtain value for tensor ${x} at layer \"${v.name}\". 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Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split(\"/\");i=o.slice(0,-2).concat([o[o.length-1]]).join(\"/\")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Pb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${uS}`,t.backend=\"TensorFlow.js\",t}toJSON(e,t=!0){let n=wm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Qr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Au(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=fa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+\"_0_0\";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=fa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U(\"Provide either a layer name or layer index\");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let N=r[x];if(N.inboundNodes.length<=k){i(m,g);return}let E=N.inboundNodes[k];b.push(E.outputTensors[T])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=ms(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!BM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e==\"object\"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e==\"object\"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]==\"object\"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function lS(e,t){return gB(e,t,\"classWeight\")}async function cS(e,t,n,s){if(t!=null||s!=null)throw new Error(\"Support sampleWeight is not implemented yet\");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Hu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Zt(i,\"float32\")}else return null}function bB(e,t){return V(e,t)}var yB=32;function dS(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=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 a=Px(\"input\",e.inputNames,n),i=Px(\"output\",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  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Use LayersModel.compile(modelCompileArgs).\");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Td?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Lx(e,this.feedInputNames,this.feedInputShapes,!1,\"input\"),t=Lx(t,this.feedOutputNames,r,!1,\"target\"),TB(e,t,null),$B(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[i,o]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error(\"sample weight is not supported yet.\");let u=null;if(s!=null){let l=lS(s,this.outputNames);u=[];for(let c=0;c<l.length;++c)u.push(await cS(o[c],null,l[c]))}return[i,o,u]}testLoop(e,t,n,s=0,r){return j(()=>{let a=this.checkNumSamples(t,n,r,\"steps\"),i=[];if(s>0)throw new Fe(\"Verbose mode is not implemented yet.\");if(r!=null)throw new Fe(\"steps mode in testLoop() is not implemented yet\");{let o=Im(a,n),u=Zt(ys(0,a));for(let l=0;l<o.length;++l){let c=o[l][0],p=o[l][1],d=ta(u,c,p-c),h=Wb(t,d),f=e(h);if(l===0)for(let m=0;m<f.length;++m)i.push(we(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=ie(i[m],V(p-c,g))}}for(let l=0;l<i.length;++l)i[l]=xe(i[l],a)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;Sx(e,s)>1&&(r+=`_${Sx(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let p=new Qr(c),d=Au(this.outputs,p,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],d[f]);r[f]!=null&&(g=bB(g,r[f]));let b=St(g);t.push(b),f===0?h=g:h=ie(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],b=this.metricsTensors[f][1];m=St(g(s[b],d[b]))}Ht(m),a.push(m)}return h=St(h),this.calculateLosses().forEach(f=>{h=ie(h,f)}),h},o=this.collectedTrainableWeights.map(c=>c.read()),u=!0;return[this.optimizer_.minimize(i,u,o)].concat(a)}}makeTestFunction(){this.testFunction=e=>j(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Qr(a),o=Au(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=St(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=St(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return CB(this,e,t,n)}async fitDataset(e,t){return xB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return Re(i),ds(n[0],e),ds(n[1],t),bn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=dm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-dm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss==\"string\")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!=\"string\")throw new Error(\"Serialization of non-string loss is not supported.\");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]==\"string\")e[s]=Ws(n[s]);else throw new Error(\"Serialization of non-string loss is not supported.\")}return e}getMetricIdentifiers(){if(typeof this.metrics==\"string\"||typeof this.metrics==\"function\")return[Ws(qc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(qc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(qc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error(\"Loading weight_metrics is not supported yet.\");if(e.loss_weights!=null)throw new Error(\"Loading loss_weights is not supported yet.\");if(e.sample_weight_mode!=null)throw new Error(\"Loading sample_weight_mode is not supported yet.\");let t=Zu(e.optimizer_config),n=ms(t),s;if(typeof e.loss==\"string\")s=Kr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>Kr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=Kr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>Kr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Kr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e==\"string\"){let u=En.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");let n=await En.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:AB,generatedBy:`TensorFlow.js tfjs-layers v${uS}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u=\"optimizer\",{data:l,specs:c}=await En.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=En.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Dx(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Dx(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className=\"Model\";ae.registerClass(pr);var hS=class extends pr{};hS.className=\"Functional\";ae.registerClass(hS);async function EB(e,t){\"modelTopology\"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Zu(n),r=ms(s,t);if(e.weightsManifest!=null){let a=await En.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function RB(e,t){if(t==null&&(t={}),typeof e==\"string\"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return DB(e,void 0,t)}async function DB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=ms(Zu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U(\"LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.\");let{modelWeights:l,optimizerWeights:c}=FB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function FB(e,t){let n=En.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group===\"optimizer\"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Cm=class extends pr{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp(\"sequential_\"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Cm||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U(\"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 U(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");let s=ZI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+\"_input\"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=QI(this.outputs[0])}this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:fa(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError(\"There are no layers in the model.\");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError(\"Sequential model cannot be built: model is empty. Add some layers first.\");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+\"_model\"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className===\"Merge\")throw new U(\"Legacy serialization format not supported yet.\");r=t}else w.assert(t.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Cm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=ms(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Ub=Cm;Ub.className=\"Sequential\";ae.registerClass(Ub);function Npe(e){return new pr(e)}function Tpe(e){return new Ub(e)}function $pe(e,t){return t==null&&(t={}),RB(e,t)}function OB(e){return ZI(e)}function _pe(e,t){zb.registerCallbackConstructor(e,t)}var In=class extends ae.Serializable{getConfig(){return{}}},fS=class extends In{apply(e,t=1){return aL(e,t)}};fS.className=\"elu\";ae.registerClass(fS);var mS=class extends In{apply(e){return gI(e)}};mS.className=\"selu\";ae.registerClass(mS);var gS=class extends In{apply(e){return Xs(e)}};gS.className=\"relu\";ae.registerClass(gS);var bS=class extends In{apply(e){return j(()=>bp(6,Xs(e)))}};bS.className=\"relu6\";ae.registerClass(bS);var yS=class extends In{apply(e){return e}};yS.className=\"linear\";ae.registerClass(yS);var vS=class extends In{apply(e){return qs(e)}};vS.className=\"sigmoid\";ae.registerClass(vS);var xS=class extends In{apply(e){return oL(e)}};xS.className=\"hardSigmoid\";ae.registerClass(xS);var wS=class extends In{apply(e){return zl(e)}};wS.className=\"softplus\";ae.registerClass(wS);var kS=class extends In{apply(e){return iL(e)}};kS.className=\"softsign\";ae.registerClass(kS);var IS=class extends In{apply(e){return qu(e)}};IS.className=\"tanh\";ae.registerClass(IS);var Gb=class extends In{apply(e,t=-1){return ub(e,t)}};Gb.className=\"softmax\";ae.registerClass(Gb);var SS=class extends In{apply(e,t=-1){return iI(e,t)}};SS.className=\"logSoftmax\";ae.registerClass(SS);var CS=class extends In{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};CS.className=\"swish\";ae.registerClass(CS);var NS=class extends In{apply(e){return j(()=>V(e,qu(zl(e))))}};NS.className=\"mish\";ae.registerClass(NS);function br(e){return e.getClassName()}function Gf(e,t={}){return Ll(e,ae.SerializationMap.getMap().classNameMap,t,\"activation\")}function yr(e){if(e==null){let t={};return t.className=\"linear\",t.config={},Gf(t)}if(typeof e==\"string\"){let t={};return t.className=e,t.config={},Gf(t)}else return e instanceof In?e:Gf(e)}function Hb(e){if(e!=null&&typeof e!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var TS=class extends ae.Serializable{},Gl=class extends TS{constructor(e){super(),Hb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Wl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Gl.className=\"L1L2\";ae.registerClass(Gl);function PB(e){return Hb(e),new Gl({l1:e!=null?e.l1:null,l2:0})}function zB(e){return Hb(e),new Gl({l2:e!=null?e.l2:null,l1:0})}var Vx={l1l2:\"L1L2\"};function it(e){return wb(e)}function Wx(e,t={}){return Ll(e,ae.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function ft(e){if(e==null)return null;if(typeof e==\"string\"){let n={className:e in Vx?Vx[e]:e,config:{}};return Wx(n)}else return e instanceof TS?e:Wx(e)}var qb=class extends Ge{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Vn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};qb.className=\"ReLU\";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Xg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jb.className=\"LeakyReLU\";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes==\"number\")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight(\"alpha\",t,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),sb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:it(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Kb.className=\"PReLU\";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return fp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Xb.className=\"ELU\";ae.registerClass(Xb);var Yb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),\"float32\"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Yb.className=\"ThresholdedReLU\";ae.registerClass(Yb);var Qb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Qb.className=\"Softmax\";ae.registerClass(Qb);function Xi(e,t,n){if(typeof e==\"number\")return fa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!tL(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function gs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n===\"same\"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s===\"valid\")e=e*t+gr([n-t,0]);else if(s===\"same\")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function Zb(e,t){return j(()=>(Ct(t),t===\"channelsFirst\"?qe(e,[0,2,3,1]):e))}function $S(e,t){return j(()=>(Ct(t),t===\"channelsFirst\"?qe(e,[0,2,3,4,1]):e))}function MB(e,t,n,s=1,r=\"valid\",a,i=1){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a===\"channelsFirst\"&&(e=qe(e,[0,2,1])),r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let o=Zk(e,t,s,r===\"same\"?\"same\":\"valid\",\"NWC\",i);return n!=null&&(o=ws(o,n)),o})}function Ux(e,t,n,s=[1,1],r=\"valid\",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Zb(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=ha.conv2d({x:u,filter:t,strides:s,pad:r===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:n,activation:o}),a===\"channelsFirst\"&&(u=qe(u,[0,3,1,2])),u})}function LB(e,t,n,s=[1,1,1],r=\"valid\",a,i){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=$S(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return o=eI(o,t,s,r===\"same\"?\"same\":\"valid\",\"NDHWC\",i),n!=null&&(o=ws(o,n)),a===\"channelsFirst\"&&(o=qe(o,[0,4,1,2,3])),o})}var Jb=class extends Ge{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",Jb.verifyArgs(t),this.rank=e,Bt(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xi(t.kernelSize,e,\"kernelSize\"),this.strides=Xi(t.strides==null?1:t.strides,e,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Ct(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Xi(t.dilationRate==null?1:t.dilationRate,e,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`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 U(`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 U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs(\"kernelSize\"in e,\"required key 'kernelSize' not in config\"),typeof e.kernelSize!=\"number\"&&!kb(e.kernelSize,\"number\",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Hl=class extends Jb{constructor(e,t){super(e,t),this.kernel=null,Hl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,\"filters\"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=UI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=MB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=LB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat===\"channelsLast\"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate==\"number\"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat===\"channelsLast\"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!(\"filters\"in e)||typeof e.filters!=\"number\"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},_S=class extends Hl{constructor(e){super(2,e),_S.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!kb(e.kernelSize,\"number\",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},zp=_S;zp.className=\"Conv2D\";ae.registerClass(zp);var AS=class extends Hl{constructor(e){super(3,e),AS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Mp=AS;Mp.className=\"Conv3D\";ae.registerClass(Mp);var ey=class extends zp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U(\"Input should have rank 4; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat===\"channelsFirst\"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,1]));let g=Jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ey.className=\"Conv2DTranspose\";ae.registerClass(ey);var ty=class extends Mp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U(\"Input should have rank 5; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat===\"channelsFirst\"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,4,1]));let k=tR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ty.className=\"Conv3DTranspose\";ae.registerClass(ty);var ES=class extends Hl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(t.padding!=null&&t.padding!==\"same\"&&t.padding!==\"valid\")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,\"float32\",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight(\"pointwise_kernel\",r,\"float32\",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(e=qe(e,[0,2,3,1])),n=c3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};ES.className=\"SeparableConv\";var ny=class extends ES{constructor(e){super(2,e)}};ny.className=\"SeparableConv2D\";ae.registerClass(ny);var RS=class extends Hl{constructor(e){super(1,e),RS.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!kb(e.kernelSize,\"number\",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},sy=RS;sy.className=\"Conv1D\";ae.registerClass(sy);var ry=class extends Ge{constructor(e){super(e),typeof e.cropping==\"number\"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]==\"number\"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?\"channelsLast\":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat===\"channelsFirst\"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat===\"channelsLast\"){let n=Gc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ry.className=\"Cropping2D\";ae.registerClass(ry);var ay=class extends Ge{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?\"nearest\":e.interpolation,ZM(this.interpolation)}computeOutputShape(e){if(this.dataFormat===\"channelsFirst\"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat===\"channelsFirst\"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation===\"nearest\"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation===\"nearest\"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ay.className=\"UpSampling2D\";ae.registerClass(ay);function BB(e,t,n=[1,1],s=\"valid\",r,a){return j(()=>{r==null&&(r=bs()),Ct(r);let i=Zb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=hp(i,t,n,s===\"same\"?\"same\":\"valid\",\"NHWC\",a),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}var iy=class extends Jb{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat===\"channelsFirst\"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=BB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=gs(t,this.kernelSize[0],this.padding,this.strides[0]),a=gs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};iy.className=\"DepthwiseConv2D\";ae.registerClass(iy);function DS(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U(\"When inputs is an array, neither initialState or constants should be provided\");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function FS(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe(\"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.\"),r!=null&&(r=ce(ce(r,\"bool\"),\"float32\"),r.rank===u-1&&(r=Pn(r,-1)),r=qe(r,l)),s&&(t=Zn(t,0),r!=null&&(r=Zn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],T=ge(Qn(k),k),N=ie(V(v[0],k),V(d[0],T)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,T)));return{output:N,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Jn(c,1)),[p,g,d]})}var OS=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(e.cell)?t=new Vp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ym(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe(\"Constants support is not implemented in RNN yet.\");ym(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=DS(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let i={training:s},u=FS((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Vl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?gm(t,[1,n]):t):this.cell.stateSize>1?[gm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===OS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=ms(s,n);return new e(Object.assign(t,{cell:r}))}},Ar=OS;Ar.className=\"RNN\";ae.registerClass(Ar);var ql=class extends Ge{},Lp=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight(\"kernel\",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Lp.className=\"SimpleRNNCell\";ae.registerClass(Lp);var oy=class extends Ar{constructor(e){e.cell=new Lp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};oy.className=\"SimpleRNN\";ae.registerClass(oy);var Bp=class extends ql{constructor(e){if(super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.resetAfter)throw new U(\"GRUCell does not support reset_after parameter set to true.\");this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Bp.className=\"GRUCell\";ae.registerClass(Bp);var uy=class extends Ar{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Bp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};uy.className=\"GRU\";ae.registerClass(uy);var jl=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new Np().apply([a]),c=r.apply([a*2]);return $x($x(u,l),c)}},t.className=\"CustomInit\",t)}else s=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};jl.className=\"LSTMCell\";ae.registerClass(jl);var ly=class extends Ar{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new jl(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ly.className=\"LSTM\";ae.registerClass(ly);var Vp=class extends ql{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){ym(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ea(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(ms(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return vm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Pb(t)}};Vp.className=\"StackedRNNCells\";ae.registerClass(Vp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):XI(t(),n),o=()=>Ul(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var PS=class extends Ar{constructor(e){if(e.unroll)throw new Fe(\"Unrolling is not possible with convolutional RNNs.\");if(Array.isArray(e.cell))throw new Fe(\"It is not possible at the moment to stack convolutional cells.\");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U(\"ConvRNN2D cell does not support constants\");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t===\"channelsFirst\",u=e[o?3:2],l=e[o?4:3],c=gs(u,s[0],r,a[0],i[0]),p=gs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};PS.className=\"ConvRNN2D\";var Wp=class extends jl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,\"filters\"),this.kernelSize=Xi(n,2,\"kernelSize\"),this.kernelSize.forEach(o=>Bt(o,\"kernelSize\")),this.strides=Xi(s||1,2,\"strides\"),this.strides.forEach(o=>Bt(o,\"strides\")),this.padding=r||\"valid\",Un(this.padding),this.dataFormat=a||\"channelsLast\",Ct(this.dataFormat),this.dilationRate=Xi(i||1,2,\"dilationRate\"),this.dilationRate.forEach(o=>Bt(o,\"dilationRate\"))}build(e){var t;e=nt(e);let n=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight(\"kernel\",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return $b([d,h,f])}},t.className=\"CustomInit\",t)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,T]=Bn(this.kernel.read(),i,y),[N,E,A,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,N,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,T,P,this.padding);let[R,F,$,z]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ca(e,t,this.strides,s||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ca(e,t,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Wp.className=\"ConvLSTM2DCell\";ae.registerClass(Wp);var cy=class extends PS{constructor(e){let t=new Wp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};cy.className=\"ConvLSTM2D\";ae.registerClass(cy);var Up=class extends Ge{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Ul(()=>XI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Up.className=\"Dropout\";ae.registerClass(Up);var dy=class extends Up{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};dy.className=\"SpatialDropout1D\";ae.registerClass(dy);var py=class extends Ge{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=UI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};py.className=\"Dense\";ae.registerClass(py);var hy=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to \"Flatten\" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return rL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};hy.className=\"Flatten\";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};fy.className=\"Activation\";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),nL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};my.className=\"RepeatVector\";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n=\"Total size of new array must be unchanged.\",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U(\"Can only specifiy one unknown dimension.\");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};gy.className=\"Reshape\";ae.registerClass(gy);var by=class extends Ge{constructor(e){if(super(e),e.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(e.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};by.className=\"Permute\";ae.registerClass(by);var yy=class extends Ge{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return pm(Xu(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=pm(Xu(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};yy.className=\"Masking\";ae.registerClass(yy);var vy=class extends Ge{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,\"inputDim\"),this.outputDim=e.outputDim,Bt(this.outputDim,\"outputDim\"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Xu(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!==\"int32\"&&(n=Sp(n,\"int32\"));let s=KI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};vy.className=\"Embedding\";ae.registerClass(vy);var bi=class extends Ge{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U(\"Operands could not be broadcast together with shapes \"+JSON.stringify(e)+\" \"+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Vl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an Array\");if(!Array.isArray(e))throw new U(\"`inputs` should be an Array\");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},xy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};xy.className=\"Add\";ae.registerClass(xy);var wy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};wy.className=\"Multiply\";ae.registerClass(wy);var ky=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};ky.className=\"Average\";ae.registerClass(ky);var Iy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$r(t,e[n]);return t})}};Iy.className=\"Maximum\";ae.registerClass(Iy);var Sy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=bp(t,e[n]);return t})}};Sy.className=\"Minimum\";ae.registerClass(Sy);var Cy=class extends bi{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \"+JSON.stringify(e))}mergeFunction(e){return j(()=>$b(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U(\"A `Concatenate` layer should be called on a list of inputs.\");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an array for Concatenate\");if(!Array.isArray(e))throw new U(\"`inputs` should be an array for Concatenate\");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Qn(e[a]),\"bool\")):t[a].rank<e[a].rank?s.push(Pn(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return qk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cy.className=\"Concatenate\";ae.registerClass(Cy);function Cu(e,t){for(;e<0;)e+=t;return e}function VB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n==\"number\"&&(n=[n,n]),e.dtype===\"complex64\"||t.dtype===\"complex64\")throw new Fe(\"batchDot is not implemented for complex64-type Tensors yet.\");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=Pn(o,1)),o})}var Ny=class extends bi{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Cu(r,e[a].shape.length)):s=[Cu(this.axes,t.shape.length),Cu(this.axes,n.shape.length)],this.normalize&&(t=Nd(t,s[0]),n=Nd(n,s[1])),VB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Cu(this.axes,e.length),Cu(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ny.className=\"Dot\";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Ul(()=>ie(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Ty.className=\"GaussianNoise\";ae.registerClass(Ty);var $y=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Ul(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};$y.className=\"GaussianDropout\";ae.registerClass($y);var _y=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Ul(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=Ko(Ml(n),this.rate);u=Sp(u,\"float32\");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};_y.className=\"AlphaDropout\";ae.registerClass(_y);function Ju(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=$E(e,t,n,s,r,a);else if(e.rank===3)i=AE(e,t,n,s,r,a);else if(e.rank===4)i=RE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function WB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance;return[Ju(e,i,o,n,t,r),i,o]})}function UB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Ju(e,l,c,d,p,r),i,o]})}function GB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?WB(e,t,n,s,r):UB(e,t,n,s,r)}var Ay=class extends Ge{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.movingMeanInitializer=ht(e.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||\"ones\"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=fa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Ju(s,b,y,v,x,this.epsilon)}else return Ju(s,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 p();let[d,h,f]=GB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),T=V(ge(k,y),x);b.write(ge(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ay.className=\"BatchNormalization\";ae.registerClass(Ay);var Ey=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=tb(n,this.axis,!0),u=fa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=ps(i,d),o=ps(o,d),c=ps(c,h),p=ps(p,h),Ju(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ey.className=\"LayerNormalization\";ae.registerClass(Ey);function HB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(n==null&&(n=bs()),n!==\"channelsLast\"&&n!==\"channelsFirst\")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n===\"channelsFirst\"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],fi(e,s)})}var Ry=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding==\"number\")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]==\"number\")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat===\"channelsFirst\"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>HB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ry.className=\"ZeroPadding2D\";ae.registerClass(Ry);function Gp(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=Zb(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=eb(e,t,n,o):i=Gg(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}function zS(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=$S(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=dI(e,t,n,o):i=Yk(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,4,1,2,3])),i})}var MS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize==\"number\")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]==\"number\")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,\"poolSize\"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides==\"number\")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]==\"number\")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=gs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Vl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Dy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"max\")}};Dy.className=\"MaxPooling1D\";ae.registerClass(Dy);var Fy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"avg\")}};Fy.className=\"AveragePooling1D\";ae.registerClass(Fy);var LS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"max\")}};Oy.className=\"MaxPooling2D\";ae.registerClass(Oy);var Py=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"avg\")}};Py.className=\"AveragePooling2D\";ae.registerClass(Py);var BS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[4]:e[3];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),s=gs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,\"max\")}};zy.className=\"MaxPooling3D\";ae.registerClass(zy);var My=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,\"avg\")}};My.className=\"AveragePooling3D\";ae.registerClass(My);var VS=class extends Ge{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Ly=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return St(n,1)})}};Ly.className=\"GlobalAveragePooling1D\";ae.registerClass(Ly);var By=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};By.className=\"GlobalMaxPooling1D\";ae.registerClass(By);var WS=class extends Ge{constructor(e){super(e),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat===\"channelsLast\"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Vy=class extends WS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?St(n,[1,2]):St(n,[2,3])})}};Vy.className=\"GlobalAveragePooling2D\";ae.registerClass(Vy);var Wy=class extends 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s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);Hn(r,t,\"TensorList shape mismatch: \");let a=Fs(e);return new eo(a,t,s)}function t4(e,t,n){return new eo([],e,t,n)}function n4(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r}  vs. ${s})`);let a=new eo([],n,e.dtype,s),i=Fs(e,0);return t.forEach((o,u)=>{a.setItem(o,i[u])}),a}function s4(e,t,n){let s=0,r=t.map(c=>(s+=c,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),i=Pm(a,n),o=s===0?0:e.size/s,u=j(()=>{let c=[];e=G(e,[1,s,o]);for(let p=0;p<t.length;++p){let d=p===0?0:r[p-1],h=[0,d,0],f=[1,t[p],o];c[p]=G(He(e,h,f),i)}return e.dispose(),c}),l=new eo([],n,e.dtype,t.length);for(let c=0;c<u.length;c++)l.setItem(c,u[c]);return l}var r4=async(e,t,n)=>{switch(e.op){case\"If\":case\"StatelessIf\":{let s=I(\"thenBranch\",e,t,n),r=I(\"elseBranch\",e,t,n),a=I(\"cond\",e,t,n),i=I(\"args\",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case\"While\":case\"StatelessWhile\":{let s=I(\"body\",e,t,n),r=I(\"cond\",e,t,n),a=I(\"args\",e,t,n),i=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),o=a.map(c=>c.id),u=await i[0].data();i.forEach(c=>{!c.kept&&o.indexOf(c.id)===-1&&c.dispose()});let l=a;for(;u[0];){let c=l;l=await n.functionMap[s].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);let p=l.map(h=>h.id);c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);u=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return l}case\"LoopCond\":{let s=I(\"pred\",e,t,n);return[Us(s)]}case\"Switch\":{let s=I(\"pred\",e,t,n),r=I(\"data\",e,t,n);return r.kept||(r=Us(r)),(await s.data())[0]?[void 0,r]:[r,void 0]}case\"Merge\":{let s=e.inputNames.find(r=>un(r,t,n)!==void 0);if(s){let r=un(s,t,n);return[Us(r)]}return}case\"Enter\":{let s=I(\"frameName\",e,t,n),r=I(\"tensor\",e,t,n);return n.enterFrame(s),[Us(r)]}case\"Exit\":{let s=I(\"tensor\",e,t,n);return n.exitFrame(),[Us(s)]}case\"NextIteration\":{let s=I(\"tensor\",e,t,n);return n.nextIteration(),[Us(s)]}case\"TensorArrayV3\":{let s=I(\"size\",e,t,n),r=I(\"dtype\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"dynamicSize\",e,t,n),o=I(\"clearAfterRead\",e,t,n),u=I(\"identicalElementShapes\",e,t,n),l=I(\"name\",e,t,n),c=new JW(l,r,s,a,u,i,o);return n.addTensorArray(c),[c.idTensor,we(1)]}case\"TensorArrayWriteV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case\"TensorArrayReadV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n);return[n.getTensorArray(s.id).read(r)]}case\"TensorArrayGatherV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"dtype\",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case\"TensorArrayScatterV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case\"TensorArrayConcatV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id),a=I(\"dtype\",e,t,n);return[r.concat(a)]}case\"TensorArraySplitV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"lengths\",e,t,n),i=n.getTensorArray(s.id);return i.split(a,r),[i.idTensor]}case\"TensorArraySizeV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return[we(r.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case\"TensorListSetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorList(s.id);return i.setItem(r,a),[i.idTensor]}case\"TensorListGetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).getItem(r,a,i)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let s=I(\"indices\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"numElements\",e,t,n),o=n4(r,s,a,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let s=I(\"elementShape\",e,t,n),r=I(\"elementDType\",e,t,n),a;e.op===\"TensorListReserve\"?a=\"numElements\":a=\"maxNumElements\";let i=I(a,e,t,n),o=t4(s,r,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListGather\":{let s=I(\"tensorListId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).gather(r,i,a)]}case\"TensorListStack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=I(\"numElements\",e,t,n);return[n.getTensorList(s.id).stack(r,a,i)]}case\"TensorListFromTensor\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=e4(s,r,a);return n.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":{let s=I(\"tensorListId\",e,t,n),r=n.getTensorList(s.id),a=I(\"dtype\",e,t,n),i=I(\"elementShape\",e,t,n);return[r.concat(a,i)]}case\"TensorListPushBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"tensor\",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case\"TensorListPopBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case\"TensorListSplit\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"lengths\",e,t,n),i=s4(s,a,r);return n.addTensorList(i),[i.idTensor]}case\"TensorListLength\":{let s=I(\"tensorListId\",e,t,n),r=n.getTensorList(s.id);return[we(r.size(),\"int32\")]}case\"TensorListResize\":{let s=I(\"tensorListId\",e,t,n),r=I(\"size\",e,t,n),i=n.getTensorList(s.id).resize(r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function 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r=I(\"keyDType\",e,t,n),a=I(\"valueDType\",e,t,n),i=new c4(r,a);return s.addHashTable(e.name,i),[i.handle]}case\"LookupTableImport\":case\"LookupTableImportV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"values\",e,t,n);return[await s.getHashTableById(r.id).import(a,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"defaultValue\",e,t,n);return[await s.getHashTableById(r.id).find(a,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let r=I(\"tableHandle\",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},p4=(e,t,n)=>{switch(e.op){case\"ResizeBilinear\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[qn.resizeBilinear(s,[r[0],r[1]],a,i)]}case\"ResizeNearestNeighbor\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[qn.resizeNearestNeighbor(s,[r[0],r[1]],a,i)]}case\"CropAndResize\":{let s=I(\"image\",e,t,n),r=I(\"boxes\",e,t,n),a=I(\"boxInd\",e,t,n),i=I(\"cropSize\",e,t,n),o=I(\"method\",e,t,n),u=I(\"extrapolationValue\",e,t,n);return[qn.cropAndResize(s,r,a,i,o,u)]}case\"ImageProjectiveTransformV3\":{let s=I(\"images\",e,t,n),r=I(\"transforms\",e,t,n),a=I(\"outputShape\",e,t,n),i=I(\"fillValue\",e,t,n),o=I(\"interpolation\",e,t,n),u=I(\"fillMode\",e,t,n);return[qn.transform(s,r,o.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},m4=(e,t,n)=>{switch(e.op){case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[ju(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"FusedBatchNormV3\":return[ju(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"LRN\":return[qR(I(\"x\",e,t,n),I(\"radius\",e,t,n),I(\"bias\",e,t,n),I(\"alpha\",e,t,n),I(\"beta\",e,t,n))];case\"Softmax\":return[ub(I(\"x\",e,t,n))];case\"LogSoftmax\":return[iI(I(\"x\",e,t,n))];case\"SparseToDense\":return[NI(I(\"sparseIndices\",e,t,n),I(\"outputShape\",e,t,n),I(\"sparseValues\",e,t,n),I(\"defaultValue\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},g4=(e,t,n)=>{switch(e.op){case\"Max\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[As(I(\"x\",e,t,n),i,o)]}case\"Mean\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[St(I(\"x\",e,t,n),i,o)]}case\"Min\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[fm(I(\"x\",e,t,n),i,o)]}case\"Sum\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[ve(I(\"x\",e,t,n),i,o)]}case\"All\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[qk(I(\"x\",e,t,n),i,o)]}case\"Any\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[pm(I(\"x\",e,t,n),i,o)]}case\"ArgMax\":{let i=I(\"axis\",e,t,n);return[Hu(I(\"x\",e,t,n),i)]}case\"ArgMin\":{let i=I(\"axis\",e,t,n);return[JA(I(\"x\",e,t,n),i)]}case\"Prod\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[pI(I(\"x\",e,t,n),i,o)]}case\"Cumprod\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[aR(I(\"x\",e,t,n),i,o,u)]}case\"Cumsum\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[sI(I(\"x\",e,t,n),i,o,u)]}case\"Bincount\":let s=I(\"x\",e,t,n),r=I(\"weights\",e,t,n),a=I(\"size\",e,t,n);return[Qk(s,r,a)];case\"DenseBincount\":{let i=I(\"x\",e,t,n),o=I(\"weights\",e,t,n),u=I(\"size\",e,t,n),l=I(\"binaryOutput\",e,t,n);return[uR(i,o,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},b4=(e,t,n)=>{switch(e.op){case\"ConcatV2\":case\"Concat\":{let s=I(\"n\",e,t,n),r=I(\"axis\",e,t,n),a=I(\"tensors\",e,t,n);return a=a.slice(0,s),[Ft(a,r)]}case\"Gather\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[Ku(s,ce(r,\"int32\"),0)]}case\"GatherV2\":{let s=I(\"axis\",e,t,n),r=I(\"batchDims\",e,t,n),a=I(\"x\",e,t,n),i=I(\"indices\",e,t,n);return[Ku(a,ce(i,\"int32\"),s,r)]}case\"Reverse\":{let s=I(\"dims\",e,t,n),r=[];for(let i=0;i<s.length;i++)s[i]&&r.push(i);let a=I(\"x\",e,t,n);return[Zn(a,r)]}case\"ReverseV2\":{let s=I(\"axis\",e,t,n),r=I(\"x\",e,t,n);return[Zn(r,s)]}case\"Slice\":{let s=I(\"begin\",e,t,n),r=I(\"size\",e,t,n);return[He(I(\"x\",e,t,n),s,r)]}case\"StridedSlice\":{let s=I(\"begin\",e,t,n),r=I(\"end\",e,t,n),a=I(\"strides\",e,t,n),i=I(\"beginMask\",e,t,n),o=I(\"endMask\",e,t,n),u=I(\"ellipsisMask\",e,t,n),l=I(\"newAxisMask\",e,t,n),c=I(\"shrinkAxisMask\",e,t,n),p=I(\"x\",e,t,n);return[D3(p,s,r,a,i,o,u,l,c)]}case\"Pack\":return j(()=>{let s=I(\"axis\",e,t,n),r=I(\"tensors\",e,t,n),a=r[0].shape,i=mr(r[0]).shape,o=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(mr(u).shape,i))throw new Error(\"the input tensors shape does not match\");return l?u:G(u,a)});return[Jn(o,s)]});case\"Unpack\":{let s=I(\"axis\",e,t,n),r=I(\"tensor\",e,t,n);return Fs(r,s)}case\"Tile\":{let s=I(\"reps\",e,t,n);return[ps(I(\"x\",e,t,n),s)]}case\"Split\":case\"SplitV\":{let s=I(\"axis\",e,t,n),r=I(\"numOrSizeSplits\",e,t,n),a=I(\"x\",e,t,n);return Bn(a,r,s)}case\"ScatterNd\":{let s=I(\"indices\",e,t,n),r=I(\"values\",e,t,n),a=I(\"shape\",e,t,n);return[X3(s,r,a)]}case\"GatherNd\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[J3(s,r)]}case\"SparseToDense\":{let s=I(\"sparseIndices\",e,t,n),r=I(\"outputShape\",e,t,n),a=I(\"sparseValues\",e,t,n),i=I(\"defaultValue\",e,t,n);return[NI(s,a,r,a.dtype===i.dtype?i:ce(i,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},y4=(e,t,n)=>{switch(e.op){case\"SparseFillEmptyRows\":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Wc.sparseFillEmptyRows(I(\"indices\",e,t,n),I(\"values\",e,t,n),I(\"denseShape\",e,t,n),I(\"defaultValue\",e,t,n));return[s,r,a,i]}case\"SparseReshape\":{let{outputIndices:s,outputShape:r}=Wc.sparseReshape(I(\"inputIndices\",e,t,n),I(\"inputShape\",e,t,n),I(\"newShape\",e,t,n));return[s,r]}case\"SparseSegmentMean\":return[Wc.sparseSegmentMean(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];case\"SparseSegmentSum\":return[Wc.sparseSegmentSum(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},v4=(e,t,n)=>{switch(e.op){case\"FFT\":return[lb(I(\"x\",e,t,n))];case\"IFFT\":return[Id(I(\"x\",e,t,n))];case\"RFFT\":return[cb(I(\"x\",e,t,n))];case\"IRFFT\":return[xI(I(\"x\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},x4=(e,t,n)=>{switch(e.op){case\"StringNGrams\":{let{nGrams:s,nGramsSplits:r}=Mf.stringNGrams(I(\"data\",e,t,n),I(\"dataSplits\",e,t,n),I(\"separator\",e,t,n),I(\"nGramWidths\",e,t,n),I(\"leftPad\",e,t,n),I(\"rightPad\",e,t,n),I(\"padWidth\",e,t,n),I(\"preserveShortSequences\",e,t,n));return[s,r]}case\"StringSplit\":{let{indices:s,values:r,shape:a}=Mf.stringSplit(I(\"input\",e,t,n),I(\"delimiter\",e,t,n),I(\"skipEmpty\",e,t,n));return[s,r,a]}case\"StringToHashBucketFast\":return[Mf.stringToHashBucketFast(I(\"input\",e,t,n),I(\"numBuckets\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},w4=(e,t,n)=>{switch(e.op){case\"Cast\":return[ce(I(\"x\",e,t,n),I(\"dtype\",e,t,n))];case\"ExpandDims\":{let s=I(\"axis\",e,t,n);return[Pn(I(\"x\",e,t,n),s)]}case\"Squeeze\":{let s=I(\"axis\",e,t,n);return[mr(I(\"x\",e,t,n),s)]}case\"Reshape\":return[G(I(\"x\",e,t,n),I(\"shape\",e,t,n))];case\"MirrorPad\":return[xD(I(\"x\",e,t,n),I(\"padding\",e,t,n),I(\"mode\",e,t,n))];case\"PadV2\":case\"Pad\":return[fi(I(\"x\",e,t,n),I(\"padding\",e,t,n),I(\"constantValue\",e,t,n))];case\"SpaceToBatchND\":{let s=I(\"blockShape\",e,t,n),r=I(\"paddings\",e,t,n);return[nb(I(\"x\",e,t,n),s,r)]}case\"BatchToSpaceND\":{let s=I(\"blockShape\",e,t,n),r=I(\"crops\",e,t,n);return[Hg(I(\"x\",e,t,n),s,r)]}case\"DepthToSpace\":{let s=I(\"blockSize\",e,t,n),r=I(\"dataFormat\",e,t,n).toUpperCase();return[cR(I(\"x\",e,t,n),s,r)]}case\"BroadcastTo\":return[nd(I(\"x\",e,t,n),I(\"shape\",e,t,n))];case\"BroadcastArgs\":return[OE(I(\"s0\",e,t,n),I(\"s1\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Yx(e,t,n,s){let r=((a,i,o)=>{switch(a.category){case\"arithmetic\":return j(()=>QW(a,i,o));case\"basic_math\":return j(()=>ZW(a,i,o));case\"control\":return r4(a,i,o);case\"convolution\":return j(()=>a4(a,i,o));case\"creation\":return j(()=>i4(a,i,o));case\"dynamic\":return o4(a,i,o);case\"evaluation\":return j(()=>u4(a,i,o));case\"image\":return j(()=>p4(a,i,o));case\"graph\":return j(()=>l4(a,i,o));case\"logical\":return j(()=>h4(a,i,o));case\"matrices\":return j(()=>f4(a,i,o));case\"normalization\":return j(()=>m4(a,i,o));case\"reduction\":return j(()=>g4(a,i,o));case\"slice_join\":return j(()=>b4(a,i,o));case\"sparse\":return j(()=>y4(a,i,o));case\"spectral\":return j(()=>v4(a,i,o));case\"string\":return j(()=>x4(a,i,o));case\"transformation\":return j(()=>w4(a,i,o));case\"hash_table\":return d4(a,i,o,s);case\"custom\":let u=XS(a.op);if(u&&u.customExecutor)return u.customExecutor(new YW(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Qx=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(\"\"),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Zx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>An(d)[0]),c=[];s!=null&&(c=s.map(d=>An(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((b0(d)||N4(d)||T4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function k4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>An(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var I4=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],S4=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],C4=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function b0(e){return I4.indexOf(e.op)>=0}function N4(e){return S4.indexOf(e.op)>=0}function T4(e){return C4.indexOf(e.op)>=0}var zm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new zm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+\"--\"+s.join(this.SEPERATOR)}compile(e,t){let n=Zx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`)}return k4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[An(c)[0]]),r=t.map(c=>An(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return j(()=>{let c=new Qx(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=An(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=Yx(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>un(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!==\"control\"){let u=_W(o.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=i[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete i[l.id]}else c!=null&&i[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=X().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new Qx(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let i=t.map(l=>un(l,this.tensorsMap,a)),o=i.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,i)=>(r[this.inputs[i].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[An(y)[0]]),i=n.map(y=>An(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=Zx(e,o,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=An(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,i,f,u);await Promise.all(y)}c==null&&!s&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=o.filter(y=>!b0(y)&&!un(y.name,h,t)).map(y=>y.name);if(b.length>0){let y=\"\";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${y}`)}return h}processStack(e,t,n,s,r,a,i,o,u){let l=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p=\"\";if(c.node.op===\"Enter\"&&I(\"isConstant\",c.node,s,n)&&([p]=Ts(c.node.name,n)),s[c.node.name]==null){let d=Yx(c.node,s,n,this._resourceManager);p||([p]=Ts(c.node.name,n));let h=n.currentContext;w.isPromise(d)?l.push(d.then(f=>(s[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u),f))):(s[p]=d,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u))}else this.processChildNodes(c.node,t,n,s,r,u)}return l}processChildNodes(e,t,n,s,r,a){e.children.forEach(i=>{let[o]=Ts(i.name,n);r[o]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=An(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((o,u)=>a[u]===-1||a[u]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let 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nU(e){if(!to(e))throw new Error(\"The argument to zip() must be an object or array.\");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return _n(async()=>{let n=await x0(e,s=>{if(s instanceof Jo)return{value:s.iterator(),recurse:!1};if(to(s))return{value:null,recurse:!0};throw new Error(\"Leaves of the structure passed to zip() must be Datasets, not primitives.\")});return W4(n,1)},t)}function sU(e){if(e===null)return null;let t=e[0];return z4(t)?{value:rU(e),recurse:!1}:{value:null,recurse:!0}}function rU(e){if(e.length===0)throw new Error(\"Can't make a batch of zero elements.\");return e[0]instanceof et?Jn(e):fs(e)}var $0=class extends Jo{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`\n`).map(s=>(s.endsWith(\"\\r\")&&(s=s.slice(0,-1)),s))}},Kc='\"',Tu=Symbol(\"out\"),ew=Symbol(\"field\"),Xc=Symbol(\"quote\"),qf=Symbol(\"quoteafterquote\"),tw=Symbol(\"quoteinquote\"),_0=class extends Jo{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=\",\",this.delimWhitespace=!1,this.base=new $0(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>\"Delimiter should not be provided when delimWhitespace is true.\"),this.delimWhitespace=!0,this.delimiter=\" \"):this.delimiter=t.delimiter?t.delimiter:\",\"}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error(\"Column names must be provided if there is no header line.\");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>\"The length of provided columnNames (\"+this.fullColumnNames.length.toString()+\") does not match the length of the header line read from file (\"+e.length.toString()+\").\"),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>\"Duplicate column names found: \"+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key \"'+s+'\" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+\").\")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error(\"No data was found for CSV parsing.\");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o===\"\")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype===\"bool\"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else 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Kc:a=Xc;break;default:}break;default:}if(a===qf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},A0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(e={}){if(!X().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let t=new A0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),fs(n,t)}},E0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Ki([a,r,o,i],[1,4])}else this.cropBox=Ki([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(e,t={}){if(!X().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!e){if(e=document.createElement(\"video\"),!t.resizeWidth||!t.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new E0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Nk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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D0{constructor(e,t){super(),this.upstream=e,this.impl=new iU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},iU=class extends Qy{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},oU=class extends Ut{decodeUTF8(){return new uU(this)}},uU=class extends D0{constructor(e){super(),this.upstream=e,this.impl=new lU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},lU=class extends Qy{constructor(e){if(super(),this.upstream=e,X().get(\"IS_BROWSER\"))this.decoder=new TextDecoder(\"utf-8\");else{let{StringDecoder:t}=Ww();this.decoder=new t(\"utf8\")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get(\"IS_BROWSER\")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},F0=class extends oU{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(X().get(\"IS_BROWSER\")?e instanceof File||e instanceof Blob:!1),()=>\"FileChunkIterator only supports File, Blob and Uint8Array right now.\"),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new 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iterator(){if(O0(this.input)&&X().get(\"IS_NODE\")){let e=tg();this.input=e.readFileSync(this.input.slice(7))}return new F0(this.input,this.options)}},z0=class extends R0{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return O0(this.url)?new P0(this.url,this.fileOptions).iterator():cU(this.url,this.fileOptions)}};function pU(e,t={}){return new _0(new z0(e),t)}function hU(e){let t=Yy(e);return _n(async()=>t)}function fU(e){return _n(async()=>{let t=await e();return Yy(()=>t.next())})}async function mU(e,t){return E0.create(e,t)}async function gU(e){return A0.create(e)}var bU=\"0.0.0\";function be(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!==\"complex64\",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var yU=xs.whereImpl,M0=class extends nl{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ud(this,Ss())}nextDataId(){return M0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().get(\"IS_NODE\")&&S.warn(`\n============================\nHi there \\u{1F44B}. 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aH={kernelName:og,backendName:\"cpu\",kernelFunc:rH};function iH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;be([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=De(i.shape,\"float32\"),T=1/(h*f),N=n.data.get(r.dataId).values,E=De(r.shape,\"float32\",N);for(let A=0;A<c.batchSize;++A)for(let P=0;P<c.inChannels;++P)for(let R=0;R<c.inHeight;++R)for(let F=0;F<c.inWidth;++F){let $=R-x,z=F-v,W=0;for(let q=0;q<b;q+=m){let K=($+q)/p;if(!(K<0||K>=c.outHeight||Math.floor(K)!==K))for(let Y=0;Y<y;Y+=g){let Z=(z+Y)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=E.get(A,K,Z,P)}}k.set(W*T,A,R,F,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var oH={kernelName:ig,backendName:\"cpu\",kernelFunc:iH};function 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p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,b=d.padInfo.left,y=d.padInfo.top,v=d.dataFormat===\"channelsLast\",x=new Vt(d.outShape,r.dtype),k=w.computeStrides(r.shape),T=w.computeStrides(a.shape),N=k[0],E=v?k[1]:k[2],A=v?k[2]:1,P=v?1:k[1],R=x.strides[0],F=v?x.strides[1]:x.strides[2],$=v?x.strides[2]:1,z=v?1:x.strides[1],W=n.data.get(r.dataId).values,q=n.data.get(a.dataId).values,K=x.values;for(let Y=0;Y<d.batchSize;++Y){let Z=Y*N,te=Y*R;for(let ee=0;ee<d.outHeight;++ee){let se=te+ee*F,ne=ee*d.strideHeight-y;for(let oe=0;oe<h;++oe){let re=ne+oe*m;if(re<0||re>=d.inHeight)continue;let le=oe*T[0],me=Z+re*E;for(let ke=0;ke<d.outWidth;++ke){let Se=se+ke*$,Ee=ke*d.strideWidth-b;for(let Pe=0;Pe<f;++Pe){let Xe=Ee+Pe*g;if(Xe<0||Xe>=d.inWidth)continue;let Je=le+Pe*T[1],Ye=me+Xe*A,tt=Je;for(let Ce=0;Ce<d.inChannels;++Ce){let ut=W[Ye+Ce*P];for(let 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Y=0;Y<d.batchSize;++Y)for(let Z=P;Z<R;++Z){let te=A+Z*h-x;for(let ee=$;ee<z;++ee){let se=F+ee*f-v;b?K+=N.get(Y,te,se,W)*E.get(Y,Z,ee,q):K+=N.get(Y,W,te,se)*E.get(Y,q,Z,ee)}}y.set(K,A,F,W,q)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var SH={kernelName:cg,backendName:\"cpu\",kernelFunc:IH};function CH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;be([r,a],\"conv2dBackpropInput\");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=S.convertConv2DDataFormat(l),f=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,\"float32\"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:T,filterHeight:N,filterWidth:E,inChannels:A,inHeight:P,inWidth:R,outChannels:F,outHeight:$,outWidth:z,strideHeight:W,strideWidth:q}=f;h=f.dataFormat;let K=N-1-f.padInfo.top,Y=E-1-f.padInfo.left,Z=h===\"channelsLast\",te=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,ne=Z?1:m.strides[1],oe=d[0],re=Z?d[1]:d[2],le=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<T;++ke)for(let Se=0;Se<A;++Se)for(let Ee=0;Ee<P;++Ee){let Pe=Ee-K,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min($,(N+Pe)/W);for(let Ye=0;Ye<R;++Ye){let tt=Ye-Y,Ce=Math.max(0,Math.ceil(tt/q)),ut=Math.min(z,(E+tt)/q),at=0;for(let Nt=Xe;Nt<Je;++Nt){let Cn=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let en=Et*q-tt,Nn=oe*ke+re*Nt+le*Et,Tn=v*(N-1-Cn)+x*(E-1-en)+k*Se;for(let Yt=0;Yt<F;++Yt){let Dn=b[Nn+me*Yt],tn=y[Tn+Yt];at+=Dn*tn}}}let Jt=te*ke+ee*Ee+se*Ye+ne*Se;g[Jt]=at}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var NH={kernelName:_a,backendName:\"cpu\",kernelFunc:CH};function TH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s;be([r,a],\"conv3d\");let l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,T=n.data.get(a.dataId).values,N=x.values,E=w.computeStrides(r.shape),A=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let R=P*E[0],F=P*x.strides[0];for(let $=0;$<l.outDepth;++$){let z=F+$*x.strides[1],W=$*l.strideDepth-b;for(let q=0;q<c;++q){let K=W+q*h;if(K<0||K>=l.inDepth)continue;let Y=q*A[0],Z=R+K*E[1];for(let te=0;te<l.outHeight;++te){let ee=z+te*x.strides[2],se=te*l.strideHeight-v;for(let ne=0;ne<p;++ne){let oe=se+ne*f;if(oe<0||oe>=l.inHeight)continue;let re=Y+ne*A[1],le=Z+oe*E[2];for(let me=0;me<l.outWidth;++me){let ke=ee+me*l.outChannels,Se=me*l.strideWidth-y;for(let Ee=0;Ee<d;++Ee){let Pe=Se+Ee*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=re+Ee*A[2],Je=le+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)N[ke+ut]+=Ce*T[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var $H={kernelName:Xd,backendName:\"cpu\",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s;be([r,a],\"conv3dBackpropFilterV2\");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=S.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,\"float32\"),v=y.values,[x,k,T,N]=y.strides,E=n.data.get(a.dataId).values,[A,P,R,F]=c,$=n.data.get(r.dataId).values,[z,W,q,K]=l,Y=p.padInfo.front,Z=p.padInfo.left,te=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((Y-ee)/d)),ne=Math.min(p.outDepth,(p.inDepth+Y-ee)/d),oe=ee*x;for(let re=0;re<g;++re){let le=Math.max(0,Math.ceil((te-re)/h)),me=Math.min(p.outHeight,(p.inHeight+te-re)/h),ke=re*k+oe;for(let Se=0;Se<b;++Se){let 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MH={kernelName:co,backendName:\"cpu\",kernelFunc:zH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumprod\");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.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=cn(l.dtype,\"int32\"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?1:h[v];else{let x=m(b,y-1);d[v]=i?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var BH={kernelName:hl,backendName:\"cpu\",kernelFunc:LH};function VH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumsum\");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.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=cn(l.dtype,\"int32\"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var WH={kernelName:lo,backendName:\"cpu\",kernelFunc:VH};function UH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=ev(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=W0(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var GH={kernelName:hg,backendName:\"cpu\",kernelFunc:UH};function HH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`,{getBroadcastDims:p1}=S;function V5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:\"\"};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=mv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 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n=e.shapeInfo.logicalShape;switch(n.length){case 0:return cK(e,t);case 1:return pK(e,t);case 2:return fK(e,t);case 3:return gK(e,t);case 4:return yK(e,t);case 5:return vK(e);case 6:return xK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function h1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return lK(e);case 1:return dK(e,t);case 2:return hK(e,t);case 3:return mK(e,t);default:return bK(e,t)}}function W5(e,t,n=!1,s){let r=\"\";n?r+=h1(e,s):r+=su(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=wK(e,t):r+=kK(e,t)),r}function U5(e,t,n){switch(e.length){case 0:return f1();case 1:return J5(e,t,n);case 2:return oK(e,t,n);case 3:return tK(e,t,n);default:return sK(e,t,n)}}function G5(e,t,n){switch(e.length){case 0:return f1();case 1:return eK(e,t,n);case 2:return uK(e,t,n);case 3:return nK(e,t,n);case 4:return rK(e,t,n);case 5:return aK(e,t);case 6:return iK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function H5(e){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${e.texture2D}(textureSampler, uv).r;\n    }\n  `}function q5(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}function j5(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}function K5(e){return`${e.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${e.varyingFs} vec2 resultUV;\n    ${e.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${e.defineSpecialNaN}\n    ${e.defineSpecialInf}\n    ${e.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${X5}\n    ${Y5}\n    ${Q5}\n  `}var X5=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, 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modCoord == 0. ? frag.r : frag.g;\n  }\n`;function f1(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function J5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${s[1]}.0);\n      }\n    `:s[1]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${s[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);\n    }\n  `}function eK(e,t,n){return t[0]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `:t[1]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}function tK(e,t,n){if(n)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function nK(e,t,n){if(n)return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${qp([\"r\",\"c\",\"d\"],e)}\n    return ivec3(r, c, d);\n  }\n`;let s=yi([\"r\",\"c\",\"d\"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec3(r, c, d);\n    }\n  `}function sK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o=\"\",u=\"b, r, c\";for(let l=2;l<e.length-1;l++)i*=e[e.length-l-1],o=`\n      int b${l} = index / ${i};\n      index -= b${l} * ${i};\n    `+o,u=`b${l}, `+u;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      ${o}\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec${e.length}(${u});\n    }\n  `}function rK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${qp([\"r\",\"c\",\"d\",\"d2\"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let s=yi([\"r\",\"c\",\"d\",\"d2\"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec4(r, c, d, d2);\n    }\n  `}function aK(e,t){let n=yi([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function iK(e,t){let n=yi([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function oK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));\n      }\n    `;let r=Math.ceil(e[1]/2);return n?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function uK(e,t,n){return w.arraysEqual(e,t)?n?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `:e[1]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:e[0]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:n?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}function vi(e){return`offset${e}`}function lK(e){let t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),s=fn();return`\n    vec4 ${n}() {\n      return ${s.texture2D}(${t}, halfCR);\n    }\n  `}function cK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`\n      float ${s}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let i=vi(n);if(t)return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;let[o,u]=e.shapeInfo.texShape;return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${o}, ${u}, ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}function dK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=fn();if(t)return`\n    vec4 ${s}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`\n    vec4 ${s}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${i[0]}, ${i[1]}, index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}function pK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${s}(int index) {\n        ${ru(e)}\n      }\n    `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`\n      float ${s}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let o=vi(n);return i===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `:a===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:t?`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}function hK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=fn();if(a!=null&&w.arraysEqual(n,a))return t?`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `:`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `;if(t)return`\n    vec4 ${r}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${r}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `}function fK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`\n      float ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `;let d=a[0],h=a[1];return`\n    float ${r}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=au(e,u),h=[\"row\",\"col\"];return`\n      ${su(d,t)}\n      float ${r}(int row, int col) {\n        return ${r}(${iu(h,o)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${ru(e)}\n      }\n    `;let l=a[0],c=a[1],p=vi(s);return c===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `:l===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${s}, uv);\n    }\n  `:t?`\n      float ${r}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s}Shape[1] + col + ${p};\n        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n  float ${r}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${p};\n    vec2 uv = uvFromFlat(${l}, ${c}, index);\n    return sampleTexture(${s}, uv);\n  }\n`}function mK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=au(e,d),m=[\"b\",\"row\",\"col\"];return`\n        ${h1(f,t)}\n        vec4 ${r}(int b, int row, int col) {\n          return ${r}(${iu(m,h)});\n        }\n      `}let o=fn();if(t)return`\n    vec4 ${r}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `;let u=i[0],l=i[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`\n    vec4 ${r}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${l}, ${p}, ${c}, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `}function gK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=au(e,l),g=[\"row\",\"col\",\"depth\"];return`\n        ${su(m,t)}\n        float ${r}(int row, int col, int depth) {\n          return ${r}(${iu(g,u)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${i}, 1)));\n        ${ru(e)}\n      }\n    `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        int stride1 = ${s}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n        float ${r}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${i}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${d}.0, ${p}.0);\n          return sampleTexture(${s}, uv);\n        }\n      `;if(d===i&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;let f=vi(s);return t?`\n    float ${r}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${s}Shape[1] * ${s}Shape[2];\n      int stride1 = ${s}Shape[2];\n      int index = row * ${a} + col * ${i} + depth + ${f};\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n      return sampleTexture(${s}, uv);\n    }\n    `:`\n      float ${r}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${i} + depth + ${f};\n        vec2 uv = uvFromFlat(${p}, ${d}, index);\n        return sampleTexture(${s}, uv);\n      }\n  `}function bK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=fn();if(t)return`\n    vec4 ${s}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);\n    }\n  `;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[i-1]/2),d=p*Math.ceil(a[i-2]/2),h=\"int b, int row, int col\",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=a[i-m-1],f=`b${m} * ${d} + `+f;return`\n    vec4 ${s}(${h}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});\n      return ${r.texture2D}(${n}, uv);\n    }\n  `}function yK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=au(e,u),v=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${su(y,t)}\n      float ${r}(int row, int col, int depth, int depth2) {\n        return ${r}(${iu(v,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${o}, ${i}, ${a}, 1)));\n        ${ru(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${i}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;if(h===a&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;let b=vi(s);return t?`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `:`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `}function vK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=au(e,u),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${su(m)}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${iu(g,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${o}, ${i}, ${a}, ${r})) +\n          depth3;\n        ${ru(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${i}, ${a}, ${r}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(h===r&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let f=vi(n);return`\n    float ${s}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} + depth * ${a} +\n          depth2 * ${r} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${d}, ${h}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function xK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=au(e,r),b=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${su(g)}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${iu(b,a)});\n      }\n    `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${l}, ${u}, ${o})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${i}, 1)));\n        ${ru(e)}\n      }\n    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${l}, ${u}, ${o}, ${i})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===i&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let m=vi(n);return`\n    float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${l} + depth * ${u} +\n          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${h}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function ru(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function wK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=p1(e.shapeInfo.logicalShape,t.logicalShape),u=rt(i),l=i-a,c,p=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?c=\"\":i<2&&o.length>=1?c=\"coords = 0;\":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`\n`);let d=\"\";i<2&&a>0?d=\"coords\":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(\", \");let h=\"return outputValue;\",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(m&&!b)i===1?h=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:h=`\n        return vec4(outputValue.x);\n      `;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h=\"return vec4(outputValue.x);\":o.indexOf(y)>-1?h=\"return vec4(outputValue.x, 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c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=V5(r,i,t),u=GC(e.gl,o),l=e.createProgram(u);return X().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...m1(e,t,l)}}function m1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,\"NAN\",!1),X().getNumber(\"WEBGL_VERSION\")===1&&(c=e.getUniformLocation(n,\"INFINITY\",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,\"outShape\",d),l=e.getUniformLocation(n,\"outShapeStrides\",d),u=e.getUniformLocation(n,\"outTexShape\",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function rw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function SK(e,t,n,s,r){t.program.enableShapeUniforms||(rw(t.inShapeInfos,n),rw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=mv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new 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4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type===\"float\")e.gl.uniform1fv(c,p);else if(u.type===\"vec2\")e.gl.uniform2fv(c,p);else if(u.type===\"vec3\")e.gl.uniform3fv(c,p);else if(u.type===\"vec4\")e.gl.uniform4fv(c,p);else if(u.type===\"int\")e.gl.uniform1iv(c,p);else if(u.type===\"ivec2\")e.gl.uniform2iv(c,p);else if(u.type===\"ivec3\")e.gl.uniform3iv(c,p);else if(u.type===\"ivec4\")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function CK(e,t,n){let s=\"\";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=mv(e.packedInputs,i.shape,u),d=\"\",h=\"\",f=\"\";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=S.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:\"\"}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+=\"_\"+s+\"_\"+r+`${X().getNumber(\"WEBGL_VERSION\")}`,a}function Sn(e){return X().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&e<=4}var NK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?qp([\"r\",\"c\",\"d\"],e):yi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${t.output} = result;\n      }\n    `}},TK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?qp([\"r\",\"c\",\"d\"],e):yi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${t.output} = result;\n      }\n    `}},$K=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${d1}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}},_K=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${d1}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${t.output} = encode_float(x);\n      }\n    `}},AK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\"),this.userCode=`\n      ${this.enableShapeUniforms?fv():hv(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${n.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}},EK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"\",r=\"result\";t&&(r=\"floor(result * 255. + 0.5)\");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`\n          localCoords = coords;\n          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?\"outShape[2]\":`${e[2]}`}) {\n          localCoords[2] += ${i};\n          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?\"outShape[1]\":`${e[1]}`}) {\n            localCoords[1] += ${a};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${o}] = values[0];\n            } else if (offset == 1) {\n              result[${o}] = values[1];\n            } else if (offset == 2) {\n              result[${o}] = values[2];\n            } else {\n              result[${o}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?fv():hv(e)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${s}\n\n          ${n.output} = ${r};\n        }\n    `}},RK={};Ae(RK,{bindVertexProgramAttributeStreams:()=>S1,createBufferFromOutputTexture:()=>T1,createFloat16MatrixTexture:()=>x1,createFloat16PackedMatrixTexture:()=>I1,createFloat32MatrixTexture:()=>v1,createIndexBuffer:()=>y1,createPackedMatrixTexture:()=>k1,createUnsignedBytesMatrixTexture:()=>w1,createVertexBuffer:()=>b1,createVertexShader:()=>g1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>_1,downloadFloat32MatrixFromBuffer:()=>$1,downloadMatrixFromPackedOutputTexture:()=>E1,downloadPackedMatrixFromBuffer:()=>A1,getInternalFormatForFloat16MatrixTexture:()=>bv,getInternalFormatForFloat16PackedMatrixTexture:()=>xv,getInternalFormatForFloat32MatrixTexture:()=>gv,getInternalFormatForPackedMatrixTexture:()=>vv,getInternalFormatForUnsignedBytesMatrixTexture:()=>yv,uploadDenseMatrixToTexture:()=>C1,uploadPixelDataToTexture:()=>N1});function g1(e){let t=fn(),n=`${t.version}\n    precision highp float;\n    ${t.attribute} vec3 clipSpacePos;\n    ${t.attribute} vec2 uv;\n    ${t.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;return UC(e,n)}function b1(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return jC(e,t)}function y1(e){let t=new Uint16Array([0,1,2,2,1,3]);return KC(e,t)}function Ql(e,t,n,s,r,a){YC(t,n);let i=XC(e),o=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(o,i)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),X().getNumber(\"WEBGL_VERSION\")===1?fe(e,()=>e.texImage2D(o,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(o,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function gv(e){return e.internalFormatFloat}function v1(e,t,n,s){let[r,a]=Yl(t,n);return Ql(e,r,a,gv(s),s.textureFormatFloat,e.FLOAT)}function bv(e){return 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fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,o,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function $1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function _1(e,t,n,s){let[r,a]=Yl(t,n),i=4,o=new Uint8Array(T5(t*n,i));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function A1(e,t,n,s,r,a,i,o){let u=e,l=new Float32Array($5(a,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 E1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Kf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=X().getNumber(\"WEBGL_VERSION\");e!=null?(this.gl=e,S5(t,e)):this.gl=vs(t);let n=\"WEBGL_color_buffer_float\",s=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),X().getNumber(\"WEBGL_VERSION\")===1){let r=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Fu(this.gl,r),Ln(this.gl,a))this.textureHalfFloatExtension=Fu(this.gl,a);else if(X().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),Ln(this.gl,s))this.colorBufferHalfFloatExtension=Fu(this.gl,s);else if(X().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\",Ln(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ln(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=b1(this.gl),this.indexBuffer=y1(this.gl),this.framebuffer=QC(this.gl),this.textureConfig=dv(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),v1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),x1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),w1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),N1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),C1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return 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fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),qC(t,n),this.debug&&rd(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=S1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&rd(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?JC(this.gl,e,t):e1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),t1(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=tu(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&rd(this.gl,this.program),Ou(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Fu(this.gl,X().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(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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e=DK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ad(this.gl,e,this.framebuffer),this.debug&&Ou(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ad(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ou(this.gl)):Vm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;ad(s,e,this.framebuffer),this.debug&&Ou(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}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 DK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:FK,bincountImpl:R1,bincountReduceImpl:OK,ceilImpl:PK,concatImpl:zK,equalImpl:MK,expImpl:LK,expm1Impl:BK,floorImpl:VK,gatherNdImpl:WK,gatherV2Impl:UK,greaterImpl:GK,greaterEqualImpl:HK,lessImpl:qK,lessEqualImpl:jK,linSpaceImpl:KK,logImpl:XK,maxImpl:YK,maximumImpl:QK,minimumImpl:ZK,multiplyImpl:JK,negImpl:eX,notEqualImpl:tX,prodImpl:nX,rangeImpl:sX,rsqrtImpl:rX,sigmoidImpl:aX,simpleAbsImpl:D1,sliceImpl:iX,sparseFillEmptyRowsImpl:oX,sparseReshapeImpl:uX,sparseSegmentReductionImpl:F1,sqrtImpl:lX,stridedSliceImpl:cX,stringNGramsImpl:dX,stringSplitImpl:pX,stringToHashBucketFastImpl:hX,subImpl:fX,tileImpl:mX,topKImpl:gX,transposeImpl:wv,uniqueImpl:bX}=Zy;function O1(e,t){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,t).map(n=>`${e}.${n}`)}function ln(e,t){return t===1?[e]:O1(e,t)}function yX(e,t){if(e===1)return\"rc\";let n=\"\";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=\",\");return n}var vX=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=ln(\"rc\",this.rank),n=rt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${n} rc = getOutputCoords();\n\n          if(${s}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?\"r\":\"rp1\"}, ${s===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${s};\n    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}},P1=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n=\"\";for(let s=0;s<4;s++){let r=\"thisRC = rc;\";s%2===1&&(r+=\"thisRC.z += 1;\"),s>1&&(r+=\"thisRC.y += 1;\"),n+=`\n        ${r}\n        ${s>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${s}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${s>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${xX(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?fv():hv(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n        int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}};function xX(e,t){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t?B5([\"r\",\"c\",\"d\"],\"inputShape\"):yi([\"r\",\"c\",\"d\"],e)}\n      return ivec3(r, c, d);\n    }\n  `}var wX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=iw(t,n),r=ow(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=aw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=iw(n,s),a=ow(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=aw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function kX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function aw(e,t,n,s,r){let a=IX(t,s),i;if(r){let[u,l]=tu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=kX(n,a);return i*o}function IX(e,t){switch(e){case 3:return vv(t);case 4:return xv(t);case 1:return gv(t);case 0:return bv(t);case 2:return yv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SX(e){return X().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?e?3:1:e?4:0}function iw(e,t){if(e===1)return 3;if(e===0||e==null)return SX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function ow(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      float unaryOperation(float x) {\n     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= min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,FX=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Zr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},OX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=ln(\"rc\",t),s=rt(t),r=yX(t,n),a=n.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${r});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}},PX=xs.whereImpl,zX=1e-7,MX=1e-4,Zc={};function LX(e){return e in Zc||(Zc[e]={}),Zc[e]}var BX=X().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),VX=600;function WX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*VX/1024/1024}var z1=class extends nl{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Kf)t=e;else{let n=vs(X().getNumber(\"WEBGL_VERSION\"),e);t=new Kf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber(\"WEBGL_VERSION\"));t=new Kf(n),this.binaryCache=LX(X().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new wX(this.gpgpu),this.numMBBeforeWarning=WX(),this.texData=new Ud(this,Ss())}nextDataId(){return z1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||X().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Zr(i,Mi):p=new Hs(i,Mi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s===\"string\")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s===\"complex64\"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=S.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Zr(s,Mi):h=new Hs(s,Mi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool(\"DEBUG\")&&!X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&X().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,l;if(a!==\"complex64\"&&X().get(\"WEBGL_BUFFER_SUPPORTED\")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let c;if(a===\"complex64\"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=S.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;o?d=new Zr(r,Mi):d=new Hs(r,Mi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!VC(n))throw X().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool(\"WEBGL_PACK\")&&s===!0,i=a?id(t):t,o=a?new _K(i):new $K(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],\"float32\"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=BX){return X().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return PX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Zr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let s=D1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,uw,e.dtype);let t=new Hs(e.shape,uw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new OX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ba(e.shape),...ya(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ba(t),...ya(t)],a=new P1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=id(r),o;s?o=new TK(i):o=new NK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.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(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!el(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=CK(e,l,c),d=this.getAndSaveBinary(p,()=>IK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),X().get(\"ENGINE_COMPILE_ONLY\")||SK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get(\"WEBGL_FLUSH_THRESHOLD\");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool(\"WEBGL_LAZILY_UNPACK\")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=X().getBool(\"DEBUG\");X().set(\"DEBUG\",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(X().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zX:MX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=r1(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=tu(c[0],c[1])),o?d=new EK(p,m):d=new AK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),T=this.texData.get(k.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(k.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=UX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await OI(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(pv(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=m1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},M1=z1;M1.nextDataId=0;function UX(e,t){if(t===\"float32\"||t===\"complex64\")return e;if(t===\"int32\"||t===\"bool\"){let n=t===\"int32\"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ppe=\"0.0.0\";function GX(){X().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}dp.isBrowser()&&pp(\"webgl\",()=>new M1,2);var zpe={forceHalfFloat:GX},L1=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,ro=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      float binaryOperation(float a, float b) {\n        ${e}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `}},jp=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`,Zl=class{constructor(e,t,n,s=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Sn(r);let a=\"\";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${rt(r)} coords = getOutputCoords();\n        `,r===1)this.enableShapeUniforms?a+=`\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `:a+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let o=ln(\"coords\",r);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= outShape[${r} - 2];\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= outShape[${r} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= ${this.outputShape[r-2]};\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= ${this.outputShape[r-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${a}\n\n        setOutput(result);\n      }\n    `}};function kn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var HX={kernelName:Ba,backendName:\"webgl\",kernelFunc:kn};function Rr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.texData.get(a.dataId),o=kn({inputs:{x:s},backend:n}),u=kn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var qX={kernelName:jd,backendName:\"webgl\",kernelFunc:Rr},B1=\"return (a < 0.) ? b * a : a;\",V1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function jX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],\"float32\",w.createScalarValue(a,\"float32\")),o=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(V1,r.shape,i.shape):new ro(B1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],\"float32\");return n.disposeIntermediateTensorInfo(i),u}var KX={kernelName:Va,backendName:\"webgl\",kernelFunc:jX},W1=\"return (a < 0.) ? b * a : a;\",U1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function XX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(U1,s.shape,r.shape):new ro(W1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],\"float32\")}var YX={kernelName:Ja,backendName:\"webgl\",kernelFunc:XX},ou=\"if (isnan(x)) return x;\",QX=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,ZX=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Zr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype===\"complex64\"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new ro(e,u.shape,l.shape);return c.runWebGLProgram(E,[T,N],cn(x.dtype,k.dtype))}),y=Rr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(f):f,b=u.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new ro(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Kp(e,t=!1){if(e===\"linear\")return t?AX:CX;if(e===\"relu\")return t?RX:TX;if(e===\"elu\")return t?EX:NX;if(e===\"relu6\")return t?DX:$X;if(e===\"prelu\")return t?U1:W1;if(e===\"leakyrelu\")return t?V1:B1;if(e===\"sigmoid\")return t?FX:_X;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var G1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?\"i * 2, rc.y\":\"rc.y, i * 2\",d=r?\"rc.z, i * 2\":\"i * 2, rc.z\",h=s?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],f=r?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],m=\"\",g=\"\";i&&(o?m=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:u?m=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:m=`vec4 activation(vec4 x) {\n          ${i}\n        }`,g=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let y=\"rc.x\",v=\"rc.x\";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${c}; i++) {\n          int batchA = ${y};\n          int batchB = ${v};\n          vec4 a = getMatrixA(batchA, ${p});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${h[0]} * ${f[0]});\n          result += (${h[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}},lw={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},cw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}},dw=\"return a * b;\";function kv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=S.upcastType(s.dtype,r.dtype);if(s.dtype===\"complex64\"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new cw(lw.REAL,s.shape,r.shape),c=new cw(lw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Rr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=JK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?i=new Zl(dw,s.shape,r.shape):i=new ro(dw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var JX={kernelName:Ya,backendName:\"webgl\",kernelFunc:kv};function e8(e,t,n){let s=[ba(e.shape),...ya(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ba(t),...ya(t)],i=new P1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!el(r.shape,u)&&!(c.texture!==null&&el(c.shape,u))?e8(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var t8={kernelName:Eo,backendName:\"webgl\",kernelFunc:he},pw=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l=\"\";r%n>0&&(l=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${l}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${o===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${o===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${o===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}},n8=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=\"0.0\",o=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",o=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",o=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let l=Math.floor(n/4)*4,c=n%4,p=`\n      if (${t===\"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${t===\"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${o}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${o}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,d=\"vec4\";t===\"all\"?(i=\"1.0\",p=`\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",p=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${h}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${i});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function s8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=S.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function xi(e,t,n,s){let r=s8(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n===\"mean\"?c=i===0?new pw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new pw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new n8({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var r8=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=a8(t);this.userCode=`\n    void main() {\n      ${s} resRC = getOutputCoords();\n      setOutput(getA(${r}));\n    }\n    `}};function a8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var i8=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=rt(this.rank),r=O1(\"rc\",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${s} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${o}) {\n        result[1] = ${u};\n      }\n      --${r[this.rank-1]};\n      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${o}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function Xp(e,t,n){let s=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new i8(e.shape,t):new r8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function o8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=S.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Xp(e,u,s),o=S.getInnerMostAxes(o.length,a)),S.assertAxesAreInnerMostDims(\"sum\",o,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=S.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=cp(e.dtype),v=xi(b,y,\"sum\",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Yp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return o8(r,a,i,n)}var u8={kernelName:oi,backendName:\"webgl\",kernelFunc:Yp};function qt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=wv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Xp(r,a,i);return l}var l8={kernelName:pi,backendName:\"webgl\",kernelFunc:qt},H1=1e3;function 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Z=N,te=E;n&&(Z=qt({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=qt({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=kv({inputs:{a:ne,b:re},backend:r});K=Yp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=cn(e.dtype,t.dtype),te=new G1(k,T,[P,h,f],n,s,F,W,$,z),ee=[N,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],\"float32\",w.createScalarValue(o,\"float32\"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function c8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return zd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var d8={kernelName:ra,backendName:\"webgl\",kernelFunc:c8},hw=\"return abs(x);\";function p8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!==\"complex64\"){let a=n.texData.get(s.dataId),i=D1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?r=new Zr(s.shape,hw):r=new Hs(s.shape,hw),n.runWebGLProgram(r,[s],s.dtype)}var h8={kernelName:io,backendName:\"webgl\",kernelFunc:p8},f8=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`,m8=Ke({opSnippet:f8}),g8={kernelName:sl,backendName:\"webgl\",kernelFunc:m8},b8=ss+`\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:rl,backendName:\"webgl\",kernelFunc:y8},fw=\"return a + 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ld(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return kn({inputs:{x:s[0]},backend:n});if(s.length>X().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(s.length/2),l=ld({inputs:s.slice(0,u),backend:n}),c=ld({inputs:s.slice(u),backend:n});return ld({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),o=X().getBool(\"WEBGL_PACK\")?new I8(s[0].shape,a):new k8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var S8={kernelName:ka,backendName:\"webgl\",kernelFunc:ld};function C8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims(\"all\",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,\"all\",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var N8={kernelName:al,backendName:\"webgl\",kernelFunc:C8};function T8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims(\"any\",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,\"any\",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var $8={kernelName:il,backendName:\"webgl\",kernelFunc:T8},_8=class{constructor(e,t,n){this.variableNames=[\"A\"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[r,a];let i=t===\"max\"?\">\":\"<\",o=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${s};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${s}; i++) {\n          int inIdx = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        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coords;\n        --${l[o-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,p),h=\".\"+d[p-1],f=d.map(N=>\"int \"+N),m=ln(\"sourceLocR\",p-1).concat(\"inIdx.r\"),g=ln(\"sourceLocG\",p-1).concat(\"inIdx.g\"),b=ln(\"sourceLocB\",p-1).concat(\"inIdx.b\"),y=ln(\"sourceLocA\",p-1).concat(\"inIdx.a\"),v=n===\"max\"?\"greaterThan\":\"lessThan\",x=s?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${y.join()})));`,k=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,T=s?\"\":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${T}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};\n        bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n          sourceLocB${h}, sourceLocA${h}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${x}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}};function q1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=S.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new _8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=q1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function j1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=S.computeOptimalWindowSize(a),o=new A8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,\"int32\");if(l.shape.length===t.shape.length){let c=j1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function K1(e,t,n,s){let r=[n];if(S.assertAxesAreInnerMostDims(\"arg\"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=q1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return j1(e,t,s)}function E8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=K1(n,u,i[0],\"max\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var R8={kernelName:Ia,backendName:\"webgl\",kernelFunc:E8};function D8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=K1(n,u,i[0],\"min\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var F8={kernelName:ol,backendName:\"webgl\",kernelFunc:D8},O8=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,P8=Ke({opSnippet:O8}),z8={kernelName:ul,backendName:\"webgl\",kernelFunc:P8},M8=ss+\"return log(x + sqrt(x * x + 1.0));\",L8=Ke({opSnippet:M8}),B8={kernelName:ll,backendName:\"webgl\",kernelFunc:L8},V8=ss+`\n  return atan(x);\n`,W8=Ke({opSnippet:V8}),U8={kernelName:cl,backendName:\"webgl\",kernelFunc:W8},G8=QX+`\n  return atan(a, b);\n`,H8=`\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+ZX+`\n  return result;\n`,q8=jt({opSnippet:G8,packedOpSnippet:H8}),j8={kernelName:pl,backendName:\"webgl\",kernelFunc:q8},K8=ss+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelName:dl,backendName:\"webgl\",kernelFunc:X8},tl=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t===\"avg\",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(f||(b=\"-1.0 / 1e-20\"),n){let N=\">=\";this.userCode=`\n        const ivec2 strides = ivec2(${i}, ${o});\n        const ivec2 pads = ivec2(${d}, ${h});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${u}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p};\n                wC += ${l}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${N} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let y=\"max\",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(v=\"avgValue / count\");let x=Math.floor(a/4)*4,k=a%4,T=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${y}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${o});\n      const ivec2 pads = ivec2(${d}, ${h});\n      const float initializationValue = ${b};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${b});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${u}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${l};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              getValue(batch, xR, xC + 3 * ${l}, d)\n            );\n\n            ${T}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${k===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${T}\n          } else if (${k===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${T}\n          } else if (${k===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              initializationValue\n            );\n\n            ${T}\n          }\n        }\n        setOutput(${v});\n      }\n    `}},Iv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t===\"avg\",v=\"0.0\";if(y||(v=\"-1.0 / 1e-20\"),n){let A=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${o}, ${u});\n        const ivec3 pads = ivec3(${m}, ${g}, ${b});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${d};\n              wD += ${l}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${h};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${p}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${A} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let x=\"max\",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(k=\"avgValue / count\");let T=Math.floor(a/4)*4,N=a%4,E=`\n      if (${y}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${x}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${o}, ${u});\n      const ivec3 pads = ivec3(${m}, ${g}, ${b});\n      const float initializationValue = ${v};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${v});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${l}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${T}; wC += 4) {\n              int xC = xCCorner + wC * ${p};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n              );\n\n              ${E}\n            }\n\n            int xC = xCCorner + ${T};\n            if (${N===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${N===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${N===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                initializationValue\n              );\n\n              ${E}\n            }\n          }\n          setOutput(${k});\n        }\n      }\n    `}};function Q8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,\"avgPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.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(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new tl(c,\"avg\",!1);return n.runWebGLProgram(p,[r],\"float32\")}var Z8={kernelName:Sa,backendName:\"webgl\",kernelFunc:Q8};function J8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Iv(p,\"avg\",!1);return n.runWebGLProgram(d,[r],\"float32\")}var eY={kernelName:qd,backendName:\"webgl\",kernelFunc:J8},tY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${l}, ${c});\n      const float avgMultiplier = float(${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${o};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${u};\n            wC+= ${i}) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},nY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${h}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${o}) {\n          float dyD = float(dyDCorner + wD) / ${r}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${p};\n              wR += ${u}) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${d};\n                wC += ${l}) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function sY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new nY(d);return n.runWebGLProgram(h,[r],i.dtype)}var rY={kernelName:og,backendName:\"webgl\",kernelFunc:sY};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;nu([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=new tY(c);return n.runWebGLProgram(p,[r],i.dtype)}var iY={kernelName:ig,backendName:\"webgl\",kernelFunc:aY};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return 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u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function uu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.texData.get(r.dataId),d=iX(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=wt.isSliceContinous(r.shape,o,u);if(l||!c){let p=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new mY(u):new hY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),gY(r,o,u,n)}var bY={kernelName:Po,backendName:\"webgl\",kernelFunc:uu},yY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=uu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},vY={kernelName:oo,backendName:\"webgl\",kernelFunc:yY};function xY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=R1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var wY={kernelName:ug,backendName:\"webgl\",kernelFunc:xY};function kY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],\"int32\",Int32Array.from(o))}var IY={kernelName:lg,backendName:\"webgl\",kernelFunc:kY},SY=\"return float(a != b);\",X1=jt({opSnippet:SY,cpuKernelImpl:tX,dtype:\"bool\"}),CY={kernelName:So,backendName:\"webgl\",kernelFunc:X1};function Jl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.real},backend:n})}var NY={kernelName:np,backendName:\"webgl\",kernelFunc:Jl},TY=\"return float(int(x));\";function $Y(e,t){let n=new Hs(e.shape,TY),s=t.runWebGLProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Hm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return kn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Hm({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=Rr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype===\"complex64\"){let i=Jl({inputs:{input:r},backend:n}),o=Hm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=kn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return $Y(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=X1({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var _Y={kernelName:Na,backendName:\"webgl\",kernelFunc:Hm},mw=\"return ceil(x);\",AY=Ke({opSnippet:mw,packedOpSnippet:mw,cpuKernelImpl:PK}),EY={kernelName:Ta,backendName:\"webgl\",kernelFunc:AY},RY=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `}},DY=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `}};function FY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;X().getBool(\"WEBGL_PACK_CLIP\")?o=new DY(r.shape):o=new RY(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var OY={kernelName:Sr,backendName:\"webgl\",kernelFunc:FY},PY=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `}};function gw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function zY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new PY(s.shape),i=[gw(s,r.complexTensorInfos.real),gw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var MY={kernelName:Kd,backendName:\"webgl\",kernelFunc:zY},LY=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join(`\n        `)}\n      }\n    `}},BY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=rt(s),a=ln(\"coords\",s),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`\n        if (${u} < ${o[f]}  && ${u} >= ${o[f-1]}) {\n          return getChannel(\n            getT${f}(${Jc(i,u,m)}),\n            vec2(${Jc(l,u,m)}));\n        }`}let d=o.length,h=o[o.length-1];p+=`\n        return getChannel(\n          getT${d}(${Jc(i,u,h)}),\n          vec2(${Jc(l,u,h)}));`,this.userCode=`\n      float getValue(${i.map(f=>\"int \"+f)}) {\n        ${p}\n      }\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[s-1]} = ${a[s-1]} + 1;\n        if (${a[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[s-2]} = ${a[s-2]} + 1;\n        if (${a[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[s-1]} = ${a[s-1]} - 1;\n        if (${a[s-2]} < ${n[s-2]} &&\n            ${a[s-1]} < ${n[s-1]}) {\n          result.b = getValue(${a});\n        }\n        setOutput(result);\n      }\n    `}};function Jc(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function Qp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var VY={kernelName:Zd,backendName:\"webgl\",kernelFunc:Qp};function Gi(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let c=e.map(m=>Jl({inputs:{input:m},backend:n})),p=e.map(m=>Qp({inputs:{input:m},backend:n})),d=Gi(c,t,n),h=Gi(p,t,n),f=Rr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=S.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=zK(p,d,s,h),m=S.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let c=Math.floor(e.length/2),p=Gi(e.slice(0,c),t,n),d=Gi(e.slice(c),t,n),h=Gi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&e[0].shape.length>1){let c=new BY(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=WY(e,t,n),o=new LY(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function WY(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function Y1(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return kn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Gi(o,a,n)}var UY={kernelName:uo,backendName:\"webgl\",kernelFunc:Y1},Q1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat===\"channelsLast\",g=m?1:2,b=m?2:3,y=m?3:1,v=\"\",x=\"\";n&&(s?v=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?v=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:v=`\n          float activation(float x) {\n            ${n}\n          }\n        `,x=\"result = activation(result);\");let k=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${v}\n\n      const ivec2 strides = ivec2(${o}, ${u});\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${y}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${g}], coords[${b}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${p}; wR++) {\n          int xR = xRCorner + wR * ${l};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${d}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${h}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${f===1}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${h}) *\n                    getW(wR, wC, ${h}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${h}, xR, xC) *\n                    getW(wR, wC, ${h}, d2);\n              }\n\n            } else if (${f===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${f===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2),\n                getW(wR, wC, ${h} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1),\n                  getX(batch, xR, xC, ${h} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC),\n                  getX(batch, ${h} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${k}\n        ${x}\n        setOutput(result);\n      }\n    `}},GY=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${r}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${n}, ${s});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${o};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${p}; wR++) {\n            int xR = xRCorner + wR * ${u};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${d}; wC++) {\n              int xC = xCCorner + wC * ${l};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${h}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${f===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${h}) *\n                  getW(wF, wR, wC, ${h}, d2);\n              } else if (${f===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${f===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1),\n                  getX(batch, xF, xR, xC, ${h} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2),\n                  getW(wF, wR, wC, ${h} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},HY=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"},{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=e,this.enableShapeUniforms=Sn(this.outputShape.length);let{dataFormat:n}=t,s=fn(),r=n===\"channelsLast\",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?\"if(blockIndex < outShape[1] && pos < outShape[0]) {\":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u=\"\";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`\n          blockIndex = rc.y + ${c};\n          pos = rc.x + ${l};\n\n          ${o}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${a}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${r}) {\n                  innerDims = vec2(d1, ch);\n                  result[${l*2+c}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${l*2+c}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${u}\n\n        ${s.output} = result;\n      }\n    `}};function Z1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat===\"channelsLast\",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>H1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(el(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let N=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=zd({a:k,b:N,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=T,A.shape=n.outShape,g=kn({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=zd({a:k,b:T,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:N},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(N)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function J1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h===\"channelsLast\",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let N=new HY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(N,[k],\"float32\",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o===\"leakyrelu\",z=o?Kp(o,!0):null,W=new G1(P.shape,T.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,T];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],\"float32\",w.createScalarValue(i,\"float32\"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,\"float32\"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function qY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type===\"SAME\"||d.padInfo.type===\"VALID\"))h=Z1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)h=J1({x:r,filter:a,convInfo:d,backend:n});else{let m=new Q1(d);h=n.runWebGLProgram(m,[r,a],\"float32\")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var jY={kernelName:$a,backendName:\"webgl\",kernelFunc:qY},KY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${a}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},XY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${a}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},YY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${r};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${a};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${s} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},QY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${o}, ${u}, ${l});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${r}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${s}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${s} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function ZY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new KY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var JY={kernelName:cg,backendName:\"webgl\",kernelFunc:ZY};function e9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new XY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var t9={kernelName:_a,backendName:\"webgl\",kernelFunc:e9};function n9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new GY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var s9={kernelName:Xd,backendName:\"webgl\",kernelFunc:n9};function r9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=S.computeConv3DInfo(r.shape,u,i,1,o),c=new YY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var a9={kernelName:dg,backendName:\"webgl\",kernelFunc:r9};function i9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=S.computeConv3DInfo(u,a.shape,o,1,i),c=new QY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var o9={kernelName:pg,backendName:\"webgl\",kernelFunc:i9},u9=ou+`\n  return cos(x);\n`,l9=Ke({opSnippet:u9}),c9={kernelName:Aa,backendName:\"webgl\",kernelFunc:l9},d9=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,p9=Ke({opSnippet:d9}),h9={kernelName:Ea,backendName:\"webgl\",kernelFunc:p9},f9=class{constructor(e,t,n,s,r){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s===\"bilinear\"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,\"(y2-y1) * height_ratio\",`y1*${h} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,\"(x2-x1) * width_ratio\",`x1*${f} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${y});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${a}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${v};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${h} ) {\n          setOutput(float(${r}));\n          return;\n        }\n        float in_x = ${x};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${r}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${d} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}},m9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new f9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],\"float32\")},g9={kernelName:co,backendName:\"webgl\",kernelFunc:m9},bw=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"1.0\":`getX(${yw(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${rt(s)} coords = getOutputCoords();\n        int end = ${vw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${vw(s,\"coords\")} = idx;\n          val *= getX(${yw(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function yw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function vw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function b9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new bw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new bw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var y9={kernelName:hl,backendName:\"webgl\",kernelFunc:b9},xw=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"0.0\":`getX(${ww(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${rt(s)} coords = getOutputCoords();\n        int end = ${kw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${kw(s,\"coords\")} = idx;\n          val += getX(${ww(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function ww(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function kw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function v9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new xw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new xw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var x9={kernelName:lo,backendName:\"webgl\",kernelFunc:v9};function w9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=R1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=OK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var k9={kernelName:hg,backendName:\"webgl\",kernelFunc:w9},I9=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}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 S9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=new I9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var C9={kernelName:po,backendName:\"webgl\",kernelFunc:S9},e2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u=\"\",l=\"\";n&&(s?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:u=`\n          float activation(float x) {\n            ${n}\n          }\n        `,l=\"result = activation(result);\");let c=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${u}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${o};\n        int q = d2 - d1 * ${o};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${a}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${i}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${l}\n        setOutput(result);\n      }\n    `}},t2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`\n          vec4 xTexelC${g*2};\n          int xTexelC${g*2}Ready;\n          vec4 xTexelC${g*2+1};\n          int xTexelC${g*2+1}Ready;\n          vec4 xC${g};`;d+=`\n    for (int r = 0; r < ${l}; r++) {\n      `;for(let g=0;g<c;g++)d+=`\n          xTexelC${g*2} = vec4(0.0);\n          xTexelC${g*2}Ready = 0;\n          xTexelC${g*2+1} = vec4(0.0);\n          xTexelC${g*2+1}Ready = 0;\n          xC${g} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,o===1){if(b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n              `,u===1&&b>0?d+=`\n                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n                `:d+=`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n                  } else {\n                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n                  }\n                  `):d+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xC${b} = xTexelC${b};\n                `,b+1<c)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${y};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${b+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${b+1}Ready = 1;\n                  }\n                  `,u>1&&(d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                      xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${b}Ready = 1;\n                    }\n                    `),d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                  `):y===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${y};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${b+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${b+1}Ready = 1;\n                    }\n\n                    xC${b+1} = xTexelC${b+1};\n                    `}}else b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n              `,b+1<c&&(d+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n                `)):(d+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(\n                  xTexelC${b}.xy, xTexelC${b+1}.xy);\n              `,b+1<c&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<c&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<c&&(d+=`\n              wTexel = getW(r, ${b+1}, d1, q);\n              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}d+=`\n    }\n  `,d+=`\n      }\n    `;let h=\"\",f=\"\";n&&(s?h=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?h=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:h=`vec4 activation(vec4 x) {\n          ${n}\n        }`,f=\"result = activation(result);\");let m=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${h}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${a};\n        int q = d2 - d1 * ${a};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${d}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${m}\n        ${f}\n        setOutput(result);\n      }\n    `}};function N9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.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(r.shape,a.shape,i,c,o,l,!0),d;X().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new t2(p):d=new e2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],\"float32\",h)}var T9={kernelName:Ra,backendName:\"webgl\",kernelFunc:N9},$9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${a} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},_9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function A9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new $9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var E9={kernelName:fg,backendName:\"webgl\",kernelFunc:A9};function R9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new _9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var D9={kernelName:mg,backendName:\"webgl\",kernelFunc:R9},F9=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function O9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new F9(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var P9={kernelName:gg,backendName:\"webgl\",kernelFunc:O9},z9=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`\n      const ivec2 strides = ivec2(${r}, ${a});\n      const ivec2 pads = ivec2(${c}, ${p});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${i}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${o}; w++) {\n              int wIn = wBeg + w * ${l};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}};function M9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeDilation2DInfo(r.shape,a.shape,i,o,\"NHWC\",u),c,p=new z9(l);c=n.runWebGLProgram(p,[r,a],\"float32\");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var L9={kernelName:Yd,backendName:\"webgl\",kernelFunc:M9};function B9(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=qt({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=kv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Yp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of 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cQ={kernelName:yg,backendName:\"webgl\",kernelFunc:lQ},dQ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `}};function ec(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new dQ(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var pQ={kernelName:ml,backendName:\"webgl\",kernelFunc:ec},hQ=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${t} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},fQ={kernelName:go,backendName:\"webgl\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new hQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Cw=\"return floor(x);\",mQ=Ke({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:VK}),gQ={kernelName:Pa,backendName:\"webgl\",kernelFunc:mQ},bQ=`\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`,yQ=`\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division 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n.disposeData(d.dataId),f}function CQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];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\"))b=Z1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)b=J1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,T=h===\"leakyrelu\",N=h?Kp(h,!1):null,E=new Q1(g,x,N,k,T),A=[r,a];if(i&&A.push(i),o&&A.push(o),T){let 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    ${r} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${a};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `}};function AQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=WK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new _Q(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var EQ={kernelName:yo,backendName:\"webgl\",kernelFunc:AQ},RQ=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=DQ(e,2);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${s}));\n      }\n    `}};function DQ(e,t){let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(\"index\"):s.push(`${n[r]}`);return s.join()}function r2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get(\"DEBUG\")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype===\"string\"){let y=n.bufferSync(h),v=n.bufferSync(d),x=UK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new RQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var FQ={kernelName:bo,backendName:\"webgl\",kernelFunc:r2},OQ=\"return float(a > b);\",PQ=`\n  return vec4(greaterThan(a, b));\n`,zQ=jt({opSnippet:OQ,packedOpSnippet:PQ,cpuKernelImpl:GK,dtype:\"bool\"}),MQ={kernelName:vo,backendName:\"webgl\",kernelFunc:zQ},LQ=\"return 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b));\n`,iZ=jt({opSnippet:rZ,packedOpSnippet:aZ,cpuKernelImpl:jK,dtype:\"bool\"}),oZ={kernelName:wo,backendName:\"webgl\",kernelFunc:iZ};function uZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=KK(s,r,a);return t.makeTensorInfo([i.length],\"float32\",i)}var lZ={kernelName:xg,backendName:\"webgl\",kernelFunc:uZ},cZ=ou+`\n  return x < 0.0 ? 0./0. : log(x);\n`,dZ=`\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`,pZ=Ke({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:XK}),hZ={kernelName:Wa,backendName:\"webgl\",kernelFunc:pZ},fZ=ou+`\n  return log(1.0 + x);\n`,mZ=Ke({opSnippet:fZ}),gZ={kernelName:vl,backendName:\"webgl\",kernelFunc:mZ},bZ=\"return float(a >= 1.0 && b >= 1.0);\",yZ=`\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`,vZ=jt({opSnippet:bZ,packedOpSnippet:yZ,dtype:\"bool\"}),xZ={kernelName:ko,backendName:\"webgl\",kernelFunc:vZ},wZ=\"return float(!(x >= 1.0));\",kZ=Ke({opSnippet:wZ}),IZ={kernelName:xl,backendName:\"webgl\",kernelFunc:kZ},SZ=\"return float(a >= 1.0 || b >= 1.0);\",CZ=`\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`,NZ=jt({opSnippet:SZ,packedOpSnippet:CZ,dtype:\"bool\"}),TZ={kernelName:Jd,backendName:\"webgl\",kernelFunc:NZ},$Z=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${a}; j <= ${a}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${i}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${o};\n        setOutput(val);\n      }\n    `}},_Z=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${a};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${a}; j <= ${a}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${o};\n        setOutput(result);\n      }\n    `}},AZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=X().getBool(\"WEBGL_PACK_NORMALIZATION\")?new _Z(r.shape,a,i,o,u):new $Z(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},EZ={kernelName:ep,backendName:\"webgl\",kernelFunc:AZ},RZ=class{constructor(e,t,n,s,r){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${s}) * norm + float(${n});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${s})\n                * float(${r})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${r});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}},DZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new RZ(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},FZ={kernelName:wg,backendName:\"webgl\",kernelFunc:DZ};function OZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=xi(o,e.dtype,\"max\",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function a2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N<x.length;N++)x[N]=r.shape[c[N]];let k=wv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let T=n.texData.get(h.dataId);T.values=k}else h=Xp(r,c,n);l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims(\"max\",l,o);let[f,m]=S.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=S.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=YK(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=OZ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var PZ={kernelName:Ua,backendName:\"webgl\",kernelFunc:a2},zZ=L1+`\n  return max(a, b);\n`,MZ=`\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+jp+`\n  return result;\n`,LZ=jt({opSnippet:zZ,packedOpSnippet:MZ,cpuKernelImpl:QK}),BZ={kernelName:Ga,backendName:\"webgl\",kernelFunc:LZ};function VZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,\"maxPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.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(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new tl(c,\"max\",!1);return n.runWebGLProgram(p,[r],r.dtype)}var WZ={kernelName:Ha,backendName:\"webgl\",kernelFunc:VZ};function UZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Iv(p,\"max\",!1);return n.runWebGLProgram(d,[r],r.dtype)}var GZ={kernelName:tp,backendName:\"webgl\",kernelFunc:UZ},HZ=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${r};\n          wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${a}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},qZ=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${p}, ${d});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? 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1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function jZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new Iv(d,\"max\",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new qZ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var KZ={kernelName:Ig,backendName:\"webgl\",kernelFunc:jZ};function XZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;nu([a,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=S.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new tl(d,\"max\",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new HZ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var YZ={kernelName:kg,backendName:\"webgl\",kernelFunc:XZ};function QZ(e,t,n,s){let r=new tl(n,\"max\",!1),a=s.runWebGLProgram(r,[e],\"float32\");r=new tl(n,\"max\",!0,!0,t);let i=s.runWebGLProgram(r,[e],\"float32\");return[a,i]}var ZZ={kernelName:Sg,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(S.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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setOutput(value);\n        } else {\n          ${r} coords = outC - start;\n          setOutput(getX(${o}));\n        }\n      }\n    `}},G7=class{constructor(e,t,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=rt(s),a=t.map(f=>f[0]).join(\",\"),i=t.map((f,m)=>f[0]+e[m]).join(\",\"),o=ln(\"rc\",s),u=ln(\"source\",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?\"source\":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;\n       if(${l}) {\n      `,s===1?\"\":`}\n       rc = outputLoc;\n       ${o[s-2]} += 1;\n       if(${o[s-2]} < ${this.outputShape[s-2]}) {`,s===1?\"\":`  ${o[s-1]} += 1;\n         if(${l}) {`],d=s===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",h=\"\";for(let f=0,m=s===1?2:4;f<m;f++)h+=`\n        ${p[f]}\n        if (${d}) {\n          result[${f}] = 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sign(a) * pow(abs(a), b);\n`,j7=`\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  `+jp+`\n  return result;\n`,K7=jt({opSnippet:q7,packedOpSnippet:j7}),X7={kernelName:Za,backendName:\"webgl\",kernelFunc:K7};function Y7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=S.getAxesPermutation(c,o),d=r;p!=null&&(d=qt({inputs:{x:r},backend:n,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,o),u.push(d)),S.assertAxesAreInnerMostDims(\"prod\",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=nX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=S.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=cp(r.dtype),v=xi(b,y,\"prod\",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=S.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Q7={kernelName:Ao,backendName:\"webgl\",kernelFunc:Y7},d2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=sX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Z7={kernelName:Il,backendName:\"webgl\",kernelFunc:d2},J7=\"return 1.0 / x;\",eJ=Ke({opSnippet:J7}),tJ={kernelName:Sl,backendName:\"webgl\",kernelFunc:eJ},nJ=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,sJ=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,rJ=Ke({opSnippet:nJ,packedOpSnippet:sJ}),aJ={kernelName:ei,backendName:\"webgl\",kernelFunc:rJ},iJ=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,oJ=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,uJ=Ke({opSnippet:iJ,packedOpSnippet:oJ}),lJ={kernelName:ni,backendName:\"webgl\",kernelFunc:uJ},cJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":p=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `}},dJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":p=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function pJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new dJ(r.shape,u,l,a,i):new cJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],\"float32\")}var hJ={kernelName:ti,backendName:\"webgl\",kernelFunc:pJ},fJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function mJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new fJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var gJ={kernelName:Tg,backendName:\"webgl\",kernelFunc:mJ},bJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}},yJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function vJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new yJ(r.shape,u,l,a,i):new bJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var xJ={kernelName:Cl,backendName:\"webgl\",kernelFunc:vJ},wJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${o[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${s}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function kJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new wJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var IJ={kernelName:Ng,backendName:\"webgl\",kernelFunc:kJ},SJ=class{constructor(e,t){this.variableNames=[\"x\"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet 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u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=mX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Gee(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var qee={kernelName:Cr,backendName:\"webgl\",kernelFunc:h2},jee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     `}},Kee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     `}};function Ur(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function _w(e){let t=1;for(;t<e;)t*=2;return t}function Xee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),u=X().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=gX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,\"int32\",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:\"int32\",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Ur(n,h);let b=_w(a),y=_w(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new jee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,\"int32\",q),Ur(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Kee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,\"int32\",z),Ur(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let T=v;v=uu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Ur(n,T);let N=r2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Ur(n,g);let E=l.slice(0,-1);E.push(a),T=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Ur(n,T);let A=N;return N=he({inputs:{x:N},attrs:{shape:E},backend:n}),Ur(n,A),[N,v]}var Yee={kernelName:Wo,backendName:\"webgl\",kernelFunc:Xee},Qee=class{constructor(e,t,n,s,r,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=n===\"nearest\"?1:2,o;switch(s){case\"constant\":o=1;break;case\"reflect\":o=2;break;case\"wrap\":o=3;break;case\"nearest\":o=4;break;default:o=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${o} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${r});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${r});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${i} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}};function Zee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Qee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],\"float32\")}var Jee={kernelName:Uo,backendName:\"webgl\",kernelFunc:Zee};function ete(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;nu(a,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=bX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],\"int32\",l)]}var tte={kernelName:Ag,backendName:\"webgl\",kernelFunc:ete};function nte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=uu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var ste={kernelName:Go,backendName:\"webgl\",kernelFunc:nte},rte=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o=\"0.0\",u=\"sumValue\",l=Math.floor(n/4)*4,c=n%4,p=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";r%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `);let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${o};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${h}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${a})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function ate(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=S.getAxesPermutation([l],o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,o)[0]);let d=S.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=cp(r.dtype),g=(x,k,T,N,E)=>{let A=x.shape[0],P=x.shape[1],R=S.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new rte(F,k),z=n.compileAndRun($,[x,T],N);if(u.push(z),z.shape[1]===E)return z;let W=d2({backend:n,attrs:{start:0,stop:E,step:1,dtype:\"float32\"}}),q=h2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,N,E)},b=g(f,\"unsortedSegmentSum\",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=S.getUndoAxesPermutation(c);v=qt({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var ite={kernelName:up,backendName:\"webgl\",kernelFunc:ate},ote=[d8,h8,g8,v8,w8,S8,N8,$8,R8,F8,z8,B8,U8,j8,Y8,Z8,eY,rY,iY,uY,pY,vY,wY,IY,_Y,EY,OY,qX,MY,UY,jY,JY,t9,s9,a9,o9,c9,h9,g9,y9,x9,k9,C9,T9,E9,D9,P9,L9,V9,H9,X9,J9,nQ,aQ,iQ,uQ,cQ,pQ,fQ,gQ,xQ,IQ,NQ,$Q,EQ,FQ,MQ,WQ,HX,GQ,VY,jQ,YQ,JQ,KX,sZ,oZ,lZ,hZ,gZ,xZ,IZ,TZ,EZ,FZ,PZ,BZ,WZ,GZ,KZ,YZ,ZZ,e7,n7,i7,c7,f7,k7,JX,N7,_7,R7,O7,CY,M7,B7,W7,H7,X7,YX,Q7,Z7,NY,y7,tJ,aJ,lJ,t8,hJ,gJ,xJ,IJ,TJ,_J,RJ,OJ,zJ,BJ,UJ,jJ,YJ,JJ,nee,bY,x7,aee,oee,lee,dee,hee,mee,bee,vee,wee,See,Nee,$ee,Eee,Dee,Oee,zee,v7,u8,Bee,Uee,qee,Yee,Jee,l8,tte,ste,ite,L7];for(let e of ote)El(e);var zs=X();zs.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);zs.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);zs.registerFlag(\"WEBGPU_MATMUL_WORK_PER_THREAD\",()=>4);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D\",()=>!1);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);zs.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);zs.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);zs.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);zs.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);zs.registerFlag(\"WEBGPU_USE_IMPORT\",()=>!1);var ute=\"return a + b;\",lte=\"return areal * breal - aimag * bimag;\",cte=\"return areal * bimag + aimag * breal;\",dte=\"return a / b;\",pte=\"return a * b;\",hte=\"return (a - b) * (a - b);\",fte=\"return a - b;\",mte=\"return f32(a == b);\",gte=\"return vec4<f32>(a == b);\",bte=\"return f32(a > b);\",yte=\"return vec4<f32>(a > b);\",vte=\"return f32(a >= b);\",xte=\"return vec4<f32>(a >= b);\",wte=\"return f32(a < b);\",kte=\"return vec4<f32>(a < b);\",Ite=\"return f32(a <= b);\",Ste=\"return vec4<f32>(a <= b);\",Cte=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",Nte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,Tte=`\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `,f2=`\n  if (isNaN.r) {\n    resultTemp.r = uniforms.NAN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = uniforms.NAN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = uniforms.NAN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = uniforms.NAN;\n  }\n  `,$te=`\n  let s = sign(a) * sign(b);\n  let ia = i32(round(a));\n  let ib = i32(round(b));\n  return f32(idiv(ia, ib, s));\n  `,_te=`\n  let ia = vec4<i32>(round(a));\n  let ib = vec4<i32>(round(b));\n  let cond = ib != vec4<i32>(0);\n  var resultTemp = vec4<i32>(0);\n  let s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4<f32>(resultTemp);\n  `,Ate=\"return f32(a != b);\",Ete=\"return vec4<f32>(a != b);\",Rte=`\n  if(a < 0.0 && floor(b) < b) {\n    return uniforms.NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  if (round(abs(b) % 2.0) != 1.0) {\n    return pow(abs(a), b);\n  }\n  return sign(a) * pow(abs(a), b);\n  `,Dte=`\n  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);\n  let isModRound1 = vec4<f32>(isModRound1Bool);\n  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);\n  var resultTemp = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  let isExpZero = b == vec4<f32>(0.0);\n  if (isExpZero.r) {\n    resultTemp.r = 1.0;\n  }\n  if (isExpZero.g) {\n    resultTemp.g = 1.0;\n  }\n  if (isExpZero.b) {\n    resultTemp.b = 1.0;\n  }\n  if (isExpZero.a) {\n    resultTemp.a = 1.0;\n  }\n  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;\n  ${f2}\n  return resultTemp;\n  `,Fte=\"if (a < 0.0) { return b * a; }  return a;\",Ote=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;function Aw(e,t){let n=t?f2:Tte;return t?`\n    var resultTemp = vec4<f32>(${e}(a, b));\n    let isNaN = isnanVec4(a) | isnanVec4(b);\n    `+n+`\n    return resultTemp;\n  `:n+`\n    return ${e}(a, b);\n  `}function tc(e,t){switch(e){case 0:return pte;case 1:return ute;case 2:return fte;case 3:return dte;case 4:return t?gte:mte;case 5:return t?yte:bte;case 6:return t?xte:vte;case 7:return t?kte:wte;case 8:return t?Ste:Ite;case 9:return t?Nte:Cte;case 10:return t?Ete:Ate;case 11:return hte;case 12:return t?_te:$te;case 14:return t?Ote:Fte;case 15:return Aw(\"max\",t);case 16:return Aw(\"min\",t);case 13:return t?Dte:Rte;case 17:return lte;case 18:return cte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Pte=\"return abs(a);\",zte=\"return ceil(a);\",Mte=\"return cos(a);\",Lte=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,Bte=\"return exp(a) - 1.0;\",Vte=\"if (a >= 0.0) { return a; }  return (exp(a) - 1.0);\",Wte=`\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,Ute=\"return exp(a);\",Gte=\"return floor(a);\",Hte=\"return a;\",qte=`if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`,jte=\"return f32(!(a >= 1.0));\",Kte=\"return -a;\",Xte=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Yte=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`,Qte=\"if(a < 0.0) { return 0.0; } return a;\",Zte=\"return clamp(a, 0.0, 6.0);\",Jte=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",ene=`\n  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));\n  let isNaN = isnanVec4(a);\n\n  if (isNaN.r) {\n    resFloat.r = a.r;\n  }\n  if (isNaN.g) {\n    resFloat.g = a.g;\n  }\n  if (isNaN.b) {\n    resFloat.b = a.b;\n  }\n  if (isNaN.a) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,tne=\"return 1.0/sqrt(a);\",nne=\"return 1.0 / (1.0 + exp(-1.0 * a));\",sne=\"return sin(a);\",rne=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,ane=\"return sqrt(a);\",ine=\"return a * a;\",one=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,une=\"return f32(i32((a)));\";function Hr(e,t){switch(e){case 0:return Pte;case 2:return Mte;case 3:return Lte;case 1:return zte;case 4:return t?Wte:Vte;case 5:return Ute;case 6:return Bte;case 7:return Gte;case 8:return Hte;case 9:return qte;case 10:return jte;case 11:return Kte;case 14:return t?Yte:Xte;case 12:return t?ene:Qte;case 13:return t?Jte:Zte;case 15:return tne;case 18:return nne;case 16:return sne;case 17:return rne;case 19:return ane;case 20:return ine;case 21:return one;case 22:return une;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e===\"linear\")return Hr(8);if(e===\"relu\")return Hr(12,t);if(e===\"elu\")return Hr(4,t);if(e===\"relu6\")return Hr(13,t);if(e===\"prelu\")return tc(14,t);if(e===\"sigmoid\")return Hr(18,t);if(e===\"leakyrelu\")return Hr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function lne(e,t){if(Math.max(...e)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return\"i32\";if(e===2)return\"vec2<i32>\";if(e===3)return\"vec3<i32>\";if(e===4)return\"vec4<i32>\";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e===\"float32\"?t?\"vec4<f32>\":\"f32\":e===\"int32\"||e===\"bool\"?t?\"vec4<i32>\":\"i32\":e}function Sv(){return`\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`}function Dr(){return`\n  ${Sv()}\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n`}function Ue(){return`\n    ${Dr()}\n      let index = getGlobalIndex();\n`}function cne(e,t,n,s=!1){let r=[];if(r.push(`\n    let workGroupSizeX = ${n.workGroupSize[0]}u;\n    let workGroupSizeY = ${n.workGroupSize[1]}u;\n    let workGroupSizeZ = ${n.workGroupSize[2]}u;\n\n    var<private> localId: vec3<u32>;\n    var<private> globalId: vec3<u32>;\n    var<private> numWorkgroups: vec3<u32>;\n\n    // Only used when the y/z dimension of workgroup size is 1.\n    fn getGlobalIndex() -> i32 {\n      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {\n        return i32(globalId.x);\n      }\n\n      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n          localId.y * workGroupSizeX + localId.x;\n      let workGroupID = (globalId - localId)/vec3<u32>(\n          workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n        workGroupID.y * numWorkgroups.x + workGroupID.x) *\n        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n        localInvocationIndex);\n    }\n  `),s===!0)return r.push(`\n      struct Uniform {\n        size            : i32,\n        numChannels     : i32,\n        outShapeStrides : vec2<i32>,\n        dispatchSize    : vec3<u32>,\n      };\n\n      @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype,n.isVec4)}>;\n      @group(0) @binding(2) var<uniform> uniforms: Uniform;\n    `),[Ew,r.join(`\n`),Rw(t.shape),n.getUserCode()].join(`\n`);let a=\"struct Uniforms { NAN : f32, \";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${Wt(e[d].shape.length)}, `}),a+=`outShape : ${Wt(t.shape.length)}, `;let i=t.shape.length-1;a+=`\n       outShapeStrides: ${Wt(i)}, `,n.size&&(a+=\"size : i32, \"),n.uniforms&&(a+=n.uniforms),a+=\"};\",r.push(a),n.atomic?r.push(`\n    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n  `):r.push(`\n    @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype,n.isVec4)}>;\n  `),n.variableNames.forEach((p,d)=>{r.push(`\n    @group(0) @binding(${1+d}) var<storage, read> ${p}: array<${cd(e[d].dtype,n.isVec4)}>;\n    `)}),a!==\"\"&&r.push(`\n    @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;\n    `);let[o,u]=gne(t.shape,n.dispatchLayout),l=[Ew,r.join(`\n`),Rw(t.shape),o,dne(t.shape.length)];if(n.atomic||l.push(pne(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>hne(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`\n`);l.push(p)}return l.push(n.getUserCode()),l.join(`\n`)}var Ew=`\n  // Checks whether coordinates lie within the bounds of the shape.\n  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {\n    return all(coord >= vec2<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {\n    return all(coord >= vec3<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {\n    return all(coord >= vec4<i32>(0)) && all(coord < shape);\n  }\n\n  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n    return coord;\n  }\n  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {\n    return dot(coords, vec2<i32>(shape.y, 1));\n  }\n  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {\n    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));\n  }\n  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n    return dot(coords, vec4<i32>(\n        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let mod: i32 = a % b;\n    if (sign < 0. && mod != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;function dne(e){let t=\"\";switch(e){case 0:case 1:t+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:t+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:t+=`\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;break;case 4:t+=`\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function pne(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${r}(value);\n    }`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${r}(value);\n    }`,s>=2){let i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=Wt(s);n?a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return a}function hne(e,t,n,s){let r=fne(e,n);return e.shape.length<=t.length&&(r+=mne(e,t,n,s)),r}function fne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=i.map(c=>`${c} : i32`).join(\", \");if(s<1)return t?`\n        fn ${a}() -> vec4<f32> {\n          return vec4<f32>(${n}[0]);\n        }\n      `:`\n      fn ${a}() ->f32 {\n        return f32(${n}[0]);\n      }\n    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l=\"1D\"),t?`\n      fn ${a}(${o}) -> vec4<f32> {\n        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n          ${u}) / 4]);\n      }\n      `:`\n    fn ${a}(${o}) -> f32 {\n      return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n        ${u})]);\n    }\n   `}function mne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i=\"get\"+a+\"ByOutput\",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`\n        fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n          return vec4<f32>(${r}[globalIndex]);\n        }\n\n        fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n          return vec4<f32>(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n        }\n        `:`\n      fn ${i}Index(globalIndex : i32) -> f32 {\n        return f32(${r}[globalIndex]);\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32 {\n        return f32(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n      }\n      `;let c=S.getBroadcastDims(e.shape,t),p=u-o,d=\"\";if(o===0)return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n        return get${a}();\n      }\n    `:`\n      fn ${i}Index(globalIndex : i32) -> f32{\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32{\n        return get${a}();\n      }\n    `;u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(g=>`coords[${g+p}] = 0;`).join(`\n`);let h=\"\";if(u<2&&o>0)h=\"coords\";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords[${v+p}]`).join(\", \");h=`${g}(${b})`}else h=\"coords\";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        var coords = getCoordsFromIndex(globalIndex);\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n\n      fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {\n        var coords = coordsIn;\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n    `:`\n    fn ${i}Index(globalIndex : i32) -> f32 {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n\n    fn ${i}Coords(coordsIn : ${l}) -> f32 {\n      var coords = coordsIn;\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n  `}function gne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{\n      let globalIndex = getGlobalIndex();\n      return getCoordsFromIndex(globalIndex);\n    }\n    `,a];let i=\"\",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=lne(h,\"uniforms.outShape\");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {\n    ${i}\n  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(\",\")}); }`,[p,u]}function Rw(e){let t=e.length;if(t<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;let a=\"var index2 = index;\"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${l};`}).join(\"\");return`\n    fn getCoordsFromIndex(index : i32) -> ${s} {\n      ${a}\n      return ${s}(${r.join(\",\")});\n    }\n  `}var m2={};Ae(m2,{ArrayBufferToTypedArray:()=>b2,GPUBytesPerElement:()=>jm,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Cv,computeWorkGroupSizeForMatMul:()=>g2,computeWorkPerThreadForConv2d:()=>Nv,flatDispatchLayout:()=>Be,isWebGPUSupported:()=>Tv,tilesFitEvenlyIntoShape:()=>Ks});var na=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(na(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(na(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(na(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function Cv(e,t){let n=na(e.x.map(r=>t[r])),s=na(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function g2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Nv(e,t){let n=na(e.x.map(r=>t[r])),s=na(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Be(e){return{x:e.map((t,n)=>n)}}function jm(e){if(e===\"float32\"||e===\"int32\"||e===\"bool\"||e===\"string\")return 4;if(e===\"complex64\")return 8;throw new Error(`Unknown dtype ${e}`)}function b2(e,t){if(t===\"float32\")return new Float32Array(e);if(t===\"int32\")return new Int32Array(e);if(t===\"bool\"||t===\"string\")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Tv(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function y2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>\"tileInner must be divisible by 4. And ColPerThread must be 4\"),`\n  var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;\n\n  let RowPerThread = ${e[1]};\n  let ColPerThread = ${e[0]};\n  let TileInner = ${s};\n\n  ${Dr()}\n\n    let tileRow = ${t===1?\"0\":\"i32(localId.y) * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${t===1?\"0\":\"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n    var acc: array<vec4<f32>, RowPerThread>;\n    var ACached : vec4<f32>;\n    var BCached : array<vec4<f32>, 4>;\n\n    // Loop over shared dimension.\n    var globalColA = tileCol;\n    let RowPerThreadB = TileInner / i32(workGroupSizeY);\n    let tileRowB = i32(localId.y) * RowPerThreadB;\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);\n        }\n        globalColA = globalColA + TileInner / ColPerThread;\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {\n            BCached[0] = mm_Bsub[k * ColPerThread][tileCol];\n            BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];\n            BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];\n            BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];\n\n            for (var i = 0; i < RowPerThread; i = i + 1) {\n                ACached = mm_Asub[tileRow + i][k];\n                acc[i] = BCached[0] * ACached.x + acc[i];\n                acc[i] = BCached[1] * ACached.y + acc[i];\n                acc[i] = BCached[2] * ACached.z + acc[i];\n                acc[i] = BCached[3] * ACached.w + acc[i];\n            }\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(globalRow + innerRow,\n                 globalCol,\n                 acc[innerRow], globalId);\n    }\n  }`}var bne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?\"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]\":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];\n        }\n        return vec4<f32>(0.0)`,t=this.fitB?\"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];\n        }\n        return vec4<f32>(0.0)`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${i}\n                }`:n=`\n            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n              ${i}\n            }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${e};\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${t};\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {\n        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])\n        {\n          var value = valueIn;\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col * 4);\n          ${r}\n          ${s}\n          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);\n        }\n      }\n      ${y2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}\n    `}};function $v(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`\n    var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;\n    ${Dr()}\n      let tileRow = i32(localId.y) * ${e[1]};\n      let tileCol = i32(localId.x) * ${e[0]};\n\n      let globalRow = i32(globalId.y) * ${e[1]};\n      let globalCol = i32(globalId.x) * ${e[0]};\n\n      let numTiles = (uniforms.dimInner - 1) / ${r} + 1;\n\n      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;\n      var ACached : f32;\n      var BCached : array<f32, ${e[0]}>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let ColPerThreadA = ${r} / ${t[0]};\n      let tileColA = i32(localId.x) * ColPerThreadA;\n      let RowPerThreadB = ${r} / ${t[1]};\n      let tileRowB = i32(localId.y) * RowPerThreadB;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileColA + innerCol;\n\n            mm_Asub[inputRow][inputCol] = mm_readA(\n                globalRow + innerRow,\n                t * ${r} + inputCol, globalId);\n          }\n        }\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n\n            mm_Bsub[inputRow][inputCol] = mm_readB(\n              t * ${r} + inputRow,\n              globalCol + innerCol, globalId);\n          }\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${r}; k = k + 1) {\n          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n            ACached = mm_Asub[tileRow + innerRow][k];\n            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n\n          if ((globalCol + innerCol) < uniforms.dimBOuter &&\n              (globalRow + innerRow) < uniforms.dimAOuter) {\n            mm_write(globalRow + innerRow,\n                     globalCol + innerCol,\n                     acc[innerRow][innerCol], globalId);\n          }\n        }\n      }\n    }\n  `}function yne(e){return`\n    let TileSize = ${e[0]*4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;\n\n    ${Dr()}\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),\n                                mm_readA(globalRow, colA + 1, globalId),\n                                mm_readA(globalRow, colA + 2, globalId),\n                                mm_readA(globalRow, colA + 3, globalId));\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),\n                              mm_readB(rowB + 1, globalCol, globalId),\n                              mm_readB(rowB + 2, globalCol, globalId),\n                              mm_readB(rowB + 3, globalCol, globalId));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {\n        mm_write(globalRow, globalCol, acc, globalId);\n      }\n    }\n  `}var v2=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=g2(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=o!=null;l&&this.variableNames.push(\"bias\"),c&&this.variableNames.push(\"preluActivationWeights\"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,u]:[this.outputShape[0],u,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let r=[t,s],a=[s,n];return[Ks(r,this.aShape.slice(1)),Ks(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?\"return A[batch * batchASize + row * uniforms.dimInner + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch * batchASize + row * uniforms.dimInner + col];\n           }\n           return 0.0;`:e=this.fitA?\"return A[batch * batchASize + col * uniforms.dimAOuter + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch* batchASize + col * uniforms.dimAOuter + row];\n           }\n           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n           }\n           return 0.0;`:t=this.fitB?\"return B[batch * batchBSize + col * uniforms.dimInner + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + col * uniforms.dimInner + row];\n           }\n           return 0.0;`;let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        var value = valueIn;\n        let batch = i32(globalId.z);\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${this.outputShape[1]>1?$v([this.workPerThread,this.workPerThread,1],this.workGroupSize):yne(this.workGroupSize)}\n    `}};function vne(){return`\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${Dr()}\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var xne=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e=\"return A[batch * batchASize + row * uniforms.dimInner + col];\":e=\"return A[batch * batchASize + col * uniforms.dimAOuter + row];\";let t;this.transposeB===!1?t=\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":t=\"return B[batch * batchBSize + col * uniforms.dimInner + row];\";let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        ${e}\n      }\n\n      fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {\n        var value = valueIn;\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${vne()}\n    `}};function wne(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`\n  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;\n  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Introduces two shared memory buffers, some logical threads could handle\n  // arithmetic operations and others handle IO operations between barrier api,\n  // makes ALUs and load/store units work simultaneously, could improves\n  // the performance.\n  ${Dr()}\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${s} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = tileRow;\n    for (var t = 0; t < numTiles; t = t + 1) {\n      if (t == 0) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        }\n      } else {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n      if (t != 0) {\n        t = t + 1;\n      }\n\n      if (t < numTiles) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub2[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};\n    if (tileRow >= ${t} && writeCol >= 0) {\n      mm_write(writeCol, globalCol, acc, globalId);\n    }\n  }\n  `}var kne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>\"This program can be only used when A width or B Height are small\"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push(\"bias\");let o=a!=null;o&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n          return A[batch * batchASize + row * uniforms.dimInner + col];\n        }\n        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n         }\n         return 0.0;`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            let b = getPreluActivationWeightsByOutputCoords(outCoord);\n            ${i}\n            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            ${i}\n        }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col);\n          var value = valueIn;\n          ${r}\n          ${s}\n          setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${wne(this.workGroupSize)}\n    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Ine={kernelName:Eo,backendName:\"webgpu\",kernelFunc:Me};function _v({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new xne([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new kne(k,T,[P,h,f],a,u,i):R?F=new bne(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),a,u,i):F=new v2(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),n,s,a,u,i);let $=[N,E];a&&$.push(a),i&&$.push(i);let z=[{type:\"int32\",data:[h]},{type:\"int32\",data:[f]},{type:\"int32\",data:[p]}];u===\"leakyrelu\"&&(z.push({type:\"float32\",data:[o]}),F.uniforms+=\" alpha : f32,\");let W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function Sne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return _v({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var Cne={kernelName:ra,backendName:\"webgpu\",kernelFunc:Sne},Dw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n\n      ${Ue()}\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}},Nne=class{constructor(e,t,n,s){this.variableNames=[\"A\",\"B\"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);\n         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];\n         let b = getBByOutputCoords(coords);`;return`\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${tc(this.op,!1)}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Ue()}\n\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${t}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `}},Tne=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`\n      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {\n        ${tc(this.op,this.isVec4)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n    `}},x2=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOperation(a : f32, b : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n      `}};function Fw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new Tne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Nne(e,t,n,a):new x2(e,t,n)}function es(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var $ne={kernelName:Ba,backendName:\"webgpu\",kernelFunc:es};function lu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.tensorMap.get(a.dataId),o=es({inputs:{x:s},backend:n}),u=es({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var _ne={kernelName:jd,backendName:\"webgpu\",kernelFunc:lu},nc=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${Hr(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype===\"complex64\"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Dw(17,i.shape,o.shape),b=new Dw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,\"float32\"),f=u.runWebGPUProgram(b,y,\"float32\")}let m=lu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(i.dtype,o.dtype);if((i.dtype===\"string\"||o.dtype===\"string\"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype===\"string\"?S.fromUint8ToStringArray(p):p,f=i.dtype===\"string\"?S.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:Ane,ceilImpl:Ene,concatImpl:Rne,equalImpl:Dne,expImpl:Fne,expm1Impl:One,floorImpl:Pne,gatherNdImpl:zne,gatherV2Impl:Mne,greaterEqualImpl:Lne,greaterImpl:Bne,lessEqualImpl:Vne,lessImpl:Wne,logImpl:Une,maxImpl:Gne,maximumImpl:Hne,minimumImpl:qne,multiplyImpl:jne,negImpl:Kne,notEqualImpl:Xne,prodImpl:Yne,rangeImpl:Qne,rsqrtImpl:Zne,simpleAbsImpl:Jne,sliceImpl:ese,stridedSliceImpl:tse,stringNGramsImpl:nse,subImpl:sse,tileImpl:rse,topKImpl:ase,transposeImpl:ise,uniqueImpl:Mpe}=Zy,ose=Kt({opType:0,cpuKernelImpl:Jne}),use={kernelName:io,backendName:\"webgpu\",kernelFunc:ose},lse=mn({opSnippet:1,cpuKernelImpl:Ane,supportsComplex:!0}),cse={kernelName:Ir,backendName:\"webgpu\",kernelFunc:lse},dse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(\" + \");return`\n      ${Ue()}\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${e.join(`\n        `)}\n            setOutputAtIndex(flatIndex, ${t});\n          }\n        }\n      }\n    `}};function pse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return es({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>cn(o,u)),a=s.map(o=>o.shape),i=new dse(a);return n.runWebGPUProgram(i,s,r)}var hse={kernelName:ka,backendName:\"webgpu\",kernelFunc:pse},w2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"axis : i32, infinityValue : f32,\",this.size=!0;let s=[t];S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n===\"min\"?\"<\":\">\";let[r]=S.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape[${r}]`;return`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${e}\n\n      // In order to get a flattened index into the input tensor, we need to\n      // add back the index along the reduced dimension to |outputCoords|.\n      // This function outputs the offset to the first value along\n      // |axis| and the stride to get the next value of the input along |axis|.\n      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        var i = ${this.outputShape.length-1};\n\n        var stride = 1;\n        var inputStride = 1;\n        var offset = 0;\n\n        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {\n          let length = ${n(`${this.inputShape.length} - r`)};\n          if (${this.inputShape.length} - r == uniforms.axis) {\n            inputStride = stride;\n          } else {\n            offset = offset + ${t(\"outputCoords\",\"i\")} * stride;\n            i = i - 1;\n          }\n          stride = stride * length;\n        }\n\n        return vec2<i32>(offset, inputStride);\n      }\n\n      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{\n        return coordInfo[0] + coordInfo[1] * index;\n      }\n\n      ${Ue()}\n        let outputIndex = index / i32(workGroupSizeX);\n        let coordInfo = getInputCoordInfo(outputIndex);\n        let Length = ${n(\"uniforms.axis\")};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n\n        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = f32(x[getInputIndex(coordInfo, k)]);\n          if (!isnan(candidate) && candidate ${this.op} bestValue) {\n            bestValue = candidate;\n            bestIndex = k;\n          }\n        }\n        xBestValues[localId.x] = bestValue;\n        xBestIndices[localId.x] = bestIndex;\n        workgroupBarrier();\n\n        var reduceSize = min(u32(Length), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `}},fse=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){return`\n      let TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;\n      ${Sv()}\n      fn main(@builtin(local_invocation_id) localId : vec3<u32>,\n              @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] = A[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}},mse=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=gse(this.newDim);return`\n      ${Ue()}\n\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function gse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function wi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=ise(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new fse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new mse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var bse={kernelName:pi,backendName:\"webgpu\",kernelFunc:wi};function yse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],\"max\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var vse={kernelName:Ia,backendName:\"webgpu\",kernelFunc:yse};function xse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],\"min\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var wse={kernelName:ol,backendName:\"webgpu\",kernelFunc:xse},k2=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,\",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${e}\n            }\n          }\n\n          setOutputAtIndex(index, ${t});\n        }\n      }\n    `}},I2=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>,\",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function kse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});let p,d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new I2(c):(p=new k2(c,\"avg\"),d.push({type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var Ise={kernelName:Sa,backendName:\"webgpu\",kernelFunc:kse};function Sse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return _v({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Cse={kernelName:Ca,backendName:\"webgpu\",kernelFunc:Sse},Nse=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Wt(this.rank),t=Tse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>\"sourceLoc = uniforms.start + coords;\"):n=this.outputShape.map((r,a)=>`sourceLoc.${Km[a]} = uniforms.start[${a}] + coords.${Km[a]};`),`\n      ${Ue()}\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${n.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},Km=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Tse(e){if(e===1)return\"sourceLoc\";if(e<=6)return Km.slice(0,e).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}function cu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.tensorMap.get(r.dataId),d=ese(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new Nse(o,u),c=[{type:\"int32\",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var $se={kernelName:Po,backendName:\"webgpu\",kernelFunc:cu},_se=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=cu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},Ase={kernelName:oo,backendName:\"webgpu\",kernelFunc:_se},S2=mn({opSnippet:10,dtype:\"bool\",cpuKernelImpl:Xne}),Ese={kernelName:So,backendName:\"webgpu\",kernelFunc:S2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.real},backend:n})}var Rse={kernelName:np,backendName:\"webgpu\",kernelFunc:sc};function Dse(e,t){let n=new nc(e.shape,22),s=t.runWebGPUProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Xm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return es({inputs:{x:r},backend:n});let i=$t(r.shape),o=Xm({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=lu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype===\"complex64\"){let i=sc({inputs:{input:r},backend:n}),o=Xm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=es({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return Dse(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=S2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Fse={kernelName:Na,backendName:\"webgpu\",kernelFunc:Xm},Ose=Kt({opType:1,cpuKernelImpl:Ene}),Pse={kernelName:Ta,backendName:\"webgpu\",kernelFunc:Ose},zse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}},Mse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function Lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:\"float32\",data:[a]},{type:\"float32\",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new zse(r.shape):o=new Mse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Bse={kernelName:Sr,backendName:\"webgpu\",kernelFunc:Lse},Vse=class{constructor(e){this.uniforms=\"\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Ue()}\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${e.join(`\n        `)}\n          }\n        }\n      }\n    `}};function Zp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Wse={kernelName:Zd,backendName:\"webgpu\",kernelFunc:Zp};function Ym(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>Zp({inputs:{input:y},backend:n})),m=Ym(h,t,n),g=Ym(f,t,n),b=lu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=S.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=Rne(f,m,s,g),y=S.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Use(e,t,n),o=a.map(h=>h.shape),u=new Vse(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:\"int32\",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:\"int32\",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Use(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return es({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Ym(o,a,n)}var Gse={kernelName:uo,backendName:\"webgpu\",kernelFunc:C2},Hse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.hasLeakyreluAlpha&&this.variableNames.push(\"leakyreluAlpha\"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);\n    let divBy4Remainder${e} = flatIndex${e} % 4;\n    let divBy4Index${e} = flatIndex${e} / 4;\n    let curData${e} = x[divBy4Index${e}];\n    if (divBy4Remainder${e} == 0) {\n      temp = curData${e};\n    } else {\n      // TODO: This could end up being a redundant load with another one in\n      // the same shader invocation. Perhaps there's an opportunity for\n      // optimization\n      let nextData${e} = x[divBy4Index${e} + 1];\n      if (divBy4Remainder${e} == 1) {\n        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);\n      } else if (divBy4Remainder${e} == 2) {\n        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);\n      } else if (divBy4Remainder${e} == 3) {\n        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);\n      }\n    }\n    `}getUserCode(){let e=y2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];\n        let outCol = r % uniforms.outShape[2];\n        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);\n        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];\n        let inChCoord = c % uniforms.xShape[3];\n        var coord = vec4<i32>(\n            batch,\n            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n            inChCoord);\n        var resData = vec4<f32>(0.0);\n        ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for\n          // the 'same' padding type.\n          if (coordsInBounds4D(coord, uniforms.xShape)) {\n            resData = x[getIndexFromCoords4D(coord, uniforms.xShape) / 4];\n          } else {\n            resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);\n          ${this.getSampleAWithRemainder(1)}\n          resData = temp;\n          if (WCol == (uniforms.filterDims[1] - 1)) {\n            coord = vec4<i32>(\n              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);\n              ${this.getSampleAWithRemainder(2)}\n            if (inChCoord == 0) {\n              resData = vec4<f32>(resData.xyz, temp.x);\n            } else if (inChCoord == 1) {\n              resData = vec4<f32>(resData.xy, temp.xy);\n            } else {\n              resData = vec4<f32>(resData.x, temp.xyz);\n            }\n          }\n          `}\n        return resData;`,r=this.fitA?`${s}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {\n          ${s}\n         }\n         return vec4<f32>(0.0);\n        `,a=this.fitB?\"return W[row * uniforms.dimBOuter / 4 + col];\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter / 4 + col];\n         }\n         return vec4<f32>(0.0);\n        `,i=\"\",o=\"\";if(this.activation){let c=Zs(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${c}\n        }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {\n          let b = getLeakyreluAlphaByOutputCoords(outCoord);\n          ${c}\n        }`,new Error(\"Leakyrelu is not supported.\");i=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          ${c}\n        }`}o=\"value = activation(value, outCoord);\"}let u=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n        ${i}\n        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          let r = row;\n          let c = col * 4;\n          var batch = i32(globalId.z);\n          ${r}\n        }\n\n        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          ${a}\n        }\n\n        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {\n          var batch = i32(globalId.z);\n          var value = valueInput;\n          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)\n          {\n            let outCoord = vec4<i32>(\n              batch,\n              row / uniforms.outShape[2],\n              row % uniforms.outShape[2],\n              col * 4);\n            ${u}\n            ${o}\n            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],\n              value);\n          }\n        }\n        ${e}\n      `}},qse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=$v(this.elementsPerThread,this.workGroupSize),t=`\n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);\n    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];\n    let coord = vec4<i32>(\n        batch,\n        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n        col % uniforms.xShape[3]);\n    // The bounds checking is always needed since we use it to pad zero for the\n    // 'same' padding type.\n    if(coordsInBounds4D(coord, uniforms.xShape)) {\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${t}\n    }\n    return 0.0;\n    `,s=this.fitB?\"return W[row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter + col];\n\t }\n\t return 0.0;\n\t `,r=\"\",a=\"\";if(this.activation){let u=Zs(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${u}\n                }`:r=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n                    ${u}\n                  }\n                `,a=\"value = activation(value, outCoord);\"}let i=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n    ${r}\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${n}\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      ${s}\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      ${i}\n      ${a}\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n    ${e}\n  `}},jse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${r}\n             }`:e=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n                    ${r}\n                  }\n                `,t=\"value = activation(value, outCoord);\"}let n=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${e}\n      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if(coordsInBounds4D(coord, uniforms.xShape)) {\n          return getX(batch, row, col, chan);\n        }\n        return 0.0;\n      }\n\n      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n        let coord = vec4<i32>(row, col, xChannel, outChannel);\n        if(coordsInBounds4D(coord, uniforms.wShape)) {\n          return getW(row, col, xChannel, outChannel);\n        }\n        return 0.0;\n      }\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          ${n}\n          ${t}\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Dr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let outChannel = coords[3];\n\n        var acc = 0.0;\n\n        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {\n              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n              let v = readInp(batch, coordRow, coordCol, xChannel);\n              let f = readFilt(row, col, xChannel, outChannel);\n              acc = acc + v * f;\n            }\n          }\n        }\n\n        writeResult(batch, coords[1], coords[2], outChannel, acc);\n      }\n    `}},Kse=class{constructor(e,t){this.variableNames=[\"A\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,\n      inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`\n    ${Ue()}\n\n      for(var i = 0; i<${this.workPerThread}; i = i + 1) {\n        let flatIndex = index * ${this.workPerThread} + i;\n\n        let rc = getCoordsFromIndex(flatIndex);\n\n        if(flatIndex < uniforms.size) {\n          let blockIndex = rc[0];\n          let pos = rc[1];\n\n          let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];\n          let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;\n          var value = 0.0;\n          if(d0 < uniforms.aShape[${e}] && d0 >= 0) {\n            let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -\n              uniforms.pad[0];\n            let d1 = offsetX + uniforms.dilation[0] * ((pos %\n              uniforms.itemsPerBlockRow) / uniforms.inChannels);\n            let ch = pos % uniforms.inChannels;\n            if(d1 < uniforms.aShape[${t}] && d1 >= 0) {\n              value = getA(d0, d1, ch);\n            }\n          }\n          setOutputAtIndex(flatIndex, value);\n        }\n      }\n    }\n  `}};function Xse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat===\"channelsLast\",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=_v({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Yse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y===\"channelsLast\",x=u*l*c,k=m*f,T=[k,x],N=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new Kse(T,v),$=[{type:\"int32\",data:[h.left,h.top]},{type:\"int32\",data:[p,d]},{type:\"int32\",data:[g,b]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*u]},{type:\"int32\",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,T[0],T[1]]}});A.push(z),A.push(W);let q=[1,T[0],T[1]],K=new v2(q,[1,k,n.outChannels],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),N,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:\"int32\",data:[Y]},{type:\"int32\",data:[te]},{type:\"int32\",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a),o===\"leakyrelu\"&&($.push({type:\"float32\",data:[i]}),K.uniforms+=\" alpha : f32,\");let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function N2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type===\"SAME\"||n.padInfo.type===\"VALID\"))return Xse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")&&e.shape[0]===1)return Yse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool(\"WEBGPU_USE_NAIVE_CONV2D\"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type===\"VALID\")&&n.outChannels%4===0,f=[n.padInfo.top,n.padInfo.left],m=[{type:\"int32\",data:[n.filterHeight,n.filterWidth]},{type:\"int32\",data:[...f]},{type:\"int32\",data:[n.strideHeight,n.strideWidth]},{type:\"int32\",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new jse(n,u,o,l);else{h?c=new Hse(n,u,o,l):c=new qse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:\"int32\",data:[b]},{type:\"int32\",data:[y]},{type:\"int32\",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),o===\"leakyrelu\"&&(m.push({type:\"float32\",data:[i]}),c.uniforms+=\" alpha : f32,\"),s.runWebGPUProgram(c,g,e.dtype,m)}function Qse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return N2({x:r,filter:a,convInfo:d,backend:s})}var Zse={kernelName:$a,backendName:\"webgpu\",kernelFunc:Qse},Jse=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      \n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n      return 0.0;\n    }\n    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n      return 0.0;\n    }\n    let coord = vec4<i32>(\n        batch,\n        i32(xR),\n        i32(xC),\n        col % uniforms.outBackprop[3]);\n    return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      let coordX = uniforms.filterDims.x - 1 -\n          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let coordY = uniforms.filterDims.y - 1 -\n          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n          coordX >= 0 && coordY >= 0) {\n        let coord = vec4<i32>(coordX, coordY, col,\n            row % uniforms.outBackprop[3]);\n        return W[getIndexFromCoords4D(coord, uniforms.wShape)];\n      }\n      return 0.0;\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n\n    ${$v(this.elementsPerThread,this.workGroupSize)}\n  `}},ere=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`\n    ${Ue()} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${n}];\n\n        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function tre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:\"int32\",data:[d.filterHeight,d.filterWidth]},{type:\"int32\",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\"))f=new ere(d);else{f=new Jse(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:\"uint32\",data:[m]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[b]})}return n.runWebGPUProgram(f,[r,a],\"float32\",h)}var nre={kernelName:_a,backendName:\"webgpu\",kernelFunc:tre},sre=Kt({opType:2}),rre={kernelName:Aa,backendName:\"webgpu\",kernelFunc:sre},are=Kt({opType:3}),ire={kernelName:Ea,backendName:\"webgpu\",kernelFunc:are},ore=class{constructor(e,t,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${n});\n        let width_ratio = f32(${a});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${s};\n        let width_scale = ${i};\n        let in_y = ${r};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${o};\n        if( in_x < 0.0 || in_x > ${t} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `}},ure=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new ore(r.shape[3],a.shape,o,u),p=[{type:\"float32\",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],\"float32\",p)},lre={kernelName:co,backendName:\"webgpu\",kernelFunc:ure},cre=class{constructor(e,t){this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function dre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=[{type:\"int32\",data:[a]}],g=new cre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var pre={kernelName:po,backendName:\"webgpu\",kernelFunc:dre},T2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,\",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n            ${r}\n          }\n        `,t=\"dotProd[i] = activation(dotProd[i], coords);\"}let n=this.addBias?\"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      ${Sv()}\n      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = 0;\n        let r = i32(globalId.x);\n        let c = i32(globalId.y) * 4;\n        let d2 = i32(globalId.z) * 4;\n        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;\n        let d1 = d2;\n        let q = 0;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n\n        var wVals : array<vec4<f32>, 9>;\n        wVals[0] = getW(0, 0, d1, q);\n        wVals[1] = getW(0, 1, d1, q);\n        wVals[2] = getW(0, 2, d1, q);\n        wVals[3] = getW(1, 0, d1, q);\n        wVals[4] = getW(1, 1, d1, q);\n        wVals[5] = getW(1, 2, d1, q);\n        wVals[6] = getW(2, 0, d1, q);\n        wVals[7] = getW(2, 1, d1, q);\n        wVals[8] = getW(2, 2, d1, q);\n\n        var xVals : array<array<vec4<f32>, 6>, 3>;\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          let xR = xRCorner + wR * uniforms.dilation[0];\n          for (var wC = 0; wC < 6; wC = wC + 1) {\n            let xC = xCCorner + wC * uniforms.dilation[1];\n            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {\n              xVals[wR][wC] = vec4<f32>(0.0);\n            } else {\n              xVals[wR][wC] = getX(batch, xR, xC, d1);\n            }\n          }\n        }\n\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          for (var wC = 0; wC < 3; wC = wC + 1) {\n            let indexW = wR * 3 + wC;\n            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];\n            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];\n            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];\n            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d2);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            ${n}\n            ${t}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n          }\n        }\n      }\n    `}},$2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,\n      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n            ${r}\n          }\n        `,t=\"dotProd = activation(dotProd, coords);\"}let n=this.addBias?\"dotProd = dotProd + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,\n          value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Dr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n        let d2 = coords[3];\n        let d1 = d2 / uniforms.channelMul;\n        let q = d2 - d1 * uniforms.channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            // Here using a constant value |this.convInfo.filterHeight| instead\n            // of uniform value is in order to loop unrolling.\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          }\n\n        ${n}\n        ${t}\n        writeResult(batch, coords[1], coords[2], d2, dotProd);\n      }\n    `}};function hre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:\"int32\",data:[p.padInfo.top,p.padInfo.left]},{type:\"int32\",data:[p.strideHeight,p.strideWidth]},{type:\"int32\",data:[p.dilationHeight,p.dilationWidth]},{type:\"int32\",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new T2(p):(h=new $2(p),d.push({type:\"int32\",data:[p.filterHeight]},{type:\"int32\",data:[p.filterWidth]},{type:\"int32\",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var fre={kernelName:Ra,backendName:\"webgpu\",kernelFunc:hre},_2=mn({opSnippet:0,cpuKernelImpl:jne,supportsComplex:!0}),mre={kernelName:Ya,backendName:\"webgpu\",kernelFunc:_2},gre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"reduceSize : i32,\",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\";this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n           {  bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"&&(e=\" bestValue = bestValue * candidate; \",t=\"1.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${`\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${Ue()}\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${e}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${n}\n        }\n       }\n     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=S.getAxesPermutation(u,a),c=e;l!=null&&(c=wi({inputs:{x:e},attrs:{perm:l},backend:r}),u=S.getInnerMostAxes(u.length,a),i.push(c)),S.assertAxesAreInnerMostDims(s,u,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=S.expandShapeToKeepDim(p,o));let f;if((s===\"max\"||s===\"prod\")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case\"max\":let g=Gne(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case\"prod\":let{outVals:b,outShape:y,outDtype:v}=Yne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s===\"mean\"?\"float32\":cp(e.dtype),x=[{type:\"int32\",data:[m]}],k=new gre(y,s),T=r.runWebGPUProgram(k,[c],v,x);i.push(T),f=Me({inputs:{x:T},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Av(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"sum\",n)}var bre={kernelName:oi,backendName:\"webgpu\",kernelFunc:Av};function yre(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=wi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=_2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Av({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var vre={kernelName:Qd,backendName:\"webgpu\",kernelFunc:yre},xre=Kt({opType:4}),wre={kernelName:Fa,backendName:\"webgpu\",kernelFunc:xre},kre=mn({opSnippet:4,dtype:\"bool\",cpuKernelImpl:Dne}),Ire={kernelName:ho,backendName:\"webgpu\",kernelFunc:kre},A2=Kt({opType:5,cpuKernelImpl:Fne,dtype:\"float32\"}),Sre={kernelName:Oa,backendName:\"webgpu\",kernelFunc:A2};function Qm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var Cre={kernelName:fo,backendName:\"webgpu\",kernelFunc:Qm},Nre=Kt({opType:6,cpuKernelImpl:One}),Tre={kernelName:mo,backendName:\"webgpu\",kernelFunc:Nre},$re=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Ue()}\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function du(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new $re(s),o=[{type:\"float32\",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var _re={kernelName:ml,backendName:\"webgpu\",kernelFunc:du},Are=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}},Ere={kernelName:go,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Are(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Rre=Kt({opType:7,cpuKernelImpl:Pne}),Dre={kernelName:Pa,backendName:\"webgpu\",kernelFunc:Rre},Fre=mn({opSnippet:12,dtype:\"int32\"}),Ore={kernelName:za,backendName:\"webgpu\",kernelFunc:Fre},Pre=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},E2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=cne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:\"main\"},label:t.constructor.name})};function R2(e,t,n,s=\"\",r=\"\"){return e.shaderKey+\"_\"+(e.workGroupSize?e.workGroupSize.join(\",\"):\"\")+t.map(i=>i.length).join(\",\")+n.join(\",\")+e.variableNames.join(\",\")+s+r}function Ow(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,\"int32\"),c=n.getFromPixelsProgram(a?\"import\":\"copyExternal\");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?\"import\":\"copyExternal\"],h=R2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>E2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var zre={kernelName:fd,backendName:\"webgpu\",kernelFunc:Mre},Bi;function Mre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let i=typeof HTMLVideoElement!=\"undefined\"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!=\"undefined\"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!=\"undefined\"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!=\"undefined\"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool(\"WEBGPU_USE_IMPORT\")&&i)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Bi==null&&(Bi=document.createElement(\"canvas\").getContext(\"2d\")),Bi.canvas.width=c,Bi.canvas.height=p,Bi.drawImage(r,0,0,c,p),r=Bi.canvas),l||u||i||o)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,\"int32\"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Lre=class{constructor(e,t,n,s,r){this.uniforms=\"varianceEpsilon : f32,\",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\")),r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\")),this.offsetShape=s,this.scaleShape=r,this.shaderKey=\"batchNorm\"}getUserCode(){let e=\"0.0\";this.offsetShape!=null&&(e=\"getOffsetByOutputIndex(index)\");let t=\"1.0\";return this.scaleShape!=null&&(t=\"getScaleByOutputIndex(index)\"),`\n      ${Ue()}\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${e};\n          let scaleValue = ${t};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `}},Bre={kernelName:Ma,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Lre(s.shape,i.shape,o.shape,p,d),f=[{type:\"float32\",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Vre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return N2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Wre={kernelName:aa,backendName:\"webgpu\",kernelFunc:Vre};function Ure(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=S.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=i!=null,y=o!=null;b&&g.push(i),y&&g.push(o);let v=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new T2(m,b,d,y):(x=new $2(m,b,d,y),v.push({type:\"int32\",data:[m.filterHeight]},{type:\"int32\",data:[m.filterWidth]},{type:\"int32\",data:[m.outChannels/m.inChannels]})),d===\"leakyrelu\"&&(v.push({type:\"float32\",data:[h]}),x.uniforms+=\" alpha : f32,\"),n.runWebGPUProgram(x,g,\"float32\",v)}var Gre={kernelName:ia,backendName:\"webgpu\",kernelFunc:Ure},Hre=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n        ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${e};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `}};function qre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=zne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Hre(i,[l,c]),m=[{type:\"int32\",data:[i]},{type:\"int32\",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var jre={kernelName:yo,backendName:\"webgpu\",kernelFunc:qre},Kre=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Xre(this.aShape);return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${e}));\n        }\n      }\n    `}};function Xre(e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let s=0;s<e.length;s++)s===2?n.push(\"indexZ\"):n.push(`${t[s]}`);return n.join()}function D2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),T=n.tensorMap.get(d.dataId).values,N=De(d.shape,d.dtype,T),E=Mne(N,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new Kre(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Yre={kernelName:bo,backendName:\"webgpu\",kernelFunc:D2},Qre=mn({opSnippet:5,cpuKernelImpl:Bne,dtype:\"bool\"}),Zre={kernelName:vo,backendName:\"webgpu\",kernelFunc:Qre},Jre=mn({opSnippet:6,dtype:\"bool\",cpuKernelImpl:Lne}),eae={kernelName:La,backendName:\"webgpu\",kernelFunc:Jre};function tae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:\"float32\",data:[a]}],o=new nc(r.shape,14);return o.uniforms=\"alpha : f32,\",n.runWebGPUProgram(o,[r],\"float32\",i)}var nae={kernelName:Va,backendName:\"webgpu\",kernelFunc:tae},sae=mn({opSnippet:7,dtype:\"bool\",cpuKernelImpl:Wne}),rae={kernelName:xo,backendName:\"webgpu\",kernelFunc:sae},aae=mn({opSnippet:8,dtype:\"bool\",cpuKernelImpl:Vne}),iae={kernelName:wo,backendName:\"webgpu\",kernelFunc:aae},oae=Kt({opType:9,cpuKernelImpl:Une}),uae={kernelName:Wa,backendName:\"webgpu\",kernelFunc:oae},lae=mn({opSnippet:9,dtype:\"bool\"}),cae={kernelName:ko,backendName:\"webgpu\",kernelFunc:lae},dae=Kt({opType:10}),pae={kernelName:xl,backendName:\"webgpu\",kernelFunc:dae};function F2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,\"max\",n)}var hae={kernelName:Ua,backendName:\"webgpu\",kernelFunc:F2},fae=mn({opSnippet:15,cpuKernelImpl:Hne}),mae={kernelName:Ga,backendName:\"webgpu\",kernelFunc:fae};function gae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});p=new I2(c),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]})}else p=new k2(c,\"max\"),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var bae={kernelName:Ha,backendName:\"webgpu\",kernelFunc:gae};function yae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,\"mean\",n)}var vae={kernelName:qa,backendName:\"webgpu\",kernelFunc:yae};function xae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"min\",n)}var wae={kernelName:ja,backendName:\"webgpu\",kernelFunc:xae},kae=mn({opSnippet:16,cpuKernelImpl:qne}),Iae={kernelName:Ka,backendName:\"webgpu\",kernelFunc:kae},Sae=class{constructor(e,t,n){this.uniforms=\"\",this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(\",\"),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:\"\"}`).join(\",\"),s=e===1?\"start\":\"start[i]\",r=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=Wt(e),o=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${i}(${t});\n          let end = ${i}(${n});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${e}; i = i + 1) {\n            if (${a} < ${s}) {\n              ${a} = ${s} * 2 - ${a} - ${this.offset};\n            } else if(${a} >= ${r}) {\n              ${a} = (${r} - 1) * 2 - ${a} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${o}));\n        }\n      }\n    `}},Cae={kernelName:Xa,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:\"int32\",data:[c[0],c[1]]})),u=new Sae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function Nae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=Kne(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var Tae={kernelName:Io,backendName:\"webgpu\",kernelFunc:Nae};function $ae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var _ae={kernelName:Co,backendName:\"webgpu\",kernelFunc:$ae};function Aae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),n.makeTensorInfo([b.length],\"float32\",new Float32Array(b))]}var Eae={kernelName:No,backendName:\"webgpu\",kernelFunc:Aae};function Ld(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=Ld({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype===\"string\"?\"\":0},backend:n})}var Rae={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Ld};function O2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=O2({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Dae={kernelName:To,backendName:\"webgpu\",kernelFunc:O2};function Fae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let o=[],u=t.map(c=>{let p=Qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=C2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Oae={kernelName:_o,backendName:\"webgpu\",kernelFunc:Fae},Pae=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(\",\"),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:\"\"}`).join(\",\"),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?\"any(outC < start)\":\"outC < start\",o=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${r};\n          let end = ${a};\n          let outC = getCoordsFromIndex(index);\n\n          if (${i} || ${o}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${u}));\n          }\n        }\n      }\n    `}},P2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return es({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return du({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:\"float32\",data:[i]}];a.map(l=>o.push({type:\"int32\",data:[l[0],l[1]]}));let u=new Pae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},zae={kernelName:Qa,backendName:\"webgpu\",kernelFunc:P2},Mae=mn({opSnippet:13}),Lae={kernelName:Za,backendName:\"webgpu\",kernelFunc:Mae};function Bae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new x2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],\"float32\")}var Vae={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Bae};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"prod\",n)}var Uae={kernelName:Ao,backendName:\"webgpu\",kernelFunc:Wae},Gae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Qne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Hae={kernelName:Il,backendName:\"webgpu\",kernelFunc:Gae},z2=mn({opSnippet:3}),qae={kernelName:Da,backendName:\"webgpu\",kernelFunc:z2},jae=Kt({opType:12}),Kae={kernelName:ei,backendName:\"webgpu\",kernelFunc:jae},Xae=Kt({opType:13}),Yae={kernelName:ni,backendName:\"webgpu\",kernelFunc:Xae},Qae=class{constructor(e,t,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Zae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[o?.5:0]}],f=new Qae(r.shape,u,l);return n.runWebGPUProgram(f,[r],\"float32\",h)}var Jae={kernelName:ti,backendName:\"webgpu\",kernelFunc:Zae},eie=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, roundBase : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))\":e=\"vec2<f32>(rc) * effectiveInputOverOutputRatioRC\",`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${e};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function tie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[a?.5:0]}],f=new eie(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var nie={kernelName:Cl,backendName:\"webgpu\",kernelFunc:tie},sie=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n          cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3<f32>,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n        ${Ue()}\n\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}},rie={kernelName:qo,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new sie(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:\"float32\",data:[l]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(r)]},{type:\"float32\",data:[Math.cos(r)]}];return typeof a==\"number\"?p.push({type:\"float32\",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:\"float32\",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},aie=Kt({opType:15,cpuKernelImpl:Zne}),iie={kernelName:si,backendName:\"webgpu\",kernelFunc:aie},oie=class{constructor(e,t,n,s,r,a,i){this.variableNames=[\"updates\",\"indices\"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",s=\"\",r=\"\",a=\"\";this.updatesRank===1?(s=\"coords[0]\",r=\"flattenedIndex\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.updatesRank===2&&(s=\"coords[0], coords[1]\",r=\"vec2<i32>(flattenedIndex, coords[1])\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        let d0 = index / uniforms.updatesShape[1];\n        let d1 = index - d0 * uniforms.updatesShape[1];\n        return vec2<i32>(d0, d1);\n      }\n      `);let i=`getUpdates(${s})`,o=this.type===\"int32\"?\"atomicAdd(&(result[flatIndex]), i32(updateValue));\":`\n     var assumed = atomicLoad(&(result[flatIndex]));\n     var success = 0;\n     for (; success == 0;) {\n       let new = bitcast<f32>(assumed) + updateValue;\n       let newI32 = bitcast<i32>(new);\n       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);\n       assumed = resValue[0];\n       success = resValue[1];\n     }\n     `;return`\n    ${a}\n\n      ${Ue()}\n\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${n};\n          }\n          let updateValue = ${i};\n          let flatIndex = getOutputIndexFromCoords(${r});\n\n         ${o}\n        }\n      }`}};function uie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=du({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:\"int32\",data:[o]},{type:\"int32\",data:c},{type:\"int32\",data:[b]}],v=new oie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var lie={kernelName:Fo,backendName:\"webgpu\",kernelFunc:uie},cie=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let s=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function die(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new cie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var pie={kernelName:Oo,backendName:\"webgpu\",kernelFunc:die},hie=Kt({opType:18}),fie={kernelName:ai,backendName:\"webgpu\",kernelFunc:hie},mie=Kt({opType:16}),gie={kernelName:ri,backendName:\"webgpu\",kernelFunc:mie},bie=Kt({opType:17}),yie={kernelName:zo,backendName:\"webgpu\",kernelFunc:bie},M2=mn({opSnippet:2,cpuKernelImpl:sse,supportsComplex:!0}),vie={kernelName:ci,backendName:\"webgpu\",kernelFunc:M2};function xie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=F2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=M2({inputs:{a:r,b:l},backend:n}),p=A2({inputs:{x:c},backend:n}),d=Av({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=z2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var wie={kernelName:ui,backendName:\"webgpu\",kernelFunc:xie},kie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=P2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},Iie={kernelName:Mo,backendName:\"webgpu\",kernelFunc:kie},Sie=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\"),this.indicesSnippet=`getIndices(${l})`;let c=\"\";s===1?c=\"i\":s===2&&(c=\"i, coords[1]\"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?\"uniforms.strides[j]\":\"uniforms.strides\"}getUserCode(){return`\n      ${Ue()}\n\n        let globalIndex = index * ${this.workPerThread};\n        if (globalIndex < uniforms.size) {\n          var sum = vec4<f32>(0.0);\n          var found = vec4<bool>(false);\n          for (var i = 0; i < uniforms.updateSize; i = i + 1) {\n            var flattenedIndex = 0;\n            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n              let indexInside = i32(round(${this.indicesSnippet}));\n              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};\n            }\n            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n              let curIndex = globalIndex + innerIndex;\n              let coords = getCoordsFromIndex(curIndex);\n              if (flattenedIndex == coords[0]) {\n                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};\n                found[innerIndex] = true;\n              }\n            }\n          }\n          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n            let curIndex = globalIndex + innerIndex;\n            if (curIndex < uniforms.size)\n            {\n              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));\n            }\n          }\n        }\n      }`}};function Cie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=[{type:\"int32\",data:[l]},{type:\"int32\",data:[u]},{type:\"int32\",data:c}],f=new Sie(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var Nie={kernelName:ip,backendName:\"webgpu\",kernelFunc:Cie};function Tie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=cu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var $ie={kernelName:Lo,backendName:\"webgpu\",kernelFunc:Tie},_ie=Kt({opType:19}),Aie={kernelName:ii,backendName:\"webgpu\",kernelFunc:_ie},Eie={kernelName:Al,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},Rie=mn({opSnippet:11}),Die={kernelName:li,backendName:\"webgpu\",kernelFunc:Rie},Fie=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Ue()}\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),N=cu({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=Me({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeData(N.dataId)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),E=De(r.shape,r.dtype,N),A=tse(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let N=new Fie(h),E=[{type:\"int32\",data:y},{type:\"int32\",data:x}],A=n.runWebGPUProgram(N,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Pie={kernelName:Bo,backendName:\"webgpu\",kernelFunc:Oie};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=nse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var Mie={kernelName:op,backendName:\"webgpu\",kernelFunc:zie},Lie=Kt({opType:21}),Bie={kernelName:di,backendName:\"webgpu\",kernelFunc:Lie},Vie=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=Wie(this.rank,\"uniforms.\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function Wie(e,t=\"\"){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Uie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=rse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Vie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var Gie={kernelName:Cr,backendName:\"webgpu\",kernelFunc:Uie},Hie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n        dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}},qie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}};function Vi(e,t){t!==null&&e.disposeData(t.dataId)}function Pw(e){let t=1;for(;t<e;)t*=2;return t}function jie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[T,N]=ase(k,o,r.dtype,a,i);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(N.shape,N.dtype,N.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,\"int32\",[])];if(u===1)return[r,du({attrs:{shape:o,dtype:\"int32\",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Pw(a),h=Pw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,T,N)=>{let E=m(),A=new Hie(N),R=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[T]}],F=f;f=n.runWebGPUProgram(A,E,\"int32\",R),Vi(n,F)};for(let k=1;k<d;k*=2){let T=k*2;for(let N=k;N>=1;N/=2)g(T,N,[c,h])}for(let k=h;k>d;k/=2){let T=m(),N=new qie([c,k/2]),A=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[d]}],P=f;f=n.runWebGPUProgram(N,T,\"int32\",A),Vi(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=cu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Vi(n,b);let y=D2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Vi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Vi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Vi(n,x),[y,f]}var Kie={kernelName:Wo,backendName:\"webgpu\",kernelFunc:jie},Xie=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${Ue()}\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `}};function Yie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Xie(g),y=i===\"nearest\"?1:2,v;switch(o){case\"constant\":v=1;break;case\"reflect\":v=2;break;case\"wrap\":v=3;break;case\"nearest\":v=4;break;default:v=1;break}let x=[{type:\"int32\",data:[y]},{type:\"int32\",data:[v]},{type:\"float32\",data:[u]}];return n.runWebGPUProgram(b,[r,a],\"float32\",x)}var Qie={kernelName:Uo,backendName:\"webgpu\",kernelFunc:Yie};function Zie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=cu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Jie={kernelName:Go,backendName:\"webgpu\",kernelFunc:Zie},eoe=[Cne,use,cse,hse,vse,wse,Ise,Cse,Ase,Fse,Pse,Bse,_ne,Gse,Zse,nre,rre,ire,lre,pre,fre,vre,wre,Ire,Sre,Cre,Tre,_re,Ere,zre,Dre,Ore,Bre,Wre,Gre,jre,Yre,Zre,eae,$ne,Wse,nae,rae,iae,uae,cae,pae,hae,mae,bae,vae,wae,Iae,Cae,mre,Tae,_ae,Eae,Ese,Dae,Oae,zae,Lae,Vae,Uae,Hae,Rse,qae,Kae,Yae,Ine,Jae,nie,rie,iie,lie,pie,fie,gie,yie,$se,Pie,Mie,wie,Iie,Nie,$ie,Aie,Eie,Die,vie,bre,Bie,Gie,Kie,Qie,bse,Jie,Rae];for(let e of eoe)El(e);var toe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=zw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=zw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function zw(e,t){return`${e}_${t}`}var L2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey=\"fromPixels\",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.useImport?\"texture_external\":\"texture_2d<f32>\"};\n\n      ${Ue()}\n        let flatIndexBase = index * uniforms.numChannels;\n        for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n          let flatIndex = flatIndexBase + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndexBase);\n            let values = ${e};\n            result[flatIndex] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:\"rgba8unorm\",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},noe=class extends L2{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},soe=X().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Mw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[a,a,a]):[a,a,1]},B2=class extends nl{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Tv())throw new Error(\"WebGPU is not supported on this device\");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new toe(this.device),this.tensorMap=new Ud(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return B2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*jm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let a=w.sizeFromShape(n)*jm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case\"copyExternal\":return this.fromPixelProgram||(this.fromPixelProgram=new L2),this.fromPixelProgram;case\"import\":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new noe),this.fromPixelImportProgram;default:w.assert(!1,()=>\"Unsupported fromPixels shape\");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(w.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype===\"complex64\"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=S.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=b2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type===\"int32\"?new Int32Array(s,o,a.data.length).set(a.data):a.type===\"uint32\"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"read-only-storage\"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"uniform\"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let N=this.tensorMap.get(r.dataId);return N.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Mw(this.device,e);let a=[{type:\"float32\",data:[NaN]}],i=t.concat(r).map(N=>N.shape),o=\"int32\";i.map(N=>{a.push({type:o,data:N})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let N=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((N,E)=>{if(N.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[E]}}),p=c.map(N=>N.dtype).concat(r.dtype),d=c.map(N=>S.getBroadcastDims(N.shape,r.shape)),h=c.map(N=>w.arraysEqual(N.shape,r.shape)).join(\"_\"),f=d.map(N=>N.join(\"_\")).join(\";\"),m=R2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>E2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Pre(this.device,g,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let T={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(T),X().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Mw(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=soe){return X().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Ev=B2;Ev.nextDataId=0;var roe={};Ae(roe,{WebGPUBackend:()=>Ev,webgpu_util:()=>m2});Tv()&&pp(\"webgpu\",async()=>{X().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let e={powerPreference:X().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has(\"timestamp-query\");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=[\"timestamp-query\"]:console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.\");let a=await t.requestDevice(s);return new Ev(a,r)},3);var It=(e=>(e[e.float32=0]=\"float32\",e[e.int32=1]=\"int32\",e[e.bool=2]=\"bool\",e[e.string=3]=\"string\",e[e.complex64=4]=\"complex64\",e))(It||{}),Jp=(e=>(e[e.linear=0]=\"linear\",e[e.relu=1]=\"relu\",e[e.relu6=2]=\"relu6\",e[e.prelu=3]=\"prelu\",e[e.leakyrelu=4]=\"leakyrelu\",e[e.sigmoid=5]=\"sigmoid\",e[e.elu=6]=\"elu\",e))(Jp||{}),V2;function aoe(e){V2=e.wasm.cwrap(ra,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ioe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Jp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return V2(d,T,r.shape.length,h,N,a.shape.length,u,l,g,f,m,p||0,k),x}var ooe={kernelName:ra,backendName:\"wasm\",setupFunc:aoe,kernelFunc:ioe};function Xt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,[\"number\",\"number\",\"number\"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,It[o.dtype],c),l}return{kernelName:e,backendName:\"wasm\",setupFunc:s,kernelFunc:r}}var uoe=Xt(io);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=S.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,It[l.dtype],y))(),m}return{kernelName:e,backendName:\"wasm\",setupFunc:r,kernelFunc:a}}var loe=!0,coe=gn(Ir,loe),W2;function doe(e){W2=e.wasm.cwrap(ka,null,[\"array\",\"number\",\"number\",\"number\"])}function poe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return W2(a,r.length,It[s.dtype],i),s}var hoe={kernelName:ka,backendName:\"wasm\",setupFunc:doe,kernelFunc:poe};function eh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var foe={kernelName:Ba,backendName:\"wasm\",kernelFunc:eh},U2;function moe(e){U2=e.wasm.cwrap(pi,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function wr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=boe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=goe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=eh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return U2(c,h,u.shape.length,It[u.dtype],p,d,a.length),l}function goe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function boe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var yoe={kernelName:pi,backendName:\"wasm\",kernelFunc:wr,setupFunc:moe};function Fr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=S.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=S.getInnerMostAxes(i.length,r),u=wr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var G2;function voe(e){G2=e.wasm.cwrap(al,null,[\"number, number, number\"])}function xoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"all\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;G2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var woe={kernelName:al,backendName:\"wasm\",setupFunc:voe,kernelFunc:xoe},H2;function koe(e){H2=e.wasm.cwrap(il,null,[\"number, number, number\"])}function Ioe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"any\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;H2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Soe={kernelName:il,backendName:\"wasm\",setupFunc:koe,kernelFunc:Ioe},q2;function Coe(e){q2=e.wasm.cwrap(Ia,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Noe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Fr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,\"int32\"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return q2(o,It[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var Toe={kernelName:Ia,backendName:\"wasm\",kernelFunc:Noe,setupFunc:Coe},j2;function $oe(e){j2=e.wasm.cwrap(Sa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function _oe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${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}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,\"float32\"),k=s.dataIdMap.get(x.dataId).id;return j2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var Aoe={kernelName:Sa,backendName:\"wasm\",setupFunc:$oe,kernelFunc:_oe};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Eoe={kernelName:Eo,backendName:\"wasm\",kernelFunc:yn},K2;function Roe(e){K2=e.wasm.cwrap(Ca,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\"])}function Doe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"BatchMatMul for non non-float32 tensors not yet supported.\");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],T=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),N=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(T.dataId).id,A=n.dataIdMap.get(N.dataId).id,P=i?T.shape[2]:T.shape[1],R=o?N.shape[1]:N.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],T.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(T.shape).buffer),q=new Uint8Array(new Int32Array(N.shape).buffer);return K2(E,W,T.shape.length,A,q,N.shape.length,i,o,z),n.disposeData(T.dataId),n.disposeData(N.dataId),$.shape=v,$}var Foe={kernelName:Ca,backendName:\"wasm\",setupFunc:Roe,kernelFunc:Doe};function va(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype===\"string\"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype===\"string\"){let f=Fd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Ooe(u,c[0],d,a,i);else if(h===3)Poe(u,c[0],c[1],d,a,i);else if(h===4)zoe(u,c[0],c[1],c[2],d,a,i);else{let f=Fd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function Ooe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Poe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function zoe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Moe={kernelName:Po,backendName:\"wasm\",kernelFunc:va};function Loe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=va({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Boe={kernelName:oo,backendName:\"wasm\",kernelFunc:Loe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Voe={kernelName:Na,backendName:\"wasm\",kernelFunc:ac},Woe=Xt(Ta),X2;function Uoe(e){X2=e.wasm.cwrap(Sr,null,[\"number\",\"number\",\"number\",\"number\"])}function Goe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return X2(o,a,i,l),u}var Hoe={kernelName:Sr,backendName:\"wasm\",setupFunc:Uoe,kernelFunc:Goe};function Y2(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=S.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return eh({inputs:{x:a[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=a.map(h=>h.shape);if(S.assertParamsConsistent(o,s),a[0].dtype===\"string\"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return yn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=S.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=tv(f,r,t[0].dtype,m),b=S.computeOutShape(a.map(v=>v.shape),s);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=S.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var qoe={kernelName:uo,backendName:\"wasm\",kernelFunc:Y2},Q2;function joe(e){Q2=e.wasm.cwrap($a,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Koe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=S.convertConv2DDataFormat(d),f=S.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,T=f.dilationWidth,N=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Qoe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=S.convertConv2DDataFormat(u),h=S.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:T,strideHeight:N,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat===\"channelsLast\",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,\"float32\"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return Z2(me,ke,f,m,g,y,v,b,k,T,x,N,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var Zoe={kernelName:_a,backendName:\"wasm\",setupFunc:Yoe,kernelFunc:Qoe},Joe=Xt(Aa),eue=Xt(Ea),J2=(e=>(e[e.bilinear=0]=\"bilinear\",e[e.nearest=1]=\"nearest\",e))(J2||{}),eN;function tue(e){eN=e.wasm.cwrap(co,null,[\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function nue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!==\"float32\"&&(m=ac({backend:t,inputs:{x:o},attrs:{dtype:\"float32\"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,\"float32\"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return eN(g,b,y,c,k,p,d,J2[r],a,x),m!=null&&t.disposeData(m.dataId),v}var sue={kernelName:co,backendName:\"wasm\",setupFunc:tue,kernelFunc:nue},tN;function rue(e){tN=e.wasm.cwrap(hl,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function aue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumprod\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;tN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var iue={kernelName:hl,backendName:\"wasm\",setupFunc:rue,kernelFunc:aue},nN;function oue(e){nN=e.wasm.cwrap(lo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function uue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;nN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var lue={kernelName:lo,backendName:\"wasm\",setupFunc:oue,kernelFunc:uue},sN;function cue(e){sN=e.wasm.cwrap(po,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function due(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,\"float32\"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return sN(b,a,i===\"NHWC\"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var pue={kernelName:po,backendName:\"wasm\",setupFunc:cue,kernelFunc:due},rN;function hue(e){rN=e.wasm.cwrap(Ra,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=S.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,T=h.strideHeight,N=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type===\"SAME\"?1:0;if(h.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(h.outShape,\"float32\"),F=s.dataIdMap.get(R.dataId).id;return rN(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,T,N,E,A,F),R}var mue={kernelName:Ra,backendName:\"wasm\",setupFunc:hue,kernelFunc:fue},gue=Xt(Fa),bue=!1,yue=gn(ho,bue,\"bool\"),vue=Xt(Oa,\"float32\");function Zm(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:o}})}var xue={kernelName:fo,backendName:\"wasm\",kernelFunc:Zm};function aN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var wue={kernelName:ml,backendName:\"wasm\",kernelFunc:aN},iN;function kue(e){iN=e.wasm.cwrap(go,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Iue(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return iN(a,o,u,l,c,i),r}var Sue={kernelName:go,backendName:\"wasm\",kernelFunc:Iue,setupFunc:kue},Cue=Xt(Pa),Nue=!1,Tue=gn(za,Nue),oN;function $ue(e){oN=e.wasm.cwrap(Ma,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function _ue(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return oN(c,p,d,h,f,r,g),m}var Aue={kernelName:Ma,backendName:\"wasm\",setupFunc:$ue,kernelFunc:_ue},uN;function Eue(e){uN=e.wasm.cwrap(aa,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 Rue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return uN(b,K,Y,Z,y,k,T,x,N,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var Due={kernelName:aa,backendName:\"wasm\",setupFunc:Eue,kernelFunc:Rue},lN;function Fue(e){lN=e.wasm.cwrap(ia,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 Oue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return lN(b,K,Y,Z,y,k,T,x,N,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var Pue={kernelName:ia,backendName:\"wasm\",setupFunc:Fue,kernelFunc:Oue},cN;function zue(e){cN=e.wasm.cwrap(yo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Mue(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,i,o,u]=$k.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(i===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return cN(h,It[s.dtype],m,i,p,o,g,b),l}var Lue={kernelName:yo,backendName:\"wasm\",setupFunc:zue,kernelFunc:Mue},dN;function Bue(e){dN=e.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Vue(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let A=0;A<l.length;++A){let P=l[A];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,T=t.dataIdMap.get(g.dataId).id,N=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),E=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return dN(v,It[r.dtype],N,b,k,p.batchSize,E,T),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Wue={kernelName:bo,backendName:\"wasm\",setupFunc:Bue,kernelFunc:Vue},Uue=!1,Gue=gn(vo,Uue,\"bool\"),Hue=!1,que=gn(La,Hue,\"bool\"),pN;function jue(e){pN=e.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\"])}function Kue(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,\"float32\");if(w.sizeFromShape(t.shape)!==0){let i=s.dataIdMap.get(a.dataId).id;pN(r,It[t.dtype],n,i)}return a}var Xue={kernelName:Va,backendName:\"wasm\",setupFunc:jue,kernelFunc:Kue},Yue=!1,Que=gn(xo,Yue,\"bool\"),Zue=!1,Jue=gn(wo,Zue,\"bool\"),ele=Xt(Wa),tle=!1,nle=gn(ko,tle,\"bool\"),hN;function sle(e){hN=e.wasm.cwrap(Ua,null,[\"number\",\"number\",\"number\",\"number\"])}function rle(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;hN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var ale={kernelName:Ua,backendName:\"wasm\",setupFunc:sle,kernelFunc:rle},ile=!1,ole=gn(Ga,ile),fN;function ule(e){fN=e.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function lle(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,T=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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x=t.dataIdMap.get(v.dataId).id;mN(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=S.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!==\"float32\"&&t.disposeData(y.dataId),v}var hle={kernelName:qa,backendName:\"wasm\",setupFunc:dle,kernelFunc:ple},gN;function fle(e){gN=e.wasm.cwrap(ja,null,[\"number\",\"number\",\"number\",\"number\"])}function mle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"min\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;gN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var gle={kernelName:ja,backendName:\"wasm\",setupFunc:fle,kernelFunc:mle},ble=!1,yle=gn(Ka,ble),bN=(e=>(e[e.reflect=0]=\"reflect\",e[e.symmetric=1]=\"symmetric\",e))(bN||{}),yN;function vle(e){yN=e.wasm.cwrap(Xa,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function xle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return yN(i,l,t.shape.length,It[t.dtype],d,h,bN[r],u),o}var wle={kernelName:Xa,backendName:\"wasm\",kernelFunc:xle,setupFunc:vle},kle=!0,Ile=gn(Ya,kle),Sle=Xt(Io);function Rv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var vN;function Cle(e){vN=e.wasm.cwrap(Co,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Nle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=vN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Rv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],\"int32\",d)}var Tle={kernelName:Co,backendName:\"wasm\",setupFunc:Cle,kernelFunc:Nle},xN;function $le(e){xN=e.wasm.cwrap(kl,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"bool\"])}function _le(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=xN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(m);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([],\"int32\",g);return[b,y]}var Ale={kernelName:kl,backendName:\"wasm\",setupFunc:$le,kernelFunc:_le},wN;function Ele(e){wN=e.wasm.cwrap(No,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Rle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=wN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(g);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([f],\"float32\",m);return[b,y]}var Dle={kernelName:No,backendName:\"wasm\",setupFunc:Ele,kernelFunc:Rle},Fle=!1,Ole=gn(So,Fle,\"bool\"),kN;function Ple(e){kN=e.wasm.cwrap($o,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function zle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],\"int32\"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return kN(p,a,i,o,l),u}var Mle={kernelName:$o,backendName:\"wasm\",setupFunc:Ple,kernelFunc:zle};function Lle(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Ble={kernelName:To,backendName:\"wasm\",kernelFunc:Lle};function Vle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Zm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack 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SN={kernelName:Qa,backendName:\"wasm\",kernelFunc:Gle,setupFunc:Ule},Hle=!1,qle=gn(Za,Hle),CN;function jle(e){CN=e.wasm.cwrap(Ja,null,[\"number\",\"number\",\"number\"])}function Kle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!==\"float32\"&&(l=ac({backend:n,inputs:{x:s},attrs:{dtype:\"float32\"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,\"float32\"),p=n.dataIdMap.get(c.dataId).id;return CN(o,i,p),u.dtype!==\"float32\"&&n.disposeData(l.dataId),c}var Xle={kernelName:Ja,backendName:\"wasm\",setupFunc:jle,kernelFunc:Kle},NN;function Yle(e){NN=e.wasm.cwrap(Ao,null,[\"number\",\"number\",\"number\",\"number\"])}function Qle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v,f=S.getInnerMostAxes(f.length,l.shape.length))}S.assertAxesAreInnerMostDims(\"prod\",f,l.shape.length);let[m,g]=S.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;NN(u,b,It[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Zle={kernelName:Ao,backendName:\"wasm\",setupFunc:Yle,kernelFunc:Qle},Jle=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=rv(s,r,a,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},ece={kernelName:Il,backendName:\"wasm\",kernelFunc:Jle},tce=!0,nce=gn(Da,tce),sce=Xt(ei),rce=Xt(ni),TN;function ace(e){TN=e.wasm.cwrap(ti,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");Bd=e,Dv=t}function Bpe(e,t=!1){if(Bu)throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");if(typeof e==\"string\")Pu=e;else{Lu=e;let n=Nde.filter(s=>Lu[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Dv=t}var HN=-1,eg=-1;function Vpe(e){HN=e}function Wpe(){if(eg===-1)throw new Error(\"WASM backend not initialized.\");return eg}var Upe=\"0.0.0\",Tde=2;pp(\"wasm\",async()=>{let{wasm:e}=await Sde();return new kde(e)},Tde);var sr=\"3.15.0-20220405\",Gpe={tfjs:sr,\"tfjs-core\":sr,\"tfjs-data\":sr,\"tfjs-layers\":sr,\"tfjs-converter\":sr,\"tfjs-backend-cpu\":sr,\"tfjs-backend-webgl\":sr,\"tfjs-backend-wasm\":sr};export{io as Abs,sl as Acos,rl as Acosh,fb as AdadeltaOptimizer,mb as AdagradOptimizer,gb as AdamOptimizer,bb as AdamaxOptimizer,Ir as Add,ka as AddN,al as All,il as Any,Ia as ArgMax,ol as ArgMin,ul as Asin,ll as Asinh,cl as Atan,pl as Atan2,dl as Atanh,Sa as AvgPool,qd as AvgPool3D,og as AvgPool3DGrad,ig as AvgPoolGrad,kde as BackendWasm,Ca as BatchMatMul,oo as BatchToSpaceND,ug as Bincount,lg as BroadcastArgs,A$ as BroadcastTo,CW as Callback,zL as CallbackList,Na as Cast,Ta as Ceil,Sr as ClipByValue,jd as Complex,Kd as ComplexAbs,uo as Concat,$a as Conv2D,cg as Conv2DBackpropFilter,_a as Conv2DBackpropInput,Xd as Conv3D,dg as Conv3DBackpropFilterV2,pg as Conv3DBackpropInputV2,Aa as Cos,Ea as Cosh,co as CropAndResize,hl as Cumprod,lo as Cumsum,BL as CustomCallback,Ud as DataStorage,hg as DenseBincount,po as DepthToSpace,Ra as DepthwiseConv2dNative,fg as DepthwiseConv2dNativeBackpropFilter,mg as DepthwiseConv2dNativeBackpropInput,gg as Diag,Yd as Dilation2D,Qf as Dilation2DBackpropFilter,Yf as Dilation2DBackpropInput,Jw as ENV,NW as EarlyStopping,Qd as Einsum,Fa as Elu,bg as EluGrad,S$ as Environment,ho as Equal,fl as Erf,Oa as Exp,fo as ExpandDims,mo as Expm1,yg as FFT,ml as Fill,go as FlipLeftRight,Pa as Floor,za as FloorDiv,fd as FromPixels,Ma as FusedBatchNorm,aa as FusedConv2D,ia as FusedDepthwiseConv2D,Kf as GPGPUContext,yo as GatherNd,bo as GatherV2,E4 as GraphModel,vo as Greater,La as GreaterEqual,LL as History,vg as IFFT,Ba as Identity,Zd as Imag,Dt as InputSpec,gl as IsFinite,bl as IsInf,yl as IsNan,nl as KernelBackend,ep as LRN,wg as LRNGrad,AL as LayerVariable,pr as LayersModel,Va as LeakyRelu,xo as Less,wo as LessEqual,xg as LinSpace,Wa as Log,vl as Log1p,E$ as LogSoftmax,ko as LogicalAnd,xl as LogicalNot,Jd as LogicalOr,L0 as MathBackendCPU,M1 as MathBackendWebGL,Ua as Max,Ha as MaxPool,tp as MaxPool3D,Ig as MaxPool3DGrad,kg as MaxPoolGrad,Sg as MaxPoolWithArgmax,Ga as Maximum,qa as Mean,ja as Min,Ka as Minimum,Xa as MirrorPad,wl as Mod,yb as MomentumOptimizer,Cg as Multinomial,Ya as Multiply,Io as Neg,Co as NonMaxSuppressionV3,kl as NonMaxSuppressionV4,No as NonMaxSuppressionV5,So as NotEqual,l_ as OP_SCOPE_SUFFIX,$o as OneHot,To as OnesLike,_r as Optimizer,Wr as OptimizerConstructors,_o as Pack,Qa as PadV2,$de as Pool,Za as Pow,Ja as Prelu,Ao as Prod,vb as RMSPropOptimizer,Ar as RNN,Il as Range,e_ as Rank,np as Real,Da as RealDiv,Sl as Reciprocal,oO as Reduction,ei as Relu,ni as Relu6,Eo as Reshape,ti as ResizeBilinear,Tg as ResizeBilinearGrad,Cl as ResizeNearestNeighbor,Ng as ResizeNearestNeighborGrad,Ro as Reverse,qo as RotateWithOffset,Do as Round,si as Rsqrt,Ip as SGDOptimizer,Fo as ScatterNd,Oo as Select,Nl as Selu,Ub as Sequential,ai as Sigmoid,Tl as Sign,ri as Sin,zo as Sinh,Po as Slice,ui as Softmax,$l as Softplus,Mo as SpaceToBatchND,sp as SparseFillEmptyRows,_l as SparseReshape,rp as SparseSegmentMean,ap as SparseSegmentSum,ip as SparseToDense,Lo as SplitV,ii as Sqrt,Al as Square,li as SquaredDifference,hi as Step,Bo as StridedSlice,op as StringNGrams,$g as StringSplit,_g as StringToHashBucketFast,ci as Sub,oi as Sum,$s as SymbolicTensor,Vo as Tan,di as Tanh,et as Tensor,Vt as TensorBuffer,Cr as Tile,Wo as TopK,Uo as Transform,pi as Transpose,Ag as Unique,Go as Unpack,up as UnsortedSegmentSum,gd as Variable,Ho as ZerosLike,ra as _FusedMatMul,Mt as abs,GA as acos,qA as acosh,ie as add,KA as addN,qk as all,pm as any,Hu as argMax,JA as argMin,tE as asin,sE as asinh,aE as atan,oE as atan2,lE as atanh,Gg as avgPool,Yk as avgPool3d,zA as backend,S as backend_util,qde as basicLSTMCell,ju as batchNorm,$E as batchNorm2d,AE as batchNorm3d,RE as batchNorm4d,Hg as batchToSpaceND,Qk as bincount,xpe as booleanMaskAsync,OE as broadcastArgs,nd as broadcastTo,jo as broadcast_util,Nk as browser,De as buffer,Ape as callbacks,ce as cast,ME as ceil,Vn as clipByValue,lr as clone,oa as complex,Ft as concat,VE as concat1d,UE as concat2d,HE as concat3d,jE as concat4d,MM as constraints,Zk as conv1d,ca as conv2d,Jk as conv2dTranspose,eI as conv3d,tR as conv3dTranspose,Ede as copyRegisteredKernels,jg as cos,nI as cosh,TI as cosineWindow,aR as cumprod,sI as cumsum,js as customGrad,R4 as data,uR as denseBincount,Gk as deprecationWarn,cR as depthToSpace,hp as depthwiseConv2d,Rpe as deregisterOp,dp as device_util,jde as diag,fR as dilation2d,Ode as disableDeprecationWarnings,Re as dispose,Pde as disposeVariables,xe as div,vR as divNoNan,Kde as dot,nF as dropout,kR as einsum,fp as elu,Fde as enableDebugMode,Dde as enableProdMode,sF as enclosingPowerOfTwo,Ss as engine,X as env,Kn as equal,CR as erf,Xn as exp,Pn as expandDims,_R as expm1,rI as eye,lb as fft,Pl as fill,Ude as findBackend,Gde as findBackendFactory,mp as floor,Hk as floorDiv,GX as forceHalfFloat,ha as fused,Ku as gather,J3 as gatherND,$k as gather_util,Vde as getBackend,nx as getGradient,Zf as getKernel,Jf as getKernelsForBackend,Wpe as getThreadsCount,RK as gpgpu_util,Qde as grad,Zde as grads,Wn as greater,Ko as greaterEqual,Id as ifft,Kg as imag,qn as image,kpe as inTopKAsync,qM as initializers,OB as input,En as io,xI as irfft,Xde as isFinite,Yde as isInf,BR as isNaN,Ht as keep,xs as kernel_impls,$L as layers,Xg as leakyRelu,aI as less,Xo as lessEqual,VO as linalg,GR as linspace,Dpe as loadGraphModel,$pe as loadLayersModel,qR as localResponseNormalization,Yn as log,Yg as log1p,tpe as logSigmoid,iI as logSoftmax,iD as logSumExp,Ds as logicalAnd,Jg as logicalNot,cI as logicalOr,npe as logicalXor,Cpe as losses,We as matMul,iA as math,As as max,eb as maxPool,dI as maxPool3d,fD as maxPoolWithArgmax,$r as maximum,St as mean,dm as memory,spe as meshgrid,rW as metrics,fm as min,bp as minimum,xD as mirrorPad,kD as mod,Npe as model,xW as models,tb as moments,wpe as movingAverage,V as mul,rpe as multiRNNCell,TD as multinomial,kt as neg,OI as nextFrame,CI as norm,Xu as notEqual,vd as oneHot,Mn as ones,Qn as onesLike,L as op,ape as outerProduct,fi as pad,ipe as pad1d,ope as pad2d,upe as pad3d,lpe as pad4d,cpe as pool,pa as pow,sb as prelu,U_ as print,pI as prod,zde as profile,dpe as rand,ppe as randomGamma,KD as randomNormal,Ml as randomUniform,Yu as range,Bde as ready,wd as real,ZD as reciprocal,pp as registerBackend,_pe as registerCallbackConstructor,D$ as registerGradient,El as registerKernel,Epe as registerOp,wW as regularizers,Xs as relu,hI as relu6,Wde as removeBackend,G as reshape,Zn as reverse,hpe as reverse1d,fpe as reverse2d,mpe as reverse3d,gpe as reverse4d,cb as rfft,fI as round,mI as rsqrt,we as scalar,X3 as scatterND,Ak as scatter_util,gI as selu,c3 as separableConv2d,Tpe as sequential,ae as serialization,Lde as setBackend,Hde as setPlatform,Vpe as setThreadsCount,Lpe as setWasmPath,Bpe as setWasmPaths,S5 as setWebGLContext,p3 as setdiff1dAsync,Zy as shared,qs as sigmoid,f3 as sign,Spe as signal,bI as sin,yI as sinh,He as slice,ib as slice1d,vI as slice2d,ob as slice3d,kd as slice4d,wt as slice_util,ub as softmax,zl as softplus,nb as spaceToBatchND,Wc as sparse,NI as sparseToDense,Ipe as spectral,Bn as split,dn as sqrt,ct as square,wI as squaredDifference,mr as squeeze,Jn as stack,yp as step,D3 as stridedSlice,Mf as string,ge as sub,ve as sum,cp as sumOutType,O3 as tan,qu as tanh,fs as tensor,Zt as tensor1d,Ki as tensor2d,pA as tensor3d,bpe as tensor4d,ype as tensor5d,vpe as tensor6d,_s as tensor_util,_A as test_util,j as tidy,ps as tile,Mde as time,z3 as topk,Pi as train,qe as transpose,db as truncatedNormal,hx as unique,Ade as unregisterGradient,_de as unregisterKernel,V3 as unsortedSegmentSum,Fs as unstack,cn as upcastType,w as util,Jde as valueAndGrad,epe as valueAndGrads,U3 as variable,XR as variableGrads,Gpe as version,Fpe as version_converter,Rde as version_core,Ope as version_cpu,uS as version_layers,Upe as version_wasm,Ppe as version_webgl,zpe as webgl,I5 as webgl_util,roe as webgpu,vn as where,II as whereAsync,$t as zeros,je as zerosLike};\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use backend file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n* @license\n* Copyright 2018 Google LLC. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n* =============================================================================\n*/\n/** @license See the LICENSE file. */\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        return { tensor: null, canvas: inCanvas };\n      }\n      fx.reset();\n      if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n      if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n      if (config.filter.negative) fx.add('negative');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.vintage) fx.add('brownie');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.kodachrome) fx.add('kodachrome');\n      if (config.filter.technicolor) fx.add('technicolor');\n      if (config.filter.polaroid) fx.add('polaroid');\n      if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n      if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  const modelUrl = join(options.modelBasePath, modelPath || '');\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  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0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.2;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else {\n    t.batch = tf.squeeze(res); // when using tfhub model\n  }\n  tf.dispose(res);\n  t.boxes = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\n// eslint-disable-next-line class-methods-use-this\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n// eslint-disable-next-line class-methods-use-this\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n    // So we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const [contours, confidence, contourCoords] = model.execute(face.tensor as Tensor) as Array<Tensor>; // first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = await confidence.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\n      tf.dispose([contourCoords, coordsReshaped, confidence, contours]);\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n      } else {\n        if (config.face.iris?.enabled) rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // augment results with iris\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.mesh?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 0.140625, y: 0.171875 },\n  { x: 0.140625, y: 0.171875 },\n  { x: 0.171875, y: 0.171875 },\n  { x: 0.171875, y: 0.171875 },\n  { x: 0.203125, y: 0.171875 },\n  { x: 0.203125, y: 0.171875 },\n  { x: 0.234375, y: 0.171875 },\n  { x: 0.234375, y: 0.171875 },\n  { x: 0.265625, y: 0.171875 },\n  { x: 0.265625, y: 0.171875 },\n  { x: 0.296875, y: 0.171875 },\n  { x: 0.296875, y: 0.171875 },\n  { x: 0.328125, y: 0.171875 },\n  { x: 0.328125, y: 0.171875 },\n  { x: 0.359375, y: 0.171875 },\n  { x: 0.359375, y: 0.171875 },\n  { x: 0.390625, y: 0.171875 },\n  { x: 0.390625, y: 0.171875 },\n  { x: 0.421875, y: 0.171875 },\n  { x: 0.421875, y: 0.171875 },\n  { x: 0.453125, y: 0.171875 },\n  { x: 0.453125, y: 0.171875 },\n  { x: 0.484375, y: 0.171875 },\n  { x: 0.484375, y: 0.171875 },\n  { x: 0.515625, y: 0.171875 },\n  { x: 0.515625, y: 0.171875 },\n  { x: 0.546875, y: 0.171875 },\n  { x: 0.546875, y: 0.171875 },\n  { x: 0.578125, y: 0.171875 },\n  { x: 0.578125, y: 0.171875 },\n  { x: 0.609375, 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x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep, now, log } from './util';\nimport { env } from './env';\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Point } from '../result';\nimport type { AnyCanvas } from '../exports';\n\n/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n\nlet drawTime = 0;\n\nconst getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nconst rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nfunction point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  z = z || 0;\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    const z = pt[2] || 0;\n    ctx.strokeStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], Math.round(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    if (localOptions.drawLabels) {\n      // silly hack since fillText does not suport new line\n      const labels:string[] = [];\n      labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n      if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n      if (f.age) labels.push(`age: ${f.age || ''}`);\n      if (f.iris) labels.push(`distance: ${f.iris}`);\n      if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n      if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n      if (f.emotion && f.emotion.length > 0) {\n        const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n        if (emotion.length > 3) emotion.length = 3;\n        labels.push(emotion.join(' '));\n      }\n      if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n        if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n        if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n      }\n      if (labels.length === 0) labels.push('face');\n      ctx.fillStyle = localOptions.color;\n      for (let i = labels.length - 1; i >= 0; i--) {\n        const x = Math.max(f.box[0], 0);\n        const y = i * localOptions.lineHeight + f.box[1];\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(labels[i], x + 5, y + 16);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(labels[i], x + 4, y + 15);\n      }\n    }\n    // ctx.lineWidth = localOptions.lineWidth;\n    ctx.lineWidth = 2;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n      }\n      if (localOptions.drawPolygons) {\n        if (f.mesh.length > 450) {\n          for (let i = 0; i < triangulation.length / 3; i++) {\n            const points = [\n              triangulation[i * 3 + 0],\n              triangulation[i * 3 + 1],\n              triangulation[i * 3 + 2],\n            ].map((index) => f.mesh[index]);\n            lines(ctx, points, localOptions);\n          }\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n          ctx.strokeStyle = 'pink';\n          const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n          const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n          const pathV = new Path2D(`\n            M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n            C\n              ${valX} ${f.box[1]},\n              ${valX} ${f.box[1] + f.box[3]},\n              ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n          `);\n          const pathH = new Path2D(`\n            M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n            C \n              ${f.box[0]} ${valY},\n              ${f.box[0] + f.box[2]} ${valY},\n              ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n          `);\n          ctx.stroke(pathH);\n          ctx.stroke(pathV);\n        }\n        if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = 'pink';\n          ctx.fillStyle = 'pink';\n          const leftGaze = [\n            f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n          const rightGaze = [\n            f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n        }\n      }\n    }\n  }\n}\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + (2 * (result[i].keypoints[pt].position[2] || 0))}, ${127.5 - (2 * (result[i].keypoints[pt].position[2] || 0))}, 255, 0.5)` : localOptions.color;\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + (2 * pt.position[2])}, ${127.5 - (2 * pt.position[2])}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * (pt[2] || 0))}, ${127.5 - (2 * (pt[2] || 0))}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || 0;\n        ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (i * z)}, ${127.5 - (i * z)}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor, GraphModel } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\n  if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n  else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n  else res = await warmupNode(instance);\n  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {\n    log('compile pass: skip');\n    return;\n  }\n  const models = Object.values(allModels).filter((m) => m !== null) as GraphModel[];\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  for (const model of models) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && model.inputs[0].dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    const res = await model.executeAsync(tensor);\n    if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n    else tf.dispose(res);\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass kernels:', kernels.length);\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    // await runCompile(instance.models);\n    const res = await runInference(instance);\n    const t1 = now();\n    if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './util/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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   "names": []
 }
diff --git a/dist/human.js b/dist/human.js
index 4db017d0..96adb35c 100644
--- a/dist/human.js
+++ b/dist/human.js
@@ -4,63 +4,63 @@
   author: <https://github.com/vladmandic>'
 */
 
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n=++this.pendingBackendInitId,a=r.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,vs(`Initialization of backend ${e} failed`),vs(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=r,{success:!0,asyncInit:!1}}catch(r){return vs(`Initialization of backend ${e} failed`),vs(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let r=e[t],{success:n,asyncInit:a}=this.initializeBackend(r);if(a||n)return{name:r,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let r=this.state.tensorInfo.get(t),n=r.backend,a=this.readSync(t),s=n.refCount(t);n.disposeData(t,!0),r.backend=e,e.move(t,a,r.shape,r.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let r=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new 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n=this.backend.numDataIds(),a=0;r.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=By(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(By(e)){let{kernelName:c,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=lf(c,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:w,shape:T,dtype:S}=b;return this.makeTensorFromDataId(w,T,S)});if(n){let b=this.getTensorsForGradient(c,f,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,f=m=>{!n||(r=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:d}=e,h=By(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,h,r,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,r){let n=e1(e);if(n!=null){let a=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=r.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,r,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",n=n||this.backend;let a=e;r==="string"&&ws(e[0])&&(a=e.map(o=>rh(o)));let s=n.write(a,t,r),i=new rt(t,r,s,this.nextTensorId());if(this.trackTensor(i,n),r==="string"){let o=this.state.tensorInfo.get(s),l=fw(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,r,n){r=r||"float32";let a=new rt(t,r,e,this.nextTensorId());return this.trackTensor(a,n),a}makeVariable(e,t=!0,r,n){r=r||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let a=new Np(e,t,r,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*Qy(e.dtype)),this.state.numBytes+=r,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:r})),e instanceof Np||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let r=e.size*Qy(e.dtype);this.state.numBytes-=r}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,r=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let n of 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+          ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(Yp,i)}var uD=W({sparseSegmentMean_:lD});function dD(e,t,r){let n=F(e,"data","sparseSegmentSum"),a=F(t,"indices","sparseSegmentSum","int32"),s=F(r,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${a.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
-         ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(Yp,i)}var pD=W({sparseSegmentSum_:dD});function hD(e,t,r,n,a,s,i,o){let l=F(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=F(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let d={separator:r,nGramWidths:n,leftPad:a,rightPad:s,padWidth:i,preserveShortSequences:o},h={data:l,dataSplits:u},p=B.runKernel(Qp,h,d);return{nGrams:p[0],nGramsSplits:p[1]}}var cD=W({stringNGrams_:hD});function fD(e,t,r=!0){let n=F(e,"input","stringSplit","string"),a=F(t,"delimiter","stringSplit","string");if(n.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${n.shape}`);if(a.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${a.shape}`);let s={skipEmpty:r},i={input:n,delimiter:a},o=B.runKernel(am,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var mD=W({stringSplit_:fD});function gD(e,t){let r=F(e,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let a={input:r};return B.runKernel(sm,a,n)}var yD=W({stringToHashBucketFast_:gD}),AD={fft:wm,ifft:Mp,rfft:km,irfft:Y2},xD={hammingWindow:qz,hannWindow:x7,frame:b7,stft:Yz},Ie={flipLeftRight:tO,grayscaleToRGB:nO,resizeNearestNeighbor:NO,resizeBilinear:SO,rotateWithOffset:sO,cropAndResize:Qz,nonMaxSuppression:oO,nonMaxSuppressionAsync:mO,nonMaxSuppressionWithScore:yO,nonMaxSuppressionWithScoreAsync:xO,nonMaxSuppressionPadded:vO,nonMaxSuppressionPaddedAsync:kO,threshold:RO,transform:FO},S7={bandPart:PO,gramSchmidt:zO,qr:DO},bD={absoluteDifference:WO,computeWeightedLoss:Ya,cosineDistance:UO,hingeLoss:jO,huberLoss:qO,logLoss:XO,meanSquaredError:YO,sigmoidCrossEntropy:eD,softmaxCrossEntropy:nD},dp={sparseFillEmptyRows:sD,sparseReshape:oD,sparseSegmentMean:uD,sparseSegmentSum:pD},qc={stringNGrams:cD,stringSplit:mD,stringToHashBucketFast:yD},Ja=class extends pk{minimize(e,t=!1,r){let{value:n,grads:a}=this.computeGradients(e,r);if(r!=null){let s=r.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return re(a),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return qk(e,t)}dispose(){this.iterations_!=null&&re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Se(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ja,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Em=class extends Ja{constructor(e,t,r=null){super();this.learningRate=e,this.rho=t,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accum_grad`,variable:K(()=>at(n).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${t}/accum_var`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[r].variable,o=this.accumulatedUpdates[r].variable;K(()=>{let l=le(L(i,this.rho),L(At(s),1-this.rho)),u=L(pe(Cr(le(o,this.epsilon)),Cr(le(i,this.epsilon))),s),d=le(L(o,this.rho),L(At(u),1-this.rho));i.assign(l),o.assign(d);let h=le(L(u,-this.learningRate),n);n.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(re(this.accumulatedGrads.map(e=>e.variable)),re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,r=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Em.className="Adadelta";Oi(Em);var Rm=class extends Ja{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accumulator`,variable:K(()=>ad(n.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[r].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[r].variable;K(()=>{let i=le(s,At(a));s.assign(i);let o=le(L(pe(a,Cr(le(i,B.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&re(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Rm.className="Adagrad";Oi(Rm);var Mm=class extends Ja{constructor(e,t,r,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],K(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(r).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=he(1,this.accBeta1),n=he(1,this.accBeta2);t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:K(()=>at(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:K(()=>at(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=le(L(d,this.beta2),L(At(l),1-this.beta2)),c=pe(h,r),f=pe(p,n);u.assign(h),d.assign(p);let m=le(L(pe(c,le(Cr(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),K(()=>{this.accBeta1.assign(_s(this.beta1,this.iterations_+1)),this.accBeta2.assign(_s(this.beta2,this.iterations_+1))});let t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Mm.className="Adam";Oi(Mm);var Fm=class extends Ja{constructor(e,t,r,n=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=he(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:at(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:at(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=L(d,this.beta2),c=er(l),f=Za(p,c);u.assign(h),d.assign(f);let m=le(L(pe(n,r),pe(h,le(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(le(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Fm.className="Adamax";Oi(Fm);var ch=class extends Ja{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];K(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=hr(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};ch.className="SGD";Oi(ch);var $m=class extends ch{constructor(e,t,r=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=r,this.accumulations=[],this.m=Se(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulations[r]==null&&(this.accumulations[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(!1))});let a=this.accumulations[r].variable,s=Array.isArray(e)?e[r].tensor:e[t];s!=null&&K(()=>{let i,o=le(L(this.m,a),s);this.useNesterov?i=le(L(this.c,le(s,L(o,this.m))),n):i=le(L(this.c,o),n),a.assign(o),n.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&re(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};$m.className="Momentum";Oi($m);var Pm=class extends Ja{constructor(e,t=.9,r=0,n=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=r,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,n==null&&(this.epsilon=B.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${t}/rms`,variable:K(()=>at(n).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${t}/mg`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[r].variable,o=this.accumulatedMoments[r].variable;K(()=>{let l=le(L(i,this.decay),L(At(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[r].variable,d=le(L(u,this.decay),L(s,1-this.decay)),h=pe(L(s,this.learningRate),Cr(he(l,le(At(d),this.epsilon)))),p=le(L(o,this.momentum),h);i.assign(l),u.assign(d),o.assign(p);let c=he(n,p);n.assign(c)}else{let u=le(L(i,this.decay),L(At(s),1-this.decay)),d=le(L(o,this.momentum),pe(L(s,this.learningRate),Cr(le(u,this.epsilon))));i.assign(u),o.assign(d);let h=he(n,d);n.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,r=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Pm.className="RMSProp";Oi(Pm);var xs=class{static sgd(e){return new ch(e)}static momentum(e,t,r=!1){return new $m(e,t,r)}static rmsprop(e,t=.9,r=0,n=null,a=!1){return new Pm(e,t,r,n,a)}static adam(e=.001,t=.9,r=.999,n=null){return new Mm(e,t,r,n)}static adadelta(e=.001,t=.95,r=null){return new Em(e,t,r)}static adamax(e=.002,t=.9,r=.999,n=null,a=0){return new Fm(e,t,r,n,a)}static adagrad(e,t=.1){return new Rm(e,t)}},oo={sgd:xs.sgd,momentum:xs.momentum,adadelta:xs.adadelta,adagrad:xs.adagrad,rmsprop:xs.rmsprop,adamax:xs.adamax,adam:xs.adam},vD=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function sA(){return new Promise(e=>vD(()=>e()))}var N={};Le(N,{ERF_A1:()=>FD,ERF_A2:()=>$D,ERF_A3:()=>PD,ERF_A4:()=>_D,ERF_A5:()=>zD,ERF_P:()=>MD,PARALLELIZE_THRESHOLD:()=>iA,SELU_SCALE:()=>N7,SELU_SCALEALPHA:()=>T7,applyActivation:()=>Nm,assertAndGetBroadcastShape:()=>bt,assertAxesAreInnerMostDims:()=>$P,assertParamsConsistent:()=>wD,assignToTypedArray:()=>VD,axesAreInnerMostDims:()=>O2,calculateShapes:()=>tk,checkEinsumDimSizes:()=>KD,checkPadOnDimRoundingMode:()=>Vr,combineLocations:()=>Kk,complexWithEvenIndex:()=>LD,complexWithOddIndex:()=>BD,computeConv2DInfo:()=>oh,computeConv3DInfo:()=>Ik,computeDefaultPad:()=>S2,computeDilation2DInfo:()=>JF,computeOptimalWindowSize:()=>ID,computeOutAndReduceShapes:()=>Xk,computeOutShape:()=>kD,computePool2DInfo:()=>kk,computePool3DInfo:()=>QF,convertConv2DDataFormat:()=>Sk,decodeEinsumEquation:()=>HD,eitherStridesOrDilationsAreOne:()=>Fa,expandShapeToKeepDim:()=>Io,exponent:()=>GD,exponents:()=>UD,fromStringArrayToUint8:()=>mL,fromUint8ToStringArray:()=>fL,getAxesPermutation:()=>Zk,getBroadcastDims:()=>Yw,getComplexWithIndex:()=>WD,getEinsumComputePath:()=>XD,getEinsumPermutation:()=>qD,getFusedBiasGradient:()=>Tm,getFusedDyActivation:()=>Sm,getImageCenter:()=>SD,getInnerMostAxes:()=>PP,getPermuted:()=>ND,getReductionAxes:()=>Xt,getReshaped:()=>TD,getReshapedPermuted:()=>CD,getSliceBeginCoords:()=>ED,getSliceSize:()=>RD,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>QD,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>eL,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>tL,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>aL,getSparseReshapeInputOutputMismatchErrorMessage:()=>iL,getSparseReshapeInputOutputMultipleErrorMessage:()=>sL,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>rL,getSparseReshapeNegativeOutputDimErrorMessage:()=>nL,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>dL,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>oL,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>lL,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>uL,getUndoAxesPermutation:()=>D2,isIdentityPermutation:()=>ZD,log:()=>TR,mergeRealAndImagArrays:()=>OD,prepareAndValidate:()=>ek,prepareSplitSize:()=>JD,segment_util:()=>C7,shouldFuse:()=>Cm,slice_util:()=>_t,splitRealAndImagArrays:()=>DD,tupleValuesAreOne:()=>Fs,upcastType:()=>Nr,validateInput:()=>g2,validateUpdateShape:()=>m2,warn:()=>vs});function wD(e,t){let r=e[0].length;e.forEach((a,s)=>{P(a.length===r,()=>`Error in concat${r}D: rank of tensors[${s}] must be the same as the rank of the rest (${r})`)}),P(t>=0&&t<r,()=>`Error in concat${r}D: axis must be between 0 and ${r-1}.`);let n=e[0];e.forEach((a,s)=>{for(let i=0;i<r;i++)P(i===t||a[i]===n[i],()=>`Error in concat${r}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${n}) along the non-concatenated axis ${s}.`)})}function kD(e,t){let r=e[0].slice();for(let n=1;n<e.length;n++)r[t]+=e[n][t];return r}var iA=30;function ID(e){return e<=iA?e:af(e,Math.floor(Math.sqrt(e)))}function SD(e,t,r){let n=r*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[n,a]}function TD(e,t,r,n=!0){let a=[];if(n)a=a.concat(t.slice(0)),a.push(e[0]/r),a=a.concat(e.slice(1));else{a=a.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)a=a.concat([e[i+1]/t[i],t[i]]);a=a.concat(e.slice(s+1))}return a}function ND(e,t,r=!0){let n=[];if(r){n.push(t);for(let 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s={skipEmpty:r},i={input:n,delimiter:a},o=B.runKernel(sm,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var mD=W({stringSplit_:fD});function gD(e,t){let r=F(e,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let a={input:r};return B.runKernel(im,a,n)}var 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this.applyGradients(a);return re(a),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Zk(e,t)}dispose(){this.iterations_!=null&&re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Se(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ja,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Rm=class extends Ja{constructor(e,t,r=null){super(),this.learningRate=e,this.rho=t,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accum_grad`,variable:K(()=>at(n).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${t}/accum_var`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[r].variable,o=this.accumulatedUpdates[r].variable;K(()=>{let l=le(L(i,this.rho),L(At(s),1-this.rho)),u=L(pe(Cr(le(o,this.epsilon)),Cr(le(i,this.epsilon))),s),d=le(L(o,this.rho),L(At(u),1-this.rho));i.assign(l),o.assign(d);let h=le(L(u,-this.learningRate),n);n.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(re(this.accumulatedGrads.map(e=>e.variable)),re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,r=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Rm.className="Adadelta";Oi(Rm);var Mm=class extends Ja{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accumulator`,variable:K(()=>sd(n.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[r].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[r].variable;K(()=>{let i=le(s,At(a));s.assign(i);let o=le(L(pe(a,Cr(le(i,B.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&re(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Mm.className="Adagrad";Oi(Mm);var Fm=class extends Ja{constructor(e,t,r,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],K(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(r).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=he(1,this.accBeta1),n=he(1,this.accBeta2);t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:K(()=>at(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:K(()=>at(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=le(L(d,this.beta2),L(At(l),1-this.beta2)),c=pe(h,r),f=pe(p,n);u.assign(h),d.assign(p);let m=le(L(pe(c,le(Cr(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),K(()=>{this.accBeta1.assign(_s(this.beta1,this.iterations_+1)),this.accBeta2.assign(_s(this.beta2,this.iterations_+1))});let t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Fm.className="Adam";Oi(Fm);var $m=class extends Ja{constructor(e,t,r,n=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=he(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};$m.className="Adamax";Oi($m);var fh=class extends Ja{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];K(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=hr(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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qt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},oa=class{constructor(e,t,r,n,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=r,this.inputs=n,this.callArgs=a,this.outputTensorIndex=i,this.id=U7(),s!=null&&(this.originalName=O7(s),this.name=D7(this.originalName)),this.rank=t.length}},oV=0,jm=class{constructor(e,t){this.callArgs=t,this.id=oV++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let r of e.inboundLayers)r!=null&&r.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},lV=0,st=class extends ue.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=lV++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let r=this.getClassName();t=Wa(r)+"_"+Gm(r)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let r;if(e.batchInputShape!=null)r=e.batchInputShape;else if(e.inputShape!=null){let a=null;e.batchSize!=null&&(a=e.batchSize),r=[a].concat(e.inputShape)}this.batchInputShape=r;let n=e.dtype;n==null&&(n=e.inputDType),n==null&&(n="float32"),this.dtype=n}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ia(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new q(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Jr(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Jr(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Ba(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let r=0;r<e.length;r++){let n=e[r],a=t[r];if(a==null)continue;let s=n.rank;if(a.ndim!=null&&s!==a.ndim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&n.dtype!==a.dtype)throw new q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${n.dtype}.`);if(a.axes){let i=n.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=n.shape[i];if(o!=null&&l!=null&&o!==l)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${n.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let r=It(e),n=!0;for(let s of r)if(!(s instanceof oa)){n=!1;break}let a=!0;for(let s of r)if(s instanceof oa){a=!1;break}if(n===a)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return Ao(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of It(e))s.push(i.shape);this.build(Jr(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=It(s),o=[];for(let l of i)r.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Jr(o),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=uV(e),i=this.computeOutputShape(s),o,l=dV(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new oa(l,u,this,It(e),t,this.name,d)):o=new oa(l,i,this,It(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,n)=>{r!=null&&e[n]!=null&&e[n]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Ba(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Ba(`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 ia(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return gf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return A1(e?this.trainableWeights:this.weights)}setWeights(e){K(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let r=[],n=A1(t);for(let a=0;a<n.length;++a){let s=n[a],i=t[a],o=e[a];if(!v.arraysEqual(s.shape,o.shape))throw new q(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);r.push([i,o])}wA(r)})}addWeight(e,t,r,n,a,s,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(n=o!=null?o():Et("zeros"));let l=n.apply(t,r),u=new G7(l,r,e,s,i);return l.dispose(),a!=null&&this.addLoss(()=>a.apply(u.read())),s==null&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=It(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,r,n,a,s,i=null){let o=It(e);t=It(t),r=It(r),n=It(n),a=mf(a),s=mf(s);let l=[],u=[],d=[];for(let h of o)l.push(h.sourceLayer),u.push(h.nodeIndex),d.push(h.tensorIndex);new jm({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:r,outputMasks:n,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function uV(e){e=It(e);let t=[];for(let r of e)t.push(r.shape);return Jr(t)}function dV(e){return"float32"}function j7(e,t,r){if((t==null||r!=null&&r>0)&&(t=e.sourceLayer,r=e.nodeIndex),t.inboundNodes.length===0)return[e];{let n=t.inboundNodes[r];if(n.inboundLayers.length===0)return n.inputTensors;{let a=[];for(let s=0;s<n.inboundLayers.length;s++){let i=n.inputTensors[s],o=n.inboundLayers[s],l=n.nodeIndices[s],u=j7(i,o,l);for(let d of u)a.indexOf(d)===-1&&a.push(d)}return a}}}var dd=class extends st{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Gm("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=e.dtype||"float32";this.batchInputShape=t,this.dtype=r,this.inputSpec=[{shape:t}];let n=new oa(this.dtype,this.batchInputShape,this,[],{},this.name);n.nodeIndex=0,n.tensorIndex=0,new jm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[n],outputTensors:[n],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`Cannot pass any input to an InputLayer's apply() method. 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R=0;R<E.length;++R){c.hasKey(E[R])||c.add(E[R],w[R],Array.isArray(T)?T[0]:T);let _=o.indexOf(E[R].name);_!==-1&&(l[_]=w[R])}a||re(x)}return c.disposeMasks(),s?l:l[0]}function LV(e,t){v.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let r=[],n={};if(e.length===1){let a=ev(e[0],t);r=a.sorted,n=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=ev(s,t);for(let l of i)a.has(l.name)||(r.push(l),a.add(l.name));for(let l in o)n[l]==null&&(n[l]=new Set),o[l].forEach(u=>n[l].add(u))}}return{sorted:r,recipientCounts:BV(n)}}function BV(e){let t={};for(let r in e)t[r]=e[r].size;return t}function ev(e,t){let r=new Set,n=[],a={};for(let o of t.names())r.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(r.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),n.push(o),r.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let u of o.inputs)a[u.name]==null&&(a[u.name]=new Set),a[u.name].add(o.name),!r.has(u.name)&&s.push(u)}}return{sorted:n,recipientMap:a}}function WV(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let r=null;for(let n=0;n<e.sourceLayer.inboundNodes.length;++n)for(let a of e.sourceLayer.inboundNodes[n].outputTensors)if(a.id===e.id){r=n;break}t=e.sourceLayer.getOutputAt(r)}return t}var ka=class extends st{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Gm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Ss(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Ss(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;Ia(x===0,"input layer has >1 nodes"),Ia(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof dd))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},r={},n={},a={},s={},i=[],o=(y,A,x,b,w,T)=>{(b==null||w==null||T==null)&&(b=y.sourceLayer,w=y.nodeIndex,T=y.tensorIndex);let S=b.inboundNodes[w];if(x.indexOf(S)!==-1)throw new ia(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(S)!==-1)return;this.containerNodes.add(ka.nodeKey(b,w)),b.id in s||(s[b.id]=Object.keys(s).length),x.indexOf(S)===-1&&x.push(S);let E=S.inboundLayers.length;for(let R=0;R<E;R++){let _=S.inputTensors[R],M=S.inboundLayers[R],I=S.nodeIndices[R],O=S.tensorIndices[R];o(_,A,x,M,I,O)}for(A.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);i.push(S)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let d=i.slice().reverse();for(let y of d){r[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=n[y.outboundLayer.id]==null?0:n[y.outboundLayer.id];A=Math.max(A,x),n[y.outboundLayer.id]=A,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let w=y.inboundLayers[b],T=y.nodeIndices[b],S=w.inboundNodes[T],E=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(A+1,E),r[S.id]=S}}let h={};for(let y in t){let A=t[y];A in h||(h[A]=[]),h[A].push(r[y])}let p={};for(let y in n){let A=n[y];A in p||(p[A]=[]),p[A].push(a[y])}let c=Object.keys(p).map(y=>parseInt(y,10)).sort(_c);this.layers=[];for(let y of c){let A=p[y];A.sort((x,b)=>{let w=s[x.id],T=s[b.id];return w<T?-1:w>T?1:0});for(let x of A)x instanceof ka&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,c=Object.keys(h).map(y=>parseInt(y,10)).sort(_c);let f=this.inputs.slice(),m=[];for(let y of c)for(let A of h[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new ia(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=h;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new ia(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new jm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(r=>r.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},n=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new q(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,n++}let a=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)a.push([r[i],e[s]]);else if(t)throw new q(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new q(`${s.length} of ${n} weights are not set: ${s}`)}wA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${CA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=b1(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return K(()=>{e=It(e);let r=new mo;for(let n=0;n<this.inputs.length;++n)r.add(this.inputs[n],e[n]);return pp(this.outputs,r,t)})}computeMask(e,t){return K(()=>{e=It(e);let r;return t==null?r=To(null,e.length):r=It(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=mf(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";r[u]=l}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(_c);if(n.length>1)for(let i of n){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let d=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=r[A];d.push(x)}let h=u.computeOutputShape(Jr(d)),p=mf(h),c=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${c}_${f}`;r[m]=p[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Ia(o in r),a.push(r[o])}return Jr(a)}runInternalGraph(e,t){t==null&&(t=To(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];r[l.id]=[u,d]}let n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(_c);for(let o of n){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,h=u.inputTensors,p=u.outputTensors,c=new Array;for(let f of h)f.id in r&&c.push(r[f.id]);if(c.length===h.length){let f={},m,g,y,A;if(u.callArgs!=null&&(f=u.callArgs),c.length===1){let[x,b]=c[0];f.mask==null&&(f.mask=b),y=It(d.call(x,f)),A=It(d.computeMask(x,b)),m=[x],g=[b]}else m=c.map(x=>x[0]),g=c.map(x=>x[1]),f.mask==null&&(f.mask=g),y=It(d.call(m,f)),A=It(d.computeMask(m,g));if(d.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],w=y[x],T=A[x];r[b.id]=[w,T]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Ia(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=r[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},r;for(let n of this.layers){r=n instanceof ka?1:0;for(let a=0;a<n.inboundNodes.length;a++){let s=ka.nodeKey(n,a);this.containerNodes.has(s)&&(t[s]=r,r+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===e)return r;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let n=ka.nodeKey(t,r);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let h=s.inboundNodes[d],p=ka.nodeKey(s,d),c={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),c=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),c={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let g=h.inboundLayers[m],y=h.nodeIndices[m],A=h.tensorIndices[m],x=ka.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,c])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,r.push(u)}e.layers=r;let n=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];n.push([i.name,u,d])}e.inputLayers=n;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];a.push([i.name,u,d])}return e.outputLayers=a,e}static fromConfig(e,t,r={},n=!1){let a={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],w=x[1],T=x[2];if(A=x[3]==null?{}:x[3],!(b in a)){i(m,g);return}let S=a[b];if(S.inboundNodes.length<=w){i(m,g);return}let E=S.inboundNodes[w];y.push(E.outputTensors[T])}y.length>0&&m.apply(Jr(y),A)}function l(m){let g=m.name,y=da(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),a[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let m of d)l(m);for(;!AW(s);)for(let m of d){let g=a[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let h=[],p=[],c=t.inputLayers;for(let m of c){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;h.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;p.push(x[A])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function VV(e,t,r){let n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(n===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==n)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${n}) outputs, so ${r} must be either an array with ${n} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function s4(e,t){return VV(e,t,"classWeight")}async function i4(e,t,r,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let a=K(()=>{if(e.shape.length===1)return Lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Nn(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());re(a);let i=[];return s.forEach(o=>{if(r[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(r[o])}),St(i,"float32")}else return null}function UV(e,t){return L(e,t)}var GV=32;function o4(e,t){let r,n,a=t;r=a.xs,n=a.ys,v.assert(r!=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=tv("input",e.inputNames,r),i=tv("output",e.outputNames,n),o=s[0].shape[0];v.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)v.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)v.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function tv(e,t,r){if(r instanceof rt)return[r];if(Array.isArray(r))return v.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let n=[];for(let a of t){if(r[a]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);n.push(r[a])}return n}}function jV(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function HV(e,t,r){let n=r.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),v.assert(!n||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),v.assert(r.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=ho(e.metrics[s])}this.compile({loss:n,metrics:a,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Sr.getSaveHandlers(e);if(i.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new q(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Sr.encodeWeights(this.getNamedWeights(t)),n=!1,a=null,s={modelTopology:this.toJSON(a,n),format:rU,generatedBy:`TensorFlow.js tfjs-layers v${CA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Sr.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Sr.concatenateArrayBuffers([r.data,o])}return this.userDefinedMetadata!=null&&(J3(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=r.data,s.weightSpecs=r.specs,e.save(s)}setUserDefinedMetadata(e){J3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ja.className="Model";ue.registerClass(ja);var u4=class extends ja{};u4.className="Functional";ue.registerClass(u4);async function nU(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let r=e.modelTopology;r.model_config!=null&&(r=r.model_config);let n=$p(r),a=da(n,t);if(e.weightsManifest!=null){let s=await Sr.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),re(s)}return a}async function aU(e,t){if(t==null&&(t={}),typeof e=="string"){let r=Sr.getLoadHandlers(e,t);if(r.length===0)r.push(Sr.browserHTTPRequest(e,t));else if(r.length>1)throw new q(`Found more than one (${r.length}) load handlers for URL '${e}'`);e=r[0]}return sU(e,void 0,t)}async function sU(e,t,r){if(r==null&&(r={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let n=await e.load(),a=n.modelTopology;a.model_config!=null&&(a=a.model_config);let s=r.strict==null?!0:r.strict,i=n.weightData!=null&&n.weightSpecs!=null&&s,o=da($p(a),t,i),l=n.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),n.userDefinedMetadata!=null&&o.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. 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compiled before being used.");return this.model.evaluate(e,t,r)}async evaluateDataset(e,t){if(!this.built)throw new ia("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,r={}){if(!this.built)throw new ia("The model needs to be compiled before being used.");return this.model.fit(e,t,r)}async fitDataset(e,t){if(!this.built)throw new ia("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,r={},n=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof I1))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=da(o,void 0,n);n&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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t={};return t.className="linear",t.config={},Ky(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Ky(t)}else return e instanceof sn?e:Ky(e)}function MA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var I4=class extends ue.Serializable{},xh=class extends I4{constructor(e){super();MA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return K(()=>{let t=Wt([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,er(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,yh(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};xh.className="L1L2";ue.registerClass(xh);function pU(e){return MA(e),new xh({l1:e!=null?e.l1:null,l2:0})}function hU(e){return MA(e),new xh({l2:e!=null?e.l2:null,l1:0})}var iv={l1l2:"L1L2"};function xt(e){return oA(e)}function ov(e,t={}){return fh(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Rt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in iv?iv[e]:e,config:{}};return ov(t)}else return e instanceof I4?e:ov(e)}var FA=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ge(e);let r=$a(e);return this.maxValue!=null&&(r=hn(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};FA.className="ReLU";ue.registerClass(FA);var $A=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ge(e);return cm(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};$A.className="LeakyReLU";ue.registerClass($A);var PA=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Et(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rt(e.alphaRegularizer),this.alphaConstraint=ar(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new qt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=Ge(e),bm(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Pt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:nr(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};PA.className="PReLU";ue.registerClass(PA);var _A=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ge(e);return uh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};_A.className="ELU";ue.registerClass(_A);var zA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let r=Ge(e);return L(r,me(cn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};zA.className="ThresholdedReLU";ue.registerClass(zA);var OA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new RA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=Ge(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};OA.className="Softmax";ue.registerClass(OA);function pu(e,t,r){if(typeof e=="number")return To(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){let a=e[n];if(!FW(a))throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function pa(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Sa(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+Os([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function DA(e,t){return K(()=>(Ut(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function S4(e,t){return K(()=>(Ut(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function cU(e,t,r,n=1,a="valid",s,i=1){return K(()=>{if(s==null&&(s=ca()),Ut(s),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=C2(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=ya(o,r)),o})}function lv(e,t,r,n=[1,1],a="valid",s,i,o=null){return K(()=>{if(s==null&&(s=ca()),Ut(s),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=DA(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=zs.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function fU(e,t,r,n=[1,1,1],a="valid",s,i){return K(()=>{if(s==null&&(s=ca()),Ut(s),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=S4(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=M2(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=ya(o,r)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var LA=class extends st{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",LA.verifyArgs(t),this.rank=e,cr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=pu(t.kernelSize,e,"kernelSize"),this.strides=pu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Pn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ut(this.dataFormat),this.activation=Ls(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Et(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ar(t.biasConstraint),this.biasRegularizer=Rt(t.biasRegularizer),this.activityRegularizer=Rt(t.activityRegularizer),this.dilationRate=pu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`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 q(`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 q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ia("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!lA(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ds(this.activation),useBias:this.useBias,biasInitializer:Pt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},bh=class extends LA{constructor(e,t){super(e,t);this.kernel=null,bh.verifyArgs(t),this.filters=t.filters,cr(this.filters,"filters"),this.kernelInitializer=Et(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ar(t.kernelConstraint),this.kernelRegularizer=Rt(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:r}}],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r,n=this.bias==null?null:this.bias.read(),a=P7(this.activation.getClassName());if(a!=null&&this.rank===2)r=lv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=cU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=lv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=fU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=ft(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=pa(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:Pt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:nr(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},T4=class extends bh{constructor(e){super(2,e);T4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!lA(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},Xm=T4;Xm.className="Conv2D";ue.registerClass(Xm);var N4=class extends bh{constructor(e){super(3,e);N4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Zm=N4;Zm.className="Conv3D";ue.registerClass(Zm);var BA=class extends Xm{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Sa(o,h,u,this.padding),f=Sa(l,p,d,this.padding),m=[a,c,f,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,1]));let g=R2(r,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=nt(g,[0,3,1,2])),this.bias!=null&&(g=ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Sa(t[n],o,s,this.padding),t[a]=Sa(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};BA.className="Conv2DTranspose";ue.registerClass(BA);var WA=class extends Zm{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Sa(l,f,h,this.padding),A=Sa(u,m,p,this.padding),x=Sa(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,4,1]));let w=_k(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=nt(w,[0,4,1,2,3])),this.bias!==null&&(w=ya(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Sa(t[n],u,i,this.padding),t[a]=Sa(t[a],d,o,this.padding),t[s]=Sa(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};WA.className="Conv3DTranspose";ue.registerClass(WA);var C4=class extends bh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rt(t.depthwiseRegularizer),this.depthwiseConstraint=ar(t.depthwiseConstraint),this.pointwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rt(t.pointwiseRegularizer),this.pointwiseConstraint=ar(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new qt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),r=n7(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=nt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.pointwiseInitializer=Pt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseConstraint),e.pointwiseConstraint=nr(this.pointwiseConstraint),e}};C4.className="SeparableConv";var VA=class extends C4{constructor(e){super(2,e)}};VA.className="SeparableConv2D";ue.registerClass(VA);var E4=class extends bh{constructor(e){super(1,e);E4.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!lA(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},UA=E4;UA.className="Conv1D";ue.registerClass(UA);var GA=class extends st{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return K(()=>{if(e=Ge(e),this.dataFormat==="channelsLast"){let r=zc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return zc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=zc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return zc(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};GA.className="Cropping2D";ue.registerClass(GA);var jA=class extends st{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,EW(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return K(()=>{let r=Ge(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=nt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};jA.className="UpSampling2D";ue.registerClass(jA);function mU(e,t,r=[1,1],n="valid",a,s){return K(()=>{a==null&&(a=ca()),Ut(a);let i=DA(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=lh(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var HA=class extends LA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Et(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ar(e.depthwiseConstraint),this.depthwiseRegularizer=Rt(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r=mU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=pa(t,this.kernelSize[0],this.padding,this.strides[0]),s=pa(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseRegularizer),e}};HA.className="DepthwiseConv2D";ue.registerClass(HA);function R4(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function M4(e,t,r,n=!1,a,s,i=!1,o=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(fa(2,l));if(t=nt(t,u),s!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=Ht(a,-1)),a=nt(a,u)),n&&(t=Fn(t,0),a!=null&&(a=Fn(a,0)));let d=[],h,p=r,c=t.shape[0],f=en(t),m;a!=null&&(m=en(a));for(let y=0;y<c;++y){let A=f[y],x=K(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=K(()=>{let w=m[y],T=he(Mn(w),w),S=le(L(x[0],w),L(p[0],T)),E=p.map((R,_)=>le(L(x[1][_],w),L(R,T)));return{output:S,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=sr(d,1)),[h,g,p]})}var F4=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Qm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new qt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return fa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){y1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");y1(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new qt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(s=>new qt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new Ba("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_=[Wt([r,this.cell.stateSize])];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_[0]=Wt([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):re(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!v.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>hr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=R4(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new qt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof oa){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=Ge(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=M4((p,c)=>{let f=this.cell.call([p].concat(c),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return K(()=>{let t=Wt(e.shape);return t=ke(t,[1,2]),t=gh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?m1(t,[1,r]):t):this.cell.stateSize>1?[m1(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===F4.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=da(n,r);return new e(Object.assign(t,{cell:a}))}},Qa=F4;Qa.className="RNN";ue.registerClass(Qa);var vh=class extends st{},Ym=class extends vh{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,cr(this.units,"units"),this.activation=Ls(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=bu([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=bu([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Bs({ones:()=>Mn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bs({ones:()=>Mn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Na(L(e,s),this.kernel.read()):a=Na(e,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,Na(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ds(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Ym.className="SimpleRNNCell";ue.registerClass(Ym);var qA=class extends Qa{constructor(e){e.cell=new Ym(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};qA.className="SimpleRNN";ue.registerClass(qA);var Jm=class extends vh{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,cr(this.units,"units"),this.activation=Ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=bu([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=bu([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Bs({ones:()=>Mn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bs({ones:()=>Mn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=Na(e,this.kernel.read());this.useBias&&(u=ya(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Kt(d,[2*this.units,this.units],d.rank-1),c=Na(n,h),[f,m,g]=Kt(u,3,u.rank-1),[y,A]=Kt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(f,y)),o=this.recurrentActivation.apply(le(m,A));let x=Na(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,zt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ds(this.activation),recurrentActivation:Ds(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Jm.className="GRUCell";ue.registerClass(Jm);var KA=class extends Qa{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Jm(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};KA.className="GRU";ue.registerClass(KA);var wh=class extends vh{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,cr(this.units,"units"),this.activation=Ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=bu([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=bu([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Hn{apply(i,o){let l=a.apply([s]),u=new Om().apply([s]),d=a.apply([s*2]);return q3(q3(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Bs({ones:()=>Mn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bs({ones:()=>Mn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Na(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,Na(n,this.recurrentKernel.read())),this.useBias&&(h=ya(h,this.bias.read()));let[p,c,f,m]=Kt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(f))),d=this.recurrentActivation.apply(m);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ds(this.activation),recurrentActivation:Ds(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};wh.className="LSTMCell";ue.registerClass(wh);var XA=class extends Qa{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new wh(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};XA.className="LSTM";ue.registerClass(XA);var Qm=class extends vh{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return K(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){y1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{Ao(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(da(a,r));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return A1(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}wA(t)}};Qm.className="StackedRNNCells";ue.registerClass(Qm);function Bs(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):W7(t(),r),o=()=>Ah(i,t,n);return!a||a<=1?hr(o().clone()):Array(a).fill(void 0).map(o).map(l=>hr(l.clone()))}var $4=class extends Qa{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new qt({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return K(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=Wt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new Ba("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[0]==null)throw new q("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(()=>Wt(a)):this.states_=[Wt(a)];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_[0]=Wt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>hr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=pa(l,n[0],a,s[0],i[0]),h=pa(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};$4.className="ConvRNN2D";var e0=class extends wh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t});this.filters=t,cr(this.filters,"filters"),this.kernelSize=pu(r,2,"kernelSize"),this.kernelSize.forEach(o=>cr(o,"kernelSize")),this.strides=pu(n||1,2,"strides"),this.strides.forEach(o=>cr(o,"strides")),this.padding=a||"valid",Pn(this.padding),this.dataFormat=s||"channelsLast",Ut(this.dataFormat),this.dilationRate=pu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>cr(o,"dilationRate"))}build(e){var t;e=ft(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Hn{apply(d,h){let p=l.apply([u]),c=pn([u]),f=l.apply([u*2]);return fA([p,c,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Bs({ones:()=>Mn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,ee,J)=>!ee||!ee[J]?V:L(ee[J],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bs({ones:()=>Mn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,f=l(a,c,0),m=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,w,T]=Kt(this.kernel.read(),i,A),[S,E,R,_]=this.useBias?Kt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,w,R,this.padding),p=this.inputConv(p,T,_,this.padding);let[M,I,O,z]=Kt(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,M),m=this.recurrentConv(m,I),g=this.recurrentConv(g,O),y=this.recurrentConv(y,z);let j=this.recurrentActivation.apply(le(u,f)),X=this.recurrentActivation.apply(le(d,m)),D=le(L(X,s),L(j,this.activation.apply(le(h,g)))),Q=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Q,Q,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=$s(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?ya(a,r,this.dataFormat):a}recurrentConv(e,t){return $s(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};e0.className="ConvLSTM2DCell";ue.registerClass(e0);var ZA=class extends $4{constructor(e){let t=new e0(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};ZA.className="ConvLSTM2D";ue.registerClass(ZA);var t0=class extends st{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return Ah(()=>W7(r,this.rate,a,this.seed),()=>r,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};t0.className="Dropout";ue.registerClass(t0);var YA=class extends t0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};YA.className="SpatialDropout1D";ue.registerClass(YA);var JA=class extends st{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,cr(this.units,"units"),this.activation=Ls(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ar(e.kernelConstraint),this.biasConstraint=ar(e.biasConstraint),this.kernelRegularizer=Rt(e.kernelRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=P7(this.activation.getClassName()),a;return n!=null?a=Na(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=Na(r,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ds(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};JA.className="Dense";ue.registerClass(JA);var QA=class extends st{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ts(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let n=[0];for(let a=2;a<r.rank;++a)n.push(a);n.push(1),r=nt(r,n)}return _W(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};QA.className="Flatten";ue.registerClass(QA);var ex=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ls(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.activation.apply(r)})}getConfig(){let e={activation:Ds(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};ex.className="Activation";ue.registerClass(ex);var tx=class extends st{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return K(()=>(e=Ge(e),$W(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};tx.className="RepeatVector";ue.registerClass(tx);var rx=class extends st{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=Ts(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return G(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};rx.className="Reshape";ue.registerClass(rx);var nx=class extends st{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=fa(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new qt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return nt(Ge(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};nx.className="Permute";ue.registerClass(nx);var ax=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let r=Ge(e),n=-1;return cf(Au(r,this.maskValue),n)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=-1,a=!0,s=cf(Au(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};ax.className="Masking";ue.registerClass(ax);var sx=class extends st{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(It(e.inputLength))}this.inputDim=e.inputDim,cr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,cr(this.outputDim,"outputDim"),this.embeddingsInitializer=Et(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rt(e.embeddingsRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.embeddingsConstraint=ar(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return K(()=>this.maskZero?(e=Ge(e),Au(e,at(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);r.dtype!=="int32"&&(r=_m(r,"int32"));let n=B7(this.embeddings.read(),G(r,[r.size]));return G(n,ft(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Pt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:nr(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};sx.className="Embedding";ue.registerClass(sx);var Nl=class extends st{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ft(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ss(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let n=e.map(a=>a.length);e.indexOf(null)===-1&&Ss(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let r=[],n=e.map(a=>a.rank);if(n.indexOf(null)===-1){let a=Os(n);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=gh(s,1);r.push(s)}return this.mergeFunction(r)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],h=u.slice(1).concat([d]),p=G(o,[d].concat(Ts(u.slice(1))));p=nt(p,[1,0]),p=G(p,h),r.push(p),a=!0}else if(l>1){let u=fa(1,l).concat([0]);r.push(nt(o,u)),a=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=G(nt(G(s,[-1,u]),[1,0]),d)}else if(i>1){let o=[i-1].concat(fa(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let a=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let r=[];for(let n of e)n!=null&&n[0]!==null&&r.push(n[0]);return r=Ss(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:Ht(n,0));let r=t[0];for(let n=1;n<t.length-1;++n)r=ha(r,t[n]);return r})}},ix=class extends Nl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return t})}};ix.className="Add";ue.registerClass(ix);var ox=class extends Nl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};ox.className="Multiply";ue.registerClass(ox);var lx=class extends Nl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return L(1/e.length,t)})}};lx.className="Average";ue.registerClass(lx);var ux=class extends Nl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=Za(t,e[r]);return t})}};ux.className="Maximum";ue.registerClass(ux);var dx=class extends Nl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=ph(t,e[r]);return t})}};dx.className="Minimum";ue.registerClass(dx);var px=class extends Nl{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let r=[];for(let n=0;n<e.length;++n){let a=e[n].slice();a.splice(this.axis,1);let s=!1;for(let i of r)if(v.arraysEqual(i,a)){s=!0;break}s||r.push(a)}if(r.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>fA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,r=t[0].slice(),n=this.axis<0?r.length+this.axis:this.axis;for(let a of t.slice(1)){if(r[n]==null||a[n]==null){r[n]=null;break}r[n]+=a[n]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let n=[];for(let s=0;s<e.length;++s)t[s]==null?n.push(me(Mn(e[s]),"bool")):t[s].rank<e[s].rank?n.push(Ht(t[s],-1)):n.push(t[s]);let a=kt(n,this.axis);return I2(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};px.className="Concatenate";ue.registerClass(px);function sp(e,t){for(;e<0;)e+=t;return e}function gU(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return K(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=G(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=G(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(nt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Je(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=et(o,u)}return o.shape.length===1&&(o=Ht(o,1)),o})}var hx=class extends Nl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>sp(a,e[s].shape.length)):n=[sp(this.axes,t.shape.length),sp(this.axes,r.shape.length)],this.normalize&&(t=yf(t,n[0]),r=yf(r,n[1])),gU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[sp(this.axes,e.length),sp(this.axes,t.length)],r}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};hx.className="Dot";ue.registerClass(hx);var cx=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return Ah(()=>le(zm(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};cx.className="GaussianNoise";ue.registerClass(cx);var fx=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.rate>0&&this.rate<1?Ah(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,zm(r.shape,1,n))},()=>r,t.training||!1):r})}};fx.className="GaussianDropout";ue.registerClass(fx);var mx=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ge(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return Ah(()=>{let n=Ge(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=wl(od(r),this.rate);o=_m(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>Ge(e),t.training||!1)}return e})}};mx.className="AlphaDropout";ue.registerClass(mx);function Pp(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=Tk(e,t,r,n,a,s);else if(e.rank===3)i=Nk(e,t,r,n,a,s);else if(e.rank===4)i=Ck(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function yU(e,t,r,n,a=.001){return K(()=>{let s=Am(e,n),i=s.mean,o=s.variance;return[Pp(e,i,o,r,t,a),i,o]})}function AU(e,t,r,n,a=.001){return K(()=>{let s=Am(e,n),i=s.mean,o=s.variance,l=[];for(let c of fa(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=G(i,l),d=G(o,l),h=t==null?null:G(t,l),p=r==null?null:G(r,l);return[Pp(e,u,d,p,h,a),i,o]})}function xU(e,t,r,n,a=.001){return v.arraysEqual(n.slice().sort(),fa(0,e.rank-1))?yU(e,t,r,n,a):AU(e,t,r,n,a)}var gx=class extends st{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.movingMeanInitializer=Et(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Et(e.movingVarianceInitializer||"ones"),this.betaConstraint=ar(e.betaConstraint),this.gammaConstraint=ar(e.gammaConstraint),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new qt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training,n=Ge(e),a=n.shape,s=a.length,i=fa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=To(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!v.arraysEqual(u,fa(0,s).slice(0,s-1)),h=()=>{if(d){let g=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),A=this.center?G(this.beta.read(),l):null,x=this.scale?G(this.gamma.read(),l):null;return Pp(n,g,y,A,x,this.epsilon)}else return Pp(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,f]=xU(n,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{K(()=>{let x=1-A,b=g.read(),w=L(he(b,y),x);g.write(he(b,w))})};return m(this.movingMean,c,this.momentum),m(this.movingVariance,f,this.momentum),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),movingMeanInitializer:Pt(this.movingMeanInitializer),movingVarianceInitializer:Pt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:nr(this.betaConstraint),gammaConstraint:nr(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};gx.className="BatchNormalization";ue.registerClass(gx);var yx=class extends st{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ss(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=Ge(e),n=r.shape,a=n.length;return K(()=>{let{mean:s,variance:i}=Am(r,this.axis,!0),o=To(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?G(c,o):c,u=l(this.gamma.read()),d=l(this.beta.read()),h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Dn(s,h),i=Dn(i,h),u=Dn(u,p),d=Dn(d,p),Pp(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};yx.className="LayerNormalization";ue.registerClass(yx);function bU(e,t,r){return K(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=ca()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return r==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Gn(e,n)})}var Ax=class extends st{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?ca():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t,r;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return K(()=>bU(Ge(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="ZeroPadding2D";ue.registerClass(Ax);function r0(e,t,r,n,a,s){return K(()=>{Ut(a),z7(s),Pn(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=ca()),s==null&&(s="max"),e=DA(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=ym(e,t,r,o):i=um(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function P4(e,t,r,n,a,s){return K(()=>{Ut(a),z7(s),Pn(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=ca()),s==null&&(s="max"),e=S4(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=B2(e,t,r,o):i=T2(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var _4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(cr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Pn(this.padding),this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=pa(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=gh(Ge(e),2);let r=this.poolingFunction(Ge(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return et(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},xx=class extends _4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),r0(e,t,r,n,a,"max")}};xx.className="MaxPooling1D";ue.registerClass(xx);var bx=class extends _4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),r0(e,t,r,n,a,"avg")}};bx.className="AveragePooling1D";ue.registerClass(bx);var z4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=pa(t,this.poolSize[0],this.padding,this.strides[0]),r=pa(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},vx=class extends z4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),r0(e,t,r,n,a,"max")}};vx.className="MaxPooling2D";ue.registerClass(vx);var wx=class extends z4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),r0(e,t,r,n,a,"avg")}};wx.className="AveragePooling2D";ue.registerClass(wx);var O4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=pa(t,this.poolSize[0],this.padding,this.strides[0]),r=pa(r,this.poolSize[1],this.padding,this.strides[1]),n=pa(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},kx=class extends O4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),P4(e,t,r,n,a,"max")}};kx.className="MaxPooling3D";ue.registerClass(kx);var Ix=class extends O4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),P4(e,t,r,n,a,"avg")}};Ix.className="AveragePooling3D";ue.registerClass(Ix);var D4=class extends st{constructor(e){super(e);this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},Sx=class extends D4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return Bt(r,1)})}};Sx.className="GlobalAveragePooling1D";ue.registerClass(Sx);var Tx=class extends D4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return fr(r,1)})}};Tx.className="GlobalMaxPooling1D";ue.registerClass(Tx);var L4=class extends st{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Nx=class extends L4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?Bt(r,[1,2]):Bt(r,[2,3])})}};Nx.className="GlobalAveragePooling2D";ue.registerClass(Nx);var Cx=class extends L4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?fr(r,[1,2]):fr(r,[2,3])})}};Cx.className="GlobalMaxPooling2D";ue.registerClass(Cx);var B4=class extends st{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,r={}){let n=t.layer,a=da(n,r);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Ex=class extends B4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ft(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return K(()=>(e=Ge(e),M4((r,n)=>[Ge(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Ex.className="TimeDistributed";ue.registerClass(Ex);function vU(e){Sl(CW,"BidirectionalMergeMode",e)}var wU="concat",Rx=class extends B4{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=da(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=da(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?wU:e.mergeMode,vU(this.mergeMode),e.weights)throw new Ve("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):Jr(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=R4(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let u=r.map(d=>new qt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(n!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof oa;for(let l of s)if(l instanceof oa!==o)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=d,h}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t.initialState,n,a;if(r==null)n=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(n)&&(s=n.slice(1).concat(a.slice(1))),n=n[0],a=a[0]),this.returnSequences&&(a=Fn(a,1));let i;return this.mergeMode==="concat"?i=fA([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ao(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ao(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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Input received: ${e}`);for(let r=0;r<e.length;r++){let n=e[r],a=t[r];if(a==null)continue;let s=n.rank;if(a.ndim!=null&&s!==a.ndim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&n.dtype!==a.dtype)throw new q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${n.dtype}.`);if(a.axes){let i=n.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=n.shape[i];if(o!=null&&l!=null&&o!==l)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${n.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let r=It(e),n=!0;for(let s of r)if(!(s instanceof oa)){n=!1;break}let a=!0;for(let s of r)if(s instanceof oa){a=!1;break}if(n===a)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return xo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of It(e))s.push(i.shape);this.build(Jr(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=It(s),o=[];for(let l of i)r.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Jr(o),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=uV(e),i=this.computeOutputShape(s),o,l=dV(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new oa(l,u,this,It(e),t,this.name,d)):o=new oa(l,i,this,It(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,n)=>{r!=null&&e[n]!=null&&e[n]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Ba(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Ba(`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 ia(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return yf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return v1(e?this.trainableWeights:this.weights)}setWeights(e){K(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let r=[],n=v1(t);for(let a=0;a<n.length;++a){let s=n[a],i=t[a],o=e[a];if(!v.arraysEqual(s.shape,o.shape))throw new q(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);r.push([i,o])}SA(r)})}addWeight(e,t,r,n,a,s,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(n=o!=null?o():Et("zeros"));let l=n.apply(t,r),u=new q7(l,r,e,s,i);return l.dispose(),a!=null&&this.addLoss(()=>a.apply(u.read())),s==null&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=It(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,r,n,a,s,i=null){let o=It(e);t=It(t),r=It(r),n=It(n),a=gf(a),s=gf(s);let l=[],u=[],d=[];for(let h of o)l.push(h.sourceLayer),u.push(h.nodeIndex),d.push(h.tensorIndex);new Hm({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:r,outputMasks:n,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function uV(e){e=It(e);let t=[];for(let r of e)t.push(r.shape);return Jr(t)}function dV(e){return"float32"}function K7(e,t,r){if((t==null||r!=null&&r>0)&&(t=e.sourceLayer,r=e.nodeIndex),t.inboundNodes.length===0)return[e];{let n=t.inboundNodes[r];if(n.inboundLayers.length===0)return n.inputTensors;{let a=[];for(let s=0;s<n.inboundLayers.length;s++){let i=n.inputTensors[s],o=n.inboundLayers[s],l=n.nodeIndices[s],u=K7(i,o,l);for(let d of u)a.indexOf(d)===-1&&a.push(d)}return a}}}var pd=class extends st{constructor(e){if(super({dtype:e.dtype,name:e.name!=null?e.name:jm("input").toString()}),e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=e.dtype||"float32";this.batchInputShape=t,this.dtype=r,this.inputSpec=[{shape:t}];let n=new oa(this.dtype,this.batchInputShape,this,[],{},this.name);n.nodeIndex=0,n.tensorIndex=0,new Hm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[n],outputTensors:[n],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`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}}};pd.className="InputLayer";ue.registerClass(pd);function X7(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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Set),a[u.name].add(o.name),!r.has(u.name)&&s.push(u)}}return{sorted:n,recipientMap:a}}function WV(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let r=null;for(let n=0;n<e.sourceLayer.inboundNodes.length;++n)for(let a of e.sourceLayer.inboundNodes[n].outputTensors)if(a.id===e.id){r=n;break}t=e.sourceLayer.getOutputAt(r)}return t}var ka=class extends st{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=jm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Ss(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. 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Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;Ia(x===0,"input layer has >1 nodes"),Ia(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof pd))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},r={},n={},a={},s={},i=[],o=(y,A,x,b,w,T)=>{(b==null||w==null||T==null)&&(b=y.sourceLayer,w=y.nodeIndex,T=y.tensorIndex);let S=b.inboundNodes[w];if(x.indexOf(S)!==-1)throw new ia(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(S)!==-1)return;this.containerNodes.add(ka.nodeKey(b,w)),b.id in s||(s[b.id]=Object.keys(s).length),x.indexOf(S)===-1&&x.push(S);let E=S.inboundLayers.length;for(let R=0;R<E;R++){let _=S.inputTensors[R],M=S.inboundLayers[R],I=S.nodeIndices[R],O=S.tensorIndices[R];o(_,A,x,M,I,O)}for(A.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);i.push(S)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let d=i.slice().reverse();for(let y of d){r[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=n[y.outboundLayer.id]==null?0:n[y.outboundLayer.id];A=Math.max(A,x),n[y.outboundLayer.id]=A,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let w=y.inboundLayers[b],T=y.nodeIndices[b],S=w.inboundNodes[T],E=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(A+1,E),r[S.id]=S}}let h={};for(let y in t){let A=t[y];A in h||(h[A]=[]),h[A].push(r[y])}let p={};for(let y in n){let A=n[y];A in p||(p[A]=[]),p[A].push(a[y])}let c=Object.keys(p).map(y=>parseInt(y,10)).sort(zc);this.layers=[];for(let y of c){let A=p[y];A.sort((x,b)=>{let w=s[x.id],T=s[b.id];return w<T?-1:w>T?1:0});for(let x of A)x instanceof ka&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,c=Object.keys(h).map(y=>parseInt(y,10)).sort(zc);let f=this.inputs.slice(),m=[];for(let y of c)for(let A of h[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new ia(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=h;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new ia(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Hm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(r=>r.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},n=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new q(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,n++}let a=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)a.push([r[i],e[s]]);else if(t)throw new q(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new q(`${s.length} of ${n} weights are not set: ${s}`)}SA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${MA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=k1(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return K(()=>{e=It(e);let r=new go;for(let n=0;n<this.inputs.length;++n)r.add(this.inputs[n],e[n]);return hp(this.outputs,r,t)})}computeMask(e,t){return K(()=>{e=It(e);let r;return t==null?r=No(null,e.length):r=It(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=gf(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";r[u]=l}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(zc);if(n.length>1)for(let i of n){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let d=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=r[A];d.push(x)}let h=u.computeOutputShape(Jr(d)),p=gf(h),c=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${c}_${f}`;r[m]=p[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Ia(o in r),a.push(r[o])}return Jr(a)}runInternalGraph(e,t){t==null&&(t=No(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];r[l.id]=[u,d]}let n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(zc);for(let o of n){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,h=u.inputTensors,p=u.outputTensors,c=new Array;for(let f of h)f.id in r&&c.push(r[f.id]);if(c.length===h.length){let f={},m,g,y,A;if(u.callArgs!=null&&(f=u.callArgs),c.length===1){let[x,b]=c[0];f.mask==null&&(f.mask=b),y=It(d.call(x,f)),A=It(d.computeMask(x,b)),m=[x],g=[b]}else m=c.map(x=>x[0]),g=c.map(x=>x[1]),f.mask==null&&(f.mask=g),y=It(d.call(m,f)),A=It(d.computeMask(m,g));if(d.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],w=y[x],T=A[x];r[b.id]=[w,T]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Ia(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=r[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},r;for(let n of this.layers){r=n instanceof ka?1:0;for(let a=0;a<n.inboundNodes.length;a++){let s=ka.nodeKey(n,a);this.containerNodes.has(s)&&(t[s]=r,r+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===e)return r;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let n=ka.nodeKey(t,r);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let h=s.inboundNodes[d],p=ka.nodeKey(s,d),c={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),c=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),c={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let g=h.inboundLayers[m],y=h.nodeIndices[m],A=h.tensorIndices[m],x=ka.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,c])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,r.push(u)}e.layers=r;let n=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];n.push([i.name,u,d])}e.inputLayers=n;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];a.push([i.name,u,d])}return e.outputLayers=a,e}static fromConfig(e,t,r={},n=!1){let a={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],w=x[1],T=x[2];if(A=x[3]==null?{}:x[3],!(b in a)){i(m,g);return}let S=a[b];if(S.inboundNodes.length<=w){i(m,g);return}let E=S.inboundNodes[w];y.push(E.outputTensors[T])}y.length>0&&m.apply(Jr(y),A)}function l(m){let g=m.name,y=da(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),a[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let m of d)l(m);for(;!AW(s);)for(let m of d){let g=a[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let h=[],p=[],c=t.inputLayers;for(let m of c){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;h.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;p.push(x[A])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function VV(e,t,r){let n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(n===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==n)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${n}) outputs, so ${r} must be either an array with ${n} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function l4(e,t){return VV(e,t,"classWeight")}async function u4(e,t,r,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let a=K(()=>{if(e.shape.length===1)return Lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Nn(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());re(a);let i=[];return s.forEach(o=>{if(r[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(r[o])}),St(i,"float32")}else return null}function UV(e,t){return L(e,t)}var GV=32;function d4(e,t){let r,n,a=t;r=a.xs,n=a.ys,v.assert(r!=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=av("input",e.inputNames,r),i=av("output",e.outputNames,n),o=s[0].shape[0];v.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)v.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)v.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function av(e,t,r){if(r instanceof rt)return[r];if(Array.isArray(r))return v.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let n=[];for(let a of t){if(r[a]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);n.push(r[a])}return n}}function jV(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function HV(e,t,r){let n=r.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),v.assert(!n||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),v.assert(r.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=r.validationData!=null,s,i;if(a)if(sv(r.validationData))v.assert(r.validationBatches==null||r.validationBatches>0&&Number.isInteger(r.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${r.validationBatches}`);else{let g=jV(r.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;a?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let d=e4(r.callbacks,r.yieldEvery),h=r.verbose==null?1:r.verbose,{callbackList:p,history:c}=t4(d,h,r.epochs,null,null,qV(t,r),null,a,u);p.setModel(e),e.history=c,await p.onTrainBegin(),e.stopTraining_=!1;let f=r.initialEpoch==null?0:r.initialEpoch,m=await t.iterator();for(;f<r.epochs;){let g={};await p.onEpochBegin(f);let y=0,A=0;for(n||(m=await t.iterator());!n||y<r.batchesPerEpoch;){let x=await m.next();if(n&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${r.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${r.batchesPerEpoch*r.epochs} batches). 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Use LayersModel.compile(modelCompileArgs).");let a=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===xf?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=ov(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=ov(t,this.feedOutputNames,a,!1,"target"),QV(e,t,null),eU(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&n!=null&&n>0&&e[0].shape[0]%n!==0)throw new q(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${n}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,r,n,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(r!=null)throw new Error("sample weight is not supported yet.");let l=null;if(n!=null){let u=l4(n,this.outputNames);l=[];for(let d=0;d<u.length;++d)l.push(await u4(o[d],null,u[d]))}return[i,o,l]}testLoop(e,t,r,n=0,a){return K(()=>{let s=this.checkNumSamples(t,r,a,"steps"),i=[];if(n>0)throw new Ve("Verbose mode is not implemented yet.");if(a!=null)throw new Ve("steps mode in testLoop() is not implemented yet");{let o=S1(s,r),l=St(fa(0,s));for(let u=0;u<o.length;++u){let d=o[u][0],h=o[u][1],p=bo(l,d,h-d),c=FA(t,p),f=e(c);if(u===0)for(let m=0;m<f.length;++m)i.push(Se(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=le(i[m],L(h-d,g))}}for(let u=0;u<i.length;++u)i[u]=pe(i[u],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let r=0;r<e.length;++r){let n=e[r],a=n;H3(e,n)>1&&(a+=`_${H3(e.slice(0,r),n)}`),t.push(a)}return t}makeTrainFunction(){return e=>{let t=[],r=e.slice(0,this.inputs.length),n=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let c=0;c<this.inputs.length;++c)u.push({key:this.inputs[c],value:r[c]});let d=new go(u),h=hp(this.outputs,d,{training:!0}),p;for(let c=0;c<this.lossFunctions.length;++c){let f=this.lossFunctions[c](n[c],h[c]);a[c]!=null&&(f=UV(f,a[c]));let m=Bt(f);t.push(m),c===0?p=f:p=le(p,f)}for(let c=0;c<this.metricsTensors.length;++c){let f;if(this.outputs.length>1&&c<this.outputs.length)f=t[c];else{let m=this.metricsTensors[c][0],g=this.metricsTensors[c][1];f=Bt(m(n[g],h[g]))}hr(f),s.push(f)}return p=Bt(p),this.calculateLosses().forEach(c=>{p=le(p,c)}),p},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>K(()=>{let t=[],r,n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:n[l]});let i=new go(s),o=hp(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],d=Bt(u(a[l],o[l]));l===0?r=d:r=le(r,d),t.push(r)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],d=this.metricsTensors[l][1],h=Bt(u(a[d],o[d]));t.push(h)}return t})}async fit(e,t,r={}){return YV(this,e,t,r)}async fitDataset(e,t){return HV(this,e,t)}async trainOnBatch(e,t){let r=await this.standardizeUserData(e,t),n=r[0],a=r[1],s=this.makeTrainFunction()(n.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return re(s),sa(r[0],e),sa(r[1],t),Jr(i)}getNamedWeights(e){let t=[],r=e!=null&&e.trainableOnly,n=r?this.trainableWeights:this.weights,a=this.getWeights(r);for(let s=0;s<n.length;++s)r&&!n[s].trainable||t.push({name:n[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=cf().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-cf().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Wa(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Wa(t))}else{let t=Object.keys(this.loss);e={};let r=this.loss;for(let n of t)if(typeof r[n]=="string")e[n]=Wa(r[n]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Wa(Lc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Wa(Lc(e)));{let e={};for(let t in this.metrics)e[t]=Wa(Lc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Pp(e.optimizer_config),r=da(t),n;if(typeof e.loss=="string")n=co(e.loss);else if(Array.isArray(e.loss))n=e.loss.map(s=>co(s));else if(e.loss!=null){n={};for(let s in e.loss)n[s]=co(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>co(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=co(e.metrics[s])}this.compile({loss:n,metrics:a,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Sr.getSaveHandlers(e);if(i.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new q(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Sr.encodeWeights(this.getNamedWeights(t)),n=!1,a=null,s={modelTopology:this.toJSON(a,n),format:rU,generatedBy:`TensorFlow.js tfjs-layers v${MA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Sr.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Sr.concatenateArrayBuffers([r.data,o])}return this.userDefinedMetadata!=null&&(tv(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=r.data,s.weightSpecs=r.specs,e.save(s)}setUserDefinedMetadata(e){tv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ja.className="Model";ue.registerClass(ja);var h4=class extends ja{};h4.className="Functional";ue.registerClass(h4);async function nU(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let r=e.modelTopology;r.model_config!=null&&(r=r.model_config);let n=Pp(r),a=da(n,t);if(e.weightsManifest!=null){let s=await Sr.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),re(s)}return a}async function aU(e,t){if(t==null&&(t={}),typeof e=="string"){let r=Sr.getLoadHandlers(e,t);if(r.length===0)r.push(Sr.browserHTTPRequest(e,t));else if(r.length>1)throw new q(`Found more than one (${r.length}) load handlers for URL '${e}'`);e=r[0]}return sU(e,void 0,t)}async function sU(e,t,r){if(r==null&&(r={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let n=await e.load(),a=n.modelTopology;a.model_config!=null&&(a=a.model_config);let s=r.strict==null?!0:r.strict,i=n.weightData!=null&&n.weightSpecs!=null&&s,o=da(Pp(a),t,i),l=n.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),n.userDefinedMetadata!=null&&o.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:d}=iU(n.weightData,n.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),re(u),re(d.map(h=>h.tensor))}return o}function iU(e,t){let r=Sr.decodeWeights(e,t),n={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:r[s.name]}):n[s.name]=r[s.name]}),{modelWeights:n,optimizerWeights:a}}var N1=class extends ja{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:jm("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof N1||e instanceof ja,r;if(t){if(r=e,r.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(r.inputs.length!==1)throw new q("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let n=X7({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(n)}if(t)this.outputs=r.outputs,this.inputs=r.inputs;else{if(e.inboundNodes.length!==1)throw new q(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=K7(this.outputs[0])}this.inboundNodes=[],new Hm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:No(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(n=>n.shape),outputShapes:this.outputs[0].shape})}else{let n=e.apply(this.outputs[0]);if(Array.isArray(n))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[n],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(ft(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new ja({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,r=console.log){this.built||this.build(),super.summary(e,t,r)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,r={}){if(!this.built)throw new ia("The model needs to be compiled before being used.");return this.model.evaluate(e,t,r)}async evaluateDataset(e,t){if(!this.built)throw new ia("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,r={}){if(!this.built)throw new ia("The model needs to be compiled before being used.");return this.model.fit(e,t,r)}async fitDataset(e,t){if(!this.built)throw new ia("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,r={},n=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");a=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof N1))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=da(o,void 0,n);n&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let r={};r.className=t.getClassName(),r.config=t.getConfig(),e.push(r)}return{name:this.name,layers:e}}},Xm=N1;Xm.className="Sequential";ue.registerClass(Xm);function oU(e){return new ja(e)}function lU(e){return new Xm(e)}function uU(e,t){return t==null&&(t={}),aU(e,t)}function c4(e){return X7(e)}function dU(e,t){TA.registerCallbackConstructor(e,t)}var sn=class extends ue.Serializable{getConfig(){return{}}},f4=class extends sn{apply(e,t=1){return zW(e,t)}};f4.className="elu";ue.registerClass(f4);var m4=class extends sn{apply(e){return Z2(e)}};m4.className="selu";ue.registerClass(m4);var g4=class extends sn{apply(e){return $a(e)}};g4.className="relu";ue.registerClass(g4);var y4=class extends sn{apply(e){return K(()=>hh(6,$a(e)))}};y4.className="relu6";ue.registerClass(y4);var A4=class extends sn{apply(e){return e}};A4.className="linear";ue.registerClass(A4);var x4=class extends sn{apply(e){return Tr(e)}};x4.className="sigmoid";ue.registerClass(x4);var b4=class extends sn{apply(e){return DW(e)}};b4.className="hardSigmoid";ue.registerClass(b4);var v4=class extends sn{apply(e){return id(e)}};v4.className="softplus";ue.registerClass(v4);var w4=class extends sn{apply(e){return OW(e)}};w4.className="softsign";ue.registerClass(w4);var k4=class extends sn{apply(e){return mu(e)}};k4.className="tanh";ue.registerClass(k4);var $A=class extends sn{apply(e,t=-1){return ud(e,t)}};$A.className="softmax";ue.registerClass($A);var I4=class extends sn{apply(e,t=-1){return L2(e,t)}};I4.className="logSoftmax";ue.registerClass(I4);var S4=class extends sn{apply(e,t=1){return K(()=>L(Tr(L(e,t)),e))}};S4.className="swish";ue.registerClass(S4);var T4=class extends sn{apply(e){return K(()=>L(e,mu(id(e))))}};T4.className="mish";ue.registerClass(T4);function Ds(e){return e.getClassName()}function Yy(e,t={}){return mh(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function Ls(e){if(e==null){let t={};return t.className="linear",t.config={},Yy(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Yy(t)}else return e instanceof sn?e:Yy(e)}function PA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var N4=class extends ue.Serializable{},bh=class extends N4{constructor(e){super(),PA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return K(()=>{let t=Wt([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,er(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,Ah(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};bh.className="L1L2";ue.registerClass(bh);function pU(e){return PA(e),new bh({l1:e!=null?e.l1:null,l2:0})}function hU(e){return PA(e),new bh({l2:e!=null?e.l2:null,l1:0})}var uv={l1l2:"L1L2"};function xt(e){return dA(e)}function dv(e,t={}){return mh(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Rt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in uv?uv[e]:e,config:{}};return dv(t)}else return e instanceof N4?e:dv(e)}var _A=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ge(e);let r=$a(e);return this.maxValue!=null&&(r=hn(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};_A.className="ReLU";ue.registerClass(_A);var zA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ge(e);return fm(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};zA.className="LeakyReLU";ue.registerClass(zA);var OA=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Et(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rt(e.alphaRegularizer),this.alphaConstraint=ar(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new qt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=Ge(e),vm(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Pt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:nr(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};OA.className="PReLU";ue.registerClass(OA);var DA=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ge(e);return dh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};DA.className="ELU";ue.registerClass(DA);var LA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let r=Ge(e);return L(r,me(cn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};LA.className="ThresholdedReLU";ue.registerClass(LA);var BA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new $A().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=Ge(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};BA.className="Softmax";ue.registerClass(BA);function pu(e,t,r){if(typeof e=="number")return No(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){let a=e[n];if(!FW(a))throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function pa(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Sa(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+Os([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function WA(e,t){return K(()=>(Ut(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function C4(e,t){return K(()=>(Ut(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function cU(e,t,r,n=1,a="valid",s,i=1){return K(()=>{if(s==null&&(s=ca()),Ut(s),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=M2(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=ya(o,r)),o})}function pv(e,t,r,n=[1,1],a="valid",s,i,o=null){return K(()=>{if(s==null&&(s=ca()),Ut(s),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=WA(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=zs.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function fU(e,t,r,n=[1,1,1],a="valid",s,i){return K(()=>{if(s==null&&(s=ca()),Ut(s),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=C4(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=P2(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=ya(o,r)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var VA=class extends st{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",VA.verifyArgs(t),this.rank=e,cr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=pu(t.kernelSize,e,"kernelSize"),this.strides=pu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Pn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ut(this.dataFormat),this.activation=Ls(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Et(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ar(t.biasConstraint),this.biasRegularizer=Rt(t.biasRegularizer),this.activityRegularizer=Rt(t.activityRegularizer),this.dilationRate=pu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`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 q(`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 q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ia("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!pA(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ds(this.activation),useBias:this.useBias,biasInitializer:Pt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},vh=class extends VA{constructor(e,t){super(e,t),this.kernel=null,vh.verifyArgs(t),this.filters=t.filters,cr(this.filters,"filters"),this.kernelInitializer=Et(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ar(t.kernelConstraint),this.kernelRegularizer=Rt(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:r}}],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r,n=this.bias==null?null:this.bias.read(),a=O7(this.activation.getClassName());if(a!=null&&this.rank===2)r=pv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=cU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=pv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=fU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=ft(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=pa(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:Pt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:nr(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},E4=class extends vh{constructor(e){super(2,e),E4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!pA(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},Zm=E4;Zm.className="Conv2D";ue.registerClass(Zm);var R4=class extends vh{constructor(e){super(3,e),R4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Ym=R4;Ym.className="Conv3D";ue.registerClass(Ym);var UA=class extends Zm{constructor(e){if(super(e),this.inputSpec=[new qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Sa(o,h,u,this.padding),f=Sa(l,p,d,this.padding),m=[a,c,f,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,1]));let g=$2(r,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=nt(g,[0,3,1,2])),this.bias!=null&&(g=ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Sa(t[n],o,s,this.padding),t[a]=Sa(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};UA.className="Conv2DTranspose";ue.registerClass(UA);var GA=class extends Ym{constructor(e){if(super(e),this.inputSpec=[new qt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Sa(l,f,h,this.padding),A=Sa(u,m,p,this.padding),x=Sa(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,4,1]));let w=Dk(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=nt(w,[0,4,1,2,3])),this.bias!==null&&(w=ya(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Sa(t[n],u,i,this.padding),t[a]=Sa(t[a],d,o,this.padding),t[s]=Sa(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};GA.className="Conv3DTranspose";ue.registerClass(GA);var M4=class extends vh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rt(t.depthwiseRegularizer),this.depthwiseConstraint=ar(t.depthwiseConstraint),this.pointwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rt(t.pointwiseRegularizer),this.pointwiseConstraint=ar(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new qt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),r=i7(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=nt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.pointwiseInitializer=Pt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseConstraint),e.pointwiseConstraint=nr(this.pointwiseConstraint),e}};M4.className="SeparableConv";var jA=class extends M4{constructor(e){super(2,e)}};jA.className="SeparableConv2D";ue.registerClass(jA);var F4=class extends vh{constructor(e){super(1,e),F4.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!pA(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},HA=F4;HA.className="Conv1D";ue.registerClass(HA);var qA=class extends st{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return K(()=>{if(e=Ge(e),this.dataFormat==="channelsLast"){let r=Oc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Oc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=Oc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Oc(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};qA.className="Cropping2D";ue.registerClass(qA);var KA=class extends st{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,EW(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return K(()=>{let r=Ge(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=nt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};KA.className="UpSampling2D";ue.registerClass(KA);function mU(e,t,r=[1,1],n="valid",a,s){return K(()=>{a==null&&(a=ca()),Ut(a);let i=WA(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=uh(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var XA=class extends VA{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Et(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ar(e.depthwiseConstraint),this.depthwiseRegularizer=Rt(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r=mU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=pa(t,this.kernelSize[0],this.padding,this.strides[0]),s=pa(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseRegularizer),e}};XA.className="DepthwiseConv2D";ue.registerClass(XA);function $4(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function P4(e,t,r,n=!1,a,s,i=!1,o=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(fa(2,l));if(t=nt(t,u),s!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=Ht(a,-1)),a=nt(a,u)),n&&(t=Fn(t,0),a!=null&&(a=Fn(a,0)));let d=[],h,p=r,c=t.shape[0],f=en(t),m;a!=null&&(m=en(a));for(let y=0;y<c;++y){let A=f[y],x=K(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=K(()=>{let w=m[y],T=he(Mn(w),w),S=le(L(x[0],w),L(p[0],T)),E=p.map((R,_)=>le(L(x[1][_],w),L(R,T)));return{output:S,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=sr(d,1)),[h,g,p]})}var _4=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new e0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new qt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return fa(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){b1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");b1(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new qt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(s=>new qt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new Ba("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_=[Wt([r,this.cell.stateSize])];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_[0]=Wt([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):re(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!v.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>hr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=$4(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new qt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof oa){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=Ge(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=P4((p,c)=>{let f=this.cell.call([p].concat(c),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return K(()=>{let t=Wt(e.shape);return t=ke(t,[1,2]),t=yh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?A1(t,[1,r]):t):this.cell.stateSize>1?[A1(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===_4.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=da(n,r);return new e(Object.assign(t,{cell:a}))}},Qa=_4;Qa.className="RNN";ue.registerClass(Qa);var wh=class extends st{},Jm=class extends wh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,cr(this.units,"units"),this.activation=Ls(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=bu([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=bu([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Bs({ones:()=>Mn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bs({ones:()=>Mn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Na(L(e,s),this.kernel.read()):a=Na(e,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,Na(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ds(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Jm.className="SimpleRNNCell";ue.registerClass(Jm);var ZA=class extends Qa{constructor(e){e.cell=new Jm(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};ZA.className="SimpleRNN";ue.registerClass(ZA);var Qm=class extends wh{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,cr(this.units,"units"),this.activation=Ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=bu([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=bu([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Bs({ones:()=>Mn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bs({ones:()=>Mn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=Na(e,this.kernel.read());this.useBias&&(u=ya(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Kt(d,[2*this.units,this.units],d.rank-1),c=Na(n,h),[f,m,g]=Kt(u,3,u.rank-1),[y,A]=Kt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(f,y)),o=this.recurrentActivation.apply(le(m,A));let x=Na(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,zt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ds(this.activation),recurrentActivation:Ds(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Qm.className="GRUCell";ue.registerClass(Qm);var YA=class extends Qa{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Qm(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};YA.className="GRU";ue.registerClass(YA);var kh=class extends wh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,cr(this.units,"units"),this.activation=Ls(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ls(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=bu([1,Os([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=bu([1,Os([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Hn{apply(i,o){let l=a.apply([s]),u=new Dm().apply([s]),d=a.apply([s*2]);return Z3(Z3(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Bs({ones:()=>Mn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bs({ones:()=>Mn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Na(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,Na(n,this.recurrentKernel.read())),this.useBias&&(h=ya(h,this.bias.read()));let[p,c,f,m]=Kt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(f))),d=this.recurrentActivation.apply(m);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ds(this.activation),recurrentActivation:Ds(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};kh.className="LSTMCell";ue.registerClass(kh);var JA=class extends Qa{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new kh(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};JA.className="LSTM";ue.registerClass(JA);var e0=class extends wh{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return K(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){b1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{xo(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(da(a,r));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return v1(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}SA(t)}};e0.className="StackedRNNCells";ue.registerClass(e0);function Bs(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):G7(t(),r),o=()=>xh(i,t,n);return!a||a<=1?hr(o().clone()):Array(a).fill(void 0).map(o).map(l=>hr(l.clone()))}var z4=class extends Qa{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new qt({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return K(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=Wt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new Ba("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[0]==null)throw new q("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(()=>Wt(a)):this.states_=[Wt(a)];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_[0]=Wt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>hr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=pa(l,n[0],a,s[0],i[0]),h=pa(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};z4.className="ConvRNN2D";var t0=class extends kh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t}),this.filters=t,cr(this.filters,"filters"),this.kernelSize=pu(r,2,"kernelSize"),this.kernelSize.forEach(o=>cr(o,"kernelSize")),this.strides=pu(n||1,2,"strides"),this.strides.forEach(o=>cr(o,"strides")),this.padding=a||"valid",Pn(this.padding),this.dataFormat=s||"channelsLast",Ut(this.dataFormat),this.dilationRate=pu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>cr(o,"dilationRate"))}build(e){var t;e=ft(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Hn{apply(d,h){let p=l.apply([u]),c=pn([u]),f=l.apply([u*2]);return yA([p,c,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Bs({ones:()=>Mn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,ee,J)=>!ee||!ee[J]?V:L(ee[J],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bs({ones:()=>Mn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,f=l(a,c,0),m=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,w,T]=Kt(this.kernel.read(),i,A),[S,E,R,_]=this.useBias?Kt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,w,R,this.padding),p=this.inputConv(p,T,_,this.padding);let[M,I,O,z]=Kt(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,M),m=this.recurrentConv(m,I),g=this.recurrentConv(g,O),y=this.recurrentConv(y,z);let j=this.recurrentActivation.apply(le(u,f)),X=this.recurrentActivation.apply(le(d,m)),D=le(L(X,s),L(j,this.activation.apply(le(h,g)))),Q=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Q,Q,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=$s(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?ya(a,r,this.dataFormat):a}recurrentConv(e,t){return $s(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};t0.className="ConvLSTM2DCell";ue.registerClass(t0);var QA=class extends z4{constructor(e){let t=new t0(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};QA.className="ConvLSTM2D";ue.registerClass(QA);var r0=class extends st{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return xh(()=>G7(r,this.rate,a,this.seed),()=>r,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};r0.className="Dropout";ue.registerClass(r0);var ex=class extends r0{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};ex.className="SpatialDropout1D";ue.registerClass(ex);var tx=class extends st{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,cr(this.units,"units"),this.activation=Ls(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ar(e.kernelConstraint),this.biasConstraint=ar(e.biasConstraint),this.kernelRegularizer=Rt(e.kernelRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=O7(this.activation.getClassName()),a;return n!=null?a=Na(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=Na(r,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ds(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};tx.className="Dense";ue.registerClass(tx);var rx=class extends st{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ts(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let n=[0];for(let a=2;a<r.rank;++a)n.push(a);n.push(1),r=nt(r,n)}return _W(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};rx.className="Flatten";ue.registerClass(rx);var nx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.activation=Ls(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.activation.apply(r)})}getConfig(){let e={activation:Ds(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};nx.className="Activation";ue.registerClass(nx);var ax=class extends st{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return K(()=>(e=Ge(e),$W(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};ax.className="RepeatVector";ue.registerClass(ax);var sx=class extends st{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=Ts(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return G(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};sx.className="Reshape";ue.registerClass(sx);var ix=class extends st{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=fa(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new qt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return nt(Ge(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};ix.className="Permute";ue.registerClass(ix);var ox=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let r=Ge(e),n=-1;return ff(Au(r,this.maskValue),n)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=-1,a=!0,s=ff(Au(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};ox.className="Masking";ue.registerClass(ox);var lx=class extends st{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(It(e.inputLength))}this.inputDim=e.inputDim,cr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,cr(this.outputDim,"outputDim"),this.embeddingsInitializer=Et(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rt(e.embeddingsRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.embeddingsConstraint=ar(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return K(()=>this.maskZero?(e=Ge(e),Au(e,at(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);r.dtype!=="int32"&&(r=zm(r,"int32"));let n=U7(this.embeddings.read(),G(r,[r.size]));return G(n,ft(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Pt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:nr(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};lx.className="Embedding";ue.registerClass(lx);var Cl=class extends st{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ft(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Ss(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let n=e.map(a=>a.length);e.indexOf(null)===-1&&Ss(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let r=[],n=e.map(a=>a.rank);if(n.indexOf(null)===-1){let a=Os(n);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=yh(s,1);r.push(s)}return this.mergeFunction(r)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],h=u.slice(1).concat([d]),p=G(o,[d].concat(Ts(u.slice(1))));p=nt(p,[1,0]),p=G(p,h),r.push(p),a=!0}else if(l>1){let u=fa(1,l).concat([0]);r.push(nt(o,u)),a=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=G(nt(G(s,[-1,u]),[1,0]),d)}else if(i>1){let o=[i-1].concat(fa(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let a=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let r=[];for(let n of e)n!=null&&n[0]!==null&&r.push(n[0]);return r=Ss(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:Ht(n,0));let r=t[0];for(let n=1;n<t.length-1;++n)r=ha(r,t[n]);return r})}},ux=class extends Cl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return t})}};ux.className="Add";ue.registerClass(ux);var dx=class extends Cl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};dx.className="Multiply";ue.registerClass(dx);var px=class extends Cl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return L(1/e.length,t)})}};px.className="Average";ue.registerClass(px);var hx=class extends Cl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=Za(t,e[r]);return t})}};hx.className="Maximum";ue.registerClass(hx);var cx=class extends Cl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=hh(t,e[r]);return t})}};cx.className="Minimum";ue.registerClass(cx);var fx=class extends Cl{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let r=[];for(let n=0;n<e.length;++n){let a=e[n].slice();a.splice(this.axis,1);let s=!1;for(let i of r)if(v.arraysEqual(i,a)){s=!0;break}s||r.push(a)}if(r.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>yA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,r=t[0].slice(),n=this.axis<0?r.length+this.axis:this.axis;for(let a of t.slice(1)){if(r[n]==null||a[n]==null){r[n]=null;break}r[n]+=a[n]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let n=[];for(let s=0;s<e.length;++s)t[s]==null?n.push(me(Mn(e[s]),"bool")):t[s].rank<e[s].rank?n.push(Ht(t[s],-1)):n.push(t[s]);let a=kt(n,this.axis);return N2(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};fx.className="Concatenate";ue.registerClass(fx);function ip(e,t){for(;e<0;)e+=t;return e}function gU(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return K(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=G(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=G(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(nt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Je(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=et(o,u)}return o.shape.length===1&&(o=Ht(o,1)),o})}var mx=class extends Cl{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>ip(a,e[s].shape.length)):n=[ip(this.axes,t.shape.length),ip(this.axes,r.shape.length)],this.normalize&&(t=Af(t,n[0]),r=Af(r,n[1])),gU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[ip(this.axes,e.length),ip(this.axes,t.length)],r}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};mx.className="Dot";ue.registerClass(mx);var gx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return xh(()=>le(Om(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};gx.className="GaussianNoise";ue.registerClass(gx);var yx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.rate>0&&this.rate<1?xh(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,Om(r.shape,1,n))},()=>r,t.training||!1):r})}};yx.className="GaussianDropout";ue.registerClass(yx);var Ax=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ge(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return xh(()=>{let n=Ge(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=kl(ld(r),this.rate);o=zm(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>Ge(e),t.training||!1)}return e})}};Ax.className="AlphaDropout";ue.registerClass(Ax);function _p(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=Ek(e,t,r,n,a,s);else if(e.rank===3)i=Rk(e,t,r,n,a,s);else if(e.rank===4)i=Mk(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function yU(e,t,r,n,a=.001){return K(()=>{let s=xm(e,n),i=s.mean,o=s.variance;return[_p(e,i,o,r,t,a),i,o]})}function AU(e,t,r,n,a=.001){return K(()=>{let s=xm(e,n),i=s.mean,o=s.variance,l=[];for(let c of fa(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=G(i,l),d=G(o,l),h=t==null?null:G(t,l),p=r==null?null:G(r,l);return[_p(e,u,d,p,h,a),i,o]})}function xU(e,t,r,n,a=.001){return v.arraysEqual(n.slice().sort(),fa(0,e.rank-1))?yU(e,t,r,n,a):AU(e,t,r,n,a)}var xx=class extends st{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.movingMeanInitializer=Et(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Et(e.movingVarianceInitializer||"ones"),this.betaConstraint=ar(e.betaConstraint),this.gammaConstraint=ar(e.gammaConstraint),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new qt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training,n=Ge(e),a=n.shape,s=a.length,i=fa(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=No(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!v.arraysEqual(u,fa(0,s).slice(0,s-1)),h=()=>{if(d){let g=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),A=this.center?G(this.beta.read(),l):null,x=this.scale?G(this.gamma.read(),l):null;return _p(n,g,y,A,x,this.epsilon)}else return _p(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,f]=xU(n,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{K(()=>{let x=1-A,b=g.read(),w=L(he(b,y),x);g.write(he(b,w))})};return m(this.movingMean,c,this.momentum),m(this.movingVariance,f,this.momentum),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),movingMeanInitializer:Pt(this.movingMeanInitializer),movingVarianceInitializer:Pt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:nr(this.betaConstraint),gammaConstraint:nr(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};xx.className="BatchNormalization";ue.registerClass(xx);var bx=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ss(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=Ge(e),n=r.shape,a=n.length;return K(()=>{let{mean:s,variance:i}=xm(r,this.axis,!0),o=No(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?G(c,o):c,u=l(this.gamma.read()),d=l(this.beta.read()),h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Dn(s,h),i=Dn(i,h),u=Dn(u,p),d=Dn(d,p),_p(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};bx.className="LayerNormalization";ue.registerClass(bx);function bU(e,t,r){return K(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=ca()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return r==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Gn(e,n)})}var vx=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ca():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t,r;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return K(()=>bU(Ge(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};vx.className="ZeroPadding2D";ue.registerClass(vx);function n0(e,t,r,n,a,s){return K(()=>{Ut(a),L7(s),Pn(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=ca()),s==null&&(s="max"),e=WA(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=Am(e,t,r,o):i=dm(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function O4(e,t,r,n,a,s){return K(()=>{Ut(a),L7(s),Pn(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=ca()),s==null&&(s="max"),e=C4(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=U2(e,t,r,o):i=E2(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var D4=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(cr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Pn(this.padding),this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=pa(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=yh(Ge(e),2);let r=this.poolingFunction(Ge(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return et(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},wx=class extends D4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),n0(e,t,r,n,a,"max")}};wx.className="MaxPooling1D";ue.registerClass(wx);var kx=class extends D4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),n0(e,t,r,n,a,"avg")}};kx.className="AveragePooling1D";ue.registerClass(kx);var L4=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=pa(t,this.poolSize[0],this.padding,this.strides[0]),r=pa(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ix=class extends L4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),n0(e,t,r,n,a,"max")}};Ix.className="MaxPooling2D";ue.registerClass(Ix);var Sx=class extends L4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),n0(e,t,r,n,a,"avg")}};Sx.className="AveragePooling2D";ue.registerClass(Sx);var B4=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=pa(t,this.poolSize[0],this.padding,this.strides[0]),r=pa(r,this.poolSize[1],this.padding,this.strides[1]),n=pa(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Tx=class extends B4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),O4(e,t,r,n,a,"max")}};Tx.className="MaxPooling3D";ue.registerClass(Tx);var Nx=class extends B4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Ut(a),Pn(n),O4(e,t,r,n,a,"avg")}};Nx.className="AveragePooling3D";ue.registerClass(Nx);var W4=class extends st{constructor(e){super(e),this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},Cx=class extends W4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return Bt(r,1)})}};Cx.className="GlobalAveragePooling1D";ue.registerClass(Cx);var Ex=class extends W4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return fr(r,1)})}};Ex.className="GlobalMaxPooling1D";ue.registerClass(Ex);var V4=class extends st{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ut(this.dataFormat),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Rx=class extends V4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?Bt(r,[1,2]):Bt(r,[2,3])})}};Rx.className="GlobalAveragePooling2D";ue.registerClass(Rx);var Mx=class extends V4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?fr(r,[1,2]):fr(r,[2,3])})}};Mx.className="GlobalMaxPooling2D";ue.registerClass(Mx);var U4=class extends st{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,r={}){let n=t.layer,a=da(n,r);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Fx=class extends U4{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ft(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return K(()=>(e=Ge(e),P4((r,n)=>[Ge(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Fx.className="TimeDistributed";ue.registerClass(Fx);function vU(e){Tl(CW,"BidirectionalMergeMode",e)}var wU="concat",$x=class extends U4{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=da(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=da(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?wU:e.mergeMode,vU(this.mergeMode),e.weights)throw new Ve("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):Jr(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=$4(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let u=r.map(d=>new qt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(n!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof oa;for(let l of s)if(l instanceof oa!==o)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=d,h}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t.initialState,n,a;if(r==null)n=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(n)&&(s=n.slice(1).concat(a.slice(1))),n=n[0],a=a[0]),this.returnSequences&&(a=Fn(a,1));let i;return this.mergeMode==="concat"?i=yA([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){xo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),xo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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n=k("tensorArrayId",e,t,r),a=k("tensor",e,t,r),s=k("lengths",e,t,r),i=r.getTensorArray(n.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let n=k("tensorArrayId",e,t,r),a=r.getTensorArray(n.id);return[Se(a.size(),"int32")]}case"TensorArrayCloseV3":{let n=k("tensorArrayId",e,t,r),a=r.getTensorArray(n.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let n=k("tensorListId",e,t,r),a=k("index",e,t,r),s=k("tensor",e,t,r),i=r.getTensorList(n.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let n=k("tensorListId",e,t,r),a=k("index",e,t,r),s=k("elementShape",e,t,r),i=k("elementDType",e,t,r);return[r.getTensorList(n.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let n=k("indices",e,t,r),a=k("tensor",e,t,r),s=k("elementShape",e,t,r),i=k("numElements",e,t,r),o=Ej(a,n,s,i);return r.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let n=k("elementShape",e,t,r),a=k("elementDType",e,t,r),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,r),o=Cj(n,a,i);return r.addTensorList(o),[o.idTensor]}case"TensorListGather":{let n=k("tensorListId",e,t,r),a=k("indices",e,t,r),s=k("elementShape",e,t,r),i=k("elementDType",e,t,r);return[r.getTensorList(n.id).gather(a,i,s)]}case"TensorListStack":{let n=k("tensorListId",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r),i=k("numElements",e,t,r);return[r.getTensorList(n.id).stack(a,s,i)]}case"TensorListFromTensor":{let n=k("tensor",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r),i=Nj(n,a,s);return r.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let n=k("tensorListId",e,t,r),a=r.getTensorList(n.id),s=k("dtype",e,t,r),i=k("elementShape",e,t,r);return[a.concat(s,i)]}case"TensorListPushBack":{let n=k("tensorListId",e,t,r),a=k("tensor",e,t,r),s=r.getTensorList(n.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let n=k("tensorListId",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r);return[r.getTensorList(n.id).popBack(a,s)]}case"TensorListSplit":{let n=k("tensor",e,t,r),a=k("elementShape",e,t,r),s=k("lengths",e,t,r),i=Rj(n,s,a);return r.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=k("tensorListId",e,t,r),a=r.getTensorList(n.id);return[Se(a.size(),"int32")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function fv(e,t,r){let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=!s,o=a==="prelu",l=n==="fusedbatchnorm",u=k("numArgs",e,t,r);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let d=k("strides",e,t,r),h=Kc(e,t,r),p=k("dataFormat",e,t,r).toUpperCase(),c=k("dilations",e,t,r),[f,m]=k("args",e,t,r);i&&(m=f,f=void 0);let g=k("leakyreluAlpha",e,t,r);return{stride:d,pad:h,dataFormat:p,dilations:c,biasArg:f,preluArg:m,activationFunc:a,leakyreluAlpha:g}}var Fj=(e,t,r)=>{switch(e.op){case"Conv1D":{let n=k("stride",e,t,r),a=k("pad",e,t,r),s=k("dataFormat",e,t,r).toUpperCase(),i=k("dilation",e,t,r);return[C2(k("x",e,t,r),k("filter",e,t,r),n,a,s,i)]}case"Conv2D":{let n=k("strides",e,t,r),a=Kc(e,t,r),s=k("dataFormat",e,t,r).toUpperCase(),i=k("dilations",e,t,r);return[$s(k("x",e,t,r),k("filter",e,t,r),[n[1],n[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=fv(e,t,r);return[zs.conv2d({x:k("x",e,t,r),filter:k("filter",e,t,r),strides:[n[1],n[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=fv(e,t,r);return[zs.depthwiseConv2d({x:k("x",e,t,r),filter:k("filter",e,t,r),strides:[n[1],n[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r),i=k("includeBatchInIndex",e,t,r),{result:o,indexes:l}=Jk(k("x",e,t,r),[s[1],s[2]],[n[1],n[2]],a,i);return[o,l]}case"AvgPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[T2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"MaxPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[B2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"Dilation2D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("dilations",e,t,r),i=n[1],o=n[2],l=s[1],u=s[2];return[Lk(k("x",e,t,r),k("filter",e,t,r),[i,o],a,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},$j=(e,t,r)=>{switch(e.op){case"Fill":{let n=k("shape",e,t,r),a=k("dtype",e,t,r),s=k("value",e,t,r);return[ad(n,s,a)]}case"LinSpace":{let n=k("start",e,t,r),a=k("stop",e,t,r),s=k("num",e,t,r);return[jk(n,a,s)]}case"Multinomial":{let 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k("x",e,t,r).map(u=>St(u.shape));case"Size":return[Se(k("x",e,t,r).size,"int32")];case"Rank":return[Se(k("x",e,t,r).rank,"int32")];case"NoOp":return[Se(1)];case"Print":let s=k("x",e,t,r),i=k("data",e,t,r),o=k("message",e,t,r),l=k("summarize",e,t,r);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let u=0;u<i.length;u++)console.log(Array.prototype.slice.call(i[u].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Oj=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Se(0),this.tensorMap=new Map,hr(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Se(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let r=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),K(()=>{let n=en(t),a=r.length,s=n.length;v.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=r[i],l=n[i];hr(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let r=await e.data();return K(()=>{let n=[];for(let a=0;a<r.length;a++){let s=r[a],i=this.findWithDefault(s,t);n.push(i)}return sr(n)})}findWithDefault(e,t){let r=this.tensorMap.get(e);return r!=null?r:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},Dj=async(e,t,r,n)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=k("keyDType",e,t,r),s=k("valueDType",e,t,r),i=new Oj(a,s);return 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n=k("image",e,t,r),a=k("boxes",e,t,r),s=k("boxInd",e,t,r),i=k("cropSize",e,t,r),o=k("method",e,t,r),l=k("extrapolationValue",e,t,r);return[Ie.cropAndResize(n,a,s,i,o,l)]}case"ImageProjectiveTransformV3":{let n=k("images",e,t,r),a=k("transforms",e,t,r),s=k("outputShape",e,t,r),i=k("fillValue",e,t,r),o=k("interpolation",e,t,r),l=k("fillMode",e,t,r);return[Ie.transform(n,a,o.toLowerCase(),l.toLowerCase(),i,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Bj=(e,t,r)=>{switch(e.op){case"Equal":return[Cn(k("a",e,t,r),k("b",e,t,r))];case"NotEqual":return[Au(k("a",e,t,r),k("b",e,t,r))];case"Greater":return[cn(k("a",e,t,r),k("b",e,t,r))];case"GreaterEqual":return[wl(k("a",e,t,r),k("b",e,t,r))];case"Less":return[_2(k("a",e,t,r),k("b",e,t,r))];case"LessEqual":return[kl(k("a",e,t,r),k("b",e,t,r))];case"LogicalAnd":return[ha(k("a",e,t,r),k("b",e,t,r))];case"LogicalNot":return[gm(k("a",e,t,r))];case"LogicalOr":return[L2(k("a",e,t,r),k("b",e,t,r))];case"Select":case"SelectV2":return[Br(k("condition",e,t,r),k("a",e,t,r),k("b",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Wj=(e,t,r)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Je(k("a",e,t,r),k("b",e,t,r),k("transposeA",e,t,r),k("transposeB",e,t,r))];case"Einsum":return[Wk(k("equation",e,t,r),...k("tensors",e,t,r))];case"Transpose":return[nt(k("x",e,t,r),k("perm",e,t,r))];case"_FusedMatMul":let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=a==="prelu",o=k("numArgs",e,t,r),l=k("leakyreluAlpha",e,t,r);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,d]=k("args",e,t,r);return[zs.matMul({a:k("a",e,t,r),b:k("b",e,t,r),transposeA:k("transposeA",e,t,r),transposeB:k("transposeB",e,t,r),bias:u,activation:a,preluActivationWeights:d,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Vj=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[gu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[gu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[Hk(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[ld(k("x",e,t,r))];case"LogSoftmax":return[z2(k("x",e,t,r))];case"SparseToDense":return[tA(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Uj=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[fr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Bt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Ps(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[I2(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[cf(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Nn(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[yk(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[W2(k("x",e,t,r),i,o)]}case"Cumprod":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[zk(k("x",e,t,r),i,o,l)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[$2(k("x",e,t,r),i,o,l)]}case"Bincount":let n=k("x",e,t,r),a=k("weights",e,t,r),s=k("size",e,t,r);return[N2(n,a,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),u=k("binaryOutput",e,t,r);return[Ok(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Gj=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let n=k("n",e,t,r),a=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,n),[kt(s,a)]}case"Gather":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[yu(n,me(a,"int32"),0)]}case"GatherV2":{let n=k("axis",e,t,r),a=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[yu(s,me(i,"int32"),n,a)]}case"Reverse":{let n=k("dims",e,t,r),a=[];for(let i=0;i<n.length;i++)n[i]&&a.push(i);let s=k("x",e,t,r);return[Fn(s,a)]}case"ReverseV2":{let n=k("axis",e,t,r),a=k("x",e,t,r);return[Fn(a,n)]}case"Slice":{let n=k("begin",e,t,r),a=k("size",e,t,r);return[Pe(k("x",e,t,r),n,a)]}case"StridedSlice":{let n=k("begin",e,t,r),a=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),u=k("newAxisMask",e,t,r),d=k("shrinkAxisMask",e,t,r),h=k("x",e,t,r);return[i7(h,n,a,s,i,o,l,u,d)]}case"Pack":return K(()=>{let n=k("axis",e,t,r),a=k("tensors",e,t,r),s=a[0].shape,i=et(a[0]).shape,o=a.map(l=>{let u=v.arraysEqual(l.shape,s);if(!u&&!v.arraysEqual(et(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[sr(o,n)]});case"Unpack":{let n=k("axis",e,t,r),a=k("tensor",e,t,r);return en(a,n)}case"Tile":{let n=k("reps",e,t,r);return[Dn(k("x",e,t,r),n)]}case"Split":case"SplitV":{let n=k("axis",e,t,r),a=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Kt(s,a,n)}case"ScatterNd":{let n=k("indices",e,t,r),a=k("values",e,t,r),s=k("shape",e,t,r);return[c7(n,a,s)]}case"GatherNd":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[f7(n,a)]}case"SparseToDense":{let n=k("sparseIndices",e,t,r),a=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[tA(n,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},jj=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:a,emptyRowIndicator:s,reverseIndexMap:i}=dp.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[n,a,s,i]}case"SparseReshape":{let{outputIndices:n,outputShape:a}=dp.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[n,a]}case"SparseSegmentMean":return[dp.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[dp.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Hj=(e,t,r)=>{switch(e.op){case"FFT":return[wm(k("x",e,t,r))];case"IFFT":return[Mp(k("x",e,t,r))];case"RFFT":return[km(k("x",e,t,r))];case"IRFFT":return[Y2(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function yv(e,t,r,n){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>un(p)[0]),d=[];n!=null&&(d=n.map(p=>un(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((x6(p)||Qj(p)||eH(p))&&i==null&&(i=p,o=i.children.map(c=>c.name).filter(c=>a.has(c))),a.add(p.name),r[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),h.push(c))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Xj(e,t,r){let{usedNodes:n,inputs:a}=r,s=[],i=Object.keys(a).map(d=>un(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(h=>{!l.has(h.name)&&n.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var Zj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Yj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Jj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function x6(e){return Zj.indexOf(e.op)>=0}function Qj(e){return Yj.indexOf(e.op)>=0}function eH(e){return Jj.indexOf(e.op)>=0}var z1=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new 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this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let r=e.map(a=>a.name).sort(),n=t.map(a=>a.name).sort();return r.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let r=yv(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:a,syncInputs:s}=r;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. 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t=Sr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Sr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,r;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?r=this.artifacts.userDefinedMetadata.signature:r=this.artifacts.signature,this.signature=r,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Sr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new z1(pv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=pv.Instance.transformGraph(e.modelInitializer);this.initializer=new z1(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let r=Sr.getSaveHandlers(e);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${e}'`);e=r[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof rt)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,r,n)=>(t[r]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=this.executor.execute(e,t);return r.length>1?r:r[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=await this.executor.executeAsync(e,t);return r.length>1?r:r[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,r)=>(t[r]=[e[r]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function aH(e,t={}){if(e==null)throw 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TextDecoder;else{let{StringDecoder:r}=ow();t=e instanceof r}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof rt)&&!(e instanceof Promise)&&!t)}function dH(e){return e==null||pH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof rt||v.isTypedArray(e)}function pH(e){return e===null||typeof e!="object"&&typeof e!="function"}function hH(e){return lH(e,cH)}function cH(e){return e instanceof rt?{value:e.clone(),recurse:!1}:wu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var I6=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),r=this.get(t);return this.set(t,this.pop()),r}},S6=class extends I6{constructor(){super(S6.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),r=this.length();for(let n=0;n<r;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}},T6=S6;T6.INITIAL_CAPACITY=32;function N6(e){return new gH(e)}function zx(e){return new yH(e)}function fH(e,t){return new C6(e,t)}function mH(e,t=E6.FAIL){return new TH(e,t)}var gr=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],r=await e.next();for(;!r.done;)t.push(r.value),r=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),r=e(t.value);for(;!t.done&&r;)t=await this.next(),r=e(t.value)}handleErrors(e){return new IH(this,e)}filter(e){return new wH(this,e)}map(e){return new kH(this,e)}mapAsync(e){return new Av(this,e)}serialMapAsync(e){return new Av(this,e).serial()}flatmap(e){return new SH(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new vH(this,e,t)}columnMajorBatch(e,t=!0,r=w6){return this.rowMajorBatch(e,t).map(n=>uH(n,r))}concatenate(e,t){return new C6(N6([this,e]),t)}take(e){return e<0||e==null?this:new bH(this,e)}skip(e){return e<0||e==null?this:new xH(this,e)}prefetch(e){return new R6(this,e)}shuffle(e,t){return new NH(this,e,t)}serial(){return new AH(this)}},gH=class extends gr{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:hH(e),done:!1}}},yH=class extends gr{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},AH=class extends gr{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},xH=class extends gr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;re(e.value)}return this.upstream.next()}},bH=class extends gr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},vH=class extends gr{constructor(e,t,r=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},wH=class extends gr{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;re(e.value)}}},kH=class extends gr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=la.getTensorsInContainer(e.value),r=this.transform(e.value),n=la.getTensorsInContainer(r);for(let a of t)la.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},IH=class extends gr{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Av=class extends gr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=la.getTensorsInContainer(e.value),r=await this.transform(e.value),n=la.getTensorsInContainer(r);for(let a of t)la.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},Ox=class extends gr{constructor(){super();this.outputQueue=new T6,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},SH=class extends Ox{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=la.getTensorsInContainer(e.value),r=this.transform(e.value),n=la.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)la.isTensorInList(a,n)||a.dispose();return!0}},C6=class extends gr{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},E6=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(E6||{}),TH=class extends gr{constructor(e,t=0){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof gr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await k6(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},R6=class extends gr{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new I6(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},NH=class extends R6{constructor(e,t,r){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=oH.alea(r||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},hd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
-      ${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),ln(async()=>(await r.iterator()).columnMajorBatch(e,t,RH),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,ln(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,ln(async()=>(await t.iterator()).filter(n=>K(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return ln(async()=>(await t.iterator()).map(r=>K(()=>e(r))),this.size)}mapAsync(e){let t=this;return ln(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return ln(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,ln(async()=>{let n=zx(async()=>({value:await t.iterator(),done:!1}));return fH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,ln(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=iH.alea(t||v.now().toString());return ln(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,ln(async()=>(await t.iterator()).take(e),r)}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()}};hd.MAX_BUFFER_SIZE=1e4;function ln(e,t=null){return new class extends hd{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function CH(e){return ln(async()=>N6(e),e.length)}function EH(e){if(!wu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return ln(async()=>{let r=await k6(e,n=>{if(n instanceof hd)return{value:n.iterator(),recurse:!1};if(wu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return mH(r,1)},t)}function RH(e){if(e===null)return null;let t=e[0];return dH(t)?{value:MH(e),recurse:!1}:{value:null,recurse:!0}}function MH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof rt?sr(e):ct(e)}var M6=class extends hd{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Bc='"',op=Symbol("out"),xv=Symbol("field"),Wc=Symbol("quote"),Zy=Symbol("quoteafterquote"),bv=Symbol("quoteinquote"),F6=class extends hd{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new M6(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(v.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=op;for(let i=0;i<a;i++)switch(s){case op:switch(e.charAt(i)){case Bc:n=i+1,s=Wc;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=op;break;default:s=xv,n=i;break}break;case xv:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=op,n=i+1;break;default:}break;case Wc:switch(e.charAt(i)){case Bc:s=Zy;break;default:}break;case Zy:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=op,n=i+1;break;case Bc:s=Wc;break;default:s=bv;break}break;case bv:switch(e.charAt(i)){case Bc:s=Wc;break;default:}break;default:}if(s===Zy?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},$6=class extends gr{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!Y().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new $6(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(v.sizeFromShape(t));return r.set(e,r.length-e.length),ct(r,t)}},P6=class extends gr{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=St([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=ua([s,a,o,i],[1,4])}else this.cropBox=ua([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Y().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new P6(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=$n.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Ht(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return G(r,n.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.")}},_6=class{},z6=class extends gr{split(e){return new FH(this,e)}},FH=class extends z6{constructor(e,t){super();this.upstream=e,this.impl=new $H(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},$H=class extends Ox{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},PH=class extends gr{decodeUTF8(){return new _H(this)}},_H=class extends z6{constructor(e){super();this.upstream=e,this.impl=new zH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zH=class extends Ox{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=ow();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return Y().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},O6=class extends PH{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},n.onabort=s=>t(new Error("Aborted")),n.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,r);n.readAsArrayBuffer(a)}this.offset=r}),done:!1}}};async function OH(e,t={},r){let n,a;typeof e=="string"?n=e:(n=e.url,a=DH(e));let s=await(r||v.fetch)(n,a);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new O6(i,t)}else throw new Error(s.statusText)}var DH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function D6(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var L6=class extends _6{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(D6(this.input)&&Y().get("IS_NODE")){let e=e2();this.input=e.readFileSync(this.input.slice(7))}return new O6(this.input,this.options)}},B6=class extends 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n=k("elementShape",e,t,r),a=k("elementDType",e,t,r),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,r),o=Cj(n,a,i);return r.addTensorList(o),[o.idTensor]}case"TensorListGather":{let n=k("tensorListId",e,t,r),a=k("indices",e,t,r),s=k("elementShape",e,t,r),i=k("elementDType",e,t,r);return[r.getTensorList(n.id).gather(a,i,s)]}case"TensorListStack":{let n=k("tensorListId",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r),i=k("numElements",e,t,r);return[r.getTensorList(n.id).stack(a,s,i)]}case"TensorListFromTensor":{let n=k("tensor",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r),i=Nj(n,a,s);return r.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let n=k("tensorListId",e,t,r),a=r.getTensorList(n.id),s=k("dtype",e,t,r),i=k("elementShape",e,t,r);return[a.concat(s,i)]}case"TensorListPushBack":{let n=k("tensorListId",e,t,r),a=k("tensor",e,t,r),s=r.getTensorList(n.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let n=k("tensorListId",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r);return[r.getTensorList(n.id).popBack(a,s)]}case"TensorListSplit":{let n=k("tensor",e,t,r),a=k("elementShape",e,t,r),s=k("lengths",e,t,r),i=Rj(n,s,a);return r.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=k("tensorListId",e,t,r),a=r.getTensorList(n.id);return[Se(a.size(),"int32")]}case"TensorListResize":{let n=k("tensorListId",e,t,r),a=k("size",e,t,r),s=r.getTensorList(n.id).resize(a);return r.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function yv(e,t,r){let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=!s,o=a==="prelu",l=n==="fusedbatchnorm",u=k("numArgs",e,t,r);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d 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n=k("strides",e,t,r),a=Xc(e,t,r),s=k("dataFormat",e,t,r).toUpperCase(),i=k("dilations",e,t,r);return[$s(k("x",e,t,r),k("filter",e,t,r),[n[1],n[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=yv(e,t,r);return[zs.conv2d({x:k("x",e,t,r),filter:k("filter",e,t,r),strides:[n[1],n[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=yv(e,t,r);return[zs.depthwiseConv2d({x:k("x",e,t,r),filter:k("filter",e,t,r),strides:[n[1],n[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r),i=k("includeBatchInIndex",e,t,r),{result:o,indexes:l}=t7(k("x",e,t,r),[s[1],s[2]],[n[1],n[2]],a,i);return[o,l]}case"AvgPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[E2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"MaxPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[U2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"Dilation2D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("dilations",e,t,r),i=n[1],o=n[2],l=s[1],u=s[2];return[Vk(k("x",e,t,r),k("filter",e,t,r),[i,o],a,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},$j=(e,t,r)=>{switch(e.op){case"Fill":{let n=k("shape",e,t,r),a=k("dtype",e,t,r),s=k("value",e,t,r);return[sd(n,s,a)]}case"LinSpace":{let n=k("start",e,t,r),a=k("stop",e,t,r),s=k("num",e,t,r);return[Kk(n,a,s)]}case"Multinomial":{let 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n=k("image",e,t,r),a=k("boxes",e,t,r),s=k("boxInd",e,t,r),i=k("cropSize",e,t,r),o=k("method",e,t,r),l=k("extrapolationValue",e,t,r);return[Ie.cropAndResize(n,a,s,i,o,l)]}case"ImageProjectiveTransformV3":{let n=k("images",e,t,r),a=k("transforms",e,t,r),s=k("outputShape",e,t,r),i=k("fillValue",e,t,r),o=k("interpolation",e,t,r),l=k("fillMode",e,t,r);return[Ie.transform(n,a,o.toLowerCase(),l.toLowerCase(),i,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Bj=(e,t,r)=>{switch(e.op){case"Equal":return[Cn(k("a",e,t,r),k("b",e,t,r))];case"NotEqual":return[Au(k("a",e,t,r),k("b",e,t,r))];case"Greater":return[cn(k("a",e,t,r),k("b",e,t,r))];case"GreaterEqual":return[kl(k("a",e,t,r),k("b",e,t,r))];case"Less":return[D2(k("a",e,t,r),k("b",e,t,r))];case"LessEqual":return[Il(k("a",e,t,r),k("b",e,t,r))];case"LogicalAnd":return[ha(k("a",e,t,r),k("b",e,t,r))];case"LogicalNot":return[ym(k("a",e,t,r))];case"LogicalOr":return[V2(k("a",e,t,r),k("b",e,t,r))];case"Select":case"SelectV2":return[Br(k("condition",e,t,r),k("a",e,t,r),k("b",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Wj=(e,t,r)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Je(k("a",e,t,r),k("b",e,t,r),k("transposeA",e,t,r),k("transposeB",e,t,r))];case"Einsum":return[Gk(k("equation",e,t,r),...k("tensors",e,t,r))];case"Transpose":return[nt(k("x",e,t,r),k("perm",e,t,r))];case"_FusedMatMul":let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=a==="prelu",o=k("numArgs",e,t,r),l=k("leakyreluAlpha",e,t,r);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,d]=k("args",e,t,r);return[zs.matMul({a:k("a",e,t,r),b:k("b",e,t,r),transposeA:k("transposeA",e,t,r),transposeB:k("transposeB",e,t,r),bias:u,activation:a,preluActivationWeights:d,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Vj=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[gu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[gu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[Xk(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[ud(k("x",e,t,r))];case"LogSoftmax":return[L2(k("x",e,t,r))];case"SparseToDense":return[aA(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Uj=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[fr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Bt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Ps(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[N2(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ff(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Nn(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[bk(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[G2(k("x",e,t,r),i,o)]}case"Cumprod":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[Lk(k("x",e,t,r),i,o,l)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[z2(k("x",e,t,r),i,o,l)]}case"Bincount":let n=k("x",e,t,r),a=k("weights",e,t,r),s=k("size",e,t,r);return[R2(n,a,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),u=k("binaryOutput",e,t,r);return[Bk(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Gj=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let n=k("n",e,t,r),a=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,n),[kt(s,a)]}case"Gather":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[yu(n,me(a,"int32"),0)]}case"GatherV2":{let n=k("axis",e,t,r),a=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[yu(s,me(i,"int32"),n,a)]}case"Reverse":{let n=k("dims",e,t,r),a=[];for(let i=0;i<n.length;i++)n[i]&&a.push(i);let s=k("x",e,t,r);return[Fn(s,a)]}case"ReverseV2":{let n=k("axis",e,t,r),a=k("x",e,t,r);return[Fn(a,n)]}case"Slice":{let n=k("begin",e,t,r),a=k("size",e,t,r);return[Pe(k("x",e,t,r),n,a)]}case"StridedSlice":{let n=k("begin",e,t,r),a=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),u=k("newAxisMask",e,t,r),d=k("shrinkAxisMask",e,t,r),h=k("x",e,t,r);return[u7(h,n,a,s,i,o,l,u,d)]}case"Pack":return K(()=>{let n=k("axis",e,t,r),a=k("tensors",e,t,r),s=a[0].shape,i=et(a[0]).shape,o=a.map(l=>{let u=v.arraysEqual(l.shape,s);if(!u&&!v.arraysEqual(et(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[sr(o,n)]});case"Unpack":{let n=k("axis",e,t,r),a=k("tensor",e,t,r);return en(a,n)}case"Tile":{let n=k("reps",e,t,r);return[Dn(k("x",e,t,r),n)]}case"Split":case"SplitV":{let n=k("axis",e,t,r),a=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Kt(s,a,n)}case"ScatterNd":{let n=k("indices",e,t,r),a=k("values",e,t,r),s=k("shape",e,t,r);return[g7(n,a,s)]}case"GatherNd":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[y7(n,a)]}case"SparseToDense":{let n=k("sparseIndices",e,t,r),a=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[aA(n,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},jj=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:a,emptyRowIndicator:s,reverseIndexMap:i}=pp.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[n,a,s,i]}case"SparseReshape":{let{outputIndices:n,outputShape:a}=pp.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[n,a]}case"SparseSegmentMean":return[pp.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[pp.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Hj=(e,t,r)=>{switch(e.op){case"FFT":return[km(k("x",e,t,r))];case"IFFT":return[Fp(k("x",e,t,r))];case"RFFT":return[Im(k("x",e,t,r))];case"IRFFT":return[eA(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not 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n=k("axis",e,t,r);return[et(k("x",e,t,r),n)]}case"Reshape":return[G(k("x",e,t,r),k("shape",e,t,r))];case"MirrorPad":return[r7(k("x",e,t,r),k("padding",e,t,r),k("mode",e,t,r))];case"PadV2":case"Pad":return[Gn(k("x",e,t,r),k("padding",e,t,r),k("constantValue",e,t,r))];case"SpaceToBatchND":{let n=k("blockShape",e,t,r),a=k("paddings",e,t,r);return[bm(k("x",e,t,r),n,a)]}case"BatchToSpaceND":{let n=k("blockShape",e,t,r),a=k("crops",e,t,r);return[pm(k("x",e,t,r),n,a)]}case"DepthToSpace":{let n=k("blockSize",e,t,r),a=k("dataFormat",e,t,r).toUpperCase();return[Wk(k("x",e,t,r),n,a)]}case"BroadcastTo":return[bp(k("x",e,t,r),k("shape",e,t,r))];case"BroadcastArgs":return[Fk(k("s0",e,t,r),k("s1",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Av(e,t,r,n){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return K(()=>Ij(s,i,o));case"basic_math":return K(()=>Sj(s,i,o));case"control":return Mj(s,i,o);case"convolution":return K(()=>Fj(s,i,o));case"creation":return K(()=>$j(s,i,o));case"dynamic":return Pj(s,i,o);case"evaluation":return K(()=>_j(s,i,o));case"image":return K(()=>Lj(s,i,o));case"graph":return K(()=>zj(s,i,o));case"logical":return K(()=>Bj(s,i,o));case"matrices":return K(()=>Wj(s,i,o));case"normalization":return K(()=>Vj(s,i,o));case"reduction":return K(()=>Uj(s,i,o));case"slice_join":return K(()=>Gj(s,i,o));case"sparse":return K(()=>jj(s,i,o));case"spectral":return K(()=>Hj(s,i,o));case"string":return K(()=>qj(s,i,o));case"transformation":return K(()=>Kj(s,i,o));case"hash_table":return Dj(s,i,o,n);case"custom":let l=e6(s.op);if(l&&l.customExecutor)return l.customExecutor(new kj(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function bv(e,t,r,n){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>un(p)[0]),d=[];n!=null&&(d=n.map(p=>un(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((w6(p)||Qj(p)||eH(p))&&i==null&&(i=p,o=i.children.map(c=>c.name).filter(c=>a.has(c))),a.add(p.name),r[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),h.push(c))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Xj(e,t,r){let{usedNodes:n,inputs:a}=r,s=[],i=Object.keys(a).map(d=>un(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(h=>{!l.has(h.name)&&n.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var Zj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Yj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Jj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function w6(e){return Zj.indexOf(e.op)>=0}function Qj(e){return Yj.indexOf(e.op)>=0}function eH(e){return Jj.indexOf(e.op)>=0}var L1=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(r=>{this._functionExecutorMap[r]=new L1(e.functions[r],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(r=>e[r].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let r=e.map(a=>a.name).sort(),n=t.map(a=>a.name).sort();return r.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let r=bv(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:a,syncInputs:s}=r;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. 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t=Sr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Sr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,r;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?r=this.artifacts.userDefinedMetadata.signature:r=this.artifacts.signature,this.signature=r,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Sr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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TextDecoder;else{let{StringDecoder:r}=dw();t=e instanceof r}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof rt)&&!(e instanceof Promise)&&!t)}function dH(e){return e==null||pH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof rt||v.isTypedArray(e)}function pH(e){return e===null||typeof e!="object"&&typeof e!="function"}function hH(e){return lH(e,cH)}function cH(e){return e instanceof rt?{value:e.clone(),recurse:!1}:ku(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var N6=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),r=this.get(t);return this.set(t,this.pop()),r}},C6=class extends N6{constructor(){super(C6.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),r=this.length();for(let n=0;n<r;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}},E6=C6;E6.INITIAL_CAPACITY=32;function R6(e){return new gH(e)}function Lx(e){return new yH(e)}function fH(e,t){return new M6(e,t)}function mH(e,t=F6.FAIL){return new TH(e,t)}var gr=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],r=await e.next();for(;!r.done;)t.push(r.value),r=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await 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e=this.items[this.trav];return this.trav++,{value:hH(e),done:!1}}},yH=class extends gr{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},AH=class extends gr{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},xH=class extends gr{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;re(e.value)}return this.upstream.next()}},bH=class extends gr{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},vH=class extends gr{constructor(e,t,r=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},wH=class extends gr{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;re(e.value)}}},kH=class extends gr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=la.getTensorsInContainer(e.value),r=this.transform(e.value),n=la.getTensorsInContainer(r);for(let a of t)la.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},IH=class extends gr{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},vv=class extends gr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=la.getTensorsInContainer(e.value),r=await this.transform(e.value),n=la.getTensorsInContainer(r);for(let a of t)la.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},Bx=class extends gr{constructor(){super(),this.outputQueue=new E6,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},SH=class extends Bx{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=la.getTensorsInContainer(e.value),r=this.transform(e.value),n=la.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)la.isTensorInList(a,n)||a.dispose();return!0}},M6=class extends gr{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},F6=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(F6||{}),TH=class extends gr{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof gr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await T6(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},$6=class extends gr{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new N6(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},NH=class extends $6{constructor(e,t,r){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=oH.alea(r||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},cd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),ln(async()=>(await r.iterator()).columnMajorBatch(e,t,RH),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,ln(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,ln(async()=>(await t.iterator()).filter(n=>K(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return ln(async()=>(await t.iterator()).map(r=>K(()=>e(r))),this.size)}mapAsync(e){let t=this;return ln(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return ln(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,ln(async()=>{let n=Lx(async()=>({value:await t.iterator(),done:!1}));return fH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,ln(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=iH.alea(t||v.now().toString());return ln(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,ln(async()=>(await t.iterator()).take(e),r)}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()}};cd.MAX_BUFFER_SIZE=1e4;function ln(e,t=null){return new class extends cd{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function CH(e){return ln(async()=>R6(e),e.length)}function EH(e){if(!ku(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return ln(async()=>{let r=await T6(e,n=>{if(n instanceof cd)return{value:n.iterator(),recurse:!1};if(ku(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return mH(r,1)},t)}function RH(e){if(e===null)return null;let t=e[0];return dH(t)?{value:MH(e),recurse:!1}:{value:null,recurse:!0}}function MH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof rt?sr(e):ct(e)}var P6=class extends cd{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Wc='"',lp=Symbol("out"),wv=Symbol("field"),Vc=Symbol("quote"),Qy=Symbol("quoteafterquote"),kv=Symbol("quoteinquote"),_6=class extends cd{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new P6(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(v.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=lp;for(let i=0;i<a;i++)switch(s){case lp:switch(e.charAt(i)){case Wc:n=i+1,s=Vc;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=lp;break;default:s=wv,n=i;break}break;case wv:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=lp,n=i+1;break;default:}break;case Vc:switch(e.charAt(i)){case Wc:s=Qy;break;default:}break;case Qy:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=lp,n=i+1;break;case Wc:s=Vc;break;default:s=kv;break}break;case kv:switch(e.charAt(i)){case Wc:s=Vc;break;default:}break;default:}if(s===Qy?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},z6=class extends gr{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!Y().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new z6(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(v.sizeFromShape(t));return r.set(e,r.length-e.length),ct(r,t)}},O6=class extends gr{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=St([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=ua([s,a,o,i],[1,4])}else this.cropBox=ua([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Y().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new O6(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=$n.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Ht(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return G(r,n.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.")}},D6=class{},L6=class extends gr{split(e){return new FH(this,e)}},FH=class extends L6{constructor(e,t){super(),this.upstream=e,this.impl=new $H(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},$H=class extends Bx{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},PH=class extends gr{decodeUTF8(){return new _H(this)}},_H=class extends L6{constructor(e){super(),this.upstream=e,this.impl=new zH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zH=class extends Bx{constructor(e){if(super(),this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=dw();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return Y().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},B6=class extends PH{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof 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I=Math.min(t.inWidth,h+_),O=Number.NEGATIVE_INFINITY,z=-1;for(let j=x;j<b;j+=i){let X=j-A;for(let D=S;D<E;D+=o){let Q=D-T;for(let V=M;V<I;V+=l){let ee=V-_,J=e.get(m,j,D,V,g);J>=O&&(O=J,z=X*d*h+Q*d+ee)}}}r.set(z,m,y,w,R,g)}}}return r}function CK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Te(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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PK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Te([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,w=We(i.shape,"float32"),T=1/(c*f),S=r.data.get(a.dataId).values,E=We(a.shape,"float32",S);for(let R=0;R<d.batchSize;++R)for(let _=0;_<d.inChannels;++_)for(let M=0;M<d.inHeight;++M)for(let I=0;I<d.inWidth;++I){let O=M-b,z=I-x,j=0;for(let X=0;X<y;X+=m){let D=(O+X)/h;if(!(D<0||D>=d.outHeight||Math.floor(D)!==D))for(let Q=0;Q<A;Q+=g){let V=(z+Q)/p;V<0||V>=d.outWidth||Math.floor(V)!==V||(j+=E.get(R,D,V,_))}}w.set(j*T,R,M,I,_)}return r.makeTensorInfo(w.shape,w.dtype,w.values)}var _K={kernelName:zf,backendName:"cpu",kernelFunc:PK};function 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r.makeTensorInfo(a.shape,a.dtype,m)}var OK={kernelName:ii,backendName:"cpu",kernelFunc:zK};function DK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Te([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Mt({inputs:{x:a},backend:r,attrs:{shape:l}}),f=rn({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Mt({inputs:{x:f},backend:r,attrs:{shape:d}}),g=Co({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),g}var LK={kernelName:_o,backendName:"cpu",kernelFunc:DK};function BK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=Bx(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var 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r.makeTensorInfo(A.shape,A.dtype,A.values)}var JK={kernelName:Bf,backendName:"cpu",kernelFunc:YK};function QK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n;Te([a,s],"conv2dBackpropInput");let h=v.computeStrides(s.shape),p=v.computeStrides(a.shape),c=N.convertConv2DDataFormat(u),f=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),m=new rr(f.inShape,"float32"),g=m.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,w]=h,{batchSize:T,filterHeight:S,filterWidth:E,inChannels:R,inHeight:_,inWidth:M,outChannels:I,outHeight:O,outWidth:z,strideHeight:j,strideWidth:X}=f;c=f.dataFormat;let D=S-1-f.padInfo.top,Q=E-1-f.padInfo.left,V=c==="channelsLast",ee=m.strides[0],J=V?m.strides[1]:m.strides[2],se=V?m.strides[2]:1,Z=V?1:m.strides[1],ae=p[0],de=V?p[1]:p[2],Ae=V?p[2]:1,be=V?1:p[1];for(let Ee=0;Ee<T;++Ee)for(let Me=0;Me<R;++Me)for(let De=0;De<_;++De){let 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pX(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[d,h,p,c]=a.shape,f=s.shape[0],[m,g]=o,y=We([f,m,g,c],"float32"),A=r.data.get(s.dataId).values,x=r.data.get(i.dataId).values,b=r.data.get(a.dataId).values,w=v.computeStrides(a.shape),T=v.computeStrides(y.shape);for(let S=0;S<f;S++){let E=S*4,R=A[E],_=A[E+1],M=A[E+2],I=A[E+3],O=x[S];if(O>=d)continue;let z=m>1?(M-R)*(h-1)/(m-1):0,j=g>1?(I-_)*(p-1)/(g-1):0;for(let X=0;X<m;X++){let D=m>1?R*(h-1)+X*z:.5*(R+M)*(h-1);if(D<0||D>h-1){for(let Q=0;Q<g;Q++)for(let V=0;V<c;V++){let ee=V+Q*T[2]+X*T[1]+S*T[0];y.values[ee]=u}continue}if(l==="bilinear"){let Q=Math.floor(D),V=Math.ceil(D),ee=D-Q;for(let J=0;J<g;J++){let se=g>1?_*(p-1)+J*j:.5*(_+I)*(p-1);if(se<0||se>p-1){for(let Ae=0;Ae<c;Ae++){let be=Ae+J*T[2]+X*T[1]+S*T[0];y.values[be]=u}continue}let Z=Math.floor(se),ae=Math.ceil(se),de=se-Z;for(let Ae=0;Ae<c;Ae++){let be=Ae+Z*w[2]+Q*w[1]+O*w[0],Ee=b[be];be=Ae+ae*w[2]+Q*w[1]+O*w[0];let 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h=Nr(u.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=i?0:c[x];else{let b=m(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=N.getUndoAxesPermutation(l),A=rn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var gX={kernelName:Oo,backendName:"cpu",kernelFunc:mX};function yX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=Bx(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=G6(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be 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j=z-S,X=m.get(g,I,z,y);X>_&&(_=X,a?M=s?((g*n.inHeight+I)*n.inWidth+z)*n.inChannels+y:(I*n.inWidth+z)*n.inChannels+y:M=O*p+j)}}i.set(M,g,A,T,y)}}return i}function zI(e,t,r,n,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,u=a.dilationDepth,d=a.dilationHeight,h=a.dilationWidth,p=a.effectiveFilterDepth,c=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,g=a.padInfo.top,y=a.padInfo.left,A=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=We(a.outShape,r),b=x.values,w=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],T=a.outShape[2]*a.outShape[3]*a.outShape[4],S=a.outShape[3]*a.outShape[4],E=a.outShape[4];for(let R=0;R<a.batchSize;++R){let _=R*w,M=R*n[0];for(let I=0;I<a.inChannels;++I)for(let O=0;O<a.outDepth;++O){let z=O*i-m,j=z;for(;j<0;)j+=u;let X=Math.min(a.inDepth,p+z),D=_+O*T;for(let Q=0;Q<a.outHeight;++Q){let V=Q*o-g,ee=V;for(;ee<0;)ee+=d;let J=Math.min(a.inHeight,c+V),se=D+Q*S;for(let Z=0;Z<a.outWidth;++Z){let 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d=N.computePool3DInfo(s.shape,i,o,1,l,u),h=d.strideDepth,p=d.strideHeight,c=d.strideWidth,f=d.filterDepth,m=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,b=d.effectiveFilterDepth,w=d.effectiveFilterHeight,T=d.effectiveFilterWidth,S=b-1-d.padInfo.front,E=T-1-d.padInfo.left,R=w-1-d.padInfo.top,_=We(s.shape,"float32"),M=1/(f*m*g),I=r.bufferSync(a);for(let O=0;O<d.batchSize;++O)for(let z=0;z<d.inChannels;++z)for(let j=0;j<d.inDepth;++j)for(let X=0;X<d.inHeight;++X)for(let D=0;D<d.inWidth;++D){let Q=j-S,V=X-R,ee=D-E,J=0;for(let se=0;se<b;se+=y){let Z=(Q+se)/h;if(!(Z<0||Z>=d.outDepth||Math.floor(Z)!==Z))for(let ae=0;ae<w;ae+=A){let de=(V+ae)/p;if(!(de<0||de>=d.outHeight||Math.floor(de)!==de))for(let Ae=0;Ae<T;Ae+=x){let be=(ee+Ae)/c;be<0||be>=d.outWidth||Math.floor(be)!==be||(J+=I.get(O,Z,de,be,z))}}}_.set(J*M,O,j,X,D,z)}return r.makeTensorInfo(_.shape,_.dtype,_.values)}var $K={kernelName:Df,backendName:"cpu",kernelFunc:FK};function PK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Te([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,w=We(i.shape,"float32"),T=1/(c*f),S=r.data.get(a.dataId).values,E=We(a.shape,"float32",S);for(let R=0;R<d.batchSize;++R)for(let _=0;_<d.inChannels;++_)for(let M=0;M<d.inHeight;++M)for(let I=0;I<d.inWidth;++I){let O=M-b,z=I-x,j=0;for(let X=0;X<y;X+=m){let D=(O+X)/h;if(!(D<0||D>=d.outHeight||Math.floor(D)!==D))for(let Q=0;Q<A;Q+=g){let V=(z+Q)/p;V<0||V>=d.outWidth||Math.floor(V)!==V||(j+=E.get(R,D,V,_))}}w.set(j*T,R,M,I,_)}return r.makeTensorInfo(w.shape,w.dtype,w.values)}var _K={kernelName:Of,backendName:"cpu",kernelFunc:PK};function 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r.makeTensorInfo(a.shape,a.dtype,m)}var OK={kernelName:ii,backendName:"cpu",kernelFunc:zK};function DK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Te([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Mt({inputs:{x:a},backend:r,attrs:{shape:l}}),f=rn({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Mt({inputs:{x:f},backend:r,attrs:{shape:d}}),g=Eo({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),g}var LK={kernelName:zo,backendName:"cpu",kernelFunc:DK};function BK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=Ux(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var WK={kernelName:Lf,backendName:"cpu",kernelFunc:BK};function VK(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.data.get(n.dataId).values,i=r.data.get(a.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var UK={kernelName:Bf,backendName:"cpu",kernelFunc:VK},GK=mt(Ka,(e,t)=>{let r=t;return e>r.clipValueMax?r.clipValueMax:e<r.clipValueMin?r.clipValueMin:e}),jK={kernelName:Ka,backendName:"cpu",kernelFunc:GK},HK=e=>{let{x:t}=e.inputs,r=e.backend,n=new Float32Array(v.sizeFromShape(t.shape)),a=r.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=r.data.get(s.dataId).values,l=r.data.get(i.dataId).values;for(let u=0;u<o.length;u++){let d=o[u],h=l[u];n[u]=Math.hypot(d,h)}return r.makeOutput(n,t.shape,"float32")},qK={kernelName:Wp,backendName:"cpu",kernelFunc:HK};function Iu(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.data.get(n.dataId).complexTensorInfos.imag,s=r.data.get(a.dataId).values;return r.makeTensorInfo(a.shape,a.dtype,s)}var KK={kernelName:jp,backendName:"cpu",kernelFunc:Iu};function Su(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(m=>m.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>v.sizeFromShape(m.shape)>0);if(o.length===1)return Ma({inputs:{x:o[0]},backend:r});let l=o.map(m=>m.shape);if(N.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(b=>Co({inputs:{input:b},backend:r})),g=o.map(b=>Iu({inputs:{input:b},backend:r})),y=Su({inputs:m,backend:r,attrs:{axis:s}}),A=Su({inputs:g,backend:r,attrs:{axis:s}}),x=dn({inputs:{real:y,imag:A},backend:r});return 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rr(p.outShape,a.dtype),w=v.computeStrides(a.shape),T=v.computeStrides(s.shape),S=w[0],E=x?w[1]:w[2],R=x?w[2]:1,_=x?1:w[1],M=b.strides[0],I=x?b.strides[1]:b.strides[2],O=x?b.strides[2]:1,z=x?1:b.strides[1],j=r.data.get(a.dataId).values,X=r.data.get(s.dataId).values,D=b.values;for(let Q=0;Q<p.batchSize;++Q){let V=Q*S,ee=Q*M;for(let J=0;J<p.outHeight;++J){let se=ee+J*I,Z=J*p.strideHeight-A;for(let ae=0;ae<c;++ae){let de=Z+ae*m;if(de<0||de>=p.inHeight)continue;let Ae=ae*T[0],be=V+de*E;for(let Ee=0;Ee<p.outWidth;++Ee){let Me=se+Ee*O,De=Ee*p.strideWidth-y;for(let Be=0;Be<f;++Be){let Ze=De+Be*g;if(Ze<0||Ze>=p.inWidth)continue;let ot=Ae+Be*T[1],dt=be+Ze*R,pt=ot;for(let $e=0;$e<p.inChannels;++$e){let vt=j[dt+$e*_];for(let yt=0;yt<p.outChannels;++yt)D[Me+yt*z]+=vt*X[pt+yt];pt+=p.outChannels}}}}}}return r.makeTensorInfo(b.shape,b.dtype,D)}var ZK={kernelName:Zs,backendName:"cpu",kernelFunc:OI};function YK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n;Te([a,s],"conv2dBackpropFilter");let h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),{strideHeight:c,strideWidth:f,filterHeight:m,filterWidth:g}=p,y=p.dataFormat==="channelsLast",A=new rr(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,w=r.data.get(a.dataId).values,T=r.data.get(s.dataId).values,S=new rr(a.shape,a.dtype,w),E=new rr(s.shape,s.dtype,T);for(let R=0;R<m;++R){let _=Math.max(0,Math.ceil((b-R)/c)),M=Math.min(p.outHeight,(p.inHeight+b-R)/c);for(let I=0;I<g;++I){let O=Math.max(0,Math.ceil((x-I)/f)),z=Math.min(p.outWidth,(p.inWidth+x-I)/f);for(let j=0;j<p.inChannels;++j)for(let X=0;X<p.outChannels;++X){let D=0;for(let Q=0;Q<p.batchSize;++Q)for(let V=_;V<M;++V){let ee=R+V*c-b;for(let J=O;J<z;++J){let se=I+J*f-x;y?D+=S.get(Q,ee,se,j)*E.get(Q,V,J,X):D+=S.get(Q,j,ee,se)*E.get(Q,X,V,J)}}A.set(D,R,I,j,X)}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var JK={kernelName:Wf,backendName:"cpu",kernelFunc:YK};function QK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n;Te([a,s],"conv2dBackpropInput");let h=v.computeStrides(s.shape),p=v.computeStrides(a.shape),c=N.convertConv2DDataFormat(u),f=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),m=new rr(f.inShape,"float32"),g=m.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,w]=h,{batchSize:T,filterHeight:S,filterWidth:E,inChannels:R,inHeight:_,inWidth:M,outChannels:I,outHeight:O,outWidth:z,strideHeight:j,strideWidth:X}=f;c=f.dataFormat;let D=S-1-f.padInfo.top,Q=E-1-f.padInfo.left,V=c==="channelsLast",ee=m.strides[0],J=V?m.strides[1]:m.strides[2],se=V?m.strides[2]:1,Z=V?1:m.strides[1],ae=p[0],de=V?p[1]:p[2],Ae=V?p[2]:1,be=V?1:p[1];for(let Ee=0;Ee<T;++Ee)for(let Me=0;Me<R;++Me)for(let De=0;De<_;++De){let Be=De-D,Ze=Math.max(0,Math.ceil(Be/j)),ot=Math.min(O,(S+Be)/j);for(let dt=0;dt<M;++dt){let pt=dt-Q,$e=Math.max(0,Math.ceil(pt/X)),vt=Math.min(z,(E+pt)/X),yt=0;for(let ur=Ze;ur<ot;++ur){let Xr=ur*j-Be;for(let Jt=$e;Jt<vt;++Jt){let dr=Jt*X-pt,Yn=ae*Ee+de*ur+Ae*Jt,Zr=x*(S-1-Xr)+b*(E-1-dr)+w*Me;for(let Qt=0;Qt<I;++Qt){let bn=y[Yn+be*Qt],vn=A[Zr+Qt];yt+=bn*vn}}}let Fr=ee*Ee+J*De+se*dt+Z*Me;g[Fr]=yt}}return r.makeTensorInfo(m.shape,m.dtype,m.values)}var eX={kernelName:Ys,backendName:"cpu",kernelFunc:QK};function tX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n;Te([a,s],"conv3d");let u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:h,filterWidth:p,dilationDepth:c,dilationHeight:f,dilationWidth:m,padInfo:g}=u,y=g.front,A=g.left,x=g.top,b=new rr(u.outShape,a.dtype),w=r.data.get(a.dataId).values,T=r.data.get(s.dataId).values,S=b.values,E=v.computeStrides(a.shape),R=v.computeStrides(s.shape);for(let _=0;_<u.batchSize;++_){let 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u=v.computeStrides(a.shape),d=v.computeStrides(s.shape),h=N.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,c=h.strideHeight,f=h.strideWidth,m=h.filterDepth,g=h.filterHeight,y=h.filterWidth,A=new rr(h.filterShape,"float32"),x=A.values,[b,w,T,S]=A.strides,E=r.data.get(s.dataId).values,[R,_,M,I]=d,O=r.data.get(a.dataId).values,[z,j,X,D]=u,Q=h.padInfo.front,V=h.padInfo.left,ee=h.padInfo.top;for(let J=0;J<m;++J){let se=Math.max(0,Math.ceil((Q-J)/p)),Z=Math.min(h.outDepth,(h.inDepth+Q-J)/p),ae=J*b;for(let de=0;de<g;++de){let Ae=Math.max(0,Math.ceil((ee-de)/c)),be=Math.min(h.outHeight,(h.inHeight+ee-de)/c),Ee=de*w+ae;for(let Me=0;Me<y;++Me){let De=Math.max(0,Math.ceil((V-Me)/f)),Be=Math.min(h.outWidth,(h.inWidth+V-Me)/f),Ze=Me*T+Ee;for(let ot=0;ot<h.inChannels;++ot){let dt=ot*S+Ze;for(let pt=0;pt<h.outChannels;++pt){let $e=0;for(let vt=0;vt<h.batchSize;++vt){let yt=vt*z,Fr=vt*R;for(let ur=se;ur<Z;++ur){let Xr=(J+ur*p-Q)*j+yt,Jt=ur*_+Fr;for(let dr=Ae;dr<be;++dr){let 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ot=Ze-be,dt=Math.max(0,Math.ceil(ot/ae)),pt=Math.min(J,(O+ot)/ae);for(let $e=0;$e<Q;++$e){let vt=$e-Ee,yt=Math.max(0,Math.ceil(vt/de)),Fr=Math.min(se,(z+vt)/de);for(let ur=0;ur<V;++ur){let Xr=ur-Me,Jt=Math.max(0,Math.ceil(Xr/Ae)),dr=Math.min(Z,(j+Xr)/Ae),Yn=0;for(let Zr=dt;Zr<pt;++Zr){let Qt=Zr*ae-ot;for(let bn=yt;bn<Fr;++bn){let vn=bn*de-vt;for(let ps=Jt;ps<dr;++ps){let Ji=ps*Ae-Xr,Jh=x*De+b*Zr+w*bn+T*ps,hs=E*(O-1-Qt)+R*(z-1-vn)+_*(j-1-Ji)+M*Be;for(let La=0;La<ee;++La){let Gd=A[Jh+La],Wl=S[hs+La];Yn+=Gd*Wl}}}}c[f*De+m*Ze+g*$e+y*ur+Be]=Yn}}}return r.makeTensorInfo(p.shape,p.dtype,p.values)}var iX={kernelName:Uf,backendName:"cpu",kernelFunc:sX},oX=mt(Js,e=>Math.cos(e)),lX={kernelName:Js,backendName:"cpu",kernelFunc:oX},uX=mt(Qs,e=>Math.cosh(e)),dX={kernelName:Qs,backendName:"cpu",kernelFunc:uX};function pX(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[d,h,p,c]=a.shape,f=s.shape[0],[m,g]=o,y=We([f,m,g,c],"float32"),A=r.data.get(s.dataId).values,x=r.data.get(i.dataId).values,b=r.data.get(a.dataId).values,w=v.computeStrides(a.shape),T=v.computeStrides(y.shape);for(let S=0;S<f;S++){let E=S*4,R=A[E],_=A[E+1],M=A[E+2],I=A[E+3],O=x[S];if(O>=d)continue;let z=m>1?(M-R)*(h-1)/(m-1):0,j=g>1?(I-_)*(p-1)/(g-1):0;for(let X=0;X<m;X++){let D=m>1?R*(h-1)+X*z:.5*(R+M)*(h-1);if(D<0||D>h-1){for(let Q=0;Q<g;Q++)for(let V=0;V<c;V++){let ee=V+Q*T[2]+X*T[1]+S*T[0];y.values[ee]=u}continue}if(l==="bilinear"){let Q=Math.floor(D),V=Math.ceil(D),ee=D-Q;for(let J=0;J<g;J++){let se=g>1?_*(p-1)+J*j:.5*(_+I)*(p-1);if(se<0||se>p-1){for(let Ae=0;Ae<c;Ae++){let be=Ae+J*T[2]+X*T[1]+S*T[0];y.values[be]=u}continue}let Z=Math.floor(se),ae=Math.ceil(se),de=se-Z;for(let Ae=0;Ae<c;Ae++){let be=Ae+Z*w[2]+Q*w[1]+O*w[0],Ee=b[be];be=Ae+ae*w[2]+Q*w[1]+O*w[0];let Me=b[be];be=Ae+Z*w[2]+V*w[1]+O*w[0];let De=b[be];be=Ae+ae*w[2]+V*w[1]+O*w[0];let Be=b[be],Ze=Ee+(Me-Ee)*de,ot=De+(Be-De)*de;be=Ae+J*T[2]+X*T[1]+S*T[0],y.values[be]=Ze+(ot-Ze)*ee}}}else for(let Q=0;Q<g;++Q){let V=g>1?_*(p-1)+Q*j:.5*(_+I)*(p-1);if(V<0||V>p-1){for(let se=0;se<c;se++){let Z=se+Q*T[2]+X*T[1]+S*T[0];y.values[Z]=u}continue}let ee=Math.round(V),J=Math.round(D);for(let se=0;se<c;se++){let Z=se+ee*w[2]+J*w[1]+O*w[0],ae=se+Q*T[2]+X*T[1]+S*T[0];y.values[ae]=b[Z]}}}}return r.makeTensorInfo(y.shape,y.dtype,y.values)}var hX={kernelName:Lo,backendName:"cpu",kernelFunc:pX};function cX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Te(a,"cumprod");let l=N.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=rn({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=N.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Nr(u.dtype,"int32"),p=v.makeOnesTypedArray(v.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=i?1:c[x];else{let b=m(y,A-1);p[x]=i?c[b]*p[b]:c[x]*p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=N.getUndoAxesPermutation(l),A=rn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var fX={kernelName:Lu,backendName:"cpu",kernelFunc:cX};function mX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Te(a,"cumsum");let l=N.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=rn({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=N.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Nr(u.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=i?0:c[x];else{let b=m(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=N.getUndoAxesPermutation(l),A=rn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var gX={kernelName:Do,backendName:"cpu",kernelFunc:mX};function yX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=Ux(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=q6(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be 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wX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n;Te([a,s],"depthwiseConv2dNativeBackpropFilter");let h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),{strideHeight:p,strideWidth:c,filterHeight:f,filterWidth:m}=h,g=new rr(h.filterShape,"float32"),y=h.padInfo.left,A=h.padInfo.top,x=h.outChannels/h.inChannels,b=r.data.get(a.dataId).values,w=new rr(a.shape,a.dtype,b),T=r.data.get(s.dataId).values,S=new rr(s.shape,s.dtype,T);for(let E=0;E<f;++E){let R=Math.max(0,Math.ceil((A-E)/p)),_=Math.min(h.outHeight,(h.inHeight+A-E)/p);for(let M=0;M<m;++M){let I=Math.max(0,Math.ceil((y-M)/c)),O=Math.min(h.outWidth,(h.inWidth+y-M)/c);for(let z=0;z<h.outChannels;++z){let j=Math.trunc(z/x),X=z%x,D=0;for(let Q=0;Q<h.batchSize;++Q)for(let V=R;V<_;++V){let ee=E+V*p-A;for(let J=I;J<O;++J){let se=M+J*c-y;D+=w.get(Q,ee,se,j)*S.get(Q,V,J,z)}}g.set(D,E,M,j,X)}}}return r.makeTensorInfo(g.shape,g.dtype,g.values)}var kX={kernelName:jf,backendName:"cpu",kernelFunc:wX};function IX(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n;Te([a,s],"depthwiseConv2DNativeBackpropInput");let h=v.computeStrides(a.shape),p=v.computeStrides(s.shape),c=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),f=new rr(c.inShape,"float32"),m=f.values,[g,y,A]=f.strides,x=r.data.get(a.dataId).values,[b,w,T]=h,S=r.data.get(s.dataId).values,[E,R,_]=p,{batchSize:M,filterHeight:I,filterWidth:O,inChannels:z,inHeight:j,inWidth:X,outChannels:D,outHeight:Q,outWidth:V,strideHeight:ee,strideWidth:J}=c,se=I-1-c.padInfo.top,Z=O-1-c.padInfo.left,ae=D/z;for(let de=0;de<M;++de)for(let Ae=0;Ae<z;++Ae)for(let be=0;be<j;++be){let Ee=be-se,Me=Math.max(0,Math.ceil(Ee/ee)),De=Math.min(Q,(I+Ee)/ee);for(let Be=0;Be<X;++Be){let Ze=Be-Z,ot=Math.max(0,Math.ceil(Ze/J)),dt=Math.min(V,(O+Ze)/J),pt=0;for(let $e=Me;$e<De;++$e){let vt=$e*ee-Ee;for(let yt=ot;yt<dt;++yt){let 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       }
-    `),{version:e,attribute:t,varyingVs:r,varyingFs:n,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Cl(e,t,r="index"){let n=v.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / ${a}`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function o0(e,t,r="index"){let n=v.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function pQ(e,t){let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function hQ(e,t,r="index"){let n=e.map((s,i)=>i),a=pQ(n,t);return a.map((s,i)=>{let o=`int ${e[i]} = ${r} / ${a[i]}`,l=i===a.length-1?`int ${e[i+1]} = ${r} - ${e[i]} * ${a[i]}`:`index -= ${e[i]} * ${a[i]}`;return`${o}; ${l};`}).join("")}function Qx(e){let t=v.computeStrides(e).map(r=>r.toString());return`
+    `),{version:e,attribute:t,varyingVs:r,varyingFs:n,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function El(e,t,r="index"){let n=v.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / ${a}`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function l0(e,t,r="index"){let n=v.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function pQ(e,t){let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function hQ(e,t,r="index"){let n=e.map((s,i)=>i),a=pQ(n,t);return a.map((s,i)=>{let o=`int ${e[i]} = ${r} / ${a[i]}`,l=i===a.length-1?`int ${e[i+1]} = ${r} - ${e[i]} * ${a[i]}`:`index -= ${e[i]} * ${a[i]}`;return`${o}; ${l};`}).join("")}function rb(e){let t=v.computeStrides(e).map(r=>r.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}function eb(){return`
+`}function nb(){return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
-`}var fS=`
+`}var yS=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -153,10 +153,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,{getBroadcastDims:mS}=N;function cQ(e,t,r){let n=[];if(e.forEach(p=>{let c=v.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:f}=tb(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.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;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
+`,{getBroadcastDims:AS}=N;function cQ(e,t,r){let n=[];if(e.forEach(p=>{let c=v.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:f}=ab(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.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;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
 `),s=e.map(p=>fQ(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
 `),i=t.texShape,o=Ur(),l=yQ(o),u,d,h=bQ(o);return t.isPacked?(u=mQ(t.logicalShape,i,r.enableShapeUniforms),d=xQ(o)):(u=gQ(t.logicalShape,i,r.enableShapeUniforms),d=AQ(o)),r.packedInputs&&(h+=IQ),[h,l,d,a,u,s,r.userCode].join(`
-`)}function gd(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return zQ(e,t);case 1:return DQ(e,t);case 2:return BQ(e,t);case 3:return VQ(e,t);case 4:return GQ(e,t);case 5:return jQ(e);case 6:return HQ(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function gS(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return _Q(e);case 1:return OQ(e,t);case 2:return LQ(e,t);case 3:return WQ(e,t);default:return UQ(e,t)}}function fQ(e,t,r=!1,n){let a="";r?a+=gS(e,n):a+=gd(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=qQ(e,t):a+=KQ(e,t)),a}function mQ(e,t,r){switch(e.length){case 0:return yS();case 1:return SQ(e,t,r);case 2:return $Q(e,t,r);case 3:return NQ(e,t,r);default:return EQ(e,t,r)}}function gQ(e,t,r){switch(e.length){case 0:return yS();case 1:return TQ(e,t,r);case 2:return PQ(e,t,r);case 3:return CQ(e,t,r);case 4:return RQ(e,t,r);case 5:return MQ(e,t);case 6:return FQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function yQ(e){return`
+`)}function yd(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return zQ(e,t);case 1:return DQ(e,t);case 2:return BQ(e,t);case 3:return VQ(e,t);case 4:return GQ(e,t);case 5:return jQ(e);case 6:return HQ(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function xS(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return _Q(e);case 1:return OQ(e,t);case 2:return LQ(e,t);case 3:return WQ(e,t);default:return UQ(e,t)}}function fQ(e,t,r=!1,n){let a="";r?a+=xS(e,n):a+=yd(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=qQ(e,t):a+=KQ(e,t)),a}function mQ(e,t,r){switch(e.length){case 0:return bS();case 1:return SQ(e,t,r);case 2:return $Q(e,t,r);case 3:return NQ(e,t,r);default:return EQ(e,t,r)}}function gQ(e,t,r){switch(e.length){case 0:return bS();case 1:return TQ(e,t,r);case 2:return PQ(e,t,r);case 3:return CQ(e,t,r);case 4:return RQ(e,t,r);case 5:return MQ(e,t);case 6:return FQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function yQ(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
@@ -266,7 +266,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function yS(){return`
+`;function bS(){return`
     int getOutputCoords() {
       return 0;
     }
@@ -363,10 +363,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
     int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-    ${o0(["r","c","d"],e)}
+    ${l0(["r","c","d"],e)}
     return ivec3(r, c, d);
   }
-`;let n=Cl(["r","c","d"],e);return`
+`;let n=El(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -420,10 +420,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
       int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      ${o0(["r","c","d","d2"],e)}
+      ${l0(["r","c","d","d2"],e)}
       return ivec4(r, c, d, d2);
     }
-  `;let n=Cl(["r","c","d","d2"],e);return`
+  `;let n=El(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -431,7 +431,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function MQ(e,t){let r=Cl(["r","c","d","d2","d3"],e);return`
+  `}function MQ(e,t){let r=El(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function FQ(e,t){let r=Cl(["r","c","d","d2","d3","d4"],e);return`
+  `}function FQ(e,t){let r=El(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -541,7 +541,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function El(e){return`offset${e}`}function _Q(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ur();return`
+  `}function Rl(e){return`offset${e}`}function _Q(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Ur();return`
     vec4 ${r}() {
       return ${n.texture2D}(${t}, halfCR);
     }
@@ -549,7 +549,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       float ${n}() {
         return sampleTexture(${r}, halfCR);
       }
-    `;let i=El(r);if(t)return`
+    `;let i=Rl(r);if(t)return`
     float ${n}() {
       vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], ${i});
       return sampleTexture(${r}, uv);
@@ -574,13 +574,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     }
   `}function DQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
-        ${yd(e)}
+        ${Ad(e)}
       }
     `;let a=e.shapeInfo.texShape,s=a[0],i=a[1];if(i===1&&s===1)return`
       float ${n}(int index) {
         return sampleTexture(${r}, halfCR);
       }
-    `;let o=El(r);return i===1?t?`
+    `;let o=Rl(r);return i===1?t?`
       float ${n}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${r}TexShape[0]));
         return sampleTexture(${r}, uv);
@@ -644,17 +644,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
       return sampleTexture(${n}, uv);
     }
-  `}let{newShape:i,keptDims:o}=v.squeezeShape(r),l=i;if(l.length<r.length){let p=Ad(e,l),c=["row","col"];return`
-      ${gd(p,t)}
+  `}let{newShape:i,keptDims:o}=v.squeezeShape(r),l=i;if(l.length<r.length){let p=xd(e,l),c=["row","col"];return`
+      ${yd(p,t)}
       float ${a}(int row, int col) {
-        return ${a}(${xd(c,o)});
+        return ${a}(${bd(c,o)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${a}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${r[1]}, 1)));
-        ${yd(e)}
+        ${Ad(e)}
       }
-    `;let u=s[0],d=s[1],h=El(n);return d===1?t?`
+    `;let u=s[0],d=s[1],h=Rl(n);return d===1?t?`
       float ${a}(int row, int col) {
         float index = dot(vec3(row, col, ${h}), vec3(${n}Shape[1], 1, 1));
         vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
@@ -692,10 +692,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${u}, ${d}, index);
     return sampleTexture(${n}, uv);
   }
-`}function WQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],f=Ad(e,p),m=["b","row","col"];return`
-        ${gS(f,t)}
+`}function WQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],f=xd(e,p),m=["b","row","col"];return`
+        ${xS(f,t)}
         vec4 ${a}(int b, int row, int col) {
-          return ${a}(${xd(m,c)});
+          return ${a}(${bd(m,c)});
         }
       `}let o=Ur();if(t)return`
     vec4 ${a}(int b, int row, int col) {
@@ -712,16 +712,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${l}, ${u}, ${h}, ${d}, b, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function VQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=v.squeezeShape(r),u=o;if(u.length<r.length){let m=Ad(e,u),g=["row","col","depth"];return`
-        ${gd(m,t)}
+  `}function VQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=v.squeezeShape(r),u=o;if(u.length<r.length){let m=xd(e,u),g=["row","col","depth"];return`
+        ${yd(m,t)}
         float ${a}(int row, int col, int depth) {
-          return ${a}(${xd(g,l)});
+          return ${a}(${bd(g,l)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${a}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${s}, ${i}, 1)));
-        ${yd(e)}
+        ${Ad(e)}
       }
     `;let d=e.shapeInfo.texShape,h=d[0],p=d[1],c=e.shapeInfo.flatOffset;if(p===s&&c==null)return t?`
       float ${a}(int row, int col, int depth) {
@@ -754,7 +754,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${h}.0);
       return sampleTexture(${n}, uv);
     }
-  `;let f=El(n);return t?`
+  `;let f=Rl(n);return t?`
     float ${a}(int row, int col, int depth) {
       // Explicitly use integer operations as dot() only works on floats.
       int stride0 = ${n}Shape[1] * ${n}Shape[2];
@@ -790,16 +790,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});
       return ${a.texture2D}(${r}, uv);
     }
-  `}function GQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(r);if(l.length<r.length){let A=Ad(e,l),x=["row","col","depth","depth2"];return`
-      ${gd(A,t)}
+  `}function GQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(r);if(l.length<r.length){let A=xd(e,l),x=["row","col","depth","depth2"];return`
+      ${yd(A,t)}
       float ${a}(int row, int col, int depth, int depth2) {
-        return ${a}(${xd(x,u)});
+        return ${a}(${bd(x,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${a}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${o}, ${i}, ${s}, 1)));
-        ${yd(e)}
+        ${Ad(e)}
       }
     `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1],f=`int stride2 = ${n}Shape[3];`,m=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(c===o&&d==null)return t?`
       float ${a}(int row, int col, int depth, int depth2) {
@@ -841,7 +841,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${c}.0, ${p}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let y=El(n);return t?`
+    `;let y=Rl(n);return t?`
     float ${a}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       ${f}
@@ -860,10 +860,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${c}, index + ${y});
       return sampleTexture(${n}, uv);
     }
-  `}function jQ(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=Ad(e,l),g=["row","col","depth","depth2","depth3"];return`
-      ${gd(m)}
+  `}function jQ(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=xd(e,l),g=["row","col","depth","depth2","depth3"];return`
+      ${yd(m)}
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
-        return ${n}(${xd(g,u)});
+        return ${n}(${bd(g,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
@@ -871,7 +871,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${s}, ${a})) +
           depth3;
-        ${yd(e)}
+        ${Ad(e)}
       }
     `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1];if(c===o&&d==null)return`
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
@@ -893,7 +893,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${c}.0, ${p}.0);
         return sampleTexture(${r}, uv);
       }
-    `;let f=El(r);return`
+    `;let f=Rl(r);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 * ${o} + col * ${i} + depth * ${s} +
@@ -901,11 +901,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${c}, index);
       return sampleTexture(${r}, uv);
     }
-  `}function HQ(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let g=Ad(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
-      ${gd(g)}
+  `}function HQ(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let g=xd(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
+      ${yd(g)}
       float ${n}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${n}(${xd(y,s)});
+        return ${n}(${bd(y,s)});
       }
     `}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,d=t[1]*u;if(e.shapeInfo.isUniform)return`
       float ${n}(int row, int col, int depth,
@@ -916,7 +916,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${yd(e)}
+        ${Ad(e)}
       }
     `;let h=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],f=p[1];if(f===d&&h==null)return`
       float ${n}(int row, int col, int depth,
@@ -942,7 +942,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${f}.0, ${c}.0);
         return sampleTexture(${r}, uv);
       }
-    `;let m=El(r);return`
+    `;let m=Rl(r);return`
     float ${n}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -951,13 +951,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${c}, ${f}, index);
       return sampleTexture(${r}, uv);
     }
-  `}function yd(e){let t=e.name,r=v.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
+  `}function Ad(e){let t=e.name,r=v.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
     for (int i = 0; i < ${r}; i++) {
       if (i == index) {
         return ${t}[i];
       }
     }
-  `}function qQ(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=mS(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${h[g+u]} = 0;`).join(`
+  `}function qQ(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=AS(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${h[g+u]} = 0;`).join(`
 `);let p="";i<2&&s>0?p="coords":p=e.shapeInfo.logicalShape.map((g,y)=>`coords.${h[y+u]}`).join(", ");let c="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)c=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(f&&!m)i===1?c=`
@@ -975,16 +975,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       float ${a}() {
         return sampleTexture(${r}, resultUV);
       }
-    `;let u=gt(l),d=mS(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,c=["x","y","z","w","u","v"];o===0?p="":l<2&&d.length>=1?p="coords = 0;":p=d.map(m=>`coords.${c[m+h]} = 0;`).join(`
+    `;let u=gt(l),d=AS(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,c=["x","y","z","w","u","v"];o===0?p="":l<2&&d.length>=1?p="coords = 0;":p=d.map(m=>`coords.${c[m+h]} = 0;`).join(`
 `);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${c[g+h]}`).join(", "),`
     float ${a}() {
       ${u} coords = getOutputCoords();
       ${p}
       return get${n}(${f});
     }
-  `}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function tb(e,t,r){let{newShape:n,keptDims:a}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function Ad(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function xd(e,t){return t.map(r=>e[r]).join(", ")}function XQ(e,t,r,n){let a=r.map((d,h)=>{let p={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(p.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[h],shapeInfo:p}}),s=a.map(d=>d.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=cQ(a,i,t),l=qI(e.gl,o),u=e.createProgram(l);return Y().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,...AS(e,t,u)}}function AS(e,t,r){let n={},a={},s={},i=[],o,l,u,d=null,h=null;h=e.getUniformLocation(r,"NAN",!1),Y().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(r,"INFINITY",!1));let p=!1;for(let c=0;c<t.variableNames.length;c++){let f=t.variableNames[c];n[f]=e.getUniformLocation(r,f,p),n[`offset${f}`]=e.getUniformLocation(r,`offset${f}`,p),t.enableShapeUniforms&&(a[`${f}Shape`]=e.getUniformLocation(r,`${f}Shape`,p),s[`${f}TexShape`]=e.getUniformLocation(r,`${f}TexShape`,p))}return t.enableShapeUniforms&&(o=e.getUniformLocation(r,"outShape",p),u=e.getUniformLocation(r,"outShapeStrides",p),l=e.getUniformLocation(r,"outTexShape",p)),t.customUniforms&&t.customUniforms.forEach((c,f)=>{i[f]=e.getUniformLocation(r,c.name,p)}),{uniformLocations:n,customUniformLocations:i,infLoc:d,nanLoc:h,inShapesLocations:a,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function kv(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function ZQ(e,t,r,n,a){t.program.enableShapeUniforms||(kv(t.inShapeInfos,r),kv([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],f=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:m}=tb(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(c,new Int32Array(m));break;case 2:e.gl.uniform2iv(c,new Int32Array(m));break;case 3:e.gl.uniform3iv(c,new Int32Array(m));break;case 4:e.gl.uniform4iv(c,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(h,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function YQ(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=tb(e.packedInputs,i.shape,l),p="",c="",f="";if(d.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let w=v.computeStrides(d);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=i.shape.length,g=d.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,A=N.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&m===r.shape.length&&v.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${Y().getNumber("WEBGL_VERSION")}`,s}function on(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var JQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ur();this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length),this.userCode=`
+  `}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function ab(e,t,r){let{newShape:n,keptDims:a}=v.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!v.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function xd(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function bd(e,t){return t.map(r=>e[r]).join(", ")}function XQ(e,t,r,n){let a=r.map((d,h)=>{let p={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(p.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[h],shapeInfo:p}}),s=a.map(d=>d.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=cQ(a,i,t),l=ZI(e.gl,o),u=e.createProgram(l);return Y().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,...vS(e,t,u)}}function vS(e,t,r){let n={},a={},s={},i=[],o,l,u,d=null,h=null;h=e.getUniformLocation(r,"NAN",!1),Y().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(r,"INFINITY",!1));let p=!1;for(let c=0;c<t.variableNames.length;c++){let f=t.variableNames[c];n[f]=e.getUniformLocation(r,f,p),n[`offset${f}`]=e.getUniformLocation(r,`offset${f}`,p),t.enableShapeUniforms&&(a[`${f}Shape`]=e.getUniformLocation(r,`${f}Shape`,p),s[`${f}TexShape`]=e.getUniformLocation(r,`${f}TexShape`,p))}return t.enableShapeUniforms&&(o=e.getUniformLocation(r,"outShape",p),u=e.getUniformLocation(r,"outShapeStrides",p),l=e.getUniformLocation(r,"outTexShape",p)),t.customUniforms&&t.customUniforms.forEach((c,f)=>{i[f]=e.getUniformLocation(r,c.name,p)}),{uniformLocations:n,customUniformLocations:i,infLoc:d,nanLoc:h,inShapesLocations:a,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function Tv(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function ZQ(e,t,r,n,a){t.program.enableShapeUniforms||(Tv(t.inShapeInfos,r),Tv([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],f=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:m}=ab(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(c,new Int32Array(m));break;case 2:e.gl.uniform2iv(c,new Int32Array(m));break;case 3:e.gl.uniform3iv(c,new Int32Array(m));break;case 4:e.gl.uniform4iv(c,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(h,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function YQ(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=ab(e.packedInputs,i.shape,l),p="",c="",f="";if(d.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let w=v.computeStrides(d);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=i.shape.length,g=d.length===2&&v.arraysEqual(i.shape,l),y=v.sizeFromShape(i.shape)===1,A=N.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&m===r.shape.length&&v.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${Y().getNumber("WEBGL_VERSION")}`,s}function on(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var JQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ur();this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?o0(["r","c","d"],e):Cl(["r","c","d"],e)}
+        ${this.enableShapeUniforms?l0(["r","c","d"],e):El(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1004,7 +1004,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
     `}},QQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ur();this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?o0(["r","c","d"],e):Cl(["r","c","d"],e)}
+        ${this.enableShapeUniforms?l0(["r","c","d"],e):El(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1023,14 +1023,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${t.output} = result;
       }
     `}},eee=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Ur();this.outputShape=e,this.userCode=`
-      ${fS}
+      ${yS}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
     `}},tee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Ur();this.outputShape=e,this.userCode=`
-      ${fS}
+      ${yS}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1038,7 +1038,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${t.output} = encode_float(x);
       }
     `}},ree=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Ur();this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
-      ${this.enableShapeUniforms?eb():Qx(e)}
+      ${this.enableShapeUniforms?nb():rb(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1096,7 +1096,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           }
         }
         `}this.userCode=`
-        ${this.enableShapeUniforms?eb():Qx(e)}
+        ${this.enableShapeUniforms?nb():rb(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -1111,7 +1111,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
           ${r.output} = ${a};
         }
-    `}},xS={};Le(xS,{bindVertexProgramAttributeStreams:()=>CS,createBufferFromOutputTexture:()=>MS,createFloat16MatrixTexture:()=>IS,createFloat16PackedMatrixTexture:()=>NS,createFloat32MatrixTexture:()=>kS,createIndexBuffer:()=>wS,createPackedMatrixTexture:()=>TS,createUnsignedBytesMatrixTexture:()=>SS,createVertexBuffer:()=>vS,createVertexShader:()=>bS,downloadByteEncodedFloatMatrixFromOutputTexture:()=>$S,downloadFloat32MatrixFromBuffer:()=>FS,downloadMatrixFromPackedOutputTexture:()=>_S,downloadPackedMatrixFromBuffer:()=>PS,getInternalFormatForFloat16MatrixTexture:()=>nb,getInternalFormatForFloat16PackedMatrixTexture:()=>ib,getInternalFormatForFloat32MatrixTexture:()=>rb,getInternalFormatForPackedMatrixTexture:()=>sb,getInternalFormatForUnsignedBytesMatrixTexture:()=>ab,uploadDenseMatrixToTexture:()=>ES,uploadPixelDataToTexture:()=>RS});function bS(e){let t=Ur(),r=`${t.version}
+    `}},wS={};Le(wS,{bindVertexProgramAttributeStreams:()=>MS,createBufferFromOutputTexture:()=>PS,createFloat16MatrixTexture:()=>NS,createFloat16PackedMatrixTexture:()=>RS,createFloat32MatrixTexture:()=>TS,createIndexBuffer:()=>SS,createPackedMatrixTexture:()=>ES,createUnsignedBytesMatrixTexture:()=>CS,createVertexBuffer:()=>IS,createVertexShader:()=>kS,downloadByteEncodedFloatMatrixFromOutputTexture:()=>zS,downloadFloat32MatrixFromBuffer:()=>_S,downloadMatrixFromPackedOutputTexture:()=>DS,downloadPackedMatrixFromBuffer:()=>OS,getInternalFormatForFloat16MatrixTexture:()=>ib,getInternalFormatForFloat16PackedMatrixTexture:()=>ub,getInternalFormatForFloat32MatrixTexture:()=>sb,getInternalFormatForPackedMatrixTexture:()=>lb,getInternalFormatForUnsignedBytesMatrixTexture:()=>ob,uploadDenseMatrixToTexture:()=>FS,uploadPixelDataToTexture:()=>$S});function kS(e){let t=Ur(),r=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -1120,7 +1120,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return HI(e,r)}function vS(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return ZI(e,t)}function wS(e){let t=new Uint16Array([0,1,2,2,1,3]);return YI(e,t)}function Nh(e,t,r,n,a,s){QI(t,r);let i=JI(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Y().getNumber("WEBGL_VERSION")===1?we(e,()=>e.texImage2D(o,0,n,t,r,0,a,s,null)):we(e,()=>e.texStorage2D(o,1,n,t,r)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[r,t]}}function rb(e){return e.internalFormatFloat}function kS(e,t,r,n){let[a,s]=Th(t,r);return Nh(e,a,s,rb(n),n.textureFormatFloat,e.FLOAT)}function nb(e){return e.internalFormatHalfFloat}function IS(e,t,r,n){let[a,s]=Th(t,r);return 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t=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,o0(t,e)):this.gl=ma(t);let r="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),Y().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=gp(this.gl,a),Tn(this.gl,s))this.textureHalfFloatExtension=gp(this.gl,s);else if(Y().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(r),Tn(this.gl,n))this.colorBufferHalfFloatExtension=gp(this.gl,n);else if(Y().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(r="EXT_color_buffer_float",Tn(this.gl,r))this.colorBufferFloatExtension=this.gl.getExtension(r);else if(Tn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=IS(this.gl),this.indexBuffer=SS(this.gl),this.framebuffer=nS(this.gl),this.textureConfig=eb(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),RS(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),ES(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(U1(this.gl,this.framebuffer),this.outputTexture=null),we(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,r){return this.downloadMatrixDriver(e,()=>zS(this.gl,t,r,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,r,n,a,s){return OS(this.gl,e,t,r,n,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return _S(this.gl,e,t)}createBufferFromTexture(e,t,r){this.bindTextureToFrameBuffer(e);let n=PS(this.gl,t,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,r;if(Y().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,a=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),r=()=>{let 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we(t,()=>t.attachShader(r,this.vertexShader)),we(t,()=>t.attachShader(r,e)),JI(t,r),this.debug&&Zc(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=MS(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&we(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Zc(this.gl,this.program),we(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,r=!0){return this.throwIfDisposed(),r?sS(this.gl,e,t):iS(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),we(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return 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this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=gp(this.gl,Y().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(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.createQuery();return r.beginQuery(n.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,r=this.getQueryTimerExtensionWebGL2();t.endQuery(r.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let r=this.gl;return r.getQueryParameter(e,r.QUERY_RESULT)/1e6}else{let r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.getQueryParameter(e,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let r=this.getQueryTimerExtensionWebGL1(),n=r.getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=aee(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:r}=this.itemsToPoll[t];r()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Yc(this.gl,e,this.framebuffer),this.debug&&yp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Yc(this.gl,this.outputTexture,this.framebuffer),this.debug&&yp(this.gl)):U1(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let r=t();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(e,t,r){this.throwIfDisposed();let n=this.gl;Yc(n,e,this.framebuffer),this.debug&&yp(n),this.outputTexture=e,we(n,()=>n.viewport(0,0,t,r)),we(n,()=>n.scissor(0,0,t,r))}setOutputMatrixWriteRegionDriver(e,t,r,n){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,r,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function aee(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:see,bincountImpl:LS,bincountReduceImpl:iee,ceilImpl:oee,concatImpl:lee,equalImpl:uee,expImpl:dee,expm1Impl:pee,floorImpl:hee,gatherNdImpl:cee,gatherV2Impl:fee,greaterImpl:mee,greaterEqualImpl:gee,lessImpl:yee,lessEqualImpl:Aee,linSpaceImpl:xee,logImpl:bee,maxImpl:vee,maximumImpl:wee,minimumImpl:kee,multiplyImpl:Iee,negImpl:See,notEqualImpl:Tee,prodImpl:Nee,rangeImpl:Cee,rsqrtImpl:Eee,sigmoidImpl:Ree,simpleAbsImpl:BS,sliceImpl:Mee,sparseFillEmptyRowsImpl:Fee,sparseReshapeImpl:$ee,sparseSegmentReductionImpl:WS,sqrtImpl:Pee,stridedSliceImpl:_ee,stringNGramsImpl:zee,stringSplitImpl:Oee,stringToHashBucketFastImpl:Dee,subImpl:Lee,tileImpl:Bee,topKImpl:Wee,transposeImpl:db,uniqueImpl:Vee}=s0;function VS(e,t){return["x","y","z","w","u","v"].slice(0,t).map(r=>`${e}.${r}`)}function Dr(e,t){return t===1?[e]:VS(e,t)}function Uee(e,t){if(e===1)return"rc";let r="";for(let n=0;n<e;n++)r+=t[n],n<e-1&&(r+=",");return r}var Gee=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=on(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -1147,7 +1147,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
             cEdge ? 0. : getA(${t[1]}),
             rEdge ? 0. : getA(${t[2]}),
-            rEdge || cEdge ? 0. : getA(${t[3]})`}},BS=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
+            rEdge || cEdge ? 0. : getA(${t[3]})`}},US=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
         ${a}
         ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -1160,7 +1160,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${n>0?"}":""}
       `}this.userCode=`
       ${jee(t,this.enableShapeUniforms)}
-      ${this.enableShapeUniforms?eb():Qx(e)}
+      ${this.enableShapeUniforms?nb():rb(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -1177,10 +1177,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
     `}};function jee(e,t){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t?hQ(["r","c","d"],"inputShape"):Cl(["r","c","d"],e)}
+      ${t?hQ(["r","c","d"],"inputShape"):El(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var Hee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=Sv(t,r),a=Tv(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Iv(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=Sv(r,n),s=Tv(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Iv(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function qee(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Iv(e,t,r,n,a){let s=Kee(t,n),i;if(a){let[l,u]=fd(e[0],e[1]);i=l*u}else{let[l,u]=Th(e[0],e[1]);i=l*u}let o=qee(r,s);return i*o}function Kee(e,t){switch(e){case 3:return sb(t);case 4:return ib(t);case 1:return rb(t);case 0:return nb(t);case 2:return ab(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Xee(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function Sv(e,t){if(e===1)return 3;if(e===0||e==null)return Xee(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function Tv(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Ga=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length),this.userCode=`
+  `}var Hee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=Cv(t,r),a=Ev(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Nv(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=Cv(r,n),s=Ev(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Nv(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function qee(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Nv(e,t,r,n,a){let s=Kee(t,n),i;if(a){let[l,u]=md(e[0],e[1]);i=l*u}else{let[l,u]=Th(e[0],e[1]);i=l*u}let o=qee(r,s);return i*o}function Kee(e,t){switch(e){case 3:return lb(t);case 4:return ub(t);case 1:return sb(t);case 0:return ib(t);case 2:return ob(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Xee(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function Cv(e,t){if(e===1)return 3;if(e===0||e==null)return Xee(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function Ev(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Ga=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1191,7 +1191,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},qn="if (isnan(x)) return x;",Zee="return x;",Nv="return abs(x);",Yee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Jee=qn+`
+    `}},qn="if (isnan(x)) return x;",Zee="return x;",Rv="return abs(x);",Yee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Jee=qn+`
   return (x < 0.0) ? 0.0 : x;
 `,Qee=qn+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
@@ -1224,7 +1224,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,ste="return 1.0 / (1.0 + exp(-1.0 * x));",go=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length),this.userCode=`
+`,ste="return 1.0 / (1.0 + exp(-1.0 * x));",yo=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=on(this.outputShape.length),this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -1242,10 +1242,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},ote=jn.whereImpl,lte=1e-7,ute=1e-4,Jy={};function dte(e){return e in Jy||(Jy[e]={}),Jy[e]}var pte=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),hte=600;function cte(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*hte/1024/1024}var WS=class extends Tu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof hu)t=e;else{let r=ma(Y().getNumber("WEBGL_VERSION"),e);t=new hu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=ma(Y().getNumber("WEBGL_VERSION"));t=new hu(r),this.binaryCache=dte(Y().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Hee(this.gpgpu),this.numMBBeforeWarning=cte(),this.texData=new Op(this,Ar())}nextDataId(){return WS.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,n,a){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:n,values:t,usage:1,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:n,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new go(i,tu):h=new Ga(i,tu);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:n}],n),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return r;let l=this.activeTimers!=null,u;l&&(u=v.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=N.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(f=>c.push(f))}let t=this.texData.get(e),{values:r,shape:n,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let c;o?c=new go(n,tu):c=new Ga(n,tu);let f=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(r!=null)return this.convertAndCacheOnCPU(e);if(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().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,u;if(s!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...Vc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=c[0],m=c[1];d=N.mergeRealAndImagArrays(f,m)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=v.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ar().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){let r=this.texData.get(e),{values:n,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new go(a,tu):p=new Ga(a,tu);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),f=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=Ar().makeTensorFromDataId(u.dataId,u.shape,u.dtype),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>v.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!GI(r))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=v.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Vc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=Y().getBool("WEBGL_PACK")&&n===!0,i=s?Yc(t):t,o=s?new tee(i):new eee(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=pte){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return ote(e.shape,t)}packedUnaryOp(e,t,r){let n=new go(e.shape,t),a=this.compileAndRun(n,[e],r);return Ar().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=OS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Nv,e.dtype);let t=new Ga(e.shape,Nv),r=this.compileAndRun(t,[e]);return Ar().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&v.isString(r[0])){let a=r.map(s=>v.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:n}=this.makeTensorInfo(e,t,r);return Ar().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new ite(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Gee(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Eo(e.shape),...Ro(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Eo(t),...Ro(t)],s=new BS(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=v.sizeFromShape(a),p=t[0]*t[1]*4;v.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Yc(a),o;n?o=new QQ(i):o=new JQ(i);let l=!0,u=[t!=null?t:Vc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Vc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!_p(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=YQ(e,u,d),p=this.getAndSaveBinary(h,()=>XQ(this.gpgpu,e,u,d)),c=this.activeTimers!=null,f;c&&(f=this.startTimer()),Y().get("ENGINE_COMPILE_ONLY")||ZQ(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=Y().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().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=K(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?lte:ute}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let d=t.texShape;if(d==null&&(d=oS(r,o),t.texShape=d),a!=null){let h=Yc(r),p,c=d[1],f=d[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!m)&&([c,f]=fd(d[0],d[1])),o?p=new nee(h,m):p=new ree(h,m);let g=m?[f,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);m?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,f,a);let x=[[f,c]],b=!0,w=this.runWebGLProgram(p,[y],n,x,b),T=this.texData.get(w.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,Y().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=fte(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let r=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(a){throw a}});e.push(r)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await sA(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(Jx(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:r,infLoc:n,nanLoc:a,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=AS(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=r,e.infLoc=n,e.nanLoc=a,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}},Ch=WS;Ch.nextDataId=0;function fte(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var mte="0.0.0";function VS(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}ah.isBrowser()&&vl("webgl",()=>new Ch,2);var gte={forceHalfFloat:VS},US=`
+    `}},ote=jn.whereImpl,lte=1e-7,ute=1e-4,t1={};function dte(e){return e in t1||(t1[e]={}),t1[e]}var pte=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),hte=600;function cte(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*hte/1024/1024}var GS=class extends Nu{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof hu)t=e;else{let r=ma(Y().getNumber("WEBGL_VERSION"),e);t=new hu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=ma(Y().getNumber("WEBGL_VERSION"));t=new hu(r),this.binaryCache=dte(Y().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Hee(this.gpgpu),this.numMBBeforeWarning=cte(),this.texData=new Dp(this,Ar())}nextDataId(){return GS.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,n,a){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:n,values:t,usage:1,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:n,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new yo(i,tu):h=new Ga(i,tu);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:n}],n),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return r;let l=this.activeTimers!=null,u;l&&(u=v.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=N.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(f=>c.push(f))}let t=this.texData.get(e),{values:r,shape:n,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let c;o?c=new yo(n,tu):c=new Ga(n,tu);let f=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(r!=null)return this.convertAndCacheOnCPU(e);if(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().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,u;if(s!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...Uc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=c[0],m=c[1];d=N.mergeRealAndImagArrays(f,m)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=v.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ar().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){let r=this.texData.get(e),{values:n,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new yo(a,tu):p=new Ga(a,tu);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),f=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=Ar().makeTensorFromDataId(u.dataId,u.shape,u.dtype),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>v.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!qI(r))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=v.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Uc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=Y().getBool("WEBGL_PACK")&&n===!0,i=s?Jc(t):t,o=s?new tee(i):new eee(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=pte){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return ote(e.shape,t)}packedUnaryOp(e,t,r){let n=new yo(e.shape,t),a=this.compileAndRun(n,[e],r);return Ar().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=BS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Rv,e.dtype);let t=new Ga(e.shape,Rv),r=this.compileAndRun(t,[e]);return Ar().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&v.isString(r[0])){let a=r.map(s=>v.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:n}=this.makeTensorInfo(e,t,r);return Ar().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new ite(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Gee(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Ro(e.shape),...Mo(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Ro(t),...Mo(t)],s=new US(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=v.sizeFromShape(a),p=t[0]*t[1]*4;v.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Jc(a),o;n?o=new QQ(i):o=new JQ(i);let l=!0,u=[t!=null?t:Uc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Uc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(i.shape)===0)return o.values=v.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!zp(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=YQ(e,u,d),p=this.getAndSaveBinary(h,()=>XQ(this.gpgpu,e,u,d)),c=this.activeTimers!=null,f;c&&(f=this.startTimer()),Y().get("ENGINE_COMPILE_ONLY")||ZQ(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=Y().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().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=K(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?lte:ute}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let d=t.texShape;if(d==null&&(d=dS(r,o),t.texShape=d),a!=null){let h=Jc(r),p,c=d[1],f=d[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!m)&&([c,f]=md(d[0],d[1])),o?p=new nee(h,m):p=new ree(h,m);let g=m?[f,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);m?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,f,a);let x=[[f,c]],b=!0,w=this.runWebGLProgram(p,[y],n,x,b),T=this.texData.get(w.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,Y().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=fte(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let r=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(a){throw a}});e.push(r)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await lA(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(tb(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:r,infLoc:n,nanLoc:a,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=vS(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=r,e.infLoc=n,e.nanLoc=a,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}},Ch=GS;Ch.nextDataId=0;function fte(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var mte="0.0.0";function jS(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}sh.isBrowser()&&wl("webgl",()=>new Ch,2);var gte={forceHalfFloat:jS},HS=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,Su=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=on(this.outputShape.length),this.userCode=`
+`,Tu=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=on(this.outputShape.length),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1255,7 +1255,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},l0=`
+    `}},u0=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
@@ -1304,13 +1304,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function nn(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var yte={kernelName:li,backendName:"webgl",kernelFunc:nn};function Li(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=nn({inputs:{x:n},backend:r}),l=nn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Ate={kernelName:Lp,backendName:"webgl",kernelFunc:Li},GS="return (a < 0.) ? b * a : a;",jS=`
+    `}};function nn(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var yte={kernelName:li,backendName:"webgl",kernelFunc:nn};function Li(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=nn({inputs:{x:n},backend:r}),l=nn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Ate={kernelName:Bp,backendName:"webgl",kernelFunc:Li},qS="return (a < 0.) ? b * a : a;",KS=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function xte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(jS,a.shape,i.shape):new Su(GS,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var bte={kernelName:ui,backendName:"webgl",kernelFunc:xte},HS="return (a < 0.) ? b * a : a;",qS=`
+`;function xte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(KS,a.shape,i.shape):new Tu(qS,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var bte={kernelName:ui,backendName:"webgl",kernelFunc:xte},XS="return (a < 0.) ? b * a : a;",ZS=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function vte(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(qS,n.shape,a.shape):new Su(HS,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var wte={kernelName:vi,backendName:"webgl",kernelFunc:vte},bd="if (isnan(x)) return x;",kte=`
+`;function vte(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(ZS,n.shape,a.shape):new Tu(XS,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var wte={kernelName:vi,backendName:"webgl",kernelFunc:vte},vd="if (isnan(x)) return x;",kte=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,Ite=`
@@ -1318,7 +1318,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new go(i.shape,t):d=new Ga(i.shape,e),o.runWebGLProgram(d,[i],l)}}function br({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let f=d.texData.get(l.dataId),m=d.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,w]=x,T={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new Su(e,l.shape,u.shape);return d.runWebGLProgram(E,[T,S],Nr(b.dtype,w.dtype))}),A=Li({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Nr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let f=d.texData.get(l.dataId).values,m=d.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),w=d.texData.get(b.dataId);return w.values=A,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Eh(t,l.shape,u.shape,r):c=new Su(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function u0(e,t=!1){if(e==="linear")return t?tte:Zee;if(e==="relu")return t?nte:Jee;if(e==="elu")return t?rte:Yee;if(e==="relu6")return t?ate:Qee;if(e==="prelu")return t?qS:HS;if(e==="leakyrelu")return t?jS:GS;if(e==="sigmoid")return t?ste:ete;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var KS=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=on(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new yo(i.shape,t):d=new Ga(i.shape,e),o.runWebGLProgram(d,[i],l)}}function br({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let f=d.texData.get(l.dataId),m=d.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,w]=x,T={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new Tu(e,l.shape,u.shape);return d.runWebGLProgram(E,[T,S],Nr(b.dtype,w.dtype))}),A=Li({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Nr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let f=d.texData.get(l.dataId).values,m=d.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),w=d.texData.get(b.dataId);return w.values=A,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Eh(t,l.shape,u.shape,r):c=new Tu(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function d0(e,t=!1){if(e==="linear")return t?tte:Zee;if(e==="relu")return t?nte:Jee;if(e==="elu")return t?rte:Yee;if(e==="relu6")return t?ate:Qee;if(e==="prelu")return t?ZS:XS;if(e==="leakyrelu")return t?KS:qS;if(e==="sigmoid")return t?ste:ete;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var YS=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=on(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1357,7 +1357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},Cv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Ev=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.userCode=`
+    `}},Mv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Fv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1370,7 +1370,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},Rv="return a * b;";function lb(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=N.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new Ev(Cv.REAL,n.shape,a.shape),d=new Ev(Cv.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Li({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=Iee(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Eh(Rv,n.shape,a.shape):i=new Su(Rv,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var Ste={kernelName:Ai,backendName:"webgl",kernelFunc:lb};function Tte(e,t,r){let n=[Eo(e.shape),...Ro(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Eo(t),...Ro(t)],i=new BS(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!_p(a.shape,l)&&!(d.texture!==null&&_p(d.shape,l))?Tte(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var Nte={kernelName:al,backendName:"webgl",kernelFunc:ve},Mv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${v.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
+    `}},$v="return a * b;";function pb(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=N.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new Fv(Mv.REAL,n.shape,a.shape),d=new Fv(Mv.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Li({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=Iee(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Eh($v,n.shape,a.shape):i=new Tu($v,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var Ste={kernelName:Ai,backendName:"webgl",kernelFunc:pb};function Tte(e,t,r){let n=[Ro(e.shape),...Mo(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Ro(t),...Mo(t)],i=new US(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),u=v.sizeFromShape(l);v.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!zp(a.shape,l)&&!(d.texture!==null&&zp(d.shape,l))?Tte(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var Nte={kernelName:sl,backendName:"webgl",kernelFunc:ve},Pv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${v.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
         if (inIdx < 0 || inIdx >= ${a}) {
           return 0.0;
         }
@@ -1515,12 +1515,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function Ete(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=N.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Rl(e,t,r,n){let a=Ete(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new Mv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Mv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new Cte({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var Rte=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=gt(this.rank),a=Mte(t);this.userCode=`
+    `}};function Ete(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=N.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Ml(e,t,r,n){let a=Ete(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new Pv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Pv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new Cte({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var Rte=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=gt(this.rank),a=Mte(t);this.userCode=`
     void main() {
       ${n} resRC = getOutputCoords();
       setOutput(getA(${a}));
     }
-    `}};function Mte(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var Fte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=gt(this.rank),a=LS("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function Mte(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var Fte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=gt(this.rank),a=VS("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${n} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1537,14 +1537,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function d0(e,t,r){let n=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Fte(e.shape,t):new Rte(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function $te(e,t,r,n){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=d0(e,l,n),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=N.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(p),m=v.sizeFromShape(e.shape)/f,g=ve({inputs:{x:d},attrs:{shape:[m,f]},backend:n}),y=nh(e.dtype),A=Rl(g,y,"sum",n),x=ve({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function p0(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return $te(a,s,i,r)}var Pte={kernelName:Ei,backendName:"webgl",kernelFunc:p0};function xr(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=ob(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=d0(a,s,i);return u}var _te={kernelName:Pi,backendName:"webgl",kernelFunc:xr},XS=1e3;function Cf({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=bl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);v.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],w=n?[A,f,p]:[A,p,f],T=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),S=ve({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[T,S],R=Math.max(y,A),_=r?T.shape[1]:T.shape[2],M=s!=null,I=i!=null,O=l==="leakyrelu",z=l!=null?u0(l,!0):null,j=M||I||O||z!=null,X;if((c===1||f===1)&&_>XS&&j===!1){let Q=T,V=S;r&&(Q=xr({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(Q)),n&&(V=xr({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let ee=f!==1,J=f===1,se=Q;ee&&(se=ve({inputs:{x:Q},backend:a,attrs:{shape:[R,_,1]}}),E.push(se));let Z=f===1?2:1,ae=V;J&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,_]}}),E.push(ae));let de=lb({inputs:{a:se,b:ae},backend:a});X=p0({inputs:{x:de},backend:a,attrs:{axis:Z,keepDims:!0}}),E.push(de)}else{let Q=Nr(e.dtype,t.dtype),V=new KS(b,w,[R,c,f],r,n,M,z,I,O),ee=[T,S];if(s!=null&&ee.push(s),I&&ee.push(i),O){let J=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));ee.push(J),E.push(J)}X=a.runWebGLProgram(V,ee,Q)}let D=ve({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let Q of E)a.disposeIntermediateTensorInfo(Q);return D}function zte(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return Cf({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Ote={kernelName:Cs,backendName:"webgl",kernelFunc:zte},Fv="return abs(x);";function Dte(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=OS(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new go(n.shape,Fv):a=new Ga(n.shape,Fv),r.runWebGLProgram(a,[n],n.dtype)}var Lte={kernelName:Po,backendName:"webgl",kernelFunc:Dte},Bte=qn+`
+    `}};function p0(e,t,r){let n=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Fte(e.shape,t):new Rte(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function $te(e,t,r,n){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=p0(e,l,n),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=N.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(p),m=v.sizeFromShape(e.shape)/f,g=ve({inputs:{x:d},attrs:{shape:[m,f]},backend:n}),y=ah(e.dtype),A=Ml(g,y,"sum",n),x=ve({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function h0(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return $te(a,s,i,r)}var Pte={kernelName:Ei,backendName:"webgl",kernelFunc:h0};function xr(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=db(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=p0(a,s,i);return u}var _te={kernelName:Pi,backendName:"webgl",kernelFunc:xr},JS=1e3;function Ef({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=vl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);v.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],w=n?[A,f,p]:[A,p,f],T=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),S=ve({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[T,S],R=Math.max(y,A),_=r?T.shape[1]:T.shape[2],M=s!=null,I=i!=null,O=l==="leakyrelu",z=l!=null?d0(l,!0):null,j=M||I||O||z!=null,X;if((c===1||f===1)&&_>JS&&j===!1){let Q=T,V=S;r&&(Q=xr({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(Q)),n&&(V=xr({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let ee=f!==1,J=f===1,se=Q;ee&&(se=ve({inputs:{x:Q},backend:a,attrs:{shape:[R,_,1]}}),E.push(se));let Z=f===1?2:1,ae=V;J&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,_]}}),E.push(ae));let de=pb({inputs:{a:se,b:ae},backend:a});X=h0({inputs:{x:de},backend:a,attrs:{axis:Z,keepDims:!0}}),E.push(de)}else{let Q=Nr(e.dtype,t.dtype),V=new YS(b,w,[R,c,f],r,n,M,z,I,O),ee=[T,S];if(s!=null&&ee.push(s),I&&ee.push(i),O){let J=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));ee.push(J),E.push(J)}X=a.runWebGLProgram(V,ee,Q)}let D=ve({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let Q of E)a.disposeIntermediateTensorInfo(Q);return D}function zte(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return Ef({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Ote={kernelName:Cs,backendName:"webgl",kernelFunc:zte},_v="return abs(x);";function Dte(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=BS(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new yo(n.shape,_v):a=new Ga(n.shape,_v),r.runWebGLProgram(a,[n],n.dtype)}var Lte={kernelName:_o,backendName:"webgl",kernelFunc:Dte},Bte=qn+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,Wte=it({opSnippet:Bte}),Vte={kernelName:Cu,backendName:"webgl",kernelFunc:Wte},Ute=qn+`
+`,Wte=it({opSnippet:Bte}),Vte={kernelName:Eu,backendName:"webgl",kernelFunc:Wte},Ute=qn+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,Gte=it({opSnippet:Ute}),jte={kernelName:Eu,backendName:"webgl",kernelFunc:Gte},$v="return a + b;",Hte=br({opSnippet:$v,packedOpSnippet:$v,supportsComplex:!0,cpuKernelImpl:see}),qte={kernelName:qa,backendName:"webgl",kernelFunc:Hte},Kte=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,Gte=it({opSnippet:Ute}),jte={kernelName:Ru,backendName:"webgl",kernelFunc:Gte},zv="return a + b;",Hte=br({opSnippet:zv,packedOpSnippet:zv,supportsComplex:!0,cpuKernelImpl:see}),qte={kernelName:qa,backendName:"webgl",kernelFunc:Hte},Kte=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${r.join(`
         `)}
@@ -1560,7 +1560,7 @@ return log(x + sqrt(x * x - 1.0));`,Gte=it({opSnippet:Ute}),jte={kernelName:Eu,b
         vec4 result = ${n};
         setOutput(result);
       }
-    `}};function ef(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return nn({inputs:{x:n[0]},backend:r});if(n.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=ef({inputs:n.slice(0,o),backend:r}),u=ef({inputs:n.slice(o),backend:r});return ef({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Nr(o,l)),s=n.map(o=>o.shape),i=Y().getBool("WEBGL_PACK")?new Xte(n[0].shape,s):new Kte(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var Zte={kernelName:Gs,backendName:"webgl",kernelFunc:ef};function Yte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=xr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Rl(m,m.dtype,"all",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Jte={kernelName:Ru,backendName:"webgl",kernelFunc:Yte};function Qte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=xr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Rl(m,m.dtype,"any",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var ere={kernelName:Mu,backendName:"webgl",kernelFunc:Qte},tre=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function tf(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return nn({inputs:{x:n[0]},backend:r});if(n.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=tf({inputs:n.slice(0,o),backend:r}),u=tf({inputs:n.slice(o),backend:r});return tf({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Nr(o,l)),s=n.map(o=>o.shape),i=Y().getBool("WEBGL_PACK")?new Xte(n[0].shape,s):new Kte(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var Zte={kernelName:Gs,backendName:"webgl",kernelFunc:tf};function Yte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=xr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Ml(m,m.dtype,"all",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Jte={kernelName:Mu,backendName:"webgl",kernelFunc:Yte};function Qte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=xr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Ml(m,m.dtype,"any",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var ere={kernelName:Fu,backendName:"webgl",kernelFunc:Qte},tre=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1642,23 +1642,23 @@ return log(x + sqrt(x * x - 1.0));`,Gte=it({opSnippet:Ute}),jte={kernelName:Eu,b
         }
         setOutput(bestIndex);
       }
-    `}};function ZS(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new tre(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=ZS(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function YS(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=N.computeOptimalWindowSize(s),o=new rre(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=YS(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function JS(e,t,r,n){let a=[r];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=N.computeOutAndReduceShapes(l.shape,a),h=v.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=ZS(e,p,n);s.push(c);let f=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return YS(e,t,n)}function nre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=JS(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var are={kernelName:js,backendName:"webgl",kernelFunc:nre};function sre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=JS(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var ire={kernelName:Fu,backendName:"webgl",kernelFunc:sre},ore=qn+`
+    `}};function QS(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new tre(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=QS(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function e8(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=N.computeOptimalWindowSize(s),o=new rre(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=e8(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function t8(e,t,r,n){let a=[r];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=N.computeOutAndReduceShapes(l.shape,a),h=v.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=QS(e,p,n);s.push(c);let f=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return e8(e,t,n)}function nre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=t8(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var are={kernelName:js,backendName:"webgl",kernelFunc:nre};function sre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=xr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=t8(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var ire={kernelName:$u,backendName:"webgl",kernelFunc:sre},ore=qn+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,lre=it({opSnippet:ore}),ure={kernelName:$u,backendName:"webgl",kernelFunc:lre},dre=qn+"return log(x + sqrt(x * x + 1.0));",pre=it({opSnippet:dre}),hre={kernelName:Pu,backendName:"webgl",kernelFunc:pre},cre=qn+`
+`,lre=it({opSnippet:ore}),ure={kernelName:Pu,backendName:"webgl",kernelFunc:lre},dre=qn+"return log(x + sqrt(x * x + 1.0));",pre=it({opSnippet:dre}),hre={kernelName:_u,backendName:"webgl",kernelFunc:pre},cre=qn+`
   return atan(x);
-`,fre=it({opSnippet:cre}),mre={kernelName:_u,backendName:"webgl",kernelFunc:fre},gre=kte+`
+`,fre=it({opSnippet:cre}),mre={kernelName:zu,backendName:"webgl",kernelFunc:fre},gre=kte+`
   return atan(a, b);
 `,yre=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+Ite+`
   return result;
-`,Are=br({opSnippet:gre,packedOpSnippet:yre}),xre={kernelName:Ou,backendName:"webgl",kernelFunc:Are},bre=qn+`
+`,Are=br({opSnippet:gre,packedOpSnippet:yre}),xre={kernelName:Du,backendName:"webgl",kernelFunc:Are},bre=qn+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernelName:zu,backendName:"webgl",kernelFunc:vre},zp=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),r){let S=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernelName:Ou,backendName:"webgl",kernelFunc:vre},Op=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),r){let S=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${p}, ${c});
 
@@ -1799,7 +1799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         }
         setOutput(${x});
       }
-    `}},ub=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
+    `}},hb=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
         const ivec3 pads = ivec3(${m}, ${g}, ${y});
@@ -1962,7 +1962,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
           setOutput(${w});
         }
       }
-    `}};function kre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;md(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return nn({inputs:{x:a},backend:r});let h=new zp(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var Ire={kernelName:Hs,backendName:"webgl",kernelFunc:kre};function Sre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new ub(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var Tre={kernelName:Dp,backendName:"webgl",kernelFunc:Sre},Nre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
+    `}};function kre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;gd(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return nn({inputs:{x:a},backend:r});let h=new Op(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var Ire={kernelName:Hs,backendName:"webgl",kernelFunc:kre};function Sre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new hb(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var Tre={kernelName:Lp,backendName:"webgl",kernelFunc:Sre},Nre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
       const ivec2 pads = ivec2(${u}, ${d});
       const float avgMultiplier = float(${h});
 
@@ -2060,7 +2060,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function Ere(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new Cre(p);return r.runWebGLProgram(c,[a],i.dtype)}var Rre={kernelName:Of,backendName:"webgl",kernelFunc:Ere};function Mre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;md([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=new Nre(d);return r.runWebGLProgram(h,[a],i.dtype)}var Fre={kernelName:zf,backendName:"webgl",kernelFunc:Mre};function $re(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return Cf({a,b:s,transposeA:i,transposeB:o,backend:r})}var Pre={kernelName:qs,backendName:"webgl",kernelFunc:$re},_re=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function Ere(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new Cre(p);return r.runWebGLProgram(c,[a],i.dtype)}var Rre={kernelName:Df,backendName:"webgl",kernelFunc:Ere};function Mre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;gd([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=new Nre(d);return r.runWebGLProgram(h,[a],i.dtype)}var Fre={kernelName:Of,backendName:"webgl",kernelFunc:Mre};function $re(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return Ef({a,b:s,transposeA:i,transposeB:o,backend:r})}var Pre={kernelName:qs,backendName:"webgl",kernelFunc:$re},_re=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -2083,7 +2083,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},Ore=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new zre(n.shape,a.shape,s.shape,d,h,l):new _re(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},Dre={kernelName:ii,backendName:"webgl",kernelFunc:Ore},Lre=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=Bre(this.rank),n,a=e.map((s,i)=>`sourceLoc.${U1[i]} = start[${i}] + coords.${U1[i]};`);n=`
+    `}},Ore=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new zre(n.shape,a.shape,s.shape,d,h,l):new _re(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},Dre={kernelName:ii,backendName:"webgl",kernelFunc:Ore},Lre=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=Bre(this.rank),n,a=e.map((s,i)=>`sourceLoc.${H1[i]} = start[${i}] + coords.${H1[i]};`);n=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${a.join(`
@@ -2093,7 +2093,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         ${n}
         setOutput(getSource(${r}));
       }
-    `}},U1=["x","y","z","w","u","v"];function Bre(e){if(e===1)return"sourceLoc";if(e<=6)return U1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Wre=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),r=Dr("coords",this.rank),n=Dr("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
+    `}},H1=["x","y","z","w","u","v"];function Bre(e){if(e===1)return"sourceLoc";if(e<=6)return H1.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Wre=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),r=Dr("coords",this.rank),n=Dr("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
       result.x = ${s};
       if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
         ++${n[this.rank-1]};
@@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         ${o}
         setOutput(result);
       }
-    `}};function Vre(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=_t.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function vd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=Mee(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=_t.isSliceContinous(a.shape,o,l);if(u||!d){let h=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Wre(l):new Lre(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),Vre(a,o,l,r)}var Ure={kernelName:ul,backendName:"webgl",kernelFunc:vd},Gre=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),m=xr({inputs:{x:f},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:d}}),y=vd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},jre={kernelName:_o,backendName:"webgl",kernelFunc:Gre};function Hre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=zS(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var qre={kernelName:Df,backendName:"webgl",kernelFunc:Hre};function Kre(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Xre={kernelName:Lf,backendName:"webgl",kernelFunc:Kre},Zre="return float(a != b);",QS=br({opSnippet:Zre,cpuKernelImpl:Tee,dtype:"bool"}),Yre={kernelName:Yo,backendName:"webgl",kernelFunc:QS};function Rh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return nn({inputs:{x:a.complexTensorInfos.real},backend:r})}var Jre={kernelName:Kp,backendName:"webgl",kernelFunc:Rh},Qre="return float(int(x));";function ene(e,t){let r=new Ga(e.shape,Qre),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function G1(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return nn({inputs:{x:a},backend:r});let i=Wt(a.shape),o=G1({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Li({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Rh({inputs:{input:a},backend:r}),o=G1({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=nn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return ene(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=QS({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var tne={kernelName:Ks,backendName:"webgl",kernelFunc:G1},Pv="return ceil(x);",rne=it({opSnippet:Pv,packedOpSnippet:Pv,cpuKernelImpl:oee}),nne={kernelName:Xs,backendName:"webgl",kernelFunc:rne},ane=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}};function Vre(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=_t.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function wd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=Mee(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=_t.isSliceContinous(a.shape,o,l);if(u||!d){let h=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Wre(l):new Lre(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),Vre(a,o,l,r)}var Ure={kernelName:dl,backendName:"webgl",kernelFunc:wd},Gre=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),m=xr({inputs:{x:f},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:d}}),y=wd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},jre={kernelName:zo,backendName:"webgl",kernelFunc:Gre};function Hre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=LS(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var qre={kernelName:Lf,backendName:"webgl",kernelFunc:Hre};function Kre(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Xre={kernelName:Bf,backendName:"webgl",kernelFunc:Kre},Zre="return float(a != b);",r8=br({opSnippet:Zre,cpuKernelImpl:Tee,dtype:"bool"}),Yre={kernelName:Jo,backendName:"webgl",kernelFunc:r8};function Rh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return nn({inputs:{x:a.complexTensorInfos.real},backend:r})}var Jre={kernelName:Xp,backendName:"webgl",kernelFunc:Rh},Qre="return float(int(x));";function ene(e,t){let r=new Ga(e.shape,Qre),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function q1(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return nn({inputs:{x:a},backend:r});let i=Wt(a.shape),o=q1({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Li({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Rh({inputs:{input:a},backend:r}),o=q1({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=nn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return ene(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=r8({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var tne={kernelName:Ks,backendName:"webgl",kernelFunc:q1},Ov="return ceil(x);",rne=it({opSnippet:Ov,packedOpSnippet:Ov,cpuKernelImpl:oee}),nne={kernelName:Xs,backendName:"webgl",kernelFunc:rne},ane=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
 
       void main() {
         float value = getAAtOutCoords();
@@ -2157,7 +2157,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function _v(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function une(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new lne(n.shape),i=[_v(n,a.complexTensorInfos.real),_v(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var dne={kernelName:Bp,backendName:"webgl",kernelFunc:une},pne=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
+    `}};function Dv(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function une(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new lne(n.shape),i=[Dv(n,a.complexTensorInfos.real),Dv(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var dne={kernelName:Wp,backendName:"webgl",kernelFunc:une},pne=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -2172,12 +2172,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
         if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
           return getChannel(
-            getT${f}(${Gc(i,l,m)}),
-            vec2(${Gc(u,l,m)}));
+            getT${f}(${jc(i,l,m)}),
+            vec2(${jc(u,l,m)}));
         }`}let p=o.length,c=o[o.length-1];h+=`
         return getChannel(
-          getT${p}(${Gc(i,l,c)}),
-          vec2(${Gc(u,l,c)}));`,this.userCode=`
+          getT${p}(${jc(i,l,c)}),
+          vec2(${jc(u,l,c)}));`,this.userCode=`
       float getValue(${i.map(f=>"int "+f)}) {
         ${h}
       }
@@ -2203,7 +2203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         }
         setOutput(result);
       }
-    `}};function Gc(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function h0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return nn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var cne={kernelName:Gp,backendName:"webgl",kernelFunc:h0};function ou(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(m=>Rh({inputs:{input:m},backend:r})),h=e.map(m=>h0({inputs:{input:m},backend:r})),p=ou(d,t,r),c=ou(h,t,r),f=Li({inputs:{real:p,imag:c},backend:r});return d.forEach(m=>r.disposeIntermediateTensorInfo(m)),h.forEach(m=>r.disposeIntermediateTensorInfo(m)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=v.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,f=lee(h,p,n,c),m=N.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(m,n,f);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=ou(e.slice(0,d),t,r),p=ou(e.slice(d),t,r),c=ou([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new hne(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=fne(e,t,r),o=new pne(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function fne(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function e8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return nn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),ou(o,s,r)}var mne={kernelName:zo,backendName:"webgl",kernelFunc:e8},t8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";r&&(n?x=`float activation(float a) {
+    `}};function jc(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function c0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return nn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var cne={kernelName:jp,backendName:"webgl",kernelFunc:c0};function ou(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(m=>Rh({inputs:{input:m},backend:r})),h=e.map(m=>c0({inputs:{input:m},backend:r})),p=ou(d,t,r),c=ou(h,t,r),f=Li({inputs:{real:p,imag:c},backend:r});return d.forEach(m=>r.disposeIntermediateTensorInfo(m)),h.forEach(m=>r.disposeIntermediateTensorInfo(m)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=v.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,f=lee(h,p,n,c),m=N.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(m,n,f);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=ou(e.slice(0,d),t,r),p=ou(e.slice(d),t,r),c=ou([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new hne(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=fne(e,t,r),o=new pne(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function fne(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function n8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return nn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),ou(o,s,r)}var mne={kernelName:Oo,backendName:"webgl",kernelFunc:n8},a8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";r&&(n?x=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${r}
         }`:a?x=`float activation(float a) {
@@ -2472,7 +2472,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
 
         ${n.output} = result;
       }
-    `}};function r8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((h===1||p===1)&&d>XS)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(_p(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let w=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(w);let T=Cf({a:x,b:w,backend:n,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),S=n.texData.get(T.dataId);v.assert(S.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,S.shape=r.outShape,g=nn({inputs:{x:T},backend:n}),g.shape=r.outShape,y.push(T)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:n,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),w=Cf({a:x,b,transposeA:f,transposeB:m,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:w},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(w)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function n8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,f=c==="channelsLast",m=l*u*d,g=p*h,y=[m,g],A=!0,x=!1,b=[],w=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),T=ve({inputs:{x:t},backend:n,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(T);let S=new yne(y,r),E=[w.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=n.runWebGLProgram(S,[w],"float32",E),_=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(_);let M=a!=null,I=s!=null,O=o==="leakyrelu",z=o?u0(o,!0):null,j=new KS(_.shape,T.shape,[1,g,r.outChannels],A,x,M,z,I,O),X=[_,T];if(a&&X.push(a),I&&X.push(s),O){let ee=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let D=n.runWebGLProgram(j,X,"float32"),Q=f?[1,p,h,r.outChannels]:[1,r.outChannels,p,h],V=ve({inputs:{x:D},backend:n,attrs:{shape:Q}});b.push(D);for(let ee of b)n.disposeIntermediateTensorInfo(ee);return V}function Ane(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=r8({x:a,filter:s,convInfo:p,backend:r});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=n8({x:a,filter:s,convInfo:p,backend:r});else{let m=new t8(p);c=r.runWebGLProgram(m,[a,s],"float32")}let f=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),f}var xne={kernelName:Zs,backendName:"webgl",kernelFunc:Ane},bne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function s8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((h===1||p===1)&&d>JS)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(zp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let w=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(w);let T=Ef({a:x,b:w,backend:n,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),S=n.texData.get(T.dataId);v.assert(S.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,S.shape=r.outShape,g=nn({inputs:{x:T},backend:n}),g.shape=r.outShape,y.push(T)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:n,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),w=Ef({a:x,b,transposeA:f,transposeB:m,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:w},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(w)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function i8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,f=c==="channelsLast",m=l*u*d,g=p*h,y=[m,g],A=!0,x=!1,b=[],w=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),T=ve({inputs:{x:t},backend:n,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(T);let S=new yne(y,r),E=[w.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=n.runWebGLProgram(S,[w],"float32",E),_=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(_);let M=a!=null,I=s!=null,O=o==="leakyrelu",z=o?d0(o,!0):null,j=new YS(_.shape,T.shape,[1,g,r.outChannels],A,x,M,z,I,O),X=[_,T];if(a&&X.push(a),I&&X.push(s),O){let ee=n.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let D=n.runWebGLProgram(j,X,"float32"),Q=f?[1,p,h,r.outChannels]:[1,r.outChannels,p,h],V=ve({inputs:{x:D},backend:n,attrs:{shape:Q}});b.push(D);for(let ee of b)n.disposeIntermediateTensorInfo(ee);return V}function Ane(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=s8({x:a,filter:s,convInfo:p,backend:r});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=i8({x:a,filter:s,convInfo:p,backend:r});else{let m=new a8(p);c=r.runWebGLProgram(m,[a,s],"float32")}let f=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),f}var xne={kernelName:Zs,backendName:"webgl",kernelFunc:Ane},bne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2666,7 +2666,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function Ine(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new bne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Sne={kernelName:Bf,backendName:"webgl",kernelFunc:Ine};function Tne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new vne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Nne={kernelName:Ys,backendName:"webgl",kernelFunc:Tne};function Cne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new gne(u);return r.runWebGLProgram(d,[a,s],"float32")}var Ene={kernelName:Wp,backendName:"webgl",kernelFunc:Cne};function Rne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=N.computeConv3DInfo(a.shape,l,i,1,o),d=new wne(u);return r.runWebGLProgram(d,[a,s],"float32")}var Mne={kernelName:Wf,backendName:"webgl",kernelFunc:Rne};function Fne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,o,1,i),d=new kne(u);return r.runWebGLProgram(d,[a,s],"float32")}var $ne={kernelName:Vf,backendName:"webgl",kernelFunc:Fne},Pne=bd+`
+    `}};function Ine(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new bne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Sne={kernelName:Wf,backendName:"webgl",kernelFunc:Ine};function Tne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new vne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Nne={kernelName:Ys,backendName:"webgl",kernelFunc:Tne};function Cne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new gne(u);return r.runWebGLProgram(d,[a,s],"float32")}var Ene={kernelName:Vp,backendName:"webgl",kernelFunc:Cne};function Rne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=N.computeConv3DInfo(a.shape,l,i,1,o),d=new wne(u);return r.runWebGLProgram(d,[a,s],"float32")}var Mne={kernelName:Vf,backendName:"webgl",kernelFunc:Rne};function Fne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,o,1,i),d=new kne(u);return r.runWebGLProgram(d,[a,s],"float32")}var $ne={kernelName:Uf,backendName:"webgl",kernelFunc:Fne},Pne=vd+`
   return cos(x);
 `,_ne=it({opSnippet:Pne}),zne={kernelName:Js,backendName:"webgl",kernelFunc:_ne},One=`
   float e2x = exp(-x);
@@ -2732,20 +2732,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
           setOutput(newValue);
         }
       }
-    `}},Wne=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Bne(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},Vne={kernelName:Do,backendName:"webgl",kernelFunc:Wne},zv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"1.0":`getX(${Ov(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
-      void main() {
-        ${gt(n)} coords = getOutputCoords();
-        int end = ${Dv(n,"coords")};
-        float val = ${a};
-        int pow2 = int(pow(2.0, index));
-        if (${i}) {
-          int idx = ${o};
-          ${Dv(n,"coords")} = idx;
-          val *= getX(${Ov(n,"coords")});
-        }
-        setOutput(val);
-      }
-    `}};function Ov(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function Dv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function Une(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=xr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=nn({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new zv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new zv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=xr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var Gne={kernelName:Du,backendName:"webgl",kernelFunc:Une},Lv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"0.0":`getX(${Bv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
+    `}},Wne=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Bne(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},Vne={kernelName:Lo,backendName:"webgl",kernelFunc:Wne},Lv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"1.0":`getX(${Bv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${gt(n)} coords = getOutputCoords();
         int end = ${Wv(n,"coords")};
@@ -2754,11 +2741,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         if (${i}) {
           int idx = ${o};
           ${Wv(n,"coords")} = idx;
-          val += getX(${Bv(n,"coords")});
+          val *= getX(${Bv(n,"coords")});
         }
         setOutput(val);
       }
-    `}};function Bv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Wv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function jne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=xr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=nn({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new Lv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new Lv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=xr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var Hne={kernelName:Oo,backendName:"webgl",kernelFunc:jne};function qne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=zS(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=iee(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Kne={kernelName:Uf,backendName:"webgl",kernelFunc:qne},Xne=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
+    `}};function Bv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function Wv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function Une(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=xr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=nn({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new Lv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new Lv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=xr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var Gne={kernelName:Lu,backendName:"webgl",kernelFunc:Une},Vv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"0.0":`getX(${Uv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
+      void main() {
+        ${gt(n)} coords = getOutputCoords();
+        int end = ${Gv(n,"coords")};
+        float val = ${a};
+        int pow2 = int(pow(2.0, index));
+        if (${i}) {
+          int idx = ${o};
+          ${Gv(n,"coords")} = idx;
+          val += getX(${Uv(n,"coords")});
+        }
+        setOutput(val);
+      }
+    `}};function Uv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Gv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function jne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=xr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=nn({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new Vv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new Vv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=xr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var Hne={kernelName:Do,backendName:"webgl",kernelFunc:jne};function qne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=LS(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=iee(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Kne={kernelName:Gf,backendName:"webgl",kernelFunc:qne},Xne=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2777,7 +2777,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
       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 Zne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=new Xne(f,s,i);return r.runWebGLProgram(m,[a],a.dtype)}var Yne={kernelName:Lo,backendName:"webgl",kernelFunc:Zne},a8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=on(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
+  `}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 Zne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=new Xne(f,s,i);return r.runWebGLProgram(m,[a],a.dtype)}var Yne={kernelName:Bo,backendName:"webgl",kernelFunc:Zne},o8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=on(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${r}
         }`:a?l=`float activation(float a) {
@@ -2830,7 +2830,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         ${u}
         setOutput(result);
       }
-    `}},s8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=on(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
+    `}},l8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=on(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<d;g++)p+=`
           vec4 xTexelC${g*2};
@@ -3017,7 +3017,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         ${f}
         setOutput(result);
       }
-    `}};function Jne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new s8(h):p=new a8(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var Qne={kernelName:ei,backendName:"webgl",kernelFunc:Jne},eae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function Jne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new l8(h):p=new o8(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var Qne={kernelName:ei,backendName:"webgl",kernelFunc:Jne},eae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -3097,13 +3097,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function rae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new eae(h);return r.runWebGLProgram(p,[a,s],"float32")}var nae={kernelName:Gf,backendName:"webgl",kernelFunc:rae};function aae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new tae(h);return r.runWebGLProgram(p,[a,s],"float32")}var sae={kernelName:jf,backendName:"webgl",kernelFunc:aae},iae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function rae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new eae(h);return r.runWebGLProgram(p,[a,s],"float32")}var nae={kernelName:jf,backendName:"webgl",kernelFunc:rae};function aae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new tae(h);return r.runWebGLProgram(p,[a,s],"float32")}var sae={kernelName:Hf,backendName:"webgl",kernelFunc:aae},iae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function oae(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new iae(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var lae={kernelName:Hf,backendName:"webgl",kernelFunc:oae},uae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
+    `}};function oae(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=v.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new iae(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var lae={kernelName:qf,backendName:"webgl",kernelFunc:oae},uae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
       const ivec2 strides = ivec2(${a}, ${s});
       const ivec2 pads = ivec2(${d}, ${h});
       const float neg_infinity = -3.4e38;
@@ -3141,7 +3141,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         float result = curVal;
         setOutput(result);
       }
-    `}};function dae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new uae(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=ve({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var pae={kernelName:Vp,backendName:"webgl",kernelFunc:dae};function hae(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=xr({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=lb({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=p0({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeIntermediateTensorInfo(m);return p}var cae={kernelName:Up,backendName:"webgl",kernelFunc:hae},fae="return (x >= 0.0) ? x : (exp(x) - 1.0);",mae=`
+    `}};function dae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new uae(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=ve({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var pae={kernelName:Up,backendName:"webgl",kernelFunc:dae};function hae(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=xr({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=pb({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=h0({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeIntermediateTensorInfo(m);return p}var cae={kernelName:Gp,backendName:"webgl",kernelFunc:hae},fae="return (x >= 0.0) ? x : (exp(x) - 1.0);",mae=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3153,9 +3153,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
 `,gae=it({opSnippet:fae,packedOpSnippet:mae}),yae={kernelName:ri,backendName:"webgl",kernelFunc:gae},Aae="return (b >= 1.0) ? a : a * (b + 1.0);",xae=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,bae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(xae,n.shape,a.shape):new Su(Aae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},vae={kernelName:qf,backendName:"webgl",kernelFunc:bae},wae=`
+`,bae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(xae,n.shape,a.shape):new Tu(Aae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},vae={kernelName:Kf,backendName:"webgl",kernelFunc:bae},wae=`
   return vec4(equal(a, b));
-`,kae="return float(a == b);",Iae=br({opSnippet:kae,packedOpSnippet:wae,dtype:"bool",cpuKernelImpl:uee}),Sae={kernelName:Bo,backendName:"webgl",kernelFunc:Iae},Tae=`
+`,kae="return float(a == b);",Iae=br({opSnippet:kae,packedOpSnippet:wae,dtype:"bool",cpuKernelImpl:uee}),Sae={kernelName:Wo,backendName:"webgl",kernelFunc:Iae},Tae=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -3170,7 +3170,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
   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));
-`,Nae=it({opSnippet:Tae}),Cae={kernelName:Lu,backendName:"webgl",kernelFunc:Nae},Eae=bd+`
+`,Nae=it({opSnippet:Tae}),Cae={kernelName:Bu,backendName:"webgl",kernelFunc:Nae},Eae=vd+`
   return exp(x);
 `,Rae=`
   vec4 result = exp(x);
@@ -3181,7 +3181,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,i8=it({opSnippet:Eae,packedOpSnippet:Rae,cpuKernelImpl:dee,dtype:"float32"}),Mae={kernelName:ni,backendName:"webgl",kernelFunc:i8};function j1(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ve({inputs:{x:s},backend:n,attrs:{shape:o}})}var Fae={kernelName:Wo,backendName:"webgl",kernelFunc:j1},Vv="return exp(x) - 1.0;",$ae=it({opSnippet:Vv,packedOpSnippet:Vv,cpuKernelImpl:pee}),Pae={kernelName:Vo,backendName:"webgl",kernelFunc:$ae},Uv=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,u8=it({opSnippet:Eae,packedOpSnippet:Rae,cpuKernelImpl:dee,dtype:"float32"}),Mae={kernelName:ni,backendName:"webgl",kernelFunc:u8};function K1(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ve({inputs:{x:s},backend:n,attrs:{shape:o}})}var Fae={kernelName:Vo,backendName:"webgl",kernelFunc:K1},jv="return exp(x) - 1.0;",$ae=it({opSnippet:jv,packedOpSnippet:jv,cpuKernelImpl:pee}),Pae={kernelName:Uo,backendName:"webgl",kernelFunc:$ae},Hv=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${a};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -3214,12 +3214,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function o8(e,t,r){let n=r.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new Uv("real",l,t),d=new Uv("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Li({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let m=ve({inputs:{x:f},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(f),m}function _ae(e){let{inputs:t,backend:r}=e,{input:n}=t;return o8(n,!1,r)}var zae={kernelName:Kf,backendName:"webgl",kernelFunc:_ae},Oae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function d8(e,t,r){let n=r.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new Hv("real",l,t),d=new Hv("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Li({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let m=ve({inputs:{x:f},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(f),m}function _ae(e){let{inputs:t,backend:r}=e,{input:n}=t;return d8(n,!1,r)}var zae={kernelName:Xf,backendName:"webgl",kernelFunc:_ae},Oae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}};function Mh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Oae(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var Dae={kernelName:Bu,backendName:"webgl",kernelFunc:Mh},Lae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function Mh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Oae(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var Dae={kernelName:Wu,backendName:"webgl",kernelFunc:Mh},Lae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3233,7 +3233,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
           }
           setOutput(outputValue);
         }
-    `}},Bae={kernelName:Uo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Lae(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},Gv="return floor(x);",Wae=it({opSnippet:Gv,packedOpSnippet:Gv,cpuKernelImpl:hee}),Vae={kernelName:ai,backendName:"webgl",kernelFunc:Wae},Uae=`
+    `}},Bae={kernelName:Go,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Lae(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},qv="return floor(x);",Wae=it({opSnippet:qv,packedOpSnippet:qv,cpuKernelImpl:hee}),Vae={kernelName:ai,backendName:"webgl",kernelFunc:Wae},Uae=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3320,7 +3320,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
 
         ${t.output} = result;
       }
-    `}},Xae={kernelName:Ip,backendName:"webgl",kernelFunc:Zae},ru;function Zae(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(ru==null&&(ru=document.createElement("canvas").getContext("2d")),ru.canvas.width=l,ru.canvas.height=u,ru.drawImage(a,0,0,l,u),a=ru.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=Y().getBool("WEBGL_PACK")?new Kae(h):new qae(h),f=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),f}function Yae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=r8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=n8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,w=o!=null,T=c==="leakyrelu",S=c?u0(c,!1):null,E=new t8(g,b,S,w,T),R=[a,s];if(i&&R.push(i),o&&R.push(o),T){let _=r.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(_),A.push(_)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Jae={kernelName:Es,backendName:"webgl",kernelFunc:Yae};function Qae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=[],m=d;m==null&&(m=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?u0(p,y):null,x=[a,s],b=i!=null,w=o!=null,T=p==="leakyrelu";if(b&&x.push(i),w&&x.push(o),T){let _=r.makeTensorInfo([],"float32",v.createScalarValue(c,"float32"));x.push(_),f.push(_)}let S;y?S=new s8(g,b,A,w,T):S=new a8(g,b,A,w,T);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(S,x,"float32",E);return f.forEach(_=>r.disposeIntermediateTensorInfo(_)),R}var ese={kernelName:Rs,backendName:"webgl",kernelFunc:Qae},tse=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=gt(t.length),a=gt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},Xae={kernelName:Sp,backendName:"webgl",kernelFunc:Zae},ru;function Zae(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(ru==null&&(ru=document.createElement("canvas").getContext("2d")),ru.canvas.width=l,ru.canvas.height=u,ru.drawImage(a,0,0,l,u),a=ru.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=Y().getBool("WEBGL_PACK")?new Kae(h):new qae(h),f=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),f}function Yae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=s8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=i8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,w=o!=null,T=c==="leakyrelu",S=c?d0(c,!1):null,E=new a8(g,b,S,w,T),R=[a,s];if(i&&R.push(i),o&&R.push(o),T){let _=r.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(_),A.push(_)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Jae={kernelName:Es,backendName:"webgl",kernelFunc:Yae};function Qae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=[],m=d;m==null&&(m=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?d0(p,y):null,x=[a,s],b=i!=null,w=o!=null,T=p==="leakyrelu";if(b&&x.push(i),w&&x.push(o),T){let _=r.makeTensorInfo([],"float32",v.createScalarValue(c,"float32"));x.push(_),f.push(_)}let S;y?S=new l8(g,b,A,w,T):S=new o8(g,b,A,w,T);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(S,x,"float32",E);return f.forEach(_=>r.disposeIntermediateTensorInfo(_)),R}var ese={kernelName:Rs,backendName:"webgl",kernelFunc:Qae},tse=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=gt(t.length),a=gt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${n} strides = ${n}(${this.strides});
          void main() {
           ${a} coords = getOutputCoords();
@@ -3331,22 +3331,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function rse(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=ve({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=ve({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=cee(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let f=new tse(i,h,[u,d]),m=r.runWebGLProgram(f,[c,p],c.dtype),g=ve({inputs:{x:m},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),g}var nse={kernelName:jo,backendName:"webgl",kernelFunc:rse},ase=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=gt(this.rank),n=sse(e,2);this.userCode=`
+      `}};function rse(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=ve({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=ve({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=cee(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let f=new tse(i,h,[u,d]),m=r.runWebGLProgram(f,[c,p],c.dtype),g=ve({inputs:{x:m},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),g}var nse={kernelName:Ho,backendName:"webgl",kernelFunc:rse},ase=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=gt(this.rank),n=sse(e,2);this.userCode=`
       void main() {
         ${r} resRC = getOutputCoords();
         int index = int(getIndices(resRC.x, resRC.z));
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${n}));
       }
-    `}};function sse(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function l8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0];if(Y().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let w=A[b];v.assert(w<=x-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.sizeFromShape(s.shape),h=[],p=ve({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=fee(x,A,f);return h.forEach(w=>r.disposeIntermediateTensorInfo(w)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new ase(p.shape,f),g=r.runWebGLProgram(m,[p,c],p.dtype);h.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var ise={kernelName:Go,backendName:"webgl",kernelFunc:l8},ose="return float(a > b);",lse=`
+    `}};function sse(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function p8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0];if(Y().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let w=A[b];v.assert(w<=x-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.sizeFromShape(s.shape),h=[],p=ve({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=fee(x,A,f);return h.forEach(w=>r.disposeIntermediateTensorInfo(w)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new ase(p.shape,f),g=r.runWebGLProgram(m,[p,c],p.dtype);h.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var ise={kernelName:jo,backendName:"webgl",kernelFunc:p8},ose="return float(a > b);",lse=`
   return vec4(greaterThan(a, b));
-`,use=br({opSnippet:ose,packedOpSnippet:lse,cpuKernelImpl:mee,dtype:"bool"}),dse={kernelName:Ho,backendName:"webgl",kernelFunc:use},pse="return float(a >= b);",hse=`
+`,use=br({opSnippet:ose,packedOpSnippet:lse,cpuKernelImpl:mee,dtype:"bool"}),dse={kernelName:qo,backendName:"webgl",kernelFunc:use},pse="return float(a >= b);",hse=`
   return vec4(greaterThanEqual(a, b));
-`,cse=br({opSnippet:pse,packedOpSnippet:hse,dtype:"bool",cpuKernelImpl:gee}),fse={kernelName:oi,backendName:"webgl",kernelFunc:cse};function mse(e){let{inputs:t,backend:r}=e,{input:n}=t;return o8(n,!0,r)}var gse={kernelName:Xf,backendName:"webgl",kernelFunc:mse},yse="return float(!isnan(x) && !isinf(x));",Ase=it({opSnippet:yse,dtype:"bool"}),xse={kernelName:Wu,backendName:"webgl",kernelFunc:Ase},bse="return float(isinf(x));",vse=it({opSnippet:bse,dtype:"bool"}),wse={kernelName:Vu,backendName:"webgl",kernelFunc:vse},kse="return float(isnan(x));",Ise=it({opSnippet:kse,dtype:"bool"}),Sse={kernelName:Uu,backendName:"webgl",kernelFunc:Ise},Tse="return float(a < b);",Nse=`
+`,cse=br({opSnippet:pse,packedOpSnippet:hse,dtype:"bool",cpuKernelImpl:gee}),fse={kernelName:oi,backendName:"webgl",kernelFunc:cse};function mse(e){let{inputs:t,backend:r}=e,{input:n}=t;return d8(n,!0,r)}var gse={kernelName:Zf,backendName:"webgl",kernelFunc:mse},yse="return float(!isnan(x) && !isinf(x));",Ase=it({opSnippet:yse,dtype:"bool"}),xse={kernelName:Vu,backendName:"webgl",kernelFunc:Ase},bse="return float(isinf(x));",vse=it({opSnippet:bse,dtype:"bool"}),wse={kernelName:Uu,backendName:"webgl",kernelFunc:vse},kse="return float(isnan(x));",Ise=it({opSnippet:kse,dtype:"bool"}),Sse={kernelName:Gu,backendName:"webgl",kernelFunc:Ise},Tse="return float(a < b);",Nse=`
   return vec4(lessThan(a, b));
-`,Cse=br({opSnippet:Tse,packedOpSnippet:Nse,cpuKernelImpl:yee,dtype:"bool"}),Ese={kernelName:qo,backendName:"webgl",kernelFunc:Cse},Rse="return float(a <= b);",Mse=`
+`,Cse=br({opSnippet:Tse,packedOpSnippet:Nse,cpuKernelImpl:yee,dtype:"bool"}),Ese={kernelName:Ko,backendName:"webgl",kernelFunc:Cse},Rse="return float(a <= b);",Mse=`
   return vec4(lessThanEqual(a, b));
-`,Fse=br({opSnippet:Rse,packedOpSnippet:Mse,cpuKernelImpl:Aee,dtype:"bool"}),$se={kernelName:Ko,backendName:"webgl",kernelFunc:Fse};function Pse(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=xee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var _se={kernelName:Zf,backendName:"webgl",kernelFunc:Pse},zse=bd+`
+`,Fse=br({opSnippet:Rse,packedOpSnippet:Mse,cpuKernelImpl:Aee,dtype:"bool"}),$se={kernelName:Xo,backendName:"webgl",kernelFunc:Fse};function Pse(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=xee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var _se={kernelName:Yf,backendName:"webgl",kernelFunc:Pse},zse=vd+`
   return x < 0.0 ? 0./0. : log(x);
 `,Ose=`
   vec4 result = log(x);
@@ -3356,18 +3356,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
   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;
-`,Dse=it({opSnippet:zse,packedOpSnippet:Ose,cpuKernelImpl:bee}),Lse={kernelName:di,backendName:"webgl",kernelFunc:Dse},Bse=bd+`
+`,Dse=it({opSnippet:zse,packedOpSnippet:Ose,cpuKernelImpl:bee}),Lse={kernelName:di,backendName:"webgl",kernelFunc:Dse},Bse=vd+`
   return log(1.0 + x);
-`,Wse=it({opSnippet:Bse}),Vse={kernelName:Gu,backendName:"webgl",kernelFunc:Wse},Use="return float(a >= 1.0 && b >= 1.0);",Gse=`
+`,Wse=it({opSnippet:Bse}),Vse={kernelName:ju,backendName:"webgl",kernelFunc:Wse},Use="return float(a >= 1.0 && b >= 1.0);",Gse=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,jse=br({opSnippet:Use,packedOpSnippet:Gse,dtype:"bool"}),Hse={kernelName:Xo,backendName:"webgl",kernelFunc:jse},qse="return float(!(x >= 1.0));",Kse=it({opSnippet:qse}),Xse={kernelName:ju,backendName:"webgl",kernelFunc:Kse},Zse="return float(a >= 1.0 || b >= 1.0);",Yse=`
+`,jse=br({opSnippet:Use,packedOpSnippet:Gse,dtype:"bool"}),Hse={kernelName:Zo,backendName:"webgl",kernelFunc:jse},qse="return float(!(x >= 1.0));",Kse=it({opSnippet:qse}),Xse={kernelName:Hu,backendName:"webgl",kernelFunc:Kse},Zse="return float(a >= 1.0 || b >= 1.0);",Yse=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,Jse=br({opSnippet:Zse,packedOpSnippet:Yse,dtype:"bool"}),Qse={kernelName:jp,backendName:"webgl",kernelFunc:Jse},eie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+`,Jse=br({opSnippet:Zse,packedOpSnippet:Yse,dtype:"bool"}),Qse={kernelName:Hp,backendName:"webgl",kernelFunc:Jse},eie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3448,7 +3448,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},rie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new tie(a.shape,s,i,o,l):new eie(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},nie={kernelName:Hp,backendName:"webgl",kernelFunc:rie},aie=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,this.userCode=`
+    `}},rie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new tie(a.shape,s,i,o,l):new eie(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},nie={kernelName:qp,backendName:"webgl",kernelFunc:rie},aie=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3503,14 +3503,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
       }
       setOutput(result);
       }
-    `}},sie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new aie(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},iie={kernelName:Yf,backendName:"webgl",kernelFunc:sie};function oie(e,t,r,n){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Rl(i,e.dtype,"max",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function u8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=a.shape[d[T]];let b=ob(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let w=r.texData.get(c.dataId);w.values=b}else c=d0(a,d,r);u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("max",u,o);let[f,m]=N.computeOutAndReduceShapes(c.shape,u),g=f;i&&(g=N.expandShapeToKeepDim(f,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=vee(A,v.sizeFromShape(m),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=oie(c,m,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var lie={kernelName:pi,backendName:"webgl",kernelFunc:u8},uie=US+`
+    `}},sie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new aie(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},iie={kernelName:Jf,backendName:"webgl",kernelFunc:sie};function oie(e,t,r,n){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Ml(i,e.dtype,"max",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function h8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=a.shape[d[T]];let b=db(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let w=r.texData.get(c.dataId);w.values=b}else c=p0(a,d,r);u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("max",u,o);let[f,m]=N.computeOutAndReduceShapes(c.shape,u),g=f;i&&(g=N.expandShapeToKeepDim(f,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=vee(A,v.sizeFromShape(m),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=oie(c,m,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var lie={kernelName:pi,backendName:"webgl",kernelFunc:h8},uie=HS+`
   return max(a, b);
 `,die=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+l0+`
+  `+u0+`
   return result;
-`,pie=br({opSnippet:uie,packedOpSnippet:die,cpuKernelImpl:wee}),hie={kernelName:hi,backendName:"webgl",kernelFunc:pie};function cie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;md(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return nn({inputs:{x:a},backend:r});let h=new zp(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var fie={kernelName:ci,backendName:"webgl",kernelFunc:cie};function mie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new ub(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var gie={kernelName:qp,backendName:"webgl",kernelFunc:mie},yie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
+`,pie=br({opSnippet:uie,packedOpSnippet:die,cpuKernelImpl:wee}),hie={kernelName:hi,backendName:"webgl",kernelFunc:pie};function cie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;gd(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;v.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return nn({inputs:{x:a},backend:r});let h=new Op(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var fie={kernelName:ci,backendName:"webgl",kernelFunc:cie};function mie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new hb(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var gie={kernelName:Kp,backendName:"webgl",kernelFunc:mie},yie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3620,12 +3620,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function xie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new ub(p,"max",!0),f=r.runWebGLProgram(c,[i],i.dtype),m=new Aie(p),g=r.runWebGLProgram(m,[a,f],i.dtype);return r.disposeIntermediateTensorInfo(f),g}var bie={kernelName:Qf,backendName:"webgl",kernelFunc:xie};function vie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;md([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=N.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,f=new zp(p,"max",c),m=r.runWebGLProgram(f,[o],o.dtype),g=new yie(p),y=r.runWebGLProgram(g,[a,m],o.dtype);return r.disposeIntermediateTensorInfo(m),y}var wie={kernelName:Jf,backendName:"webgl",kernelFunc:vie};function kie(e,t,r,n){let a=new zp(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new zp(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var Iie={kernelName:em,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=N.computePool2DInfo(n.shape,a,s,u,i),[h,p]=kie(n,o,d,l);return[h,p]}};function Sie(e,t,r,n){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Rl(i,"float32","mean",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var Tie={kernelName:fi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],f=n;if(h){if(p){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let S=0;S<b.length;S++)b[S]=n.shape[d[S]];let w=ob(x,n.shape,n.dtype,d,b);f=i.makeTensorInfo(b,n.dtype);let T=i.texData.get(f.dataId);T.values=w}else f=d0(n,d,i);c.push(f),u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=N.expandShapeToKeepDim(m,l));let A=Sie(f,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function Nie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=xr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,a.shape.length)),N.assertAxesAreInnerMostDims("min",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Rl(m,m.dtype,"min",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Cie={kernelName:mi,backendName:"webgl",kernelFunc:Nie},Eie=US+`
+    `}};function xie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new hb(p,"max",!0),f=r.runWebGLProgram(c,[i],i.dtype),m=new Aie(p),g=r.runWebGLProgram(m,[a,f],i.dtype);return r.disposeIntermediateTensorInfo(f),g}var bie={kernelName:em,backendName:"webgl",kernelFunc:xie};function vie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;gd([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=N.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,f=new Op(p,"max",c),m=r.runWebGLProgram(f,[o],o.dtype),g=new yie(p),y=r.runWebGLProgram(g,[a,m],o.dtype);return r.disposeIntermediateTensorInfo(m),y}var wie={kernelName:Qf,backendName:"webgl",kernelFunc:vie};function kie(e,t,r,n){let a=new Op(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new Op(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var Iie={kernelName:tm,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;v.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=N.computePool2DInfo(n.shape,a,s,u,i),[h,p]=kie(n,o,d,l);return[h,p]}};function Sie(e,t,r,n){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Ml(i,"float32","mean",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var Tie={kernelName:fi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=v.parseAxisParam(s,n.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],f=n;if(h){if(p){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let S=0;S<b.length;S++)b[S]=n.shape[d[S]];let w=db(x,n.shape,n.dtype,d,b);f=i.makeTensorInfo(b,n.dtype);let T=i.texData.get(f.dataId);T.values=w}else f=p0(n,d,i);c.push(f),u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=N.expandShapeToKeepDim(m,l));let A=Sie(f,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function Nie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=v.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=xr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,a.shape.length)),N.assertAxesAreInnerMostDims("min",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=v.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Ml(m,m.dtype,"min",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Cie={kernelName:mi,backendName:"webgl",kernelFunc:Nie},Eie=HS+`
   return min(a, b);
 `,Rie=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+l0+`
+  `+u0+`
   return result;
 `,Mie=br({opSnippet:Eie,packedOpSnippet:Rie,cpuKernelImpl:kee}),Fie={kernelName:gi,backendName:"webgl",kernelFunc:Mie},$ie=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let n=e.length,a=gt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=r==="reflect"?0:1;if(n===1){this.userCode=`
         int start = ${s};
@@ -3716,9 +3716,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vre=it({opSnippet:bre}),wre={kernel
   return mod(a, b);`,Die=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+l0+`
+  `+u0+`
   return result;
-`,Lie=br({opSnippet:Oie,packedOpSnippet:Die}),Bie={kernelName:Hu,backendName:"webgl",kernelFunc:Lie},Wie=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
+`,Lie=br({opSnippet:Oie,packedOpSnippet:Die}),Bie={kernelName:qu,backendName:"webgl",kernelFunc:Lie},Wie=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -3760,7 +3760,7 @@ return a / b;`,Uie=`
   }
 
   return result;
-`,d8=br({opSnippet:Vie,packedOpSnippet:Uie,checkOutOfBounds:!0}),Gie={kernelName:ti,backendName:"webgl",kernelFunc:d8},jv="return a - b;",p8=br({opSnippet:jv,packedOpSnippet:jv,supportsComplex:!0,cpuKernelImpl:Lee}),jie={kernelName:Fi,backendName:"webgl",kernelFunc:p8};function h8(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=u8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=p8({inputs:{a,b:u},backend:r}),h=i8({inputs:{x:d},backend:r}),p=p0({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),f=d8({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}var Hie={kernelName:Ri,backendName:"webgl",kernelFunc:h8};function qie(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:h8({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new Wie(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var Kie={kernelName:tm,backendName:"webgl",kernelFunc:qie},Xie=qn+`
+`,c8=br({opSnippet:Vie,packedOpSnippet:Uie,checkOutOfBounds:!0}),Gie={kernelName:ti,backendName:"webgl",kernelFunc:c8},Kv="return a - b;",f8=br({opSnippet:Kv,packedOpSnippet:Kv,supportsComplex:!0,cpuKernelImpl:Lee}),jie={kernelName:Fi,backendName:"webgl",kernelFunc:f8};function m8(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=h8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=f8({inputs:{a,b:u},backend:r}),h=u8({inputs:{x:d},backend:r}),p=h0({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),f=c8({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}var Hie={kernelName:Ri,backendName:"webgl",kernelFunc:m8};function qie(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:m8({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new Wie(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var Kie={kernelName:rm,backendName:"webgl",kernelFunc:qie},Xie=qn+`
   return -x;
 `,Zie=`
   vec4 result = -x;
@@ -3772,14 +3772,14 @@ return a / b;`,Uie=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`;function Yie(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=See(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new go(n.shape,Zie):a=new Ga(n.shape,Xie),r.runWebGLProgram(a,[n],n.dtype)}var Jie={kernelName:Zo,backendName:"webgl",kernelFunc:Yie},Qie=jn.nonMaxSuppressionV3Impl;function eoe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Qie(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var toe={kernelName:Jo,backendName:"webgl",kernelFunc:eoe},roe=jn.nonMaxSuppressionV4Impl;function noe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=roe(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var aoe={kernelName:qu,backendName:"webgl",kernelFunc:noe},soe=jn.nonMaxSuppressionV5Impl;function ioe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=soe(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var ooe={kernelName:Qo,backendName:"webgl",kernelFunc:ioe},loe=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`;function Yie(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=See(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new yo(n.shape,Zie):a=new Ga(n.shape,Xie),r.runWebGLProgram(a,[n],n.dtype)}var Jie={kernelName:Yo,backendName:"webgl",kernelFunc:Yie},Qie=jn.nonMaxSuppressionV3Impl;function eoe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Qie(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var toe={kernelName:Qo,backendName:"webgl",kernelFunc:eoe},roe=jn.nonMaxSuppressionV4Impl;function noe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=roe(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var aoe={kernelName:Ku,backendName:"webgl",kernelFunc:noe},soe=jn.nonMaxSuppressionV5Impl;function ioe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=soe(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var ooe={kernelName:el,backendName:"webgl",kernelFunc:ioe},loe=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${n}), float(${r}),
                       float(index == coords.y)));
       }
-    `}},uoe=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=v.sizeFromShape(a.shape),u=new loe(l,s,i,o),d=ve({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=ve({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},doe={kernelName:tl,backendName:"webgl",kernelFunc:uoe};function Ef(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Rh({inputs:{input:n},backend:r}),s=Ef({inputs:{x:a},backend:r}),i=h0({inputs:{input:n},backend:r}),o=Ef({inputs:{x:i},backend:r}),l=Li({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Mh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var poe={kernelName:Al,backendName:"webgl",kernelFunc:Ef};function c8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Rh({inputs:{input:n},backend:r}),s=c8({inputs:{x:a},backend:r}),i=h0({inputs:{input:n},backend:r}),o=Ef({inputs:{x:i},backend:r}),l=Li({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Mh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var hoe={kernelName:el,backendName:"webgl",kernelFunc:c8};function coe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return j1({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=j1({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=e8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var foe={kernelName:rl,backendName:"webgl",kernelFunc:coe},moe=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=gt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
+    `}},uoe=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=v.sizeFromShape(a.shape),u=new loe(l,s,i,o),d=ve({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=ve({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},doe={kernelName:rl,backendName:"webgl",kernelFunc:uoe};function Rf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Rh({inputs:{input:n},backend:r}),s=Rf({inputs:{x:a},backend:r}),i=c0({inputs:{input:n},backend:r}),o=Rf({inputs:{x:i},backend:r}),l=Li({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Mh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var poe={kernelName:xl,backendName:"webgl",kernelFunc:Rf};function g8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Rh({inputs:{input:n},backend:r}),s=g8({inputs:{x:a},backend:r}),i=c0({inputs:{input:n},backend:r}),o=Rf({inputs:{x:i},backend:r}),l=Li({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Mh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var hoe={kernelName:tl,backendName:"webgl",kernelFunc:g8};function coe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return K1({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=K1({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=n8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var foe={kernelName:nl,backendName:"webgl",kernelFunc:coe},moe=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=gt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3828,7 +3828,7 @@ return a / b;`,Uie=`
         ${c}
         setOutput(result);
       }
-    `}},f8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Mh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new goe(a.shape,s,i):new moe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},yoe={kernelName:xi,backendName:"webgl",kernelFunc:f8},Aoe=`
+    `}},y8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Mh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new goe(a.shape,s,i):new moe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},yoe={kernelName:xi,backendName:"webgl",kernelFunc:y8},Aoe=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3851,9 +3851,9 @@ return a / b;`,Uie=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+l0+`
+  `+u0+`
   return result;
-`,boe=br({opSnippet:Aoe,packedOpSnippet:xoe}),voe={kernelName:bi,backendName:"webgl",kernelFunc:boe};function woe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),d=u,h=N.getAxesPermutation(d,o),p=a;h!=null&&(p=xr({inputs:{x:a},backend:r,attrs:{perm:h}}),d=N.getInnerMostAxes(d.length,o),l.push(p)),N.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let f=r.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=Nee(p.shape,p.dtype,f,d);c=r.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,d),g=v.sizeFromShape(m),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=nh(a.dtype),x=Rl(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(c);let f=N.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:f}})}return l.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var koe={kernelName:nl,backendName:"webgl",kernelFunc:woe},m8=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Cee(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},Ioe={kernelName:Ku,backendName:"webgl",kernelFunc:m8},Soe="return 1.0 / x;",Toe=it({opSnippet:Soe}),Noe={kernelName:Xu,backendName:"webgl",kernelFunc:Toe},Coe=qn+`
+`,boe=br({opSnippet:Aoe,packedOpSnippet:xoe}),voe={kernelName:bi,backendName:"webgl",kernelFunc:boe};function woe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=v.parseAxisParam(s,a.shape),d=u,h=N.getAxesPermutation(d,o),p=a;h!=null&&(p=xr({inputs:{x:a},backend:r,attrs:{perm:h}}),d=N.getInnerMostAxes(d.length,o),l.push(p)),N.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let f=r.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=Nee(p.shape,p.dtype,f,d);c=r.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,d),g=v.sizeFromShape(m),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=ah(a.dtype),x=Ml(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(c);let f=N.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:f}})}return l.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var koe={kernelName:al,backendName:"webgl",kernelFunc:woe},A8=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Cee(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},Ioe={kernelName:Xu,backendName:"webgl",kernelFunc:A8},Soe="return 1.0 / x;",Toe=it({opSnippet:Soe}),Noe={kernelName:Zu,backendName:"webgl",kernelFunc:Toe},Coe=qn+`
   return (x < 0.0) ? 0.0 : x;
 `,Eoe=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
@@ -4068,7 +4068,7 @@ return a / b;`,Uie=`
 
         setOutput(accumulator);
       }
-    `}};function Woe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Boe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Voe={kernelName:nm,backendName:"webgl",kernelFunc:Woe},Uoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function Woe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Boe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Voe={kernelName:am,backendName:"webgl",kernelFunc:Woe},Uoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${u[0]/d[0]},
           ${u[1]/d[1]});
@@ -4131,7 +4131,7 @@ return a / b;`,Uie=`
 
         setOutput(newValue);
       }
-    `}};function joe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Goe(a.shape,l,u,s,i):new Uoe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var Hoe={kernelName:Zu,backendName:"webgl",kernelFunc:joe},qoe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
+    `}};function joe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Goe(a.shape,l,u,s,i):new Uoe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var Hoe={kernelName:Yu,backendName:"webgl",kernelFunc:joe},qoe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4201,7 +4201,7 @@ return a / b;`,Uie=`
 
         setOutput(accumulator);
       }
-    `}};function Koe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new qoe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Xoe={kernelName:rm,backendName:"webgl",kernelFunc:Koe},Zoe=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
+    `}};function Koe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new qoe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Xoe={kernelName:nm,backendName:"webgl",kernelFunc:Koe},Zoe=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
@@ -4239,7 +4239,7 @@ return a / b;`,Uie=`
           }
           setOutput(result);
         }
-    `;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let f=e.map((y,A)=>p(A,c)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(c,f){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${f[c]} - 1`:`${f[c]}`}}};function Joe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return nn({inputs:{x:a},backend:r});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Yoe(a.shape,o):new Zoe(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var Qoe={kernelName:sl,backendName:"webgl",kernelFunc:Joe},ele=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],n=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
+    `;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let f=e.map((y,A)=>p(A,c)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(c,f){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${f[c]} - 1`:`${f[c]}`}}};function Joe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return nn({inputs:{x:a},backend:r});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Yoe(a.shape,o):new Zoe(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var Qoe={kernelName:il,backendName:"webgl",kernelFunc:Joe},ele=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],n=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -4258,7 +4258,7 @@ return a / b;`,Uie=`
           }
           setOutput(outputValue);
         }
-    `}},tle={kernelName:xl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new ele(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},rle=`
+    `}},tle={kernelName:bl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new ele(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},rle=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -4273,7 +4273,7 @@ return a / b;`,Uie=`
       return base + 1.0;
     }
   }
-`,nle=it({opSnippet:rle}),ale={kernelName:il,backendName:"webgl",kernelFunc:nle},sle="return inversesqrt(x);",ile=it({opSnippet:sle,cpuKernelImpl:Eee}),ole={kernelName:Si,backendName:"webgl",kernelFunc:ile},g8=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(a.length),l=gt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
+`,nle=it({opSnippet:rle}),ale={kernelName:ol,backendName:"webgl",kernelFunc:nle},sle="return inversesqrt(x);",ile=it({opSnippet:sle,cpuKernelImpl:Eee}),ole={kernelName:Si,backendName:"webgl",kernelFunc:ile},x8=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(a.length),l=gt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${a});
 
         void main() {
@@ -4293,7 +4293,7 @@ return a / b;`,Uie=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function lle(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=ve({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new g8(l,o,c.shape.length,f.shape.length,d,p),y=r.runWebGLProgram(g,[f,c,m],f.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(m),A}var ule={kernelName:ol,backendName:"webgl",kernelFunc:lle},dle=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=gt(r);this.userCode=`
+      `}};function lle(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=ve({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new x8(l,o,c.shape.length,f.shape.length,d,p),y=r.runWebGLProgram(g,[f,c,m],f.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(m),A}var ule={kernelName:ll,backendName:"webgl",kernelFunc:lle},dle=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=gt(r);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${n});
@@ -4303,13 +4303,13 @@ return a / b;`,Uie=`
           setOutput(getB(${a}));
         }
       }
-    `}};function ple(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new dle(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Nr(a.dtype,s.dtype))}var hle={kernelName:ll,backendName:"webgl",kernelFunc:ple},cle=`
+    `}};function ple(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new dle(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Nr(a.dtype,s.dtype))}var hle={kernelName:ul,backendName:"webgl",kernelFunc:ple},cle=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${N.SELU_SCALEALPHA};
   float scale = ${N.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,fle=it({opSnippet:cle}),mle={kernelName:Yu,backendName:"webgl",kernelFunc:fle},gle=bd+`
+`,fle=it({opSnippet:cle}),mle={kernelName:Ju,backendName:"webgl",kernelFunc:fle},gle=vd+`
   return 1.0 / (1.0 + exp(-1.0 * x));
 `,yle=`
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
@@ -4324,12 +4324,12 @@ return a / b;`,Uie=`
 `,Ale=it({opSnippet:gle,packedOpSnippet:yle,cpuKernelImpl:Ree}),xle={kernelName:Ni,backendName:"webgl",kernelFunc:Ale},ble=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,vle=it({opSnippet:ble}),wle={kernelName:Ju,backendName:"webgl",kernelFunc:vle},kle=bd+`
+`,vle=it({opSnippet:ble}),wle={kernelName:Qu,backendName:"webgl",kernelFunc:vle},kle=vd+`
   return sin(x);
 `,Ile=it({opSnippet:kle}),Sle={kernelName:Ti,backendName:"webgl",kernelFunc:Ile},Tle=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,Nle=it({opSnippet:Tle}),Cle={kernelName:dl,backendName:"webgl",kernelFunc:Nle},Ele=`
+`,Nle=it({opSnippet:Tle}),Cle={kernelName:pl,backendName:"webgl",kernelFunc:Nle},Ele=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -4349,15 +4349,15 @@ return a / b;`,Uie=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,Rle=it({opSnippet:Ele}),Mle={kernelName:Qu,backendName:"webgl",kernelFunc:Rle},Fle=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=f8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),m=xr({inputs:{x:f},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},$le={kernelName:pl,backendName:"webgl",kernelFunc:Fle};function Ple(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
+`,Rle=it({opSnippet:Ele}),Mle={kernelName:ed,backendName:"webgl",kernelFunc:Rle},Fle=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=y8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),m=xr({inputs:{x:f},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},$le={kernelName:hl,backendName:"webgl",kernelFunc:Fle};function Ple(e){let{inputs:t,backend:r}=e,{indices:n,values:a,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(a.shape.length!==1)throw new Error(`Values must be a vector, saw:
          ${a.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
-        ${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,f,m]=Fee(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var _le={kernelName:Xp,backendName:"webgl",kernelFunc:Ple};function zle(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=$ee(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Ole={kernelName:ed,backendName:"webgl",kernelFunc:zle};function Dle(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+        ${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,f,m]=Fee(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var _le={kernelName:Zp,backendName:"webgl",kernelFunc:Ple};function zle(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=$ee(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Ole={kernelName:td,backendName:"webgl",kernelFunc:zle};function Dle(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
               ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-              ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=DS(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var Lle={kernelName:Zp,backendName:"webgl",kernelFunc:Dle};function Ble(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+              ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=WS(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var Lle={kernelName:Yp,backendName:"webgl",kernelFunc:Dle};function Ble(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-             ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=DS(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var Wle={kernelName:Yp,backendName:"webgl",kernelFunc:Ble};function Vle(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=new g8(u,l,a.shape.length,s.shape.length,d,[h,1],p),f=r.runWebGLProgram(c,[s,a,i],s.dtype),m=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),m}var Ule={kernelName:Jp,backendName:"webgl",kernelFunc:Vle};function Gle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=vd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var jle={kernelName:hl,backendName:"webgl",kernelFunc:Gle},Hv="return sqrt(x);",Hle=it({opSnippet:Hv,packedOpSnippet:Hv,cpuKernelImpl:Pee}),qle={kernelName:Ci,backendName:"webgl",kernelFunc:Hle},Kle="return x * x;",Xle=it({opSnippet:Kle}),Zle={kernelName:td,backendName:"webgl",kernelFunc:Xle},qv="return (a - b) * (a - b);",Yle=br({opSnippet:qv,packedOpSnippet:qv}),Jle={kernelName:Mi,backendName:"webgl",kernelFunc:Yle};function Qle({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=qn+`
+             ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=WS(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var Wle={kernelName:Jp,backendName:"webgl",kernelFunc:Ble};function Vle(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=new x8(u,l,a.shape.length,s.shape.length,d,[h,1],p),f=r.runWebGLProgram(c,[s,a,i],s.dtype),m=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),m}var Ule={kernelName:Qp,backendName:"webgl",kernelFunc:Vle};function Gle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=wd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var jle={kernelName:cl,backendName:"webgl",kernelFunc:Gle},Xv="return sqrt(x);",Hle=it({opSnippet:Xv,packedOpSnippet:Xv,cpuKernelImpl:Pee}),qle={kernelName:Ci,backendName:"webgl",kernelFunc:Hle},Kle="return x * x;",Xle=it({opSnippet:Kle}),Zle={kernelName:rd,backendName:"webgl",kernelFunc:Xle},Zv="return (a - b) * (a - b);",Yle=br({opSnippet:Zv,packedOpSnippet:Zv}),Jle={kernelName:Mi,backendName:"webgl",kernelFunc:Yle};function Qle({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=qn+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
   `,s=new Ga(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var eue={kernelName:_i,backendName:"webgl",kernelFunc:Qle},tue=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=gt(r.length),s=gt(r.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=r.map((l,u)=>(o++,r.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
       ${a} begin = ${a}(${e});
@@ -4367,7 +4367,7 @@ return a / b;`,Uie=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function rue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(m)w=ve({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let S=_t.computeOutShape(A,x,b),E=vd({inputs:{x:a},backend:r,attrs:{begin:A,size:S}});w=ve({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let S=r.readSync(a.dataId),E=We(a.shape,a.dtype,S),R=_ee(c,E,b,A);w=r.makeTensorInfo(f,a.dtype,R.values)}else{let S=new tue(A,b,c);w=r.runWebGLProgram(S,[a],a.dtype)}let T=ve({inputs:{x:w},backend:r,attrs:{shape:f}});return r.disposeIntermediateTensorInfo(w),T}var nue={kernelName:cl,backendName:"webgl",kernelFunc:rue};function aue(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=zee(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var sue={kernelName:Qp,backendName:"webgl",kernelFunc:aue};function iue(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=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 o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=Oee(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var oue={kernelName:am,backendName:"webgl",kernelFunc:iue};function lue(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=Dee(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var uue={kernelName:sm,backendName:"webgl",kernelFunc:lue},due="return tan(x);",pue=it({opSnippet:due}),hue={kernelName:fl,backendName:"webgl",kernelFunc:pue},cue=`
+    `}};function rue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(m)w=ve({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let S=_t.computeOutShape(A,x,b),E=wd({inputs:{x:a},backend:r,attrs:{begin:A,size:S}});w=ve({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let S=r.readSync(a.dataId),E=We(a.shape,a.dtype,S),R=_ee(c,E,b,A);w=r.makeTensorInfo(f,a.dtype,R.values)}else{let S=new tue(A,b,c);w=r.runWebGLProgram(S,[a],a.dtype)}let T=ve({inputs:{x:w},backend:r,attrs:{shape:f}});return r.disposeIntermediateTensorInfo(w),T}var nue={kernelName:fl,backendName:"webgl",kernelFunc:rue};function aue(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=zee(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var sue={kernelName:eh,backendName:"webgl",kernelFunc:aue};function iue(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=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: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=Oee(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var oue={kernelName:sm,backendName:"webgl",kernelFunc:iue};function lue(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=Dee(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var uue={kernelName:im,backendName:"webgl",kernelFunc:lue},due="return tan(x);",pue=it({opSnippet:due}),hue={kernelName:ml,backendName:"webgl",kernelFunc:pue},cue=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
 `,fue=it({opSnippet:cue}),mue={kernelName:$i,backendName:"webgl",kernelFunc:fue},gue=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let n=gt(this.rank),a=yue(e);this.userCode=`
@@ -4375,7 +4375,7 @@ return a / b;`,Uie=`
         ${n} resRC = getOutputCoords();
         setOutput(getA(${a}));
       }
-    `}};function yue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function y8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>v.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Bee(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new gue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var Aue={kernelName:Xa,backendName:"webgl",kernelFunc:y8},xue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
+    `}};function yue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function b8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>v.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Bee(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new gue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var Aue={kernelName:Xa,backendName:"webgl",kernelFunc:b8},xue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
        void main() {
          ivec2 coords = getOutputCoords();
          int batch = coords[0];
@@ -4449,7 +4449,7 @@ return a / b;`,Uie=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function so(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Kv(e){let t=1;for(;t<e;)t*=2;return t}function vue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[_,M]=Wee(R,u,a.dtype,s,i);return[r.makeTensorInfo(_.shape,_.dtype,_.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Mh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,f=v.sizeFromShape(u)/d,m=ve({inputs:{x:c},attrs:{shape:[f,d]},backend:r});p&&so(r,c);let g=Kv(s),y=Kv(d),A=null,x=()=>A===null?[m,m]:[m,A],b=(R,_,M)=>{let I=x(),O=new xue(M),z=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[_]],j=A;A=r.runWebGLProgram(O,I,"int32",z),so(r,j)};for(let R=1;R<g;R*=2){let _=R*2;for(let M=R;M>=1;M/=2)b(_,M,[f,y])}for(let R=y;R>g;R/=2){let _=x(),M=new bue([f,R/2]),I=[[d],[A===null?1:0],[g]],O=A;A=r.runWebGLProgram(M,_,"int32",I),so(r,O);let z=g/2,j=z*2;for(let X=z;X>=1;X/=2)b(j,X,A.shape)}let w=A;A=vd({inputs:{x:A},backend:r,attrs:{begin:0,size:[f,s]}}),so(r,w);let T=l8({inputs:{x:m,indices:A},backend:r,attrs:{axis:1,batchDims:1}});so(r,m);let S=u.slice(0,-1);S.push(s),w=A,A=ve({inputs:{x:A},attrs:{shape:S},backend:r}),so(r,w);let E=T;return T=ve({inputs:{x:T},attrs:{shape:S},backend:r}),so(r,E),[T,A]}var wue={kernelName:ml,backendName:"webgl",kernelFunc:vue},kue=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+     `}};function io(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Yv(e){let t=1;for(;t<e;)t*=2;return t}function vue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[_,M]=Wee(R,u,a.dtype,s,i);return[r.makeTensorInfo(_.shape,_.dtype,_.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Mh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,f=v.sizeFromShape(u)/d,m=ve({inputs:{x:c},attrs:{shape:[f,d]},backend:r});p&&io(r,c);let g=Yv(s),y=Yv(d),A=null,x=()=>A===null?[m,m]:[m,A],b=(R,_,M)=>{let I=x(),O=new xue(M),z=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[_]],j=A;A=r.runWebGLProgram(O,I,"int32",z),io(r,j)};for(let R=1;R<g;R*=2){let _=R*2;for(let M=R;M>=1;M/=2)b(_,M,[f,y])}for(let R=y;R>g;R/=2){let _=x(),M=new bue([f,R/2]),I=[[d],[A===null?1:0],[g]],O=A;A=r.runWebGLProgram(M,_,"int32",I),io(r,O);let z=g/2,j=z*2;for(let X=z;X>=1;X/=2)b(j,X,A.shape)}let w=A;A=wd({inputs:{x:A},backend:r,attrs:{begin:0,size:[f,s]}}),io(r,w);let T=p8({inputs:{x:m,indices:A},backend:r,attrs:{axis:1,batchDims:1}});io(r,m);let S=u.slice(0,-1);S.push(s),w=A,A=ve({inputs:{x:A},attrs:{shape:S},backend:r}),io(r,w);let E=T;return T=ve({inputs:{x:T},attrs:{shape:S},backend:r}),io(r,E),[T,A]}var wue={kernelName:gl,backendName:"webgl",kernelFunc:vue},kue=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4561,7 +4561,7 @@ return a / b;`,Uie=`
               }
               setOutput(outputValue);
             }
-        `}};function Iue(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new kue(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var Sue={kernelName:gl,backendName:"webgl",kernelFunc:Iue};function Tue(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;md(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=Vee(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var Nue={kernelName:im,backendName:"webgl",kernelFunc:Tue};function Cue(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=vd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var Eue={kernelName:yl,backendName:"webgl",kernelFunc:Cue},Rue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
+        `}};function Iue(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new kue(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var Sue={kernelName:yl,backendName:"webgl",kernelFunc:Iue};function Tue(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;gd(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=Vee(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var Nue={kernelName:om,backendName:"webgl",kernelFunc:Tue};function Cue(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=wd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var Eue={kernelName:Al,backendName:"webgl",kernelFunc:Cue},Rue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
         sumValue += dot(values, segFilter);
     `,p="";a%r>0&&(p=`
         if (inIdx < 0 || inIdx >= ${a}) {
@@ -4667,11 +4667,11 @@ return a / b;`,Uie=`
         }
         setOutput(${l});
       }
-    `}};function Mue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=N.getAxesPermutation([u],o),h=a;d!=null&&(h=xr({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=N.getInnerMostAxes(1,o)[0]);let p=N.segment_util.computeOutShape(h.shape,u,i),c=v.sizeFromShape([h.shape[u]]),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(f);let m=nh(a.dtype),g=(b,w,T,S,E)=>{let R=b.shape[0],_=b.shape[1],M=N.segment_util.segOpComputeOptimalWindowSize(_,E),I={windowSize:M,inSize:_,batchSize:R,numSegments:E},O=new Rue(I,w),z=r.compileAndRun(O,[b,T],S);if(l.push(z),z.shape[1]===E)return z;let j=m8({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=y8({inputs:{x:j},backend:r,attrs:{reps:[_/M]}});return l.push(j),l.push(X),g(z,w,X,S,E)},y=g(f,"unsortedSegmentSum",s,m,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=N.getUndoAxesPermutation(d);x=xr({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Fue={kernelName:eh,backendName:"webgl",kernelFunc:Mue},$ue=[Ote,Lte,Vte,jte,qte,Zte,Jte,ere,are,ire,ure,hre,mre,xre,wre,Ire,Tre,Rre,Fre,Pre,Dre,jre,qre,Xre,tne,nne,one,Ate,dne,mne,xne,Sne,Nne,Ene,Mne,$ne,zne,Lne,Vne,Gne,Hne,Kne,Yne,Qne,nae,sae,lae,pae,cae,yae,vae,Sae,Cae,Mae,Fae,Pae,zae,Dae,Bae,Vae,Hae,Xae,Jae,ese,nse,ise,dse,fse,yte,gse,cne,xse,wse,Sse,bte,Ese,$se,_se,Lse,Vse,Hse,Xse,Qse,nie,iie,lie,hie,fie,gie,bie,wie,Iie,Tie,Cie,Fie,zie,Bie,Kie,Ste,Jie,toe,aoe,ooe,Yre,doe,hoe,foe,yoe,voe,wte,koe,Ioe,Jre,Gie,Noe,Moe,_oe,Nte,Loe,Voe,Hoe,Xoe,Qoe,tle,ale,ole,ule,hle,mle,xle,wle,Sle,Cle,Ure,Hie,Mle,$le,_le,Ole,Lle,Wle,Ule,jle,qle,Zle,Jle,eue,nue,sue,oue,uue,jie,Pte,hue,mue,Aue,wue,Sue,_te,Nue,Eue,Fue,poe];for(let e of $ue)Vn(e);var Pa=Y();Pa.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Pa.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Pa.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Pa.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Pa.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Pa.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Pa.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Pa.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Pue="return a + b;",_ue="return areal * breal - aimag * bimag;",zue="return areal * bimag + aimag * breal;",Oue="return a / b;",Due="return a * b;",Lue="return (a - b) * (a - b);",Bue="return a - b;",Wue="return f32(a == b);",Vue="return vec4<f32>(a == b);",Uue="return f32(a > b);",Gue="return vec4<f32>(a > b);",jue="return f32(a >= b);",Hue="return vec4<f32>(a >= b);",que="return f32(a < b);",Kue="return vec4<f32>(a < b);",Xue="return f32(a <= b);",Zue="return vec4<f32>(a <= b);",Yue="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Jue=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
+    `}};function Mue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=N.getAxesPermutation([u],o),h=a;d!=null&&(h=xr({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=N.getInnerMostAxes(1,o)[0]);let p=N.segment_util.computeOutShape(h.shape,u,i),c=v.sizeFromShape([h.shape[u]]),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(f);let m=ah(a.dtype),g=(b,w,T,S,E)=>{let R=b.shape[0],_=b.shape[1],M=N.segment_util.segOpComputeOptimalWindowSize(_,E),I={windowSize:M,inSize:_,batchSize:R,numSegments:E},O=new Rue(I,w),z=r.compileAndRun(O,[b,T],S);if(l.push(z),z.shape[1]===E)return z;let j=A8({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=b8({inputs:{x:j},backend:r,attrs:{reps:[_/M]}});return l.push(j),l.push(X),g(z,w,X,S,E)},y=g(f,"unsortedSegmentSum",s,m,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=N.getUndoAxesPermutation(d);x=xr({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Fue={kernelName:th,backendName:"webgl",kernelFunc:Mue},$ue=[Ote,Lte,Vte,jte,qte,Zte,Jte,ere,are,ire,ure,hre,mre,xre,wre,Ire,Tre,Rre,Fre,Pre,Dre,jre,qre,Xre,tne,nne,one,Ate,dne,mne,xne,Sne,Nne,Ene,Mne,$ne,zne,Lne,Vne,Gne,Hne,Kne,Yne,Qne,nae,sae,lae,pae,cae,yae,vae,Sae,Cae,Mae,Fae,Pae,zae,Dae,Bae,Vae,Hae,Xae,Jae,ese,nse,ise,dse,fse,yte,gse,cne,xse,wse,Sse,bte,Ese,$se,_se,Lse,Vse,Hse,Xse,Qse,nie,iie,lie,hie,fie,gie,bie,wie,Iie,Tie,Cie,Fie,zie,Bie,Kie,Ste,Jie,toe,aoe,ooe,Yre,doe,hoe,foe,yoe,voe,wte,koe,Ioe,Jre,Gie,Noe,Moe,_oe,Nte,Loe,Voe,Hoe,Xoe,Qoe,tle,ale,ole,ule,hle,mle,xle,wle,Sle,Cle,Ure,Hie,Mle,$le,_le,Ole,Lle,Wle,Ule,jle,qle,Zle,Jle,eue,nue,sue,oue,uue,jie,Pte,hue,mue,Aue,wue,Sue,_te,Nue,Eue,Fue,poe];for(let e of $ue)Vn(e);var Pa=Y();Pa.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Pa.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Pa.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Pa.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Pa.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Pa.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Pa.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Pa.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Pue="return a + b;",_ue="return areal * breal - aimag * bimag;",zue="return areal * bimag + aimag * breal;",Oue="return a / b;",Due="return a * b;",Lue="return (a - b) * (a - b);",Bue="return a - b;",Wue="return f32(a == b);",Vue="return vec4<f32>(a == b);",Uue="return f32(a > b);",Gue="return vec4<f32>(a > b);",jue="return f32(a >= b);",Hue="return vec4<f32>(a >= b);",que="return f32(a < b);",Kue="return vec4<f32>(a < b);",Xue="return f32(a <= b);",Zue="return vec4<f32>(a <= b);",Yue="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Jue=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
   vec4<f32>(b >= vec4<f32>(1.0)));`,Que=`
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
-  `,A8=`
+  `,v8=`
   if (isNaN.r) {
     resultTemp.r = uniforms.NAN;
   }
@@ -4742,19 +4742,19 @@ return a / b;`,Uie=`
     resultTemp.a = 1.0;
   }
   let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
-  ${A8}
+  ${v8}
   return resultTemp;
   `,ide="if (a < 0.0) { return b * a; }  return a;",ode=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-  `;function Xv(e,t){let r=t?A8:Que;return t?`
+  `;function Jv(e,t){let r=t?v8:Que;return t?`
     var resultTemp = vec4<f32>(${e}(a, b));
     let isNaN = isnanVec4(a) | isnanVec4(b);
     `+r+`
     return resultTemp;
   `:r+`
     return ${e}(a, b);
-  `}function Fh(e,t){switch(e){case 0:return Due;case 1:return Pue;case 2:return Bue;case 3:return Oue;case 4:return t?Vue:Wue;case 5:return t?Gue:Uue;case 6:return t?Hue:jue;case 7:return t?Kue:que;case 8:return t?Zue:Xue;case 9:return t?Jue:Yue;case 10:return t?nde:rde;case 11:return Lue;case 12:return t?tde:ede;case 14:return t?ode:ide;case 15:return Xv("max",t);case 16:return Xv("min",t);case 13:return t?sde:ade;case 17:return _ue;case 18:return zue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var lde="return abs(a);",ude="return ceil(a);",dde="return cos(a);",pde=`
+  `}function Fh(e,t){switch(e){case 0:return Due;case 1:return Pue;case 2:return Bue;case 3:return Oue;case 4:return t?Vue:Wue;case 5:return t?Gue:Uue;case 6:return t?Hue:jue;case 7:return t?Kue:que;case 8:return t?Zue:Xue;case 9:return t?Jue:Yue;case 10:return t?nde:rde;case 11:return Lue;case 12:return t?tde:ede;case 14:return t?ode:ide;case 15:return Jv("max",t);case 16:return Jv("min",t);case 13:return t?sde:ade;case 17:return _ue;case 18:return zue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var lde="return abs(a);",ude="return ceil(a);",dde="return cos(a);",pde=`
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
 `,hde="return exp(a) - 1.0;",cde="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",fde=`
@@ -4773,7 +4773,10 @@ return a / b;`,Uie=`
   }
   return resFloat;
 `,mde="return exp(a);",gde="return floor(a);",yde="return a;",Ade=`if (a < 0.0) { return 1.0/0.0; }
-  return log(a);`,xde="return f32(!(a >= 1.0));",bde="return -a;",vde="if (a < 0.0) { return uniforms.alpha * a; } return a;",wde="if(a < 0.0) { return 0.0; } return a;",kde="return clamp(a, 0.0, 6.0);",Ide="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Sde=`
+  return log(a);`,xde="return f32(!(a >= 1.0));",bde="return -a;",vde="if (a < 0.0) { return uniforms.alpha * a; } return a;",wde=`
+  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
+  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
+`,kde="if(a < 0.0) { return 0.0; } return a;",Ide="return clamp(a, 0.0, 6.0);",Sde="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Tde=`
   var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
   let isNaN = isnanVec4(a);
 
@@ -4790,16 +4793,16 @@ return a / b;`,Uie=`
     resFloat.a = a.a;
   }
   return resFloat;
-`,Tde="return 1.0/sqrt(a);",Nde="return 1.0 / (1.0 + exp(-1.0 * a));",Cde="return sin(a);",Ede=`
+`,Nde="return 1.0/sqrt(a);",Cde="return 1.0 / (1.0 + exp(-1.0 * a));",Ede="return sin(a);",Rde=`
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
-`,Rde="return sqrt(a);",Mde="return a * a;",Fde=`
+`,Mde="return sqrt(a);",Fde="return a * a;",$de=`
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`,$de="return f32(i32((a)));";function lo(e,t){switch(e){case 0:return lde;case 2:return dde;case 3:return pde;case 1:return ude;case 4:return t?fde:cde;case 5:return mde;case 6:return hde;case 7:return gde;case 8:return yde;case 9:return Ade;case 10:return xde;case 11:return bde;case 14:return vde;case 12:return t?Sde:wde;case 13:return t?Ide:kde;case 15:return Tde;case 18:return Nde;case 16:return Cde;case 17:return Ede;case 19:return Rde;case 20:return Mde;case 21:return Fde;case 22:return $de;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function ts(e,t=!1){if(e===null)return null;if(e==="linear")return lo(8);if(e==="relu")return lo(12,t);if(e==="elu")return lo(4,t);if(e==="relu6")return lo(13,t);if(e==="prelu")return Fh(14,t);if(e==="sigmoid")return lo(18);if(e==="leakyrelu")return lo(14);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function Pde(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function mr(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function tf(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function db(){return`
+`,Pde="return f32(i32((a)));";function uo(e,t){switch(e){case 0:return lde;case 2:return dde;case 3:return pde;case 1:return ude;case 4:return t?fde:cde;case 5:return mde;case 6:return hde;case 7:return gde;case 8:return yde;case 9:return Ade;case 10:return xde;case 11:return bde;case 14:return t?wde:vde;case 12:return t?Tde:kde;case 13:return t?Sde:Ide;case 15:return Nde;case 18:return Cde;case 16:return Ede;case 17:return Rde;case 19:return Mde;case 20:return Fde;case 21:return $de;case 22:return Pde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function ts(e,t=!1){if(e===null)return null;if(e==="linear")return uo(8);if(e==="relu")return uo(12,t);if(e==="elu")return uo(4,t);if(e==="relu6")return uo(13,t);if(e==="prelu")return Fh(14,t);if(e==="sigmoid")return uo(18,t);if(e==="leakyrelu")return uo(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function _de(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function mr(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function rf(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function cb(){return`
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
 `}function Bi(){return`
-  ${db()}
+  ${cb()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
@@ -4809,7 +4812,7 @@ return a / b;`,Uie=`
 `}function tt(){return`
     ${Bi()}
       let index = getGlobalIndex();
-`}function _de(e,t,r,n=!1){let a=[];if(a.push(`
+`}function zde(e,t,r,n=!1){let a=[];if(a.push(`
     let workGroupSizeX = ${r.workGroupSize[0]}u;
     let workGroupSizeY = ${r.workGroupSize[1]}u;
     let workGroupSizeZ = ${r.workGroupSize[2]}u;
@@ -4842,23 +4845,23 @@ return a / b;`,Uie=`
         dispatchSize    : vec3<u32>,
       };
 
-      @group(0) @binding(0) var<storage, write> result: array<${tf(t.dtype,r.isVec4)}>;
+      @group(0) @binding(0) var<storage, write> result: array<${rf(t.dtype,r.isVec4)}>;
       @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `),[Zv,a.join(`
-`),Yv(t.shape),r.getUserCode()].join(`
+    `),[Qv,a.join(`
+`),ew(t.shape),r.getUserCode()].join(`
 `);let s="struct Uniforms { NAN : f32, ";r.variableNames.forEach((d,h)=>{s+=`${d.charAt(0).toLowerCase()+d.slice(1)}Shape : ${mr(e[h].shape.length)}, `}),s+=`outShape : ${mr(t.shape.length)}, `;let i=t.shape.length-1;s+=`
        outShapeStrides: ${mr(i)}, `,r.size&&(s+="size : i32, "),r.uniforms&&(s+=r.uniforms),s+="};",a.push(s),r.atomic?a.push(`
     @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
   `):a.push(`
-    @group(0) @binding(0) var<storage, write> result: array<${tf(t.dtype,r.isVec4)}>;
+    @group(0) @binding(0) var<storage, write> result: array<${rf(t.dtype,r.isVec4)}>;
   `),r.variableNames.forEach((d,h)=>{a.push(`
-    @group(0) @binding(${1+h}) var<storage, read> ${d}: array<${tf(e[h].dtype,r.isVec4)}>;
+    @group(0) @binding(${1+h}) var<storage, read> ${d}: array<${rf(e[h].dtype,r.isVec4)}>;
     `)}),s!==""&&a.push(`
     @group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms: Uniforms;
-    `);let[o,l]=Wde(t.shape,r.dispatchLayout),u=[Zv,a.join(`
-`),Yv(t.shape),o,zde(t.shape.length)];if(r.atomic||u.push(Ode(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let d=e.map(h=>Dde(h,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
+    `);let[o,l]=Vde(t.shape,r.dispatchLayout),u=[Qv,a.join(`
+`),ew(t.shape),o,Ode(t.shape.length)];if(r.atomic||u.push(Dde(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let d=e.map(h=>Lde(h,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
 `);u.push(d)}return u.push(r.getUserCode()),u.join(`
-`)}var Zv=`
+`)}var Qv=`
   // Checks whether coordinates lie within the bounds of the shape.
   fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
     return all(coord >= vec2<i32>(0)) && all(coord < shape);
@@ -4905,7 +4908,7 @@ return a / b;`,Uie=`
   fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
-`;function zde(e){let t="";switch(e){case 0:case 1:t+=`
+`;function Ode(e){let t="";switch(e){case 0:case 1:t+=`
         fn getOutputIndexFromCoords(coords : i32) -> i32 {
           return coords;
         }
@@ -4922,7 +4925,7 @@ return a / b;`,Uie=`
           return dot(coords, vec4<i32>(
             uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
         }
-        `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Ode(e,t,r){let n=e.length,a=tf(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Dde(e,t,r){let n=e.length,a=rf(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${a}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -4950,7 +4953,7 @@ return a / b;`,Uie=`
         let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
         setOutputAtIndexI32(flatIndex, value);
       }
-    `}return s}function Dde(e,t,r,n){let a=Lde(e,r);return e.shape.length<=t.length&&(a+=Bde(e,t,r,n)),a}function Lde(e,t){let r=e.name,n=e.shape.length,a=mr(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3"].slice(0,n),o=i.map(d=>`${d} : i32`).join(", ");if(n<1)return t?`
+    `}return s}function Lde(e,t,r,n){let a=Bde(e,r);return e.shape.length<=t.length&&(a+=Wde(e,t,r,n)),a}function Bde(e,t){let r=e.name,n=e.shape.length,a=mr(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3"].slice(0,n),o=i.map(d=>`${d} : i32`).join(", ");if(n<1)return t?`
         fn ${s}() -> vec4<f32> {
           return vec4<f32>(${r}[0]);
         }
@@ -4968,7 +4971,7 @@ return a / b;`,Uie=`
       return f32(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
         ${l})]);
     }
-   `}function Bde(e,t,r,n){let a=e.name,s=a.charAt(0).toUpperCase()+a.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=mr(l);if(v.arraysEqual(e.shape,t)&&n)return r?`
+   `}function Wde(e,t,r,n){let a=e.name,s=a.charAt(0).toUpperCase()+a.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=mr(l);if(v.arraysEqual(e.shape,t)&&n)return r?`
         fn ${i}Index(globalIndex : i32) -> vec4<f32> {
           return vec4<f32>(${a}[globalIndex]);
         }
@@ -5025,13 +5028,13 @@ return a / b;`,Uie=`
       ${p}
       return f32(${a}[getIndexFromCoords${m}(${c}, ${f})]);
     }
-  `}function Wde(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${mr(s)}{
+  `}function Vde(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${mr(s)}{
       let globalIndex = getGlobalIndex();
       return getCoordsFromIndex(globalIndex);
     }
-    `,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let f=Pde(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)i+=`let d${c[m]} = index${p} / ${f[m]};`,m===f.length-1?i+=`let d${c[m+1]} = index${p} - d${c[m]} * ${f[m]};`:i+=`index${p} = index${p} - d${c[m]} * ${f[m]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=mr(l),h=`fn getOutputCoords() -> ${d} {
+    `,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let f=_de(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)i+=`let d${c[m]} = index${p} / ${f[m]};`,m===f.length-1?i+=`let d${c[m+1]} = index${p} - d${c[m]} * ${f[m]};`:i+=`index${p} = index${p} - d${c[m]} * ${f[m]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=mr(l),h=`fn getOutputCoords() -> ${d} {
     ${i}
-  `;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function Yv(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=v.computeStrides(e),n=mr(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+  `;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function ew(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=v.computeStrides(e),n=mr(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
       let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
       return vec2<i32>(d0, d1);
     }`;let s="var index2 = index;"+r.map((i,o)=>{let l=`let ${a[o]} = index2 / uniforms.outShapeStrides[${o}]`,u=o===r.length-1?`let ${a[o+1]} = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`;return`${l}; ${u};`}).join("");return`
@@ -5039,7 +5042,7 @@ return a / b;`,Uie=`
       ${s}
       return ${n}(${a.join(",")});
     }
-  `}var x8={};Le(x8,{ArrayBufferToTypedArray:()=>v8,GPUBytesPerElement:()=>H1,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>pb,computeWorkGroupSizeForMatMul:()=>b8,computeWorkPerThreadForConv2d:()=>hb,flatDispatchLayout:()=>Xe,isWebGPUSupported:()=>cb,tilesFitEvenlyIntoShape:()=>Ha});var bo=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function Ha(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function Oe(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(bo(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil(bo(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil(bo(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function pb(e,t){let r=bo(e.x.map(a=>t[a])),n=bo(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function b8(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function hb(e,t){let r=bo(e.x.map(a=>t[a])),n=bo(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Xe(e){return{x:e.map((t,r)=>r)}}function H1(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function v8(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function cb(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function w8(e,t,r,n){return v.assert(n%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
+  `}var w8={};Le(w8,{ArrayBufferToTypedArray:()=>I8,GPUBytesPerElement:()=>X1,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>fb,computeWorkGroupSizeForMatMul:()=>k8,computeWorkPerThreadForConv2d:()=>mb,flatDispatchLayout:()=>Xe,isWebGPUSupported:()=>gb,tilesFitEvenlyIntoShape:()=>Ha});var vo=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function Ha(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function Oe(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(vo(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil(vo(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil(vo(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function fb(e,t){let r=vo(e.x.map(a=>t[a])),n=vo(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function k8(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function mb(e,t){let r=vo(e.x.map(a=>t[a])),n=vo(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Xe(e){return{x:e.map((t,r)=>r)}}function X1(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function I8(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function gb(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function S8(e,t,r,n){return v.assert(n%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
   var<workgroup> mm_Asub : array<array<vec4<f32>, ${n/e[0]}>, ${t}>;
   var<workgroup> mm_Bsub : array<array<vec4<f32>, ${r/e[0]}>, ${n}>;
 
@@ -5106,7 +5109,7 @@ return a / b;`,Uie=`
                  globalCol,
                  acc[innerRow], globalId);
     }
-  }`}var Vde=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],r=[this.outputShape[0],e,t],n=[this.tileAOuter,this.tileInner],a=[this.tileInner,this.tileBOuter];return[Ha(n,this.aShape.slice(1)),Ha(a,r.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  }`}var Ude=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],r=[this.outputShape[0],e,t],n=[this.tileAOuter,this.tileInner],a=[this.tileInner,this.tileBOuter];return[Ha(n,this.aShape.slice(1)),Ha(a,r.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
         }
         return vec4<f32>(0.0)`,t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
@@ -5143,8 +5146,8 @@ return a / b;`,Uie=`
           setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
         }
       }
-      ${w8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
-    `}};function fb(e,t){let r=t[1]*e[1],n=t[0]*e[0],a=r>n?r:n;return`
+      ${S8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
+    `}};function yb(e,t){let r=t[1]*e[1],n=t[0]*e[0],a=r>n?r:n;return`
     var<workgroup> mm_Asub : array<array<f32, ${a}>, ${r}>;
     var<workgroup> mm_Bsub : array<array<f32, ${n}>, ${a}>;
     ${Bi()}
@@ -5228,7 +5231,7 @@ return a / b;`,Uie=`
         }
       }
     }
-  `}function Ude(e){return`
+  `}function Gde(e){return`
     let TileSize = ${e[0]*4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
 
@@ -5271,7 +5274,7 @@ return a / b;`,Uie=`
         mm_write(globalRow, globalCol, acc, globalId);
       }
     }
-  `}var k8=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=n?e[1]:e[2];this.workGroupSize=b8(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let u=s!=null,d=o!=null;u&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=n,this.transposeB=a,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=d;let h=this.outputShape[2],p=this.transposeB?[this.outputShape[0],h,l]:[this.outputShape[0],l,h];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${n}_${a}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,n=t>r?t:r;this.outputShape[1]===1&&(n*=4),v.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[t,n],s=[n,r];return[Ha(a,this.aShape.slice(1)),Ha(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var T8=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=n?e[1]:e[2];this.workGroupSize=k8(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let u=s!=null,d=o!=null;u&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=n,this.transposeB=a,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=d;let h=this.outputShape[2],p=this.transposeB?[this.outputShape[0],h,l]:[this.outputShape[0],l,h];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${n}_${a}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,n=t>r?t:r;this.outputShape[1]===1&&(n*=4),v.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[t,n],s=[n,r];return[Ha(a,this.aShape.slice(1)),Ha(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
              return A[batch * batchASize + row * uniforms.dimInner + col];
            }
            return 0.0;`:e=this.fitA?"return A[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
@@ -5313,8 +5316,8 @@ return a / b;`,Uie=`
         ${n}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${this.outputShape[1]>1?fb([this.workPerThread,this.workPerThread,1],this.workGroupSize):Ude(this.workGroupSize)}
-    `}};function Gde(){return`
+      ${this.outputShape[1]>1?yb([this.workPerThread,this.workPerThread,1],this.workGroupSize):Gde(this.workGroupSize)}
+    `}};function jde(){return`
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
     ${Bi()}
       let coords = getOutputCoords();
@@ -5345,7 +5348,7 @@ return a / b;`,Uie=`
         mm_write(batch, row, col, sum);
       }
     }
-  `}var jde=class{constructor(e,t=!1,r=!1,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=r,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${r}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let r="",n="";if(this.activation){let s=ts(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+  `}var Hde=class{constructor(e,t=!1,r=!1,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=r,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${r}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let r="",n="";if(this.activation){let s=ts(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${s}
             }`:r=`
@@ -5372,8 +5375,8 @@ return a / b;`,Uie=`
         ${n}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${Gde()}
-    `}};function Hde(e){let t=e[1]/2,r=e[0],n=t>r?t:r;return`
+      ${jde()}
+    `}};function qde(e){let t=e[1]/2,r=e[0],n=t>r?t:r;return`
   var<workgroup> mm_Asub1 : array<array<f32, ${n}>, ${t}>;
   var<workgroup> mm_Bsub1 : array<array<f32, ${r}>, ${n}>;
   var<workgroup> mm_Asub2 : array<array<f32, ${n}>, ${t}>;
@@ -5460,7 +5463,7 @@ return a / b;`,Uie=`
       mm_write(writeCol, globalCol, acc, globalId);
     }
   }
-  `}var qde=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var Kde=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
           return A[batch * batchASize + row * uniforms.dimInner + col];
         }
         return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
@@ -5494,8 +5497,8 @@ return a / b;`,Uie=`
           setOutputAtCoords(batch, row, col, value);
         }
       }
-      ${Hde(this.workGroupSize)}
-    `}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Kde={kernelName:al,backendName:"webgpu",kernelFunc:qe};function mb({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=bl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);v.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],w=n?[A,f,p]:[A,p,f],T=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),S=qe({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[T,S],R=Math.max(y,A),_=h%4===0&&f%4===0&&!r&&!n&&f>=32,M;c*f<=32?M=new jde([R,c,f],r,n,s,l,i):!r&&!n&&(c<=16&&(f<=512||p>=2*f)||f<=16&&(c<=512||h>=2*c))?M=new qde(b,w,[R,c,f],s,l,i):_?M=new Vde(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),s,l,i):M=new k8(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),r,n,s,l,i);let I=[T,S];s&&I.push(s),i&&I.push(i);let O=[{type:"int32",data:[c]},{type:"int32",data:[f]},{type:"int32",data:[h]}];l==="leakyrelu"&&(O.push({type:"float32",data:[o]}),M.uniforms+=" alpha : f32,");let z=a.runWebGPUProgram(M,I,e.dtype,O),j=qe({inputs:{x:z},backend:a,attrs:{shape:x}});E.push(z);for(let X of E)a.disposeData(X.dataId);return j}function Xde(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return mb({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Zde={kernelName:Cs,backendName:"webgpu",kernelFunc:Xde},Jv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+      ${qde(this.workGroupSize)}
+    `}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s),o=v.sizeFromShape(i);return v.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Xde={kernelName:sl,backendName:"webgpu",kernelFunc:qe};function Ab({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),A=v.sizeFromShape(g),x=vl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);v.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],w=n?[A,f,p]:[A,p,f],T=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),S=qe({inputs:{x:t},backend:a,attrs:{shape:w}}),E=[T,S],R=Math.max(y,A),_=h%4===0&&f%4===0&&!r&&!n&&f>=32,M;c*f<=32?M=new Hde([R,c,f],r,n,s,l,i):!r&&!n&&(c<=16&&(f<=512||p>=2*f)||f<=16&&(c<=512||h>=2*c))?M=new Kde(b,w,[R,c,f],s,l,i):_?M=new Ude(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),s,l,i):M=new T8(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),r,n,s,l,i);let I=[T,S];s&&I.push(s),i&&I.push(i);let O=[{type:"int32",data:[c]},{type:"int32",data:[f]},{type:"int32",data:[h]}];l==="leakyrelu"&&(O.push({type:"float32",data:[o]}),M.uniforms+=" alpha : f32,");let z=a.runWebGPUProgram(M,I,e.dtype,O),j=qe({inputs:{x:z},backend:a,attrs:{shape:x}});E.push(z);for(let X of E)a.disposeData(X.dataId);return j}function Zde(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return Ab({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Yde={kernelName:Cs,backendName:"webgpu",kernelFunc:Zde},tw=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
         ${Fh(this.op,!1)}
@@ -5510,7 +5513,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
         }
       }
-    `}},Yde=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
+    `}},Jde=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
          let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
          let b = getBByOutputCoords(coords);`;return`
         fn binaryOperation(a : f32, b : f32) -> f32 {
@@ -5537,7 +5540,7 @@ return a / b;`,Uie=`
             }
           }
         }
-        `}},Jde=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
+        `}},Qde=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
       fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
         ${Fh(this.op,this.isVec4)}
       }
@@ -5548,7 +5551,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-    `}},I8=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
+    `}},N8=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
       fn binaryOperation(a : f32, b : f32) -> f32 {
         ${Fh(this.op,!1)}
       }
@@ -5559,9 +5562,9 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-      `}};function Qv(e,t,r){if(v.arraysEqual(t,r)&&v.sizeFromShape(t)%4===0)return new Jde(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new Yde(e,t,r,a):new I8(e,t,r)}function Bn(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Qde={kernelName:li,backendName:"webgpu",kernelFunc:Bn};function wd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=Bn({inputs:{x:n},backend:r}),l=Bn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var epe={kernelName:Lp,backendName:"webgpu",kernelFunc:wd},$h=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+      `}};function rw(e,t,r){if(v.arraysEqual(t,r)&&v.sizeFromShape(t)%4===0)return new Qde(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new Jde(e,t,r,a):new N8(e,t,r)}function Bn(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var epe={kernelName:li,backendName:"webgpu",kernelFunc:Bn};function kd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=Bn({inputs:{x:n},backend:r}),l=Bn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var tpe={kernelName:Bp,backendName:"webgpu",kernelFunc:kd},$h=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
-        ${lo(this.op,!1)}
+        ${uo(this.op,!1)}
       }
       ${tt()}
         if (index < uniforms.size) {
@@ -5569,7 +5572,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function vr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new $h(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Gr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,f;if(e!==0)[c,f]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},w=Qv(e,i.shape,o.shape);return l.runWebGPUProgram(w,[x,b],Nr(y.dtype,A.dtype))});else{let g=new Jv(17,i.shape,o.shape),y=new Jv(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=wd({inputs:{real:c,imag:f},backend:l});return l.disposeData(c.dataId),l.disposeData(f.dataId),m}let u=n||Nr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?N.fromUint8ToStringArray(h):h,f=i.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(i.shape,o.shape,c,f,u);return l.makeTensorInfo(g,u,m)}let d=Qv(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:tpe,ceilImpl:rpe,concatImpl:npe,equalImpl:ape,expImpl:spe,expm1Impl:ipe,floorImpl:ope,gatherNdImpl:lpe,gatherV2Impl:upe,greaterEqualImpl:dpe,greaterImpl:ppe,lessEqualImpl:hpe,lessImpl:cpe,logImpl:fpe,maxImpl:mpe,maximumImpl:gpe,minimumImpl:ype,multiplyImpl:Ape,negImpl:xpe,notEqualImpl:bpe,prodImpl:vpe,rangeImpl:wpe,rsqrtImpl:kpe,simpleAbsImpl:Ipe,sliceImpl:Spe,stridedSliceImpl:Tpe,stringNGramsImpl:Npe,subImpl:Cpe,tileImpl:Epe,topKImpl:Rpe,transposeImpl:Mpe,uniqueImpl:_Ae}=a0,Fpe=vr({opType:0,cpuKernelImpl:Ipe}),$pe={kernelName:Po,backendName:"webgpu",kernelFunc:Fpe},Ppe=Gr({opSnippet:1,cpuKernelImpl:tpe,supportsComplex:!0}),_pe={kernelName:qa,backendName:"webgpu",kernelFunc:Ppe},zpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
+      `}};function vr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new $h(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Gr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,f;if(e!==0)[c,f]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},w=rw(e,i.shape,o.shape);return l.runWebGPUProgram(w,[x,b],Nr(y.dtype,A.dtype))});else{let g=new tw(17,i.shape,o.shape),y=new tw(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=kd({inputs:{real:c,imag:f},backend:l});return l.disposeData(c.dataId),l.disposeData(f.dataId),m}let u=n||Nr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?N.fromUint8ToStringArray(h):h,f=i.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(i.shape,o.shape,c,f,u);return l.makeTensorInfo(g,u,m)}let d=rw(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:rpe,ceilImpl:npe,concatImpl:ape,equalImpl:spe,expImpl:ipe,expm1Impl:ope,floorImpl:lpe,gatherNdImpl:upe,gatherV2Impl:dpe,greaterEqualImpl:ppe,greaterImpl:hpe,lessEqualImpl:cpe,lessImpl:fpe,logImpl:mpe,maxImpl:gpe,maximumImpl:ype,minimumImpl:Ape,multiplyImpl:xpe,negImpl:bpe,notEqualImpl:vpe,prodImpl:wpe,rangeImpl:kpe,rsqrtImpl:Ipe,simpleAbsImpl:Spe,sliceImpl:Tpe,stridedSliceImpl:Npe,stringNGramsImpl:Cpe,subImpl:Epe,tileImpl:Rpe,topKImpl:Mpe,transposeImpl:Fpe,uniqueImpl:zAe}=s0,$pe=vr({opType:0,cpuKernelImpl:Spe}),Ppe={kernelName:_o,backendName:"webgpu",kernelFunc:$pe},_pe=Gr({opSnippet:1,cpuKernelImpl:rpe,supportsComplex:!0}),zpe={kernelName:qa,backendName:"webgpu",kernelFunc:_pe},Ope=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
       ${tt()}
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5581,7 +5584,7 @@ return a / b;`,Uie=`
           }
         }
       }
-    `}};function Ope(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return Bn({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Nr(o,l)),s=n.map(o=>o.shape),i=new zpe(s);return r.runWebGPUProgram(i,n,a)}var Dpe={kernelName:Gs,backendName:"webgpu",kernelFunc:Ope},S8=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let n=[t];N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=N.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
+    `}};function Dpe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return Bn({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Nr(o,l)),s=n.map(o=>o.shape),i=new Ope(s);return r.runWebGPUProgram(i,n,a)}var Lpe={kernelName:Gs,backendName:"webgpu",kernelFunc:Dpe},C8=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let n=[t];N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=N.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
       var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
       var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
     `,t=(n,a)=>this.outputShape.length===1?n:`${n}[${a}]`,r=n=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${n}]`;return`
@@ -5661,10 +5664,10 @@ return a / b;`,Uie=`
           setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
         }
       }
-    `}},Lpe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
+    `}},Bpe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
-      ${db()}
+      ${cb()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -5683,7 +5686,7 @@ return a / b;`,Uie=`
             [localId.y]);
         }
       }
-    `}},Bpe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=mr(this.outputShape.length),t=Wpe(this.newDim);return`
+    `}},Wpe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=mr(this.outputShape.length),t=Vpe(this.newDim);return`
       ${tt()}
 
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
@@ -5695,7 +5698,7 @@ return a / b;`,Uie=`
           }
         }
       }
-    `}};function Wpe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC[${n}]`;return r.join()}function Ml(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=Mpe(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&v.arraysEqual(s,[1,0])){let d=new Lpe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new Bpe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var Vpe={kernelName:Pi,backendName:"webgpu",kernelFunc:Ml};function Upe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ml({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new S8(l.shape,i[0],"max"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Gpe={kernelName:js,backendName:"webgpu",kernelFunc:Upe};function jpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ml({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new S8(l.shape,i[0],"min"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Hpe={kernelName:Fu,backendName:"webgpu",kernelFunc:jpe},T8=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+    `}};function Vpe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC[${n}]`;return r.join()}function Fl(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=Fpe(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&v.arraysEqual(s,[1,0])){let d=new Bpe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new Wpe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var Upe={kernelName:Pi,backendName:"webgpu",kernelFunc:Fl};function Gpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Fl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new C8(l.shape,i[0],"max"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var jpe={kernelName:js,backendName:"webgpu",kernelFunc:Gpe};function Hpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=v.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Fl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new C8(l.shape,i[0],"min"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var qpe={kernelName:$u,backendName:"webgpu",kernelFunc:Hpe},E8=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
       ${tt()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -5728,7 +5731,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, ${t});
         }
       }
-    `}},N8=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
+    `}},R8=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
       ${tt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -5743,7 +5746,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function qpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return Bn({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new N8(d):(h=new T8(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var Kpe={kernelName:Hs,backendName:"webgpu",kernelFunc:qpe};function Xpe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return mb({a,b:s,transposeA:i,transposeB:o,backend:r})}var Zpe={kernelName:qs,backendName:"webgpu",kernelFunc:Xpe},Ype=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${mr(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=mr(this.rank),t=Jpe(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${q1[a]} = uniforms.start[${a}] + coords.${q1[a]};`),`
+    `}};function Kpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&v.arraysEqual(d.inShape,d.outShape))return Bn({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new R8(d):(h=new E8(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var Xpe={kernelName:Hs,backendName:"webgpu",kernelFunc:Kpe};function Zpe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return Ab({a,b:s,transposeA:i,transposeB:o,backend:r})}var Ype={kernelName:qs,backendName:"webgpu",kernelFunc:Zpe},Jpe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${mr(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=mr(this.rank),t=Qpe(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${Z1[a]} = uniforms.start[${a}] + coords.${Z1[a]};`),`
       ${tt()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -5753,7 +5756,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, getSource(${t}));
         }
       }
-    `}},q1=["x","y","z","w","u","v"];function Jpe(e){if(e===1)return"sourceLoc";if(e<=6)return q1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function kd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=Spe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new Ype(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var Qpe={kernelName:ul,backendName:"webgpu",kernelFunc:kd},ehe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Ml({inputs:{x:f},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:d}}),y=kd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},the={kernelName:_o,backendName:"webgpu",kernelFunc:ehe},C8=Gr({opSnippet:10,dtype:"bool",cpuKernelImpl:bpe}),rhe={kernelName:Yo,backendName:"webgpu",kernelFunc:C8};function Ph(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Bn({inputs:{x:a.complexTensorInfos.real},backend:r})}var nhe={kernelName:Kp,backendName:"webgpu",kernelFunc:Ph};function ahe(e,t){let r=new $h(e.shape,22),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function K1(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return Bn({inputs:{x:a},backend:r});let i=Wt(a.shape),o=K1({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=wd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=Ph({inputs:{input:a},backend:r}),o=K1({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Bn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return ahe(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=C8({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var she={kernelName:Ks,backendName:"webgpu",kernelFunc:K1},ihe=vr({opType:1,cpuKernelImpl:rpe}),ohe={kernelName:Xs,backendName:"webgpu",kernelFunc:ihe},lhe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+    `}},Z1=["x","y","z","w","u","v"];function Qpe(e){if(e===1)return"sourceLoc";if(e<=6)return Z1.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Id(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=Tpe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(v.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new Jpe(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var ehe={kernelName:dl,backendName:"webgpu",kernelFunc:Id},the=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Fl({inputs:{x:f},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:d}}),y=Id({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},rhe={kernelName:zo,backendName:"webgpu",kernelFunc:the},M8=Gr({opSnippet:10,dtype:"bool",cpuKernelImpl:vpe}),nhe={kernelName:Jo,backendName:"webgpu",kernelFunc:M8};function Ph(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Bn({inputs:{x:a.complexTensorInfos.real},backend:r})}var ahe={kernelName:Xp,backendName:"webgpu",kernelFunc:Ph};function she(e,t){let r=new $h(e.shape,22),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Y1(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return Bn({inputs:{x:a},backend:r});let i=Wt(a.shape),o=Y1({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=kd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=Ph({inputs:{input:a},backend:r}),o=Y1({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Bn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return she(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=M8({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var ihe={kernelName:Ks,backendName:"webgpu",kernelFunc:Y1},ohe=vr({opType:1,cpuKernelImpl:npe}),lhe={kernelName:Xs,backendName:"webgpu",kernelFunc:ohe},uhe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
       ${tt()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5769,7 +5772,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, clampedValue);
         }
       }
-    `}},uhe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
+    `}},dhe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
       ${tt()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5780,7 +5783,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
         }
       }
-    `}};function dhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(a.shape)%4===0?o=new lhe(a.shape):o=new uhe(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var phe={kernelName:Ka,backendName:"webgpu",kernelFunc:dhe},hhe=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
+    `}};function phe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return v.sizeFromShape(a.shape)%4===0?o=new uhe(a.shape):o=new dhe(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var hhe={kernelName:Ka,backendName:"webgpu",kernelFunc:phe},che=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
       ${tt()}
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5794,7 +5797,7 @@ return a / b;`,Uie=`
           }
         }
       }
-    `}};function c0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Bn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var che={kernelName:Gp,backendName:"webgpu",kernelFunc:c0};function X1(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>Ph({inputs:{input:A},backend:r})),f=e.map(A=>c0({inputs:{input:A},backend:r})),m=X1(c,t,r),g=X1(f,t,r),y=wd({inputs:{real:m,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),f.forEach(A=>r.disposeData(A.dataId)),r.disposeData(m.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let w=v.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,w]}})}),f=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=npe(f,m,n,g),A=N.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=fhe(e,t,r),o=s.map(c=>c.shape),l=new hhe(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function fhe(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function E8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return Bn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),X1(o,s,r)}var mhe={kernelName:zo,backendName:"webgpu",kernelFunc:E8},ghe=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+    `}};function f0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Bn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var fhe={kernelName:jp,backendName:"webgpu",kernelFunc:f0};function J1(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>Ph({inputs:{input:A},backend:r})),f=e.map(A=>f0({inputs:{input:A},backend:r})),m=J1(c,t,r),g=J1(f,t,r),y=kd({inputs:{real:m,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),f.forEach(A=>r.disposeData(A.dataId)),r.disposeData(m.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let w=v.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,w]}})}),f=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=ape(f,m,n,g),A=N.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=mhe(e,t,r),o=s.map(c=>c.shape),l=new che(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function mhe(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function F8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=v.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(v.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>v.sizeFromShape(u.shape)>0);if(o.length===1)return Bn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),J1(o,s,r)}var ghe={kernelName:Oo,backendName:"webgpu",kernelFunc:F8},yhe=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.hasLeakyreluAlpha=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],r=this.outputShape[1]*this.outputShape[2],n=this.outputShape[3],a=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ha(e,[r,a]),Ha(t,[a,n])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
     let divBy4Remainder${e} = flatIndex${e} % 4;
     let divBy4Index${e} = flatIndex${e} / 4;
@@ -5814,7 +5817,7 @@ return a / b;`,Uie=`
         temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
       }
     }
-    `}getUserCode(){let e=w8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`let outRow = r / uniforms.outShape[2];
+    `}getUserCode(){let e=S8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`let outRow = r / uniforms.outShape[2];
         let outCol = r % uniforms.outShape[2];
         let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
         let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
@@ -5893,7 +5896,7 @@ return a / b;`,Uie=`
           }
         }
         ${e}
-      `}},yhe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=pb(this.dispatchLayout,this.outputShape),this.elementsPerThread=hb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;v.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[e,r],a=[r,t],s=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ha(n,[s,o]),Ha(a,[o,i])]}getUserCode(){let e=fb(this.elementsPerThread,this.workGroupSize),t=`
+      `}},Ahe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=fb(this.dispatchLayout,this.outputShape),this.elementsPerThread=mb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;v.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[e,r],a=[r,t],s=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ha(n,[s,o]),Ha(a,[o,i])]}getUserCode(){let e=yb(this.elementsPerThread,this.workGroupSize),t=`
     let outRow = row / uniforms.outShape[2];
     let outCol = row % uniforms.outShape[2];
 
@@ -5948,7 +5951,7 @@ return a / b;`,Uie=`
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
     ${e}
-  `}},Ahe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=ts(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
+  `}},xhe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=ts(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${n}
              }`:e=`
@@ -6003,7 +6006,7 @@ return a / b;`,Uie=`
 
         writeResult(batch, coords[1], coords[2], outChannel, acc);
       }
-    `}},xhe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
+    `}},bhe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
       inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
     ${tt()}
 
@@ -6033,7 +6036,7 @@ return a / b;`,Uie=`
         }
       }
     }
-  `}};function bhe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.dataFormat==="channelsLast",d=!1,h=!1,p=r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",c,f;if(p){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else{let y=u?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,y,r.inChannels]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}})}let m=mb({a:c,b:f,transposeA:d,transposeB:h,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(c.dataId),n.disposeData(f.dataId),n.disposeData(m.dataId),g}function vhe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,strideWidth:h,strideHeight:p,padInfo:c,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*u*d,w=m*f,T=[w,b],S=!1,E=!1,R=[],_=qe({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),M=qe({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});R.push(_),R.push(M);let I=new xhe(T,x),O=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[h,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[d*l]},{type:"int32",data:[d]}],z=n.runWebGPUProgram(I,[_],_.dtype,O),j=qe({inputs:{x:z},backend:n,attrs:{shape:[1,T[0],T[1]]}});R.push(z),R.push(j);let X=[1,T[0],T[1]],D=new k8(X,[1,w,r.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,E,a,o,s),Q=X[1],V=X[2],ee=r.outChannels,J=[{type:"int32",data:[Q]},{type:"int32",data:[ee]},{type:"int32",data:[V]}],se=[j,M];a&&se.push(a),s&&se.push(s),o==="leakyrelu"&&(O.push({type:"float32",data:[i]}),D.uniforms+=" alpha : f32,");let Z=n.runWebGPUProgram(D,se,j.dtype,J),ae=x?[1,m,f,r.outChannels]:[1,r.outChannels,m,f],de=qe({inputs:{x:Z},backend:n,attrs:{shape:ae}});R.push(Z);for(let Ae of R)n.disposeData(Ae.dataId);return de}function R8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d;if(r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return bhe({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return vhe({x:e,filter:t,convInfo:r,backend:n,bias:a,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let h=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),p=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0&&r.outChannels>=32,c=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(h)d=new Ahe(r,l,o,u);else{p?d=new ghe(r,l,o,u):d=new yhe(r,l,o,u);let g=r.outShape[1]*r.outShape[2],y=r.outShape[3],A=r.filterHeight*r.filterWidth*r.inShape[3];f.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[A]})}let m=[e,t];return l&&m.push(a),u&&m.push(s),o==="leakyrelu"&&(f.push({type:"float32",data:[i]}),d.uniforms+=" alpha : f32,"),n.runWebGPUProgram(d,m,e.dtype,f)}function whe(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return R8({x:a,filter:s,convInfo:p,backend:n})}var khe={kernelName:Zs,backendName:"webgpu",kernelFunc:whe},Ihe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=pb(this.dispatchLayout,this.outputShape),this.elementsPerThread=hb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
+  `}};function vhe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.dataFormat==="channelsLast",d=!1,h=!1,p=r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",c,f;if(p){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else{let y=u?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,y,r.inChannels]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}})}let m=Ab({a:c,b:f,transposeA:d,transposeB:h,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(c.dataId),n.disposeData(f.dataId),n.disposeData(m.dataId),g}function whe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,strideWidth:h,strideHeight:p,padInfo:c,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*u*d,w=m*f,T=[w,b],S=!1,E=!1,R=[],_=qe({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),M=qe({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});R.push(_),R.push(M);let I=new bhe(T,x),O=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[h,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[d*l]},{type:"int32",data:[d]}],z=n.runWebGPUProgram(I,[_],_.dtype,O),j=qe({inputs:{x:z},backend:n,attrs:{shape:[1,T[0],T[1]]}});R.push(z),R.push(j);let X=[1,T[0],T[1]],D=new T8(X,[1,w,r.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,E,a,o,s),Q=X[1],V=X[2],ee=r.outChannels,J=[{type:"int32",data:[Q]},{type:"int32",data:[ee]},{type:"int32",data:[V]}],se=[j,M];a&&se.push(a),s&&se.push(s),o==="leakyrelu"&&(O.push({type:"float32",data:[i]}),D.uniforms+=" alpha : f32,");let Z=n.runWebGPUProgram(D,se,j.dtype,J),ae=x?[1,m,f,r.outChannels]:[1,r.outChannels,m,f],de=qe({inputs:{x:Z},backend:n,attrs:{shape:ae}});R.push(Z);for(let Ae of R)n.disposeData(Ae.dataId);return de}function $8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d;if(r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return vhe({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return whe({x:e,filter:t,convInfo:r,backend:n,bias:a,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let h=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),p=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0,c=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(h)d=new xhe(r,l,o,u);else{p?d=new yhe(r,l,o,u):d=new Ahe(r,l,o,u);let g=r.outShape[1]*r.outShape[2],y=r.outShape[3],A=r.filterHeight*r.filterWidth*r.inShape[3];f.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[A]})}let m=[e,t];return l&&m.push(a),u&&m.push(s),o==="leakyrelu"&&(f.push({type:"float32",data:[i]}),d.uniforms+=" alpha : f32,"),n.runWebGPUProgram(d,m,e.dtype,f)}function khe(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return $8({x:a,filter:s,convInfo:p,backend:n})}var Ihe={kernelName:Zs,backendName:"webgpu",kernelFunc:khe},She=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=fb(this.dispatchLayout,this.outputShape),this.elementsPerThread=mb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
     fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
       var batch = i32(globalId.z);
       if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
@@ -6086,8 +6089,8 @@ return a / b;`,Uie=`
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
 
-    ${fb(this.elementsPerThread,this.workGroupSize)}
-  `}},She=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
+    ${yb(this.elementsPerThread,this.workGroupSize)}
+  `}},The=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
     ${tt()} {
       if(index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6136,7 +6139,7 @@ return a / b;`,Uie=`
         setOutputAtIndex(index, dotProd);
       }
     }
-  `}};function The(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new She(p);else{f=new Ihe(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(f,[a,s],"float32",c)}var Nhe={kernelName:Ys,backendName:"webgpu",kernelFunc:The},Che=vr({opType:2}),Ehe={kernelName:Js,backendName:"webgpu",kernelFunc:Che},Rhe=vr({opType:3}),Mhe={kernelName:Qs,backendName:"webgpu",kernelFunc:Rhe},Fhe=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,n,a]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
+  `}};function Nhe(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new The(p);else{f=new She(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(f,[a,s],"float32",c)}var Che={kernelName:Ys,backendName:"webgpu",kernelFunc:Nhe},Ehe=vr({opType:2}),Rhe={kernelName:Js,backendName:"webgpu",kernelFunc:Ehe},Mhe=vr({opType:3}),Fhe={kernelName:Qs,backendName:"webgpu",kernelFunc:Mhe},$he=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,n,a]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
       ${tt()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6192,7 +6195,7 @@ return a / b;`,Uie=`
         }
       }
     }
-    `}},$he=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Fhe(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},Phe={kernelName:Do,backendName:"webgpu",kernelFunc:$he},_he=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+    `}},Phe=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new $he(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},_he={kernelName:Lo,backendName:"webgpu",kernelFunc:Phe},zhe=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
       ${tt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6212,7 +6215,7 @@ return a / b;`,Uie=`
           let rlt = ${this.getInputSamplingString()};
           setOutputAtIndex(index, rlt);
         }
-      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function zhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=[{type:"int32",data:[s]}],g=new _he(f,i);return r.runWebGPUProgram(g,[a],a.dtype,m)}var Ohe={kernelName:Lo,backendName:"webgpu",kernelFunc:zhe},M8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=ts(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Ohe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=[{type:"int32",data:[s]}],g=new zhe(f,i);return r.runWebGPUProgram(g,[a],a.dtype,m)}var Dhe={kernelName:Bo,backendName:"webgpu",kernelFunc:Ohe},P8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=ts(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${n}
         }`:e=`
@@ -6222,7 +6225,7 @@ return a / b;`,Uie=`
         `,t="dotProd[i] = activation(dotProd[i], coords);"}let r=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
       ${e}
 
-      ${db()}
+      ${cb()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -6284,7 +6287,7 @@ return a / b;`,Uie=`
           }
         }
       }
-    `}},F8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+    `}},_8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
       channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=ts(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
@@ -6366,7 +6369,7 @@ return a / b;`,Uie=`
         ${t}
         writeResult(batch, coords[1], coords[2], d2, dotProd);
       }
-    `}};function Dhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.filterHeight===3&&h.inChannels%4===0?c=new M8(h):(c=new F8(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var Lhe={kernelName:ei,backendName:"webgpu",kernelFunc:Dhe},$8=Gr({opSnippet:0,cpuKernelImpl:Ape,supportsComplex:!0}),Bhe={kernelName:Ai,backendName:"webgpu",kernelFunc:$8},Whe=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
+    `}};function Lhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.filterHeight===3&&h.inChannels%4===0?c=new P8(h):(c=new _8(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var Bhe={kernelName:ei,backendName:"webgpu",kernelFunc:Lhe},z8=Gr({opSnippet:0,cpuKernelImpl:xpe,supportsComplex:!0}),Whe={kernelName:Ai,backendName:"webgpu",kernelFunc:z8},Vhe=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
          if (isnan(candidate)) {
           bestValue = uniforms.NAN;
          } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
@@ -6414,13 +6417,13 @@ return a / b;`,Uie=`
           ${r}
         }
        }
-     `}};function _h(e,t,r,n,a){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=N.getAxesPermutation(l,s),d=e;u!=null&&(d=Ml({inputs:{x:e},attrs:{perm:u},backend:a}),l=N.getInnerMostAxes(l.length,s),i.push(d)),N.assertAxesAreInnerMostDims(n,l,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=N.expandShapeToKeepDim(h,o));let f;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let m=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=mpe(m,v.sizeFromShape(p),c,e.dtype);f=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=vpe(d.shape,d.dtype,m,l);f=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),g=v.sizeFromShape(d.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":nh(e.dtype),x=[{type:"int32",data:[m]}],b=new Whe(y,n),w=a.runWebGPUProgram(b,[d],A,x);i.push(w),f=qe({inputs:{x:w},attrs:{shape:c},backend:a})}return i.forEach(m=>a.disposeData(m.dataId)),f}function gb(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return _h(a,s,i,"sum",r)}var Vhe={kernelName:Ei,backendName:"webgpu",kernelFunc:gb};function Uhe(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Ml({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=$8({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=gb({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeData(m.dataId);return p}var Ghe={kernelName:Up,backendName:"webgpu",kernelFunc:Uhe},jhe=vr({opType:4}),Hhe={kernelName:ri,backendName:"webgpu",kernelFunc:jhe},qhe=Gr({opSnippet:4,dtype:"bool",cpuKernelImpl:ape}),Khe={kernelName:Bo,backendName:"webgpu",kernelFunc:qhe},P8=vr({opType:5,cpuKernelImpl:spe,dtype:"float32"}),Xhe={kernelName:ni,backendName:"webgpu",kernelFunc:P8};function Z1(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var Zhe={kernelName:Wo,backendName:"webgpu",kernelFunc:Z1},Yhe=vr({opType:6,cpuKernelImpl:ipe}),Jhe={kernelName:Vo,backendName:"webgpu",kernelFunc:Yhe},Qhe=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+     `}};function _h(e,t,r,n,a){let s=e.shape.length,i=[],o=v.parseAxisParam(t,e.shape),l=o,u=N.getAxesPermutation(l,s),d=e;u!=null&&(d=Fl({inputs:{x:e},attrs:{perm:u},backend:a}),l=N.getInnerMostAxes(l.length,s),i.push(d)),N.assertAxesAreInnerMostDims(n,l,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=N.expandShapeToKeepDim(h,o));let f;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let m=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=gpe(m,v.sizeFromShape(p),c,e.dtype);f=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=wpe(d.shape,d.dtype,m,l);f=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),g=v.sizeFromShape(d.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":ah(e.dtype),x=[{type:"int32",data:[m]}],b=new Vhe(y,n),w=a.runWebGPUProgram(b,[d],A,x);i.push(w),f=qe({inputs:{x:w},attrs:{shape:c},backend:a})}return i.forEach(m=>a.disposeData(m.dataId)),f}function xb(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return _h(a,s,i,"sum",r)}var Uhe={kernelName:Ei,backendName:"webgpu",kernelFunc:xb};function Ghe(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Fl({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let w=0;w<A.length;++w)b.splice(A[w],0,1);v.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=z8({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=xb({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeData(m.dataId);return p}var jhe={kernelName:Gp,backendName:"webgpu",kernelFunc:Ghe},Hhe=vr({opType:4}),qhe={kernelName:ri,backendName:"webgpu",kernelFunc:Hhe},Khe=Gr({opSnippet:4,dtype:"bool",cpuKernelImpl:spe}),Xhe={kernelName:Wo,backendName:"webgpu",kernelFunc:Khe},O8=vr({opType:5,cpuKernelImpl:ipe,dtype:"float32"}),Zhe={kernelName:ni,backendName:"webgpu",kernelFunc:O8};function Q1(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var Yhe={kernelName:Vo,backendName:"webgpu",kernelFunc:Q1},Jhe=vr({opType:6,cpuKernelImpl:ope}),Qhe={kernelName:Uo,backendName:"webgpu",kernelFunc:Jhe},ece=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
     ${tt()}
       if (index < uniforms.size) {
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function Id(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Qhe(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var ece={kernelName:Bu,backendName:"webgpu",kernelFunc:Id},tce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+  `}};function Sd(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new ece(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var tce={kernelName:Wu,backendName:"webgpu",kernelFunc:Sd},rce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
       ${tt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6429,7 +6432,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, outputValue);
         }
       }
-    `}},rce={kernelName:Uo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new tce(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},nce=vr({opType:7,cpuKernelImpl:ope}),ace={kernelName:ai,backendName:"webgpu",kernelFunc:nce},sce=Gr({opSnippet:12,dtype:"int32"}),ice={kernelName:si,backendName:"webgpu",kernelFunc:sce},oce=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},_8=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=_de(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function z8(e,t,r,n="",a=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(s=>s.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a}function ew(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=v.sizeFromShape(a),l=v.computeStrides(a),u=r.makeTensorInfo(a,"int32"),d=r.getFromPixelsProgram(s?"import":"copyExternal");d.updateOutputShape(a);let h=[u.shape],p=[u.dtype,s?"import":"copyExternal"],c=z8(d,h,p),f=d.getLayout(r.device),m=r.getAndSavePipeline(c,()=>_8(r.device,d,f.pipelineLayout,[],u,!0));d.setPipeline(m),s||r.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:d.makeInputTexture(r.device,a[1],a[0])},[a[1],a[0]]);let g=r.tensorMap.get(u.dataId);g.bufferInfo.buffer=r.acquireBuffer(g.bufferInfo.byteSize);let y=[o,i,...l,...d.dispatch];d.setUniform(r.device,y);let A;if(s){let x={source:t};A=r.device.importExternalTexture(x)}else A=d.inputTexture.createView();return r.runFromPixelsProgram(d,g.bufferInfo.buffer,f,A,u.dataId),u}var lce={kernelName:Ip,backendName:"webgpu",kernelFunc:uce},nu;function uce(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(Y().getBool("WEBGPU_USE_IMPORT")&&i)return ew({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(nu==null&&(nu=document.createElement("canvas").getContext("2d")),nu.canvas.width=d,nu.canvas.height=h,nu.drawImage(a,0,0,d,h),a=nu.canvas),u||l||i||o)return ew({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,f=c;if(s!=null&&s!==4){f=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(f[A++]=c[x])}let m=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(m.dataId);return g.values=new Int32Array(f),r.maybeReleaseBuffer(m.dataId),r.uploadToGPU(m.dataId),m}var dce=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+    `}},nce={kernelName:Go,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new rce(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},ace=vr({opType:7,cpuKernelImpl:lpe}),sce={kernelName:ai,backendName:"webgpu",kernelFunc:ace},ice=Gr({opSnippet:12,dtype:"int32"}),oce={kernelName:si,backendName:"webgpu",kernelFunc:ice},lce=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},D8=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=zde(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function L8(e,t,r,n="",a=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(s=>s.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a}function nw(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=v.sizeFromShape(a),l=v.computeStrides(a),u=r.makeTensorInfo(a,"int32"),d=r.getFromPixelsProgram(s?"import":"copyExternal");d.updateOutputShape(a);let h=[u.shape],p=[u.dtype,s?"import":"copyExternal"],c=L8(d,h,p),f=d.getLayout(r.device),m=r.getAndSavePipeline(c,()=>D8(r.device,d,f.pipelineLayout,[],u,!0));d.setPipeline(m),s||r.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:d.makeInputTexture(r.device,a[1],a[0])},[a[1],a[0]]);let g=r.tensorMap.get(u.dataId);g.bufferInfo.buffer=r.acquireBuffer(g.bufferInfo.byteSize);let y=[o,i,...l,...d.dispatch];d.setUniform(r.device,y);let A;if(s){let x={source:t};A=r.device.importExternalTexture(x)}else A=d.inputTexture.createView();return r.runFromPixelsProgram(d,g.bufferInfo.buffer,f,A,u.dataId),u}var uce={kernelName:Sp,backendName:"webgpu",kernelFunc:dce},nu;function dce(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(Y().getBool("WEBGPU_USE_IMPORT")&&i)return nw({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(nu==null&&(nu=document.createElement("canvas").getContext("2d")),nu.canvas.width=d,nu.canvas.height=h,nu.drawImage(a,0,0,d,h),a=nu.canvas),u||l||i||o)return nw({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,f=c;if(s!=null&&s!==4){f=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(f[A++]=c[x])}let m=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(m.dataId);return g.values=new Int32Array(f),r.maybeReleaseBuffer(m.dataId),r.uploadToGPU(m.dataId),m}var pce=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
       ${tt()}
         if (index < uniforms.size)
         {
@@ -6442,7 +6445,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
         }
       }
-  `}},pce={kernelName:ii,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new dce(n.shape,i.shape,o.shape,h,p),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,f)}};function hce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m);return R8({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:c})}var cce={kernelName:Es,backendName:"webgpu",kernelFunc:hce};function fce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=d;f==null&&(f=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=N.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),g=[a,s],y=i!=null,A=o!=null;y&&g.push(i),A&&g.push(o);let x=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?b=new M8(m,y,p,A):(b=new F8(m,y,p,A),x.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),p==="leakyrelu"&&(x.push({type:"float32",data:[c]}),b.uniforms+=" alpha : f32,"),r.runWebGPUProgram(b,g,"float32",x)}var mce={kernelName:Rs,backendName:"webgpu",kernelFunc:fce},gce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${mr(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+  `}},hce={kernelName:ii,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new pce(n.shape,i.shape,o.shape,h,p),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,f)}};function cce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m);return $8({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:c})}var fce={kernelName:Es,backendName:"webgpu",kernelFunc:cce};function mce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=d;f==null&&(f=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=N.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),g=[a,s],y=i!=null,A=o!=null;y&&g.push(i),A&&g.push(o);let x=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?b=new P8(m,y,p,A):(b=new _8(m,y,p,A),x.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),p==="leakyrelu"&&(x.push({type:"float32",data:[c]}),b.uniforms+=" alpha : f32,"),r.runWebGPUProgram(b,g,"float32",x)}var gce={kernelName:Rs,backendName:"webgpu",kernelFunc:mce},yce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${mr(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
         ${tt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6456,7 +6459,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, getA(flattenIndex, coords[1]));
         }
       }
-      `}};function yce(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=qe({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=qe({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=lpe(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let f=new gce(i,[u,d]),m=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(f,[c,p],c.dtype,m),y=qe({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var Ace={kernelName:jo,backendName:"webgpu",kernelFunc:yce},xce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=bce(this.aShape);return`
+      `}};function Ace(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=v.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=qe({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=qe({inputs:{x:n},backend:r,attrs:{shape:[v.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=upe(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let f=new yce(i,[u,d]),m=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(f,[c,p],c.dtype,m),y=qe({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var xce={kernelName:Ho,backendName:"webgpu",kernelFunc:Ace},bce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=vce(this.aShape);return`
       ${tt()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6465,7 +6468,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, inBounds * getA(${e}));
         }
       }
-    `}};function bce(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let n=0;n<e.length;n++)n===2?r.push("indexZ"):r.push(`${t[n]}`);return r.join()}function O8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=We(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,w=We(p.shape,p.dtype,b),T=upe(w,x,f);return h.forEach(S=>r.disposeData(S.dataId)),r.makeTensorInfo(u.outputShape,T.dtype,T.values)}let m=new xce(p.shape,f),g=r.runWebGPUProgram(m,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var vce={kernelName:Go,backendName:"webgpu",kernelFunc:O8},wce=Gr({opSnippet:5,cpuKernelImpl:ppe,dtype:"bool"}),kce={kernelName:Ho,backendName:"webgpu",kernelFunc:wce},Ice=Gr({opSnippet:6,dtype:"bool",cpuKernelImpl:dpe}),Sce={kernelName:oi,backendName:"webgpu",kernelFunc:Ice};function Tce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new $h(a.shape,14);return o.uniforms="alpha : f32,",r.runWebGPUProgram(o,[a],"float32",i)}var Nce={kernelName:ui,backendName:"webgpu",kernelFunc:Tce},Cce=Gr({opSnippet:7,dtype:"bool",cpuKernelImpl:cpe}),Ece={kernelName:qo,backendName:"webgpu",kernelFunc:Cce},Rce=Gr({opSnippet:8,dtype:"bool",cpuKernelImpl:hpe}),Mce={kernelName:Ko,backendName:"webgpu",kernelFunc:Rce},Fce=vr({opType:9,cpuKernelImpl:fpe}),$ce={kernelName:di,backendName:"webgpu",kernelFunc:Fce},Pce=Gr({opSnippet:9,dtype:"bool"}),_ce={kernelName:Xo,backendName:"webgpu",kernelFunc:Pce},zce=vr({opType:10}),Oce={kernelName:ju,backendName:"webgpu",kernelFunc:zce};function D8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return _h(a,s,i,"max",r)}var Dce={kernelName:pi,backendName:"webgpu",kernelFunc:D8},Lce=Gr({opSnippet:15,cpuKernelImpl:gpe}),Bce={kernelName:hi,backendName:"webgpu",kernelFunc:Lce};function Wce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(v.arraysEqual(d.inShape,d.outShape))return Bn({inputs:{x:a},backend:r});h=new N8(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new T8(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var Vce={kernelName:ci,backendName:"webgpu",kernelFunc:Wce};function Uce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return _h(a,i,s,"mean",r)}var Gce={kernelName:fi,backendName:"webgpu",kernelFunc:Uce};function jce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return _h(a,s,i,"min",r)}var Hce={kernelName:mi,backendName:"webgpu",kernelFunc:jce},qce=Gr({opSnippet:16,cpuKernelImpl:ype}),Kce={kernelName:gi,backendName:"webgpu",kernelFunc:qce},Xce=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>,`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=mr(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}};function vce(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let n=0;n<e.length;n++)n===2?r.push("indexZ"):r.push(`${t[n]}`);return r.join()}function B8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=v.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=We(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,w=We(p.shape,p.dtype,b),T=dpe(w,x,f);return h.forEach(S=>r.disposeData(S.dataId)),r.makeTensorInfo(u.outputShape,T.dtype,T.values)}let m=new bce(p.shape,f),g=r.runWebGPUProgram(m,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var wce={kernelName:jo,backendName:"webgpu",kernelFunc:B8},kce=Gr({opSnippet:5,cpuKernelImpl:hpe,dtype:"bool"}),Ice={kernelName:qo,backendName:"webgpu",kernelFunc:kce},Sce=Gr({opSnippet:6,dtype:"bool",cpuKernelImpl:ppe}),Tce={kernelName:oi,backendName:"webgpu",kernelFunc:Sce};function Nce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new $h(a.shape,14);return o.uniforms="alpha : f32,",r.runWebGPUProgram(o,[a],"float32",i)}var Cce={kernelName:ui,backendName:"webgpu",kernelFunc:Nce},Ece=Gr({opSnippet:7,dtype:"bool",cpuKernelImpl:fpe}),Rce={kernelName:Ko,backendName:"webgpu",kernelFunc:Ece},Mce=Gr({opSnippet:8,dtype:"bool",cpuKernelImpl:cpe}),Fce={kernelName:Xo,backendName:"webgpu",kernelFunc:Mce},$ce=vr({opType:9,cpuKernelImpl:mpe}),Pce={kernelName:di,backendName:"webgpu",kernelFunc:$ce},_ce=Gr({opSnippet:9,dtype:"bool"}),zce={kernelName:Zo,backendName:"webgpu",kernelFunc:_ce},Oce=vr({opType:10}),Dce={kernelName:Hu,backendName:"webgpu",kernelFunc:Oce};function W8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return _h(a,s,i,"max",r)}var Lce={kernelName:pi,backendName:"webgpu",kernelFunc:W8},Bce=Gr({opSnippet:15,cpuKernelImpl:ype}),Wce={kernelName:hi,backendName:"webgpu",kernelFunc:Bce};function Vce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(v.arraysEqual(d.inShape,d.outShape))return Bn({inputs:{x:a},backend:r});h=new R8(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new E8(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var Uce={kernelName:ci,backendName:"webgpu",kernelFunc:Vce};function Gce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return _h(a,i,s,"mean",r)}var jce={kernelName:fi,backendName:"webgpu",kernelFunc:Gce};function Hce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return _h(a,s,i,"min",r)}var qce={kernelName:mi,backendName:"webgpu",kernelFunc:Hce},Kce=Gr({opSnippet:16,cpuKernelImpl:Ape}),Xce={kernelName:gi,backendName:"webgpu",kernelFunc:Kce},Zce=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>,`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=mr(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${tt()}
         if (index < uniforms.size) {
           let start = ${i}(${t});
@@ -6482,7 +6485,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, getX(${o}));
         }
       }
-    `}},Zce={kernelName:yi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new Xce(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function Yce(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=xpe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new $h(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var Jce={kernelName:Zo,backendName:"webgpu",kernelFunc:Yce};function Qce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=jn.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var efe={kernelName:Jo,backendName:"webgpu",kernelFunc:Qce};function tfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=jn.nonMaxSuppressionV5Impl(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var rfe={kernelName:Qo,backendName:"webgpu",kernelFunc:tfe};function Rf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Ph({inputs:{input:n},backend:r}),s=Rf({inputs:{x:a},backend:r}),i=c0({inputs:{input:n},backend:r}),o=Rf({inputs:{x:i},backend:r}),l=wd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Id({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var nfe={kernelName:Al,backendName:"webgpu",kernelFunc:Rf};function L8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Ph({inputs:{input:n},backend:r}),s=L8({inputs:{x:a},backend:r}),i=c0({inputs:{input:n},backend:r}),o=Rf({inputs:{x:i},backend:r}),l=wd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Id({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var afe={kernelName:el,backendName:"webgpu",kernelFunc:L8};function sfe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return Z1({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=Z1({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=E8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var ife={kernelName:rl,backendName:"webgpu",kernelFunc:sfe},ofe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=mr(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}},Yce={kernelName:yi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new Zce(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function Jce(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=bpe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new $h(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var Qce={kernelName:Yo,backendName:"webgpu",kernelFunc:Jce};function efe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=jn.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var tfe={kernelName:Qo,backendName:"webgpu",kernelFunc:efe};function rfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=jn.nonMaxSuppressionV5Impl(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var nfe={kernelName:el,backendName:"webgpu",kernelFunc:rfe};function Mf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Ph({inputs:{input:n},backend:r}),s=Mf({inputs:{x:a},backend:r}),i=f0({inputs:{input:n},backend:r}),o=Mf({inputs:{x:i},backend:r}),l=kd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Sd({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var afe={kernelName:xl,backendName:"webgpu",kernelFunc:Mf};function V8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Ph({inputs:{input:n},backend:r}),s=V8({inputs:{x:a},backend:r}),i=f0({inputs:{input:n},backend:r}),o=Mf({inputs:{x:i},backend:r}),l=kd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Sd({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var sfe={kernelName:tl,backendName:"webgpu",kernelFunc:V8};function ife(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return Q1({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=Q1({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=F8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var ofe={kernelName:nl,backendName:"webgpu",kernelFunc:ife},lfe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=mr(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${tt()}
         if (index < uniforms.size) {
           let start = ${a};
@@ -6497,7 +6500,7 @@ return a / b;`,Uie=`
           }
         }
       }
-    `}},B8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return Bn({inputs:{x:a},backend:r});if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Id({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new ofe(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},lfe={kernelName:xi,backendName:"webgpu",kernelFunc:B8},ufe=Gr({opSnippet:13}),dfe={kernelName:bi,backendName:"webgpu",kernelFunc:ufe};function pfe(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new I8(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var hfe={kernelName:vi,backendName:"webgpu",kernelFunc:pfe};function cfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return _h(a,s,i,"prod",r)}var ffe={kernelName:nl,backendName:"webgpu",kernelFunc:cfe},mfe=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=wpe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},gfe={kernelName:Ku,backendName:"webgpu",kernelFunc:mfe},W8=Gr({opSnippet:3}),yfe={kernelName:ti,backendName:"webgpu",kernelFunc:W8},Afe=vr({opType:12}),xfe={kernelName:wi,backendName:"webgpu",kernelFunc:Afe},bfe=vr({opType:13}),vfe={kernelName:Ii,backendName:"webgpu",kernelFunc:bfe},wfe=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+    `}},U8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>v.arraysEqual(u,[0,0])))return Bn({inputs:{x:a},backend:r});if(v.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Sd({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new lfe(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},ufe={kernelName:xi,backendName:"webgpu",kernelFunc:U8},dfe=Gr({opSnippet:13}),pfe={kernelName:bi,backendName:"webgpu",kernelFunc:dfe};function hfe(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new N8(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var cfe={kernelName:vi,backendName:"webgpu",kernelFunc:hfe};function ffe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return _h(a,s,i,"prod",r)}var mfe={kernelName:al,backendName:"webgpu",kernelFunc:ffe},gfe=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=kpe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},yfe={kernelName:Xu,backendName:"webgpu",kernelFunc:gfe},G8=Gr({opSnippet:3}),Afe={kernelName:ti,backendName:"webgpu",kernelFunc:G8},xfe=vr({opType:12}),bfe={kernelName:wi,backendName:"webgpu",kernelFunc:xfe},vfe=vr({opType:13}),wfe={kernelName:Ii,backendName:"webgpu",kernelFunc:vfe},kfe=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
       ${tt()}
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6540,7 +6543,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function kfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new wfe(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var Ife={kernelName:ki,backendName:"webgpu",kernelFunc:kfe},Sfe=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
+    `}};function Ife(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new kfe(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var Sfe={kernelName:ki,backendName:"webgpu",kernelFunc:Ife},Tfe=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
       ${tt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6571,7 +6574,7 @@ return a / b;`,Uie=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Tfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new Sfe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var Nfe={kernelName:Zu,backendName:"webgpu",kernelFunc:Tfe},Cfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
+    `}};function Nfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new Tfe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var Cfe={kernelName:Yu,backendName:"webgpu",kernelFunc:Nfe},Efe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
         ${tt()}
 
@@ -6593,7 +6596,7 @@ return a / b;`,Uie=`
             setOutputAtIndex(index, outputValue);
           }
         }
-      `}},Efe={kernelName:xl,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new Cfe(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},Rfe=vr({opType:15,cpuKernelImpl:kpe}),Mfe={kernelName:Si,backendName:"webgpu",kernelFunc:Rfe},Ffe=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Xe(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=mr(a.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=n,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",a="",s="";this.updatesRank===1?(n="coords[0]",a="flattenedIndex",s=`
+      `}},Rfe={kernelName:bl,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new Efe(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},Mfe=vr({opType:15,cpuKernelImpl:Ipe}),Ffe={kernelName:Si,backendName:"webgpu",kernelFunc:Mfe},$fe=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Xe(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=mr(a.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=n,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",a="",s="";this.updatesRank===1?(n="coords[0]",a="flattenedIndex",s=`
       fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
         return index;
       }
@@ -6630,7 +6633,7 @@ return a / b;`,Uie=`
 
          ${o}
         }
-      }`}};function $fe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=f.dtype,g=Id({backend:r,attrs:{shape:p,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new Ffe(f.shape,o,c.shape.length,f.shape.length,d,p,m),b=r.runWebGPUProgram(x,[f,c],m,A,g),w=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(b.dataId),w}var Pfe={kernelName:ol,backendName:"webgpu",kernelFunc:$fe},_fe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
+      }`}};function Pfe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=f.dtype,g=Sd({backend:r,attrs:{shape:p,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new $fe(f.shape,o,c.shape.length,f.shape.length,d,p,m),b=r.runWebGPUProgram(x,[f,c],m,A,g),w=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(b.dataId),w}var _fe={kernelName:ll,backendName:"webgpu",kernelFunc:Pfe},zfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
       ${tt()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6642,7 +6645,7 @@ return a / b;`,Uie=`
           }
         }
       }
-    `}};function zfe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new _fe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Nr(a.dtype,s.dtype))}var Ofe={kernelName:ll,backendName:"webgpu",kernelFunc:zfe},Dfe=vr({opType:18}),Lfe={kernelName:Ni,backendName:"webgpu",kernelFunc:Dfe},Bfe=vr({opType:16}),Wfe={kernelName:Ti,backendName:"webgpu",kernelFunc:Bfe},Vfe=vr({opType:17}),Ufe={kernelName:dl,backendName:"webgpu",kernelFunc:Vfe},V8=Gr({opSnippet:2,cpuKernelImpl:Cpe,supportsComplex:!0}),Gfe={kernelName:Fi,backendName:"webgpu",kernelFunc:V8};function jfe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=D8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=V8({inputs:{a,b:u},backend:r}),h=P8({inputs:{x:d},backend:r}),p=gb({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),f=W8({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),f}var Hfe={kernelName:Ri,backendName:"webgpu",kernelFunc:jfe},qfe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=B8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Ml({inputs:{x:f},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeData(y.dataId)),g},Kfe={kernelName:pl,backendName:"webgpu",kernelFunc:qfe},Xfe=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=mr(a.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
+    `}};function Ofe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new zfe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Nr(a.dtype,s.dtype))}var Dfe={kernelName:ul,backendName:"webgpu",kernelFunc:Ofe},Lfe=vr({opType:18}),Bfe={kernelName:Ni,backendName:"webgpu",kernelFunc:Lfe},Wfe=vr({opType:16}),Vfe={kernelName:Ti,backendName:"webgpu",kernelFunc:Wfe},Ufe=vr({opType:17}),Gfe={kernelName:pl,backendName:"webgpu",kernelFunc:Ufe},j8=Gr({opSnippet:2,cpuKernelImpl:Epe,supportsComplex:!0}),jfe={kernelName:Fi,backendName:"webgpu",kernelFunc:j8};function Hfe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=v.parseAxisParam([s],a.shape),o=W8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=j8({inputs:{a,b:u},backend:r}),h=O8({inputs:{x:d},backend:r}),p=xb({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),f=G8({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),f}var qfe={kernelName:Ri,backendName:"webgpu",kernelFunc:Hfe},Kfe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=U8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Fl({inputs:{x:f},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeData(y.dataId)),g},Xfe={kernelName:hl,backendName:"webgpu",kernelFunc:Kfe},Zfe=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=mr(a.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
       ${tt()}
 
         let globalIndex = index * ${this.workPerThread};
@@ -6672,21 +6675,21 @@ return a / b;`,Uie=`
             }
           }
         }
-      }`}};function Zfe(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:d}],f=new Xfe(u,l,a.shape.length,s.shape.length,d,[h,1],p),m=r.runWebGPUProgram(f,[s,a,i],s.dtype,c),g=qe({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeData(m.dataId),g}var Yfe={kernelName:Jp,backendName:"webgpu",kernelFunc:Zfe};function Jfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=kd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var Qfe={kernelName:hl,backendName:"webgpu",kernelFunc:Jfe},eme=vr({opType:19}),tme={kernelName:Ci,backendName:"webgpu",kernelFunc:eme},rme={kernelName:td,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new $h(r.shape,20);return n.runWebGPUProgram(a,[r],r.dtype)}},nme=Gr({opSnippet:11}),ame={kernelName:Mi,backendName:"webgpu",kernelFunc:nme},sme=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=mr(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+      }`}};function Yfe(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:d}],f=new Zfe(u,l,a.shape.length,s.shape.length,d,[h,1],p),m=r.runWebGPUProgram(f,[s,a,i],s.dtype,c),g=qe({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeData(m.dataId),g}var Jfe={kernelName:Qp,backendName:"webgpu",kernelFunc:Yfe};function Qfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=v.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=Id({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var eme={kernelName:cl,backendName:"webgpu",kernelFunc:Qfe},tme=vr({opType:19}),rme={kernelName:Ci,backendName:"webgpu",kernelFunc:tme},nme={kernelName:rd,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new $h(r.shape,20);return n.runWebGPUProgram(a,[r],r.dtype)}},ame=Gr({opSnippet:11}),sme={kernelName:Mi,backendName:"webgpu",kernelFunc:ame},ime=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=mr(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
        ${tt()}
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            setOutputAtIndex(index, getX(${t}));
          }
        }
-     `}};function ime(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(m)w=qe({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let T=_t.computeOutShape(A,x,b),S=kd({inputs:{x:a},backend:r,attrs:{begin:A,size:T}});w=qe({inputs:{x:S},backend:r,attrs:{shape:f}}),r.disposeData(S.dataId)}else if(r.shouldExecuteOnCPU([a])){let T=r.readSync(a.dataId),S=We(a.shape,a.dtype,T),E=Tpe(c,S,b,A);w=r.makeTensorInfo(f,a.dtype,E.values)}else{let T=new sme(c),S=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(T,[a],a.dtype,S);w=qe({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeData(E.dataId)}return w}var ome={kernelName:cl,backendName:"webgpu",kernelFunc:ime};function lme(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Npe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var ume={kernelName:Qp,backendName:"webgpu",kernelFunc:lme},dme=vr({opType:21}),pme={kernelName:$i,backendName:"webgpu",kernelFunc:dme},hme=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=cme(this.rank,"uniforms.");return`
+     `}};function ome(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),w;if(m)w=qe({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){v.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let T=_t.computeOutShape(A,x,b),S=Id({inputs:{x:a},backend:r,attrs:{begin:A,size:T}});w=qe({inputs:{x:S},backend:r,attrs:{shape:f}}),r.disposeData(S.dataId)}else if(r.shouldExecuteOnCPU([a])){let T=r.readSync(a.dataId),S=We(a.shape,a.dtype,T),E=Npe(c,S,b,A);w=r.makeTensorInfo(f,a.dtype,E.values)}else{let T=new ime(c),S=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(T,[a],a.dtype,S);w=qe({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeData(E.dataId)}return w}var lme={kernelName:fl,backendName:"webgpu",kernelFunc:ome};function ume(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Cpe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var dme={kernelName:eh,backendName:"webgpu",kernelFunc:ume},pme=vr({opType:21}),hme={kernelName:$i,backendName:"webgpu",kernelFunc:pme},cme=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=fme(this.rank,"uniforms.");return`
       ${tt()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function cme(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e;a++)n.push(`(${r[a]} % ${t}aShape[${a}])`);return n.join()}function fme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(r.shouldExecuteOnCPU([a])||a.dtype==="string"||a.shape.length>=5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>v.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Epe(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new hme(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var mme={kernelName:Xa,backendName:"webgpu",kernelFunc:fme},gme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
+    `}};function fme(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e;a++)n.push(`(${r[a]} % ${t}aShape[${a}])`);return n.join()}function mme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(r.shouldExecuteOnCPU([a])||a.dtype==="string"||a.shape.length>=5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>v.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Rpe(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new cme(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var gme={kernelName:Xa,backendName:"webgpu",kernelFunc:mme},yme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
         ${tt()}
           if (index < uniforms.size) {
@@ -6754,7 +6757,7 @@ return a / b;`,Uie=`
             }
           }
         }
-      `}},yme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
+      `}},Ame=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
         ${tt()}
           if (index < uniforms.size) {
             let outC = getCoordsFromIndex(index);
@@ -6813,7 +6816,7 @@ return a / b;`,Uie=`
             }
           }
         }
-      `}};function au(e,t){t!==null&&e.disposeData(t.dataId)}function tw(e){let t=1;for(;t<e;)t*=2;return t}function Ame(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[w,T]=Rpe(b,o,a.dtype,s,i);return[r.makeTensorInfo(w.shape,w.dtype,w.values),r.makeTensorInfo(T.shape,T.dtype,T.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,Id({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=v.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=tw(s),p=tw(l),c=null,f=()=>c===null?[d,d]:[d,c],m=(b,w,T)=>{let S=f(),E=new gme(T),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[w]}],_=c;c=r.runWebGPUProgram(E,S,"int32",R),au(r,_)};for(let b=1;b<h;b*=2){let w=b*2;for(let T=b;T>=1;T/=2)m(w,T,[u,p])}for(let b=p;b>h;b/=2){let w=f(),T=new yme([u,b/2]),S=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(T,w,"int32",S),au(r,E);let R=h/2,_=R*2;for(let M=R;M>=1;M/=2)m(_,M,c.shape)}let g=c;c=kd({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),au(r,g);let y=O8({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});au(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),au(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),au(r,x),[y,c]}var xme={kernelName:ml,backendName:"webgpu",kernelFunc:Ame},bme=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function au(e,t){t!==null&&e.disposeData(t.dataId)}function aw(e){let t=1;for(;t<e;)t*=2;return t}function xme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[w,T]=Mpe(b,o,a.dtype,s,i);return[r.makeTensorInfo(w.shape,w.dtype,w.values),r.makeTensorInfo(T.shape,T.dtype,T.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,Sd({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=v.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=aw(s),p=aw(l),c=null,f=()=>c===null?[d,d]:[d,c],m=(b,w,T)=>{let S=f(),E=new yme(T),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[w]}],_=c;c=r.runWebGPUProgram(E,S,"int32",R),au(r,_)};for(let b=1;b<h;b*=2){let w=b*2;for(let T=b;T>=1;T/=2)m(w,T,[u,p])}for(let b=p;b>h;b/=2){let w=f(),T=new Ame([u,b/2]),S=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(T,w,"int32",S),au(r,E);let R=h/2,_=R*2;for(let M=R;M>=1;M/=2)m(_,M,c.shape)}let g=c;c=Id({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),au(r,g);let y=B8({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});au(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),au(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),au(r,x),[y,c]}var bme={kernelName:gl,backendName:"webgpu",kernelFunc:xme},vme=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
           fn mapCoord(outCoord : f32, len : f32) -> f32{
             var inCoord = outCoord;
             if(uniforms.fillModeId == 2) {
@@ -6929,7 +6932,7 @@ return a / b;`,Uie=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function vme(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new bme(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var wme={kernelName:gl,backendName:"webgpu",kernelFunc:vme};function kme(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=kd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeData(m.dataId)),f}var Ime={kernelName:yl,backendName:"webgpu",kernelFunc:kme},Sme=[Zde,$pe,_pe,Dpe,Gpe,Hpe,Kpe,Zpe,the,she,ohe,phe,epe,mhe,khe,Nhe,Ehe,Mhe,Phe,Ohe,Lhe,Ghe,Hhe,Khe,Xhe,Zhe,Jhe,ece,rce,lce,ace,ice,pce,cce,mce,Ace,vce,kce,Sce,Qde,che,Nce,Ece,Mce,$ce,_ce,Oce,Dce,Bce,Vce,Gce,Hce,Kce,Zce,Bhe,Jce,efe,rfe,rhe,afe,ife,lfe,dfe,hfe,ffe,gfe,nhe,yfe,xfe,vfe,Kde,Ife,Nfe,Efe,Mfe,Pfe,Ofe,Lfe,Wfe,Ufe,Qpe,ome,ume,Hfe,Kfe,Yfe,Qfe,tme,rme,ame,Gfe,Vhe,pme,mme,xme,wme,Vpe,Ime,nfe];for(let e of Sme)Vn(e);var Tme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=rw(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=rw(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function rw(e,t){return`${e}_${t}`}var U8=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){v.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+        `}};function wme(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new vme(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var kme={kernelName:yl,backendName:"webgpu",kernelFunc:wme};function Ime(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=Id({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeData(m.dataId)),f}var Sme={kernelName:Al,backendName:"webgpu",kernelFunc:Ime},Tme=[Yde,Ppe,zpe,Lpe,jpe,qpe,Xpe,Ype,rhe,ihe,lhe,hhe,tpe,ghe,Ihe,Che,Rhe,Fhe,_he,Dhe,Bhe,jhe,qhe,Xhe,Zhe,Yhe,Qhe,tce,nce,uce,sce,oce,hce,fce,gce,xce,wce,Ice,Tce,epe,fhe,Cce,Rce,Fce,Pce,zce,Dce,Lce,Wce,Uce,jce,qce,Xce,Yce,Whe,Qce,tfe,nfe,nhe,sfe,ofe,ufe,pfe,cfe,mfe,yfe,ahe,Afe,bfe,wfe,Xde,Sfe,Cfe,Rfe,Ffe,_fe,Dfe,Bfe,Vfe,Gfe,ehe,lme,dme,qfe,Xfe,Jfe,eme,rme,nme,sme,jfe,Uhe,hme,gme,bme,kme,Upe,Sme,afe];for(let e of Tme)Vn(e);var Nme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=sw(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=sw(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function sw(e,t){return`${e}_${t}`}var H8=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){v.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
       @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
 
       ${tt()}
@@ -6943,10 +6946,10 @@ return a / b;`,Uie=`
           }
         }
       }
-  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let r=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=r}!t||t.length===this.lastUniformData.length&&t.every((r,n)=>r===this.lastUniformData[n])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,r){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==r)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,r],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=r),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},Nme=class extends U8{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},Cme=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),nw=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;v.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),v.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},G8=class extends Tu{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!cb())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Tme(this.device),this.tensorMap=new Op(this,Ar()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return G8.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=v.sizeFromShape(t)*H1(r);return this.tensorMap.set(n,{dtype:r,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=v.sizeFromShape(r)*H1(n);this.tensorMap.set(e,{dtype:n,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new U8),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Nme),this.fromPixelImportProgram;default:v.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let r=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=N.mergeRealAndImagArrays(s,i)}else{let a=await this.getBufferData(t);n=v8(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>v.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&v.isString(r[0])){let a=r.map(s=>v.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=[];e.forEach(s=>{s.data.length===0&&(s.data=[1]);let i;switch(s.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:v.assert(!1,()=>`Unsupported ${s.data.length}D shape`)}t=Math.ceil(t/i)*i,r.push(t),t+=s.data.length*4});let n=new ArrayBuffer(t);e.forEach((s,i)=>{let o=r[i];s.type==="int32"?new Int32Array(n,o,s.data.length).set(s.data):s.type==="uint32"?new Uint32Array(n,o,s.data.length).set(s.data):new Float32Array(n,o,s.data.length).set(s.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(a,0,n,0,t),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),v.sizeFromShape(a.shape)===0){let S=this.tensorMap.get(a.dataId);return S.values=v.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=nw(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(S=>S.shape),o="int32";i.map(S=>{s.push({type:o,data:S})});let l=v.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let S=v.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?S/4:S]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((S,E)=>{if(S.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. 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Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let s=await t.requestDevice(n);return new yb(s,a)},3);var Vt=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(Vt||{}),f0=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(f0||{}),H8;function Eme(e){H8=e.wasm.cwrap(Cs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Rme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:r.dataIdMap.get(o.dataId).id,g=f0[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=bl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),w=r.dataIdMap.get(b.dataId).id,T=new Uint8Array(new Int32Array(a.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return H8(p,T,a.shape.length,c,S,s.shape.length,l,u,g,f,m,h||0,w),b}var Mme={kernelName:Cs,backendName:"wasm",setupFunc:Eme,kernelFunc:Rme};function wr(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||r(l,Vt[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Fme=wr(Po);function jr(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,f=N.assertAndGetBroadcastShape(u.shape,d.shape),m=o.makeOutput(f,c);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(m.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Vt[u.dtype],A),m}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var $me=!0,Pme=jr(qa,$me),q8;function _me(e){q8=e.wasm.cwrap(Gs,null,["array","number","number","number"])}function zme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return q8(s,a.length,Vt[n.dtype],i),n}var Ome={kernelName:Gs,backendName:"wasm",setupFunc:_me,kernelFunc:zme};function m0(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var Dme={kernelName:li,backendName:"wasm",kernelFunc:m0},K8;function Lme(e){K8=e.wasm.cwrap(Pi,null,["number","array","number","number","number","array","number"])}function Vs(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=Wme(t.x.shape,n.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=Bme(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=m0({inputs:t,backend:r});return f.shape=o,f}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return K8(d,c,l.shape.length,Vt[l.dtype],h,p,s.length),u}function Bme(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function Wme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var Vme={kernelName:Pi,backendName:"wasm",kernelFunc:Vs,setupFunc:Lme};function Wi(e,t,r){let n=e.shape,a=e.shape.length,s=v.parseAxisParam(t,n),i=s,o=N.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=N.getInnerMostAxes(i.length,a),l=Vs({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var X8;function Ume(e){X8=e.wasm.cwrap(Ru,null,["number, number, number"])}function Gme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Wi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("all",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;X8(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var jme={kernelName:Ru,backendName:"wasm",setupFunc:Ume,kernelFunc:Gme},Z8;function Hme(e){Z8=e.wasm.cwrap(Mu,null,["number, number, number"])}function qme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Wi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("any",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;Z8(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Kme={kernelName:Mu,backendName:"wasm",setupFunc:Hme,kernelFunc:qme},Y8;function Xme(e){Y8=e.wasm.cwrap(js,null,["number","number","number","number","number"])}function Zme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=Wi(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),f=t.dataIdMap.get(c.dataId).id,m=v.sizeFromShape(c.shape),g=l.shape[d[0]];return Y8(o,Vt[l.dtype],m,g,f),h&&t.disposeData(u.dataId),c}var Yme={kernelName:js,backendName:"wasm",kernelFunc:Zme,setupFunc:Xme},J8;function Jme(e){J8=e.wasm.cwrap(Hs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qme(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let b=n.makeOutput(d.outShape,"float32"),w=n.dataIdMap.get(b.dataId).id;return J8(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,w),b}var e0e={kernelName:Hs,backendName:"wasm",setupFunc:Jme,kernelFunc:Qme};function Qr(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s);return v.assert(s===v.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var t0e={kernelName:al,backendName:"wasm",kernelFunc:Qr},Q8;function r0e(e){Q8=e.wasm.cwrap(qs,null,["number","array","number","number","array","number","number","number","number"])}function n0e(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=bl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);v.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],w=Qr({inputs:{x:a},backend:r,attrs:{shape:x}}),T=Qr({inputs:{x:s},backend:r,attrs:{shape:b}}),S=r.dataIdMap.get(w.dataId).id,E=r.dataIdMap.get(T.dataId).id,R=i?w.shape[2]:w.shape[1],_=o?T.shape[1]:T.shape[2],M=Math.max(g,y),I=r.makeOutput([M,R,_],w.dtype),O=r.dataIdMap.get(I.dataId).id,z=new Uint8Array(new Int32Array(w.shape).buffer),j=new Uint8Array(new Int32Array(T.shape).buffer);return Q8(S,z,w.shape.length,E,j,T.shape.length,i,o,O),r.disposeData(w.dataId),r.disposeData(T.dataId),I.shape=A,I}var a0e={kernelName:qs,backendName:"wasm",setupFunc:r0e,kernelFunc:n0e};function Mo(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=_t.parseSliceParams(t,r,n),o=_t.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=v.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=_t.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(f,f+v.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+v.sizeFromShape(i))),u}if(t.dtype==="string"){let f=Sf(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)s0e(l,d[0],p,s,i);else if(c===3)i0e(l,d[0],d[1],p,s,i);else if(c===4)o0e(l,d[0],d[1],d[2],p,s,i);else{let f=Sf(l,s,i,t.shape,t.dtype);p.set(f)}return u}function s0e(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function i0e(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let f=p*t+c*r+u;n.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function o0e(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],f=s[3];for(let m=l;m<h;m++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=m*t+g*r+y*n+f;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var l0e={kernelName:ul,backendName:"wasm",kernelFunc:Mo};function u0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Qr({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Vs({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Qr({inputs:{x:f},backend:r,attrs:{shape:d}}),g=Mo({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(c.dataId),g}var d0e={kernelName:_o,backendName:"wasm",kernelFunc:u0e};function zh(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var p0e={kernelName:Ks,backendName:"wasm",kernelFunc:zh},h0e=wr(Xs),eT;function c0e(e){eT=e.wasm.cwrap(Ka,null,["number","number","number","number"])}function 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D0e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h}=r,p=u==null?[1,1]:u,c=N.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),f=c.filterHeight,m=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,w=c.dilationWidth,T=c.strideHeight,S=c.strideWidth,E=c.inChannels,R=c.outChannels,_=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let M=n.makeOutput(c.outShape,"float32"),I=n.dataIdMap.get(M.dataId).id;return uT(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,g,y,A,x,_,b,w,T,S,E,R,I),M}var L0e={kernelName:ei,backendName:"wasm",setupFunc:O0e,kernelFunc:D0e},B0e=wr(ri),W0e=!1,V0e=jr(Bo,W0e,"bool"),U0e=wr(ni,"float32");function Y1(e){let{inputs:t,attrs:r,backend:n}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Qr({inputs:{x:a},backend:n,attrs:{shape:o}})}var G0e={kernelName:Wo,backendName:"wasm",kernelFunc:Y1};function dT(e){let{attrs:{shape:t,value:r,dtype:n},backend:a}=e,s=a.makeOutput(t,n);return a.typedArrayFromHeap(s).fill(r),s}var j0e={kernelName:Bu,backendName:"wasm",kernelFunc:dT},pT;function H0e(e){pT=e.wasm.cwrap(Uo,null,["number","number","number","number","number","number"])}function q0e(e){let{inputs:t,backend:r}=e,{image:n}=t,a=r.makeOutput(n.shape,n.dtype),s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,[o,l,u,d]=n.shape;return pT(s,o,l,u,d,i),a}var K0e={kernelName:Uo,backendName:"wasm",kernelFunc:q0e,setupFunc:H0e},X0e=wr(ai),Z0e=!1,Y0e=jr(si,Z0e),hT;function J0e(e){hT=e.wasm.cwrap(ii,null,["number","number","number","number","number","number","number"])}function Q0e(e){let{backend:t,inputs:r,attrs:n}=e,{varianceEpsilon:a}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=r,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return hT(d,h,p,c,f,a,g),m}var ege={kernelName:ii,backendName:"wasm",setupFunc:J0e,kernelFunc:Q0e},cT;function tge(e){cT=e.wasm.cwrap(Es,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function rge(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p),g=f0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let w=m.filterHeight,T=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,O=m.strideHeight,z=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. 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Please use 'NHWC'.`);let ee=n.makeOutput(m.outShape,"float32"),J=n.dataIdMap.get(ee.dataId).id,se=o==null?0:n.dataIdMap.get(o.dataId).id;return fT(y,D,Q,V,A,w,T,b,S,E,R,_,X,M,I,O,z,j,x,g,se,f||0,J),ee}var ige={kernelName:Rs,backendName:"wasm",setupFunc:age,kernelFunc:sge},mT;function oge(e){mT=e.wasm.cwrap(jo,null,["number","number","number","number","number","number","array","number"])}function lge(e){let{backend:t,inputs:r}=e,{params:n,indices:a}=r,[s,i,o,l]=c2.prepareAndValidate(n,a),u=t.makeOutput(s,n.dtype);if(i===0)return u;let d=a.shape,h=d[d.length-1],p=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(u.dataId).id;return mT(p,Vt[n.dtype],c,i,h,o,f,m),u}var uge={kernelName:jo,backendName:"wasm",setupFunc:oge,kernelFunc:lge},gT;function dge(e){gT=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function pge(e){let{backend:t,inputs:r,attrs:n}=e,{x:a,indices:s}=r,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=t.readSync(s.dataId),d=a.shape[l];for(let S=0;S<u.length;++S){let E=u[S];v.assert(E<=d-1&&E>=0,()=>`GatherV2: the index value ${E} is not in [0, ${d-1}]`)}let h=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),p=Qr({inputs:{x:a},attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]},backend:t}),c=v.sizeFromShape(s.shape),f=Qr({inputs:{x:s},attrs:{shape:[h.batchSize,c/h.batchSize]},backend:t}),m=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],g=t.makeOutput(m,a.dtype);if(v.sizeFromShape(a.shape)===0)return g;let y=p.shape.length-1,A=t.dataIdMap.get(p.dataId).id,x=t.dataIdMap.get(f.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer);return gT(A,Vt[a.dtype],w,y,x,h.batchSize,T,b),t.disposeData(p.dataId),t.disposeData(f.dataId),g.shape=h.outputShape,g}var hge={kernelName:Go,backendName:"wasm",setupFunc:dge,kernelFunc:pge},cge=!1,fge=jr(Ho,cge,"bool"),mge=!1,gge=jr(oi,mge,"bool"),yT;function yge(e){yT=e.wasm.cwrap(ui,null,["number","number","number","number"])}function Age(e){let{inputs:{x:t},attrs:{alpha:r},backend:n}=e,a=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;yT(a,Vt[t.dtype],r,i)}return s}var xge={kernelName:ui,backendName:"wasm",setupFunc:yge,kernelFunc:Age},bge=!1,vge=jr(qo,bge,"bool"),wge=!1,kge=jr(Ko,wge,"bool"),Ige=wr(di),Sge=!1,Tge=jr(Xo,Sge,"bool"),AT;function Nge(e){AT=e.wasm.cwrap(pi,null,["number","number","number","number"])}function Cge(e){let{backend:t,inputs:r,attrs:n}=e,{reductionIndices:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Wi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("max",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;AT(o,Vt[i.dtype],g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Ege={kernelName:pi,backendName:"wasm",setupFunc:Nge,kernelFunc:Cge},Rge=!1,Mge=jr(hi,Rge),xT;function Fge(e){xT=e.wasm.cwrap(ci,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $ge(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id;v.assert(a.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${a.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,b=d.strideWidth,w=d.inChannels,T=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let S=n.makeOutput(d.outShape,"float32"),E=n.dataIdMap.get(S.dataId).id;return xT(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,b,w,T,E),S}var Pge={kernelName:ci,backendName:"wasm",setupFunc:Fge,kernelFunc:$ge},bT;function _ge(e){bT=e.wasm.cwrap(fi,null,["number, number, number"])}function zge(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Wi(i,a,t),f=h;if(c){let b=t.dataIdMap.get(d.dataId).id;b!==o&&(u=d,l=b,f=N.getInnerMostAxes(f.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,g]=N.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(g),A=u;u.dtype!=="float32"&&(A=zh({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(m,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;bT(l,y,b)}if(c&&t.disposeData(d.dataId),s){let b=N.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var Oge={kernelName:fi,backendName:"wasm",setupFunc:_ge,kernelFunc:zge},vT;function Dge(e){vT=e.wasm.cwrap(mi,null,["number","number","number","number"])}function Lge(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Wi(i,a,t);if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let f=u.shape.length;N.assertAxesAreInnerMostDims("min",h,f);let[m,g]=N.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;vT(l,Vt[i.dtype],y,x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Bge={kernelName:mi,backendName:"wasm",setupFunc:Dge,kernelFunc:Lge},Wge=!1,Vge=jr(gi,Wge),wT=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(wT||{}),kT;function Uge(e){kT=e.wasm.cwrap(yi,null,["number","array","number","number","array","array","number","number"])}function Gge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return kT(i,u,t.shape.length,Vt[t.dtype],p,c,wT[a],l),o}var jge={kernelName:yi,backendName:"wasm",kernelFunc:Gge,setupFunc:Uge},Hge=!0,qge=jr(Ai,Hge),Kge=wr(Zo);function Ab(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var IT;function Xge(e){IT=e.wasm.cwrap(Jo,"number",["number","number","number","number","number"])}function Zge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=IT(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:f,pValidOutputs:m}=Ab(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([c],"int32",p)}var Yge={kernelName:Jo,backendName:"wasm",setupFunc:Xge,kernelFunc:Zge},ST;function Jge(e){ST=e.wasm.cwrap(qu,"number",["number","number","number","number","number","bool"])}function Qge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=ST(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Ab(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var eye={kernelName:qu,backendName:"wasm",setupFunc:Jge,kernelFunc:Qge},TT;function tye(e){TT=e.wasm.cwrap(Qo,"number",["number","number","number","number","number","number"])}function rye(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=TT(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Ab(t,p);t.wasm._free(g);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([f],"float32",m);return[y,A]}var nye={kernelName:Qo,backendName:"wasm",setupFunc:tye,kernelFunc:rye},aye=!1,sye=jr(Yo,aye,"bool"),NT;function iye(e){NT=e.wasm.cwrap(tl,null,["number","number","number","number","number"])}function oye(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=r.makeOutput([...a.shape,s],"int32"),u=r.dataIdMap.get(l.dataId).id,d=r.dataIdMap.get(a.dataId).id;return NT(d,s,i,o,u),l}var lye={kernelName:tl,backendName:"wasm",setupFunc:iye,kernelFunc:oye};function uye(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype);return r.typedArrayFromHeap(n).fill(1),n}var dye={kernelName:el,backendName:"wasm",kernelFunc:uye};function pye(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return Y1({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=Y1({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=tT({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var hye={kernelName:rl,backendName:"wasm",kernelFunc:pye},CT;function cye(e){CT=e.wasm.cwrap(xi,null,["number","array","number","number","array","array","number","number"])}function fye(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]);if(v.sizeFromShape(t.shape)===0)return dT({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return CT(i,u,t.shape.length,Vt[t.dtype],p,c,a,l),o}var ET={kernelName:xi,backendName:"wasm",kernelFunc:fye,setupFunc:cye},mye=!1,gye=jr(bi,mye),RT;function yye(e){RT=e.wasm.cwrap(vi,null,["number","number","number"])}function Aye(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=zh({backend:r,inputs:{x:n},attrs:{dtype:"float32"}}),o=r.dataIdMap.get(u.dataId).id);let d=r.makeOutput(n.shape,"float32"),h=r.dataIdMap.get(d.dataId).id;return RT(o,i,h),l.dtype!=="float32"&&r.disposeData(u.dataId),d}var xye={kernelName:vi,backendName:"wasm",setupFunc:yye,kernelFunc:Aye},MT;function bye(e){MT=e.wasm.cwrap(nl,null,["number","number","number","number"])}function vye(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Wi(i,a,t),f=h;if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x,f=N.getInnerMostAxes(f.length,u.shape.length))}N.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=N.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;MT(l,y,Vt[A.dtype],x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var wye={kernelName:nl,backendName:"wasm",setupFunc:bye,kernelFunc:vye},kye=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Gx(n,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Iye={kernelName:Ku,backendName:"wasm",kernelFunc:kye},Sye=!0,Tye=jr(ti,Sye),Nye=wr(wi),Cye=wr(Ii),FT;function Eye(e){FT=e.wasm.cwrap(ki,null,["number","number","number","number","number","number","number","number","number","number"])}function Rye(e){let{backend:t,inputs:r,attrs:n}=e,{images:a}=r,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[d,h,p,c]=a.shape,f=[d,l,u,c],m=t.dataIdMap.get(a.dataId),g;m.dtype!=="float32"&&(g=zh({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let y=m.id,A=t.makeOutput(f,"float32");if(v.sizeFromShape(a.shape)===0)return A;let x=t.dataIdMap.get(A.dataId).id;return FT(y,d,h,p,c,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),A}var Mye={kernelName:ki,backendName:"wasm",setupFunc:Eye,kernelFunc:Rye},$T;function Fye(e){$T=e.wasm.cwrap(sl,null,["number","array","number","array","number","number"])}function $ye(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=v.parseAxisParam(s,a.shape);if(a.shape.length===0)return m0({inputs:{x:a},backend:r});let o=r.makeOutput(a.shape,a.dtype),l=r.dataIdMap.get(a.dataId).id,u=r.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);$T(l,d,i.length,h,a.shape.length,u);let p=Qr({inputs:{x:o},attrs:{shape:a.shape},backend:r});return r.disposeData(o.dataId),p}var Pye={kernelName:sl,backendName:"wasm",kernelFunc:$ye,setupFunc:Fye},PT;function _ye(e){PT=e.wasm.cwrap(xl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function zye(e){let{inputs:t,backend:r,attrs:n}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=n,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(a.dataId).id,d=r.dataIdMap.get(l.dataId).id,[h,p,c,f]=a.shape,[m,g]=N.getImageCenter(o,p,c),y=i===0,A=255,x=typeof i=="number"?[i,i,i,y?0:A]:[...i,A],b=new Uint8Array(new Int32Array(x).buffer);return PT(u,h,p,c,f,s,m,g,b,x.length,d),l}var Oye={kernelName:xl,backendName:"wasm",kernelFunc:zye,setupFunc:_ye},Dye=wr(il),Lye=wr(Si),_T;function Bye(e){_T=e.wasm.cwrap(ol,null,["number","number","number","number","number","number","array","number","number"])}function Wye(e){let{backend:t,inputs:r,attrs:n}=e,{indices:a,updates:s}=r,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(v.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=f2.calculateShapes(s,a,i),c=t.dataIdMap.get(a.dataId).id,f=t.dataIdMap.get(s.dataId).id,m=new Uint8Array(new Int32Array(h).buffer),g=t.dataIdMap.get(o.dataId).id;return _T(c,f,Vt[s.dtype],l,u,d,m,p,g),o}var Vye={kernelName:ol,backendName:"wasm",setupFunc:Bye,kernelFunc:Wye},zT;function Uye(e){zT=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function Gye(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=r.dataIdMap.get(n.dataId).id,o=r.dataIdMap.get(a.dataId).id,l=r.dataIdMap.get(s.dataId).id,u=r.makeOutput(a.shape,a.dtype),d=r.dataIdMap.get(u.dataId).id,h=n.shape.length,p=a.shape.length,c=h===0||h>1||p===1?1:v.sizeFromShape(a.shape.slice(1));return zT(i,o,l,c,d),u}var jye={kernelName:ll,backendName:"wasm",kernelFunc:Gye,setupFunc:Uye},OT;function Hye(e){OT=e.wasm.cwrap(Ni,null,["number","number"])}function qye(e){let{backend:t,inputs:{x:r}}=e,n=t.dataIdMap.get(r.dataId).id,a=t.makeOutput(r.shape,r.dtype),s=t.dataIdMap.get(a.dataId).id;return v.sizeFromShape(a.shape)===0||OT(n,s),a}var Kye={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Hye,kernelFunc:qye},Xye=wr(Ti),DT;function Zye(e){DT=e.wasm.cwrap(Ri,null,["number","number","number","number"])}function Yye(e){let{backend:t,inputs:{logits:r},attrs:{dim:n}}=e,a=t.dataIdMap.get(r.dataId).id,s=t.makeOutput(r.shape,r.dtype),i=t.dataIdMap.get(s.dataId).id,o=r.shape[n],l=v.sizeFromShape(r.shape)/o;return v.sizeFromShape(s.shape)===0||DT(a,i,o,l),s}var Jye={kernelName:Ri,backendName:"wasm",setupFunc:Zye,kernelFunc:Yye};function Qye(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n,o=v.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<a.shape.length;++g)l.push([0,0]);let u=ET.kernelFunc({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,s,o,!1),h=N.getPermuted(d.length,s.length,!1),p=N.getReshapedPermuted(u.shape,s,o,!1),c=Qr({inputs:{x:u},backend:r,attrs:{shape:d}}),f=Vs({inputs:{x:c},backend:r,attrs:{perm:h}}),m=Qr({inputs:{x:f},backend:r,attrs:{shape:p}});return r.disposeData(u.dataId),r.disposeData(c.dataId),r.disposeData(f.dataId),m}var e1e={kernelName:pl,backendName:"wasm",kernelFunc:Qye},LT;function t1e(e){LT=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function r1e(e){let{backend:t,inputs:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=r,o=n.shape[0],l=n.shape[1],u=t.readSync(s.dataId)[0],d=[o+u,l],h=t.dataIdMap.get(n.dataId).id,p=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(d,n.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(d.slice(0,1),a.dtype),y=t.dataIdMap.get(g.dataId).id,A=t.makeOutput([u],"bool"),x=t.dataIdMap.get(A.dataId).id,b=t.makeOutput([o],n.dtype),w=t.dataIdMap.get(b.dataId).id,T=t.makeOutput([4],"int32"),S=t.dataIdMap.get(T.dataId).id,E=LT(h,p,Vt[a.dtype],o,u,l,c,m,y,x,w,S),R=t.readSync(T.dataId),_;switch(R[0]){case 1:{_=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 2:{_=N.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1],R[2]);break}case 3:_=N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1],R[2],R[3]);break;default:_=""}if(t.disposeData(T.dataId),_)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(A.dataId),t.disposeData(b.dataId),new Error(_);let M=f,I=g;return E!==d[0]&&(M=Mo({inputs:{x:f},attrs:{begin:0,size:[E,l]},backend:t}),I=Mo({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[M,I,A,b]}var n1e={kernelName:Xp,backendName:"wasm",setupFunc:t1e,kernelFunc:r1e},BT;function a1e(e){BT=e.wasm.cwrap(ed,null,["number","number","number","number","number","number","number"])}function s1e(e){let{backend:t,inputs:r}=e,{inputIndices:n,inputShape:a,newShape:s}=r;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
+  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let r=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=r}!t||t.length===this.lastUniformData.length&&t.every((r,n)=>r===this.lastUniformData[n])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,r){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==r)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,r],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=r),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},Cme=class extends H8{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},Eme=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),iw=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;v.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),v.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},q8=class extends Nu{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!gb())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Nme(this.device),this.tensorMap=new Dp(this,Ar()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return q8.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=v.sizeFromShape(t)*X1(r);return this.tensorMap.set(n,{dtype:r,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=v.sizeFromShape(r)*X1(n);this.tensorMap.set(e,{dtype:n,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new H8),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Cme),this.fromPixelImportProgram;default:v.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let r=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=N.mergeRealAndImagArrays(s,i)}else{let a=await this.getBufferData(t);n=I8(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>v.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&v.isString(r[0])){let a=r.map(s=>v.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=[];e.forEach(s=>{s.data.length===0&&(s.data=[1]);let i;switch(s.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:v.assert(!1,()=>`Unsupported ${s.data.length}D shape`)}t=Math.ceil(t/i)*i,r.push(t),t+=s.data.length*4});let n=new ArrayBuffer(t);e.forEach((s,i)=>{let o=r[i];s.type==="int32"?new Int32Array(n,o,s.data.length).set(s.data):s.type==="uint32"?new Uint32Array(n,o,s.data.length).set(s.data):new Float32Array(n,o,s.data.length).set(s.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(a,0,n,0,t),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),v.sizeFromShape(a.shape)===0){let S=this.tensorMap.get(a.dataId);return S.values=v.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=iw(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(S=>S.shape),o="int32";i.map(S=>{s.push({type:o,data:S})});let l=v.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let S=v.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?S/4:S]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((S,E)=>{if(S.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. 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Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let s=await t.requestDevice(n);return new bb(s,a)},3);var Vt=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(Vt||{}),m0=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(m0||{}),X8;function Rme(e){X8=e.wasm.cwrap(Cs,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Mme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:r.dataIdMap.get(o.dataId).id,g=m0[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=vl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),w=r.dataIdMap.get(b.dataId).id,T=new Uint8Array(new Int32Array(a.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return X8(p,T,a.shape.length,c,S,s.shape.length,l,u,g,f,m,h||0,w),b}var Fme={kernelName:Cs,backendName:"wasm",setupFunc:Rme,kernelFunc:Mme};function wr(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||r(l,Vt[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var $me=wr(_o);function jr(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,f=N.assertAndGetBroadcastShape(u.shape,d.shape),m=o.makeOutput(f,c);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(m.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Vt[u.dtype],A),m}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var Pme=!0,_me=jr(qa,Pme),Z8;function zme(e){Z8=e.wasm.cwrap(Gs,null,["array","number","number","number"])}function Ome(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return Z8(s,a.length,Vt[n.dtype],i),n}var Dme={kernelName:Gs,backendName:"wasm",setupFunc:zme,kernelFunc:Ome};function g0(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var Lme={kernelName:li,backendName:"wasm",kernelFunc:g0},Y8;function Bme(e){Y8=e.wasm.cwrap(Pi,null,["number","array","number","number","number","array","number"])}function Vs(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=Vme(t.x.shape,n.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=Wme(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=g0({inputs:t,backend:r});return f.shape=o,f}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return Y8(d,c,l.shape.length,Vt[l.dtype],h,p,s.length),u}function Wme(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function Vme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var Ume={kernelName:Pi,backendName:"wasm",kernelFunc:Vs,setupFunc:Bme};function Wi(e,t,r){let n=e.shape,a=e.shape.length,s=v.parseAxisParam(t,n),i=s,o=N.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=N.getInnerMostAxes(i.length,a),l=Vs({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var J8;function Gme(e){J8=e.wasm.cwrap(Mu,null,["number, number, number"])}function jme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Wi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("all",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;J8(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Hme={kernelName:Mu,backendName:"wasm",setupFunc:Gme,kernelFunc:jme},Q8;function qme(e){Q8=e.wasm.cwrap(Fu,null,["number, number, number"])}function Kme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Wi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("any",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;Q8(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Xme={kernelName:Fu,backendName:"wasm",setupFunc:qme,kernelFunc:Kme},eT;function Zme(e){eT=e.wasm.cwrap(js,null,["number","number","number","number","number"])}function Yme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=Wi(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),f=t.dataIdMap.get(c.dataId).id,m=v.sizeFromShape(c.shape),g=l.shape[d[0]];return eT(o,Vt[l.dtype],m,g,f),h&&t.disposeData(u.dataId),c}var Jme={kernelName:js,backendName:"wasm",kernelFunc:Yme,setupFunc:Zme},tT;function Qme(e){tT=e.wasm.cwrap(Hs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function e0e(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let b=n.makeOutput(d.outShape,"float32"),w=n.dataIdMap.get(b.dataId).id;return tT(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,w),b}var t0e={kernelName:Hs,backendName:"wasm",setupFunc:Qme,kernelFunc:e0e};function Qr(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=v.sizeFromShape(n.shape),i=v.inferFromImplicitShape(a,s);return v.assert(s===v.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var r0e={kernelName:sl,backendName:"wasm",kernelFunc:Qr},rT;function n0e(e){rT=e.wasm.cwrap(qs,null,["number","array","number","number","array","number","number","number","number"])}function a0e(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=vl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);v.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],w=Qr({inputs:{x:a},backend:r,attrs:{shape:x}}),T=Qr({inputs:{x:s},backend:r,attrs:{shape:b}}),S=r.dataIdMap.get(w.dataId).id,E=r.dataIdMap.get(T.dataId).id,R=i?w.shape[2]:w.shape[1],_=o?T.shape[1]:T.shape[2],M=Math.max(g,y),I=r.makeOutput([M,R,_],w.dtype),O=r.dataIdMap.get(I.dataId).id,z=new Uint8Array(new Int32Array(w.shape).buffer),j=new Uint8Array(new Int32Array(T.shape).buffer);return rT(S,z,w.shape.length,E,j,T.shape.length,i,o,O),r.disposeData(w.dataId),r.disposeData(T.dataId),I.shape=A,I}var s0e={kernelName:qs,backendName:"wasm",setupFunc:n0e,kernelFunc:a0e};function Fo(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=_t.parseSliceParams(t,r,n),o=_t.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=v.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=_t.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(f,f+v.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+v.sizeFromShape(i))),u}if(t.dtype==="string"){let f=Tf(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)i0e(l,d[0],p,s,i);else if(c===3)o0e(l,d[0],d[1],p,s,i);else if(c===4)l0e(l,d[0],d[1],d[2],p,s,i);else{let f=Tf(l,s,i,t.shape,t.dtype);p.set(f)}return u}function i0e(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function o0e(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let f=p*t+c*r+u;n.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function l0e(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],f=s[3];for(let m=l;m<h;m++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=m*t+g*r+y*n+f;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var u0e={kernelName:dl,backendName:"wasm",kernelFunc:Fo};function d0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Qr({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Vs({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Qr({inputs:{x:f},backend:r,attrs:{shape:d}}),g=Fo({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(c.dataId),g}var p0e={kernelName:zo,backendName:"wasm",kernelFunc:d0e};function zh(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var h0e={kernelName:Ks,backendName:"wasm",kernelFunc:zh},c0e=wr(Xs),nT;function f0e(e){nT=e.wasm.cwrap(Ka,null,["number","number","number","number"])}function 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L0e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h}=r,p=u==null?[1,1]:u,c=N.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),f=c.filterHeight,m=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,w=c.dilationWidth,T=c.strideHeight,S=c.strideWidth,E=c.inChannels,R=c.outChannels,_=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let M=n.makeOutput(c.outShape,"float32"),I=n.dataIdMap.get(M.dataId).id;return hT(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,g,y,A,x,_,b,w,T,S,E,R,I),M}var B0e={kernelName:ei,backendName:"wasm",setupFunc:D0e,kernelFunc:L0e},W0e=wr(ri),V0e=!1,U0e=jr(Wo,V0e,"bool"),G0e=wr(ni,"float32");function e2(e){let{inputs:t,attrs:r,backend:n}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(v.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Qr({inputs:{x:a},backend:n,attrs:{shape:o}})}var j0e={kernelName:Vo,backendName:"wasm",kernelFunc:e2};function cT(e){let{attrs:{shape:t,value:r,dtype:n},backend:a}=e,s=a.makeOutput(t,n);return a.typedArrayFromHeap(s).fill(r),s}var H0e={kernelName:Wu,backendName:"wasm",kernelFunc:cT},fT;function q0e(e){fT=e.wasm.cwrap(Go,null,["number","number","number","number","number","number"])}function K0e(e){let{inputs:t,backend:r}=e,{image:n}=t,a=r.makeOutput(n.shape,n.dtype),s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,[o,l,u,d]=n.shape;return fT(s,o,l,u,d,i),a}var X0e={kernelName:Go,backendName:"wasm",kernelFunc:K0e,setupFunc:q0e},Z0e=wr(ai),Y0e=!1,J0e=jr(si,Y0e),mT;function Q0e(e){mT=e.wasm.cwrap(ii,null,["number","number","number","number","number","number","number"])}function ege(e){let{backend:t,inputs:r,attrs:n}=e,{varianceEpsilon:a}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=r,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(v.sizeFromShape(s.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return mT(d,h,p,c,f,a,g),m}var tge={kernelName:ii,backendName:"wasm",setupFunc:Q0e,kernelFunc:ege},gT;function rge(e){gT=e.wasm.cwrap(Es,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nge(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p),g=m0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let w=m.filterHeight,T=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,O=m.strideHeight,z=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ee=n.makeOutput(m.outShape,"float32"),J=n.dataIdMap.get(ee.dataId).id,se=o==null?0:n.dataIdMap.get(o.dataId).id;return gT(y,D,Q,V,A,w,T,b,S,E,R,_,X,M,I,O,z,j,x,g,se,f||0,J),ee}var age={kernelName:Es,backendName:"wasm",setupFunc:rge,kernelFunc:nge},yT;function sge(e){yT=e.wasm.cwrap(Rs,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 ige(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p,!0),g=m0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let w=m.filterHeight,T=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,O=m.strideHeight,z=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ee=n.makeOutput(m.outShape,"float32"),J=n.dataIdMap.get(ee.dataId).id,se=o==null?0:n.dataIdMap.get(o.dataId).id;return yT(y,D,Q,V,A,w,T,b,S,E,R,_,X,M,I,O,z,j,x,g,se,f||0,J),ee}var oge={kernelName:Rs,backendName:"wasm",setupFunc:sge,kernelFunc:ige},AT;function lge(e){AT=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number","array","number"])}function uge(e){let{backend:t,inputs:r}=e,{params:n,indices:a}=r,[s,i,o,l]=g2.prepareAndValidate(n,a),u=t.makeOutput(s,n.dtype);if(i===0)return u;let d=a.shape,h=d[d.length-1],p=t.dataIdMap.get(n.dataId).id,c=t.dataIdMap.get(a.dataId).id,f=new Uint8Array(new Int32Array(l).buffer),m=t.dataIdMap.get(u.dataId).id;return AT(p,Vt[n.dtype],c,i,h,o,f,m),u}var dge={kernelName:Ho,backendName:"wasm",setupFunc:lge,kernelFunc:uge},xT;function pge(e){xT=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function hge(e){let{backend:t,inputs:r,attrs:n}=e,{x:a,indices:s}=r,{axis:i,batchDims:o}=n,l=v.parseAxisParam(i,a.shape)[0],u=t.readSync(s.dataId),d=a.shape[l];for(let S=0;S<u.length;++S){let E=u[S];v.assert(E<=d-1&&E>=0,()=>`GatherV2: the index value ${E} is not in [0, ${d-1}]`)}let h=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),p=Qr({inputs:{x:a},attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]},backend:t}),c=v.sizeFromShape(s.shape),f=Qr({inputs:{x:s},attrs:{shape:[h.batchSize,c/h.batchSize]},backend:t}),m=[h.batchSize,h.outerSize,c/h.batchSize,h.sliceSize],g=t.makeOutput(m,a.dtype);if(v.sizeFromShape(a.shape)===0)return g;let y=p.shape.length-1,A=t.dataIdMap.get(p.dataId).id,x=t.dataIdMap.get(f.dataId).id,b=t.dataIdMap.get(g.dataId).id,w=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),T=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer);return xT(A,Vt[a.dtype],w,y,x,h.batchSize,T,b),t.disposeData(p.dataId),t.disposeData(f.dataId),g.shape=h.outputShape,g}var cge={kernelName:jo,backendName:"wasm",setupFunc:pge,kernelFunc:hge},fge=!1,mge=jr(qo,fge,"bool"),gge=!1,yge=jr(oi,gge,"bool"),bT;function Age(e){bT=e.wasm.cwrap(ui,null,["number","number","number","number"])}function xge(e){let{inputs:{x:t},attrs:{alpha:r},backend:n}=e,a=n.dataIdMap.get(t.dataId).id,s=n.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let i=n.dataIdMap.get(s.dataId).id;bT(a,Vt[t.dtype],r,i)}return s}var bge={kernelName:ui,backendName:"wasm",setupFunc:Age,kernelFunc:xge},vge=!1,wge=jr(Ko,vge,"bool"),kge=!1,Ige=jr(Xo,kge,"bool"),Sge=wr(di),Tge=!1,Nge=jr(Zo,Tge,"bool"),vT;function Cge(e){vT=e.wasm.cwrap(pi,null,["number","number","number","number"])}function Ege(e){let{backend:t,inputs:r,attrs:n}=e,{reductionIndices:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Wi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("max",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=v.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(v.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;vT(o,Vt[i.dtype],g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Rge={kernelName:pi,backendName:"wasm",setupFunc:Cge,kernelFunc:Ege},Mge=!1,Fge=jr(hi,Mge),wT;function $ge(e){wT=e.wasm.cwrap(ci,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Pge(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id;v.assert(a.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${a.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,b=d.strideWidth,w=d.inChannels,T=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);let S=n.makeOutput(d.outShape,"float32"),E=n.dataIdMap.get(S.dataId).id;return wT(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,b,w,T,E),S}var _ge={kernelName:ci,backendName:"wasm",setupFunc:$ge,kernelFunc:Pge},kT;function zge(e){kT=e.wasm.cwrap(fi,null,["number, number, number"])}function Oge(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Wi(i,a,t),f=h;if(c){let b=t.dataIdMap.get(d.dataId).id;b!==o&&(u=d,l=b,f=N.getInnerMostAxes(f.length,u.shape.length))}N.assertAxesAreInnerMostDims("mean",f,u.shape.length);let[m,g]=N.computeOutAndReduceShapes(u.shape,f),y=v.sizeFromShape(g),A=u;u.dtype!=="float32"&&(A=zh({backend:t,inputs:{x:u},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(A.dataId).id);let x=t.makeOutput(m,"float32");if(v.sizeFromShape(u.shape)!==0){let b=t.dataIdMap.get(x.dataId).id;kT(l,y,b)}if(c&&t.disposeData(d.dataId),s){let b=N.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var Dge={kernelName:fi,backendName:"wasm",setupFunc:zge,kernelFunc:Oge},IT;function Lge(e){IT=e.wasm.cwrap(mi,null,["number","number","number","number"])}function Bge(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Wi(i,a,t);if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let f=u.shape.length;N.assertAxesAreInnerMostDims("min",h,f);let[m,g]=N.computeOutAndReduceShapes(u.shape,h),y=v.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;IT(l,Vt[i.dtype],y,x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Wge={kernelName:mi,backendName:"wasm",setupFunc:Lge,kernelFunc:Bge},Vge=!1,Uge=jr(gi,Vge),ST=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(ST||{}),TT;function Gge(e){TT=e.wasm.cwrap(yi,null,["number","array","number","number","array","array","number","number"])}function jge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return TT(i,u,t.shape.length,Vt[t.dtype],p,c,ST[a],l),o}var Hge={kernelName:yi,backendName:"wasm",kernelFunc:jge,setupFunc:Gge},qge=!0,Kge=jr(Ai,qge),Xge=wr(Yo);function vb(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var NT;function Zge(e){NT=e.wasm.cwrap(Qo,"number",["number","number","number","number","number"])}function Yge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=NT(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:f,pValidOutputs:m}=vb(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([c],"int32",p)}var Jge={kernelName:Qo,backendName:"wasm",setupFunc:Zge,kernelFunc:Yge},CT;function Qge(e){CT=e.wasm.cwrap(Ku,"number",["number","number","number","number","number","bool"])}function eye(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=CT(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=vb(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var tye={kernelName:Ku,backendName:"wasm",setupFunc:Qge,kernelFunc:eye},ET;function rye(e){ET=e.wasm.cwrap(el,"number",["number","number","number","number","number","number"])}function nye(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=ET(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=vb(t,p);t.wasm._free(g);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([f],"float32",m);return[y,A]}var aye={kernelName:el,backendName:"wasm",setupFunc:rye,kernelFunc:nye},sye=!1,iye=jr(Jo,sye,"bool"),RT;function oye(e){RT=e.wasm.cwrap(rl,null,["number","number","number","number","number"])}function lye(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=r.makeOutput([...a.shape,s],"int32"),u=r.dataIdMap.get(l.dataId).id,d=r.dataIdMap.get(a.dataId).id;return RT(d,s,i,o,u),l}var uye={kernelName:rl,backendName:"wasm",setupFunc:oye,kernelFunc:lye};function dye(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype);return r.typedArrayFromHeap(n).fill(1),n}var pye={kernelName:tl,backendName:"wasm",kernelFunc:dye};function hye(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return e2({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{v.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=e2({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=aT({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var cye={kernelName:nl,backendName:"wasm",kernelFunc:hye},MT;function fye(e){MT=e.wasm.cwrap(xi,null,["number","array","number","number","array","array","number","number"])}function mye(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]);if(v.sizeFromShape(t.shape)===0)return cT({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return MT(i,u,t.shape.length,Vt[t.dtype],p,c,a,l),o}var FT={kernelName:xi,backendName:"wasm",kernelFunc:mye,setupFunc:fye},gye=!1,yye=jr(bi,gye),$T;function Aye(e){$T=e.wasm.cwrap(vi,null,["number","number","number"])}function 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x=t.dataIdMap.get(A.dataId).id;PT(l,y,Vt[A.dtype],x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var kye={kernelName:al,backendName:"wasm",setupFunc:vye,kernelFunc:wye},Iye=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=qx(n,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},Sye={kernelName:Xu,backendName:"wasm",kernelFunc:Iye},Tye=!0,Nye=jr(ti,Tye),Cye=wr(wi),Eye=wr(Ii),_T;function Rye(e){_T=e.wasm.cwrap(ki,null,["number","number","number","number","number","number","number","number","number","number"])}function Mye(e){let{backend:t,inputs:r,attrs:n}=e,{images:a}=r,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[d,h,p,c]=a.shape,f=[d,l,u,c],m=t.dataIdMap.get(a.dataId),g;m.dtype!=="float32"&&(g=zh({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let y=m.id,A=t.makeOutput(f,"float32");if(v.sizeFromShape(a.shape)===0)return A;let 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t1e={kernelName:hl,backendName:"wasm",kernelFunc:e1e},VT;function r1e(e){VT=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function n1e(e){let{backend:t,inputs:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=r,o=n.shape[0],l=n.shape[1],u=t.readSync(s.dataId)[0],d=[o+u,l],h=t.dataIdMap.get(n.dataId).id,p=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(d,n.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(d.slice(0,1),a.dtype),y=t.dataIdMap.get(g.dataId).id,A=t.makeOutput([u],"bool"),x=t.dataIdMap.get(A.dataId).id,b=t.makeOutput([o],n.dtype),w=t.dataIdMap.get(b.dataId).id,T=t.makeOutput([4],"int32"),S=t.dataIdMap.get(T.dataId).id,E=VT(h,p,Vt[a.dtype],o,u,l,c,m,y,x,w,S),R=t.readSync(T.dataId),_;switch(R[0]){case 1:{_=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 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d2e=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],p2e=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],h2e=[33,133,362,263,1,78,308],nxe=d2e.map(e=>Bh[e]),axe=p2e.map(e=>Bh[e]),sxe=h2e.map(e=>Bh[e]);var Nd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],v0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],Lb=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],Bb=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],EN=(e,t)=>{let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:r,endPoint:n,landmarks:e.landmarks,confidence:e.confidence}},Ob=(e,t,r)=>{let n=t.shape[1],a=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a],i=Ie.cropAndResize(t,[s],[0],r),o=pe(i,Qe.tf255);return re(i),o},w0=(e,t)=>{let r=v0(e),n=Nd(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},k0=e=>{let t=v0(e),r=Nd(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},RN=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},Db=[[1,0,0],[0,1,0],[0,0,1]],c2e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),f2e=(e,t)=>c2e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var NN=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],$l=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},m2e=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},CN=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push($l(e[a],m2e(t,s)))}return r},MN=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=NN(t[0],t[1]),i=CN(s,a),o=NN(-t[0],-t[1]);return CN(i,o)},g2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-$l(t[0],r),-$l(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},y2e=(e,t)=>[$l(e,t[0]),$l(e,t[1])];function FN(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function $N(e,t,r,n,a){let s=Nd(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?MN(r,[0,0]):Db,u=o?i.map(c=>[...y2e(c,l),c[2]]):i,d=o?g2e(n):Db,h=v0(t),p=[$l(h,d[0]),$l(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function PN(e,t,r,n){let a=t.landmarks.length>=Pb.count?Pb.symmetryLine:Lh.symmetryLine,s=0,i=Db,o;if(e&&ce.kernels.includes("rotatewithoffset"))if(s=f2e(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=v0(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=MN(-s,u),o=Ob(t,h,[n,n]),re(h)}else o=Ob(t,r,[n,n]);else o=Ob(t,r,[n,n]);return[s,i,o]}var A2e=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},_N=(e,t)=>{let r=A2e(e),n=Nd(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var zN=6,x2e=1.2,_a,ON=null,Vi=0,Wh=null,I0=()=>Vi;async function DN(e){var t;return ce.initial&&(_a=null),_a?e.debug&&ie("cached model:",_a.modelUrl):_a=await je((t=e.face.detector)==null?void 0:t.modelPath),Vi=_a.inputs[0].shape?_a.inputs[0].shape[2]:0,Wh=Se(Vi,"int32"),ON=ua(FN(Vi)),_a}function b2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,ON),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Wh),t.centersNormalized=pe(t.centers,Wh),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=he(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Wh),t.endNormalized=L(t.ends,Wh);let r=nd([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>re(t[n])),r}async function LN(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Vi,Vi]),r.div=pe(r.resized,Qe.tf127),r.normalized=he(r.div,Qe.tf05);let n=_a==null?void 0:_a.execute(r.normalized);if(Array.isArray(n)){let h=n.sort((p,c)=>p.size-c.size);r.concat384=kt([h[0],h[2]],2),r.concat512=kt([h[1],h[3]],2),r.concat=kt([r.concat512,r.concat384],1),r.batch=et(r.concat,0)}else r.batch=et(n);re(n),r.boxes=b2e(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Tr(r.logits),r.scores=et(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=Pe(r.boxes,[a[h],0],[1,-1]),c.slice=Pe(r.batch,[a[h],zN-1],[1,-1]),c.squeeze=et(c.slice),c.landmarks=G(c.squeeze,[zN,-1]);let f=await c.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await c.landmarks.array(),confidence:p},g=EN(m,[(e.shape[2]||0)/Vi,(e.shape[1]||0)/Vi]),y=w0(g,t.face.scale||x2e),A=k0(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(h=>re(r[h])),s}var S0={};gs(S0,{connected:()=>Ub,kpt:()=>Vb});var Vb=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Ub={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var WN=224,v2e,w2e=5,T0=[8,16,32,32,32];async function VN(){let e=[],t=0;for(;t<w2e;){let r=0,n=t;for(;n<T0.length&&T0[n]===T0[t];)r+=2,n++;let a=T0[t],s=Math.ceil(WN/a),i=Math.ceil(WN/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}v2e={x:St(e.map(r=>r.x)),y:St(e.map(r=>r.y))}}function as(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function UN(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function N0(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var HN={initial:!0},mn={detector:null,landmarks:null},Cd={detector:[224,224],landmarks:[256,256]},Gb=Number.MAX_SAFE_INTEGER,I2e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},E0=null,Vh,Ui=[[0,0],[0,0],[0,0],[0,0]],GN=0,jN=e=>1-1/(1+Math.exp(e));async function qN(e){if(HN.initial&&(mn.detector=null),!mn.detector&&e.body.detector&&e.body.detector.modelPath){mn.detector=await je(e.body.detector.modelPath);let t=Object.values(mn.detector.modelSignature.inputs);Cd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Cd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&mn.detector&&ie("cached model:",mn.detector.modelUrl);return await VN(),mn.detector}async function KN(e){if(HN.initial&&(mn.landmarks=null),mn.landmarks)e.debug&&ie("cached model:",mn.landmarks.modelUrl);else{mn.landmarks=await je(e.body.modelPath);let t=Object.values(mn.landmarks.modelSignature.inputs);Cd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Cd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return mn.landmarks}async function S2e(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Vh&&(r.cropped=Ie.cropAndResize(e,[Vh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Ui=[[0,0],a,s,[0,0]],r.pad=Gn(r.cropped||e,Ui),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>re(r[a])),n}function T2e(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Ui[2][0]+Ui[2][1])/t[0]-Ui[2][0]),Math.trunc(r.position[1]*(t[1]+Ui[1][0]+Ui[1][1])/t[1]-Ui[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Vh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Vh[1],r.positionRaw[1]+Vh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function N2e(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function C2e(e,t,r){var f;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(f=mn.landmarks)==null?void 0:f.execute(e,I2e.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>re(n[m]));let o=[],l=5;for(let m=0;m<s.length/l;m++){let g=jN(s[l*m+3]),y=jN(s[l*m+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*m+0]/Cd.landmarks[0],s[l*m+1]/Cd.landmarks[1],s[l*m+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],w=[i[l*m+0],i[l*m+1],i[l*m+2]+0];o.push({part:Vb[m],positionRaw:x,position:b,distance:w,score:A})}if(a<(t.body.minConfidence||0))return null;N2e(o);let u=T2e(o,r),d=u.map(m=>m.position),h=as(d,[r[0],r[1]]),p={};for(let[m,g]of Object.entries(Ub)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(w=>w.part===g[A]),b=u.find(w=>w.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[m]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function jb(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-GN,a=Gb<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&E0!==null)Gb++;else{let s={};s.landmarks=await S2e(e,256),E0=await C2e(s.landmarks,t,r),Object.keys(s).forEach(i=>re(s[i])),GN=oe(),Gb=0}return E0?[E0]:[]}var Ed=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var ss,Pl=0,Hb=[],ZN=0,qb=Number.MAX_SAFE_INTEGER;async function YN(e){if(ce.initial&&(ss=null),ss)e.debug&&ie("cached model:",ss.modelUrl);else{ss=await je(e.object.modelPath);let t=Object.values(ss.modelSignature.inputs);Pl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return ss}async function E2e(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=et(e);let i=Kt(n.squeeze,6,1);n.stack=sr([i[1],i[0],i[3],i[2]],1),n.boxes=et(n.stack),n.scores=et(i[4]),n.classes=et(i[5]),re([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=Ed[h].label,[c,f]=[s[0][u][0]/Pl,s[0][u][1]/Pl],m=[c,f,s[0][u][2]/Pl-c,s[0][u][3]/Pl-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:m})}return Object.keys(n).forEach(u=>re(n[u])),a}async function Kb(e,t){let r=(t.object.skipTime||0)>oe()-ZN,n=qb<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&Hb.length>0?(qb++,Hb):(qb=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Pl,Pl]),o=t.object.enabled?ss==null?void 0:ss.execute(i,["tower_0/detections"]):null;ZN=oe(),re(i);let l=await E2e(o,s,t);Hb=l,a(l)}))}var R0={};gs(R0,{connected:()=>Zb,kpt:()=>Xb});var Xb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Zb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Rr,QN=0,qr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Yb=Number.MAX_SAFE_INTEGER;async function eC(e){return ce.initial&&(Rr=null),Rr?e.debug&&ie("cached model:",Rr.modelUrl):Rr=await je(e.body.modelPath),Rr}async function R2e(e,t){let[r,n]=e.shape,a=G(e,[n*r]),s=fr(a,0),i=(await s.data())[0];if(re([a,s]),i>t){let o=Nn(a,0),l=id(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return re([l,d]),[u,h,i]}return[0,0,i]}async function Jb(e,t){let r=(t.body.skipTime||0)>oe()-QN,n=Yb<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(qr.keypoints).length>0?(Yb++,[qr]):(Yb=0,new Promise(async a=>{var h;let s=K(()=>{if(!(Rr!=null&&Rr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Rr.inputs[0].shape[2],Rr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return he(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Rr==null?void 0:Rr.execute(s)),QN=oe(),re(s),i){qr.keypoints.length=0;let p=i.squeeze();re(i);let c=p.unstack(2);re(p);for(let f=0;f<c.length;f++){let[m,g,y]=await R2e(c[f],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&qr.keypoints.push({score:Math.round(100*y)/100,part:Xb[f],positionRaw:[m/Rr.inputs[0].shape[2],g/Rr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Rr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Rr.inputs[0].shape[1])]})}c.forEach(f=>re(f))}qr.score=qr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=qr.keypoints.map(p=>p.position[0]),l=qr.keypoints.map(p=>p.position[1]);qr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=qr.keypoints.map(p=>p.positionRaw[0]),d=qr.keypoints.map(p=>p.positionRaw[1]);qr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(Zb)){let f=[];for(let m=0;m<c.length-1;m++){let g=qr.keypoints.find(A=>A.part===c[m]),y=qr.keypoints.find(A=>A.part===c[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}qr.annotations[p]=f}a([qr])}))}var M2e=["angry","disgust","fear","happy","sad","surprise","neutral"],_n,M0=[],rC=0,nC=0,Qb=Number.MAX_SAFE_INTEGER;async function aC(e){var t;return ce.initial&&(_n=null),_n?e.debug&&ie("cached model:",_n.modelUrl):_n=await je((t=e.face.emotion)==null?void 0:t.modelPath),_n}async function e5(e,t,r,n){var i,o;if(!_n)return[];let a=Qb<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-nC;return t.skipAllowed&&s&&a&&rC===n&&M0[r]&&M0[r].length>0?(Qb++,M0[r]):(Qb=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=_n!=null&&_n.inputs[0].shape?_n.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=he(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=_n==null?void 0:_n.execute(p.grayscaleMul),nC=oe();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:M2e[m]});u.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>re(p[m]))}M0[r]=u,rC=n,l(u)}))}var gn,t5=[],iC=0,oC=0,lC=Number.MAX_SAFE_INTEGER;async function uC(e){return ce.initial&&(gn=null),gn?e.debug&&ie("cached model:",gn.modelUrl):gn=await je(e.face.mobilefacenet.modelPath),gn}async function r5(e,t,r,n){var i,o;if(!gn)return[];let a=lC<(((i=t.face.embedding)==null?void 0:i.skipFrames)||0),s=(((o=t.face.embedding)==null?void 0:o.skipTime)||0)>oe()-oC;return t.skipAllowed&&s&&a&&iC===n&&t5[r]?(lC++,t5[r]):new Promise(async l=>{var d;let u=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(gn==null?void 0:gn.inputs[0].shape)){let h={};h.crop=Ie.resizeBilinear(e,[gn.inputs[0].shape[2],gn.inputs[0].shape[1]],!1),h.data=gn==null?void 0:gn.execute(h.crop);let p=await h.data.data();u=Array.from(p)}t5[r]=u,iC=n,oC=oe(),l(u)})}var is,Gi=0,F2e=2.3,n5=Zn.leftEyeLower0,a5=Zn.rightEyeLower0,Rd={leftBounds:[n5[0],n5[n5.length-1]],rightBounds:[a5[0],a5[a5.length-1]]},Md={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function fC(e){var t;return ce.initial&&(is=null),is?e.debug&&ie("cached model:",is.modelUrl):is=await je((t=e.face.iris)==null?void 0:t.modelPath),Gi=is.inputs[0].shape?is.inputs[0].shape[2]:0,Gi===-1&&(Gi=64),is}function F0(e,t,r,n){for(let a=0;a<_b.length;a++){let{key:s,indices:i}=_b[a],o=Zn[`${r}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var $2e=e=>{let t=e[Rd.leftBounds[0]][2],r=e[Rd.rightBounds[0]][2];return t-r},pC=(e,t,r,n,a,s=!1)=>{let i=k0(w0(RN([e[r],e[n]]),F2e)),o=Nd(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/a,i.startPoint[0]/a,i.endPoint[1]/a,i.endPoint[0]/a]],[0],[Gi,Gi]);if(s&&ce.kernels.includes("flipleftright")){let u=Ie.flipLeftRight(l);re(l),l=u}return{box:i,boxSize:o,crop:l}},hC=(e,t,r,n=!1)=>{let a=[];for(let s=0;s<Md.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(n?1-i/Gi:i/Gi)*r[0]+t.startPoint[0],o/Gi*r[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(Md.index)}},cC=(e,t,r)=>{let n=e[Zn[`${r}EyeUpper0`][Md.upperCenter]][2],a=e[Zn[`${r}EyeLower0`][Md.lowerCenter]][2],s=(n+a)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=a),[i[0],i[1],l]})};async function mC(e,t,r,n){if(!is)return r.debug&&ie("face mesh iris detection requested, but model is not loaded"),e;let{box:a,boxSize:s,crop:i}=pC(e,t,Rd.leftBounds[0],Rd.leftBounds[1],n,!0),{box:o,boxSize:l,crop:u}=pC(e,t,Rd.rightBounds[0],Rd.rightBounds[1],n,!0),d=kt([i,u]);re(i),re(u);let h=is.execute(d);re(d);let p=await h.data();re(h);let c=p.slice(0,Md.numCoordinates*3),{rawCoords:f,iris:m}=hC(c,a,s,!0),g=p.slice(Md.numCoordinates*3),{rawCoords:y,iris:A}=hC(g,o,l),x=$2e(e);Math.abs(x)<30?(F0(e,f,"left",null),F0(e,y,"right",null)):x<1?F0(e,f,"left",["EyeUpper0","EyeLower0"]):F0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=cC(e,m,"left"),w=cC(e,A,"right");return e.concat(b).concat(w)}var za={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},os=null,Fd=0;async function yC(e,t){var o,l,u,d,h,p,c,f,m;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-za.timestamp,n=za.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||za.boxes.length===0?(za.boxes=await LN(e,t),za.timestamp=oe(),za.skipped=0):za.skipped++;let a=[],s=[],i=0;for(let g=0;g<za.boxes.length;g++){let y=za.boxes[g],A=0,x,b={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([A,x,b.tensor]=PN((u=t.face.detector)==null?void 0:u.rotation,y,e,(d=t.face.mesh)!=null&&d.enabled?Fd:I0()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let w=await g0(b.tensor);re(b.tensor),b.tensor=w}if(b.boxScore=Math.round(100*y.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!os)t.debug&&ie("face mesh detection requested, but model is not loaded");else{let[w,T,S]=os.execute(b.tensor),E=await T.data();b.faceScore=Math.round(100*E[0])/100;let R=G(S,[-1,3]),_=await R.array();if(re([S,R,T,w]),b.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))y.confidence=b.faceScore;else{(f=t.face.iris)!=null&&f.enabled&&(_=await mC(_,b.tensor,t,Fd)),b.mesh=$N(_,y,A,x,Fd),b.meshRaw=b.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/Fd]);for(let I of Object.keys(Zn))b.annotations[I]=Zn[I].map(O=>b.mesh[O]);b.score=b.faceScore;let M={..._N(b.mesh,y),confidence:y.confidence,landmarks:y.landmarks};b.box=Lb(M,e),b.boxRaw=Bb(M,e),s.push(M)}}else{b.box=Lb(y,e),b.boxRaw=Bb(y,e),b.score=b.boxScore,b.mesh=y.landmarks.map(w=>[(y.startPoint[0]+y.endPoint[0])/2+(y.endPoint[0]+y.startPoint[0])*w[0]/I0(),(y.startPoint[1]+y.endPoint[1])/2+(y.endPoint[1]+y.startPoint[1])*w[1]/I0()]),b.meshRaw=b.mesh.map(w=>[w[0]/(e.shape[2]||0),w[1]/(e.shape[1]||0),(w[2]||0)/Fd]);for(let w of Object.keys(Lh))b.annotations[w]=[b.mesh[Lh[w]]]}b.score>(((m=t.face.detector)==null?void 0:m.minConfidence)||1)?a.push(b):re(b.tensor)}return za.boxes=s,a}async function AC(e){var t;return ce.initial&&(os=null),os?e.debug&&ie("cached model:",os.modelUrl):os=await je((t=e.face.mesh)==null?void 0:t.modelPath),Fd=os.inputs[0].shape?os.inputs[0].shape[2]:0,os}var xC=Fl,bC=Bh;var yn,$0=[],vC=0,wC=0,i5=Number.MAX_SAFE_INTEGER;async function kC(e){var t;return ce.initial&&(yn=null),yn?e.debug&&ie("cached model:",yn.modelUrl):yn=await je((t=e.face.description)==null?void 0:t.modelPath),yn}function o5(e){let t=e.image||e.tensor||e;if(!(yn!=null&&yn.inputs[0].shape))return t;let r=Ie.resizeBilinear(t,[yn.inputs[0].shape[2],yn.inputs[0].shape[1]],!1),n=L(r,Qe.tf255);return re(r),n}async function l5(e,t,r,n){var i,o,l,u;if(!yn)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let a=i5<(((i=t.face.description)==null?void 0:i.skipFrames)||0),s=(((o=t.face.description)==null?void 0:o.skipTime)||0)>oe()-vC;return t.skipAllowed&&a&&s&&wC===n&&((l=$0[r])==null?void 0:l.age)&&((u=$0[r])==null?void 0:u.age)>0?(i5++,$0[r]):(i5=0,new Promise(async d=>{var p,c;let h={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)!=null&&p.enabled){let f=o5(e),m=yn==null?void 0:yn.execute(f);vC=oe(),re(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((c=t.face.description)==null?void 0:c.minConfidence)||0)&&(h.gender=y[0]<=.5?"female":"male",h.genderScore=Math.min(.99,A));let x=Nn(m.find(R=>R.shape[1]===100),1),b=(await x.data())[0];re(x);let T=await m.find(R=>R.shape[1]===100).data();h.age=Math.round(T[b-1]>T[b+1]?10*b-100*T[b-1]:10*b+100*T[b+1])/10;let S=m.find(R=>R.shape[1]===1024),E=S?await S.data():[];h.descriptor=Array.from(E),m.forEach(R=>re(R))}$0[r]=h,wC=n,d(h)}))}function P0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Uh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function TC(e,t,r){let n=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a]];return Ie.cropAndResize(t,s,[0],r)}function NC(e,t){let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:r,endPoint:n,palmLandmarks:a,confidence:e.confidence}}function _0(e,t=1.5){let r=Uh(e),n=P0(e),a=[t*n[0]/2,t*n[1]/2],s=[r[0]-a[0],r[1]-a[1]],i=[r[0]+a[0],r[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function z0(e){let t=Uh(e),r=P0(e),a=Math.max(...r)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function P2e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function CC(e,t){let r=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return P2e(r)}var IC=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ji(e,t){let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r}function _2e(e,t){let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r}function SC(e,t){let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(ji(e[a],_2e(t,s)))}return r}function d5(e,t){let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=IC(t[0],t[1]),i=SC(s,a),o=IC(-t[0],-t[1]);return SC(i,o)}function EC(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-ji(t[0],r),-ji(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]}function p5(e,t){return[ji(e,t[0]),ji(e,t[1])]}var 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n={};n.reshape=G(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>re(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=he(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=et(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Tr(n.slice),n.scores=et(n.sigmoid);let a=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=Pe(n.norm,[o,0],[1,-1]),l.slice=Pe(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=G(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},f=NC(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(f),Object.keys(l).forEach(m=>re(l[m]))}return Object.keys(n).forEach(o=>re(n[o])),i}};var D2e=5,FC=1.65,$C=[0,5,9,13,17,1,2],L2e=0,B2e=2,PC=0,c5=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>p5([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return _0(z0(a),D2e)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=_0(z0(r),FC);n.palmLandmarks=[];for(let a=0;a<$C.length;a++)n.palmLandmarks.push(t[$C[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=P0(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=d5(n,[0,0]),u=o.map(c=>[...p5(c,l),c[2]]),d=EC(a),h=[...Uh(r),1],p=[ji(h,d[0]),ji(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-PC,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?CC(u.palmLandmarks[L2e],u.palmLandmarks[B2e]):0,h=Uh(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&ce.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),f=d5(-d,h),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=TC(m,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);PC=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let w=G(x,[-1,3]),T=await w.array();re(x),re(w);let S=this.transformRawCoords(T,m,d,f),E=this.getBoxForHandLandmarks(S);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:S,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let d=_0(z0(u),FC),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Kr={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Kr.nameMapping[e],getPoints:e=>Kr.pointsMapping[e]},Hi={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Hi.nameMapping[e]},Dt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Dt.nameMapping[e]},_l=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:Aa,index:ls,middle:us,ring:zl,pinky:Ol}=Kr,{none:xa,half:V2e,full:ba}=Hi,{verticalUp:$d,verticalDown:Kxe,horizontalLeft:f5,horizontalRight:U2e,diagonalUpRight:G2e,diagonalUpLeft:Pd,diagonalDownRight:Xxe,diagonalDownLeft:Zxe}=Dt,qi=new _l("thumbs up");qi.curl(Aa,xa,1);qi.direction(Aa,$d,1);qi.direction(Aa,Pd,.25);qi.direction(Aa,G2e,.25);for(let e of[Kr.index,Kr.middle,Kr.ring,Kr.pinky])qi.curl(e,ba,1),qi.direction(e,f5,1),qi.direction(e,U2e,1);var Yt=new _l("victory");Yt.curl(Aa,V2e,.5);Yt.curl(Aa,xa,.5);Yt.direction(Aa,$d,1);Yt.direction(Aa,Pd,1);Yt.curl(ls,xa,1);Yt.direction(ls,$d,.75);Yt.direction(ls,Pd,1);Yt.curl(us,xa,1);Yt.direction(us,$d,1);Yt.direction(us,Pd,.75);Yt.curl(zl,ba,1);Yt.direction(zl,$d,.2);Yt.direction(zl,Pd,1);Yt.direction(zl,f5,.2);Yt.curl(Ol,ba,1);Yt.direction(Ol,$d,.2);Yt.direction(Ol,Pd,1);Yt.direction(Ol,f5,.2);Yt.weight(ls,2);Yt.weight(us,2);var Ki=new _l("point");Ki.curl(Aa,ba,1);Ki.curl(ls,xa,.5);Ki.curl(us,ba,.5);Ki.curl(zl,ba,.5);Ki.curl(Ol,ba,.5);Ki.weight(ls,2);Ki.weight(us,2);var Xi=new _l("middle finger");Xi.curl(Aa,xa,1);Xi.curl(ls,ba,.5);Xi.curl(us,ba,.5);Xi.curl(zl,ba,.5);Xi.curl(Ol,ba,.5);Xi.weight(ls,2);Xi.weight(us,2);var _d=new _l("open palm");_d.curl(Aa,xa,.75);_d.curl(ls,xa,.75);_d.curl(us,xa,.75);_d.curl(zl,xa,.75);_d.curl(Ol,xa,.75);var _C=[qi,Yt,Ki,Xi,_d];var j2e=.7,Dl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function zC(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function DC(e,t){if(!e||!t)return[0,0];let r=zC(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=zC(e[1],e[2],t[1],t[2]);return[r,n]}function OC(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function H2e(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),f=Math.sqrt(s*s+l*l+h*h),m=(f*f+p*p-c*c)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Dl.NO_CURL_START_LIMIT?y=Hi.none:g>Dl.HALF_CURL_START_LIMIT?y=Hi.half:y=Hi.full,y}function LC(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:n===Math.abs(t)?t>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:r>0?a=Dt.horizontalLeft:a=Dt.horizontalRight,a}function BC(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Dt.verticalDown:a=Dt.verticalUp:n===Math.abs(t)?t<0?a=Dt.verticalDown:a=Dt.verticalUp:r<0?a=Dt.verticalDown:a=Dt.verticalUp,a}function q2e(e,t,r,n,a,s,i,o){let l,u=BC(e,t,r,n),d=LC(a,s,i,o);return u===Dt.verticalUp?d===Dt.horizontalLeft?l=Dt.diagonalUpLeft:l=Dt.diagonalUpRight:d===Dt.horizontalLeft?l=Dt.diagonalDownLeft:l=Dt.diagonalDownRight,l}function K2e(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,f=0,m=h/(d+1e-5);m>1.5?p+=Dl.DISTANCE_VOTE_POWER:m>.66?c+=Dl.DISTANCE_VOTE_POWER:f+=Dl.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],w=e[1],T=r[0],S=r[1];x===g?(T=r[0],S=r[1]):x===A&&(b=t[0],w=t[1]);let _=DC([b,w],[T,S]),M=OC(_,Dl.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],f+=M[2];for(let O of n){let z=OC(O,Dl.SINGLE_ANGLE_VOTE_POWER);p+=z[0],c+=z[1],f+=z[2]}let I;return p===Math.max(p,c,f)?I=BC(l,o,u,h):f===Math.max(c,f)?I=LC(s,a,i,d):I=q2e(l,o,u,h,s,a,i,d),I}function WC(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Kr.all){let i=Kr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=DC(d,h),c=p[0],f=p[1];o.push(c),l.push(f)}t.push(o),r.push(l)}for(let s of Kr.all){let i=s===Kr.thumb?1:0,o=Kr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=H2e(l,u,d),p=K2e(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function O0(e){if(!e||e.length===0)return null;let t=WC(e),r={};for(let n of Kr.all)r[Kr.getName(n)]={curl:Hi.getName(t.curls[n]),direction:Dt.getName(t.directions[n])};return r}function VC(e){let t=[];if(!e||e.length===0)return t;let r=WC(e);for(let n of _C){let a=n.matchAgainst(r.curls,r.directions);a>=j2e&&t.push({name:n.name,confidence:a})}return t}var UC={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},zd,Od,GC;async function g5(e,t){let r=await GC.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(UC))s[d]=UC[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=O0(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function y5(e){var r,n;ce.initial&&(zd=null,Od=null),!zd||!Od?[zd,Od]=await Promise.all([e.hand.enabled?je((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?je((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&ie("cached model:",zd.modelUrl),e.debug&&ie("cached model:",Od.modelUrl));let t=new h5(zd);return GC=new c5(t,Od),[zd,Od]}var lr=[null,null],X2e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Zi=[[0,0],[0,0]],Z2e=["hand","fist","pinch","point","face","tip","pinchtip"],HC=4,qC=1.6,Y2e=512,J2e=1.4,D0=Number.MAX_SAFE_INTEGER,A5=0,ds=[0,0],Gt={boxes:[],hands:[]},KC={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function XC(e){var t;if(ce.initial&&(lr[0]=null),lr[0])e.debug&&ie("cached model:",lr[0].modelUrl);else{L0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),lr[0]=await je((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(lr[0].modelSignature.inputs);Zi[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Zi[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[0]}async function ZC(e){var t;if(ce.initial&&(lr[1]=null),lr[1])e.debug&&ie("cached model:",lr[1].modelUrl);else{lr[1]=await je((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(lr[1].modelSignature.inputs);Zi[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Zi[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[1]}async function Q2e(e,t){let r=[];if(!e||!lr[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,Y2e),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await lr[0].executeAsync(n.cast,X2e),n.boxes=et(n.rawBoxes,[0,2]),n.scores=et(n.rawScores,[0]);let o=en(n.scores,1);re(o[HC]),o.splice(HC,1),n.filtered=sr(o,1),re(o),n.max=fr(n.filtered,1),n.argmax=Nn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=Pe(n.boxes,p,1),f=await c.data();re(c);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=N0(m,J2e),y=[Math.trunc(m[0]*ds[0]),Math.trunc(m[1]*ds[1]),Math.trunc(m[2]*ds[0]),Math.trunc(m[3]*ds[1])],A=d[p],x=Z2e[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>re(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function x5(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&lr[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[Zi[1][0],Zi[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=lr[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=G(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/Zi[1][1],h[1]/Zi[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[ds[0]*(h[0]+t.boxRaw[0]),ds[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=O0(n.keypoints);for(let h of Object.keys(KC))n.annotations[h]=KC[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>re(a[l]))}return n}async function b5(e,t){var a,s;if(!lr[0]||!lr[1]||!((a=lr[0])!=null&&a.inputs[0].shape)||!((s=lr[1])!=null&&s.inputs[0].shape))return[];ds=[e.shape[2]||0,e.shape[1]||0],D0++;let r=(t.hand.skipTime||0)>oe()-A5,n=D0<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Gt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-A5,l=D0<3*(t.hand.skipFrames||0);t.skipAllowed&&Gt.hands.length===t.hand.maxDetected?Gt.hands=await Promise.all(Gt.boxes.map(d=>x5(e,d,t))):t.skipAllowed&&o&&l&&Gt.hands.length>0?Gt.hands=await Promise.all(Gt.boxes.map(d=>x5(e,d,t))):(Gt.boxes=await Q2e(e,t),A5=oe(),Gt.hands=await Promise.all(Gt.boxes.map(d=>x5(e,d,t))),D0=0);let u=[...Gt.boxes];if(Gt.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Gt.hands.length;d++){let h=UN(Gt.hands[d].keypoints,ds);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&Gt.hands[d].fingerScore&&Gt.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=N0(h.box,qC),c=N0(h.boxRaw,qC);Gt.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Gt.hands.length;d++){let h=as(Gt.hands[d].keypoints,ds);Gt.hands[d].box=h.box,Gt.hands[d].boxRaw=h.boxRaw}i(Gt.hands)})}var Mr,B0=[],v5=Number.MAX_SAFE_INTEGER,JC=0,QC=0;async function e9(e){var t;return ce.initial&&(Mr=null),Mr?e.debug&&ie("cached model:",Mr.modelUrl):Mr=await je((t=e.face.liveness)==null?void 0:t.modelPath),Mr}async function w5(e,t,r,n){var i,o;if(!Mr)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-QC,s=v5<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&JC===n&&B0[r]?(v5++,B0[r]):(v5=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[Mr!=null&&Mr.inputs[0].shape?Mr.inputs[0].shape[2]:0,Mr!=null&&Mr.inputs[0].shape?Mr.inputs[0].shape[1]:0],!1),d=Mr==null?void 0:Mr.execute(u),h=(await d.data())[0];B0[r]=Math.round(100*h)/100,JC=n,QC=oe(),re([u,d]),l(B0[r])}))}var Gh={};gs(Gh,{connected:()=>V0,horizontal:()=>k5,kpt:()=>W0,relative:()=>S5,vertical:()=>I5});var W0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],k5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],I5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],S5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],V0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var r9=.005,An={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function T5(e){for(let t of k5){let r=e.keypoints.findIndex(a=>a.part===t[0]),n=e.keypoints.findIndex(a=>a.part===t[1]);if(e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[0]<e.keypoints[n].position[0]){let a=e.keypoints[r];e.keypoints[r]=e.keypoints[n],e.keypoints[n]=a}}for(let t of I5){let r=e.keypoints.findIndex(a=>a&&a.part===t[0]),n=e.keypoints.findIndex(a=>a&&a.part===t[1]);e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(r,1)}for(let[t,r]of S5){let n=e.keypoints.findIndex(u=>u&&u.part===t[0]),a=e.keypoints.findIndex(u=>u&&u.part===t[1]),s=e.keypoints.findIndex(u=>u&&u.part===r[0]),i=e.keypoints.findIndex(u=>u&&u.part===r[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[a]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[a].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[a].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[a],e.keypoints[a]=u}}}function n9(e){for(let t=0;t<e.length;t++)if(e[t]&&An.keypoints[t]){let r=[Math.abs(e[t].positionRaw[0]-An.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-An.keypoints[t].positionRaw[1])];r[0]<r9&&r[1]<r9?e[t]=An.keypoints[t]:An.keypoints[t]=e[t]}else An.keypoints[t]=e[t];return e}function a9(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;An.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],r.pad=Gn(e,An.padding),r.resize=Ie.resizeBilinear(r.pad,[t,t]);let n=me(r.resize,"int32");return Object.keys(r).forEach(a=>re(r[a])),n}function s9(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+An.padding[2][0]+An.padding[2][1])/t[0]-An.padding[2][0],n.position[1]*(t[1]+An.padding[1][0]+An.padding[1][1])/t[1]-An.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let r=as(e.keypoints.map(n=>n.position),t);return e.box=r.box,e.boxRaw=r.boxRaw,e}var xn,U0=0,N5=Number.MAX_SAFE_INTEGER,Ll={boxes:[],bodies:[],last:0};async function i9(e){return ce.initial&&(xn=null),xn?e.debug&&ie("cached model:",xn.modelUrl):(L0(["size"],e),xn=await je(e.body.modelPath)),U0=xn.inputs[0].shape?xn.inputs[0].shape[2]:0,U0<64&&(U0=256),xn}async function tAe(e,t,r){let n=e[0][0],a=[],s=0;for(let d=0;d<n.length;d++)if(s=n[d][2],s>t.body.minConfidence){let h=[n[d][1],n[d][0]];a.push({score:Math.round(100*s)/100,part:W0[d],positionRaw:h,position:[Math.round((r.shape[2]||0)*h[0]),Math.round((r.shape[1]||0)*h[1])]})}s=a.reduce((d,h)=>h.score>d?h.score:d,0);let i=[],o=as(a.map(d=>d.position),[r.shape[2],r.shape[1]]),l={};for(let[d,h]of Object.entries(V0)){let p=[];for(let c=0;c<h.length-1;c++){let f=a.find(g=>g.part===h[c]),m=a.find(g=>g.part===h[c+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}l[d]=p}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:a,annotations:l};return T5(u),i.push(u),i}async function rAe(e,t,r){let n=[];for(let a=0;a<e[0].length;a++){let s=e[0][a],i=Math.round(100*s[51+4])/100;if(i>t.body.minConfidence){let o=[];for(let h=0;h<17;h++){let p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:W0[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=as(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(V0)){let c=[];for(let f=0;f<p.length-1;f++){let m=o.find(y=>y.part===p[f]),g=o.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([m.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};T5(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function C5(e,t){if(!xn||!(xn!=null&&xn.inputs[0].shape))return[];t.skipAllowed||(Ll.boxes.length=0),N5++;let r=(t.body.skipTime||0)>oe()-Ll.last,n=N5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?Ll.bodies:new Promise(async a=>{let s={};N5=0,s.input=a9(e,U0),s.res=xn==null?void 0:xn.execute(s.input),Ll.last=oe();let i=await s.res.array();Ll.bodies=s.res.shape[2]===17?await tAe(i,t,e):await rAe(i,t,e);for(let o of Ll.bodies)s9(o,[e.shape[2]||1,e.shape[1]||1]),n9(o.keypoints);Object.keys(s).forEach(o=>re(s[o])),a(Ll.bodies)})}var Dd,G0=[],l9=0,E5=Number.MAX_SAFE_INTEGER,H0=0,j0=2.5;async function u9(e){if(!Dd||ce.initial){Dd=await je(e.object.modelPath);let t=Object.values(Dd.modelSignature.inputs);H0=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&ie("cached model:",Dd.modelUrl);return Dd}async function nAe(e,t,r){let n=0,a=[];for(let l of[1,2,4])K(async()=>{let u=l*13,d=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)===Ed.length)),h=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)<Ed.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),f=await d.array();for(let m=0;m<d.shape[0];m++)for(let g=0;g<d.shape[1];g++){let y=f[m][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(m%u))/u,x=(.5+Math.trunc(m/u))/u,b=c[m].map(I=>I*(u/l/H0)),[w,T]=[A-j0/l*b[0],x-j0/l*b[1]],[S,E]=[A+j0/l*b[2]-w,x+j0/l*b[3]-T],R=[w,T,S,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let _=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:n++,score:Math.round(100*y)/100,class:g+1,label:Ed[g].label,box:_.map(I=>Math.trunc(I)),boxRaw:R};a.push(M)}}});e.forEach(l=>re(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function R5(e,t){let r=(t.object.skipTime||0)>oe()-l9,n=E5<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&G0.length>0?(E5++,G0):(E5=0,!ce.kernels.includes("mod")||!ce.kernels.includes("sparsetodense")?G0:new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[H0,H0],!1),o=pe(i,Qe.tf255),l=o.transpose([0,3,1,2]);re(o),re(i);let u;t.object.enabled&&(u=Dd.execute(l)),l9=oe(),re(l);let d=await nAe(u,s,t);G0=d,a(d)}))}var Hh=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],aAe=Hh.length,jh=Hh.reduce((e,t,r)=>(e[t]=r,e),{}),sAe=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],bbe=sAe.map(([e,t])=>[jh[e],jh[t]]),p9=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function h9(e){let t=e.reduce(({maxX:r,maxY:n,minX:a,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(r,i),maxY:Math.max(n,o),minX:Math.min(a,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function c9(e,[t,r],[n,a]){let s=t/n,i=r/a,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/a,u.box[1]/n,u.box[2]/a,u.box[3]/n],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:h,part:p,position:c})=>({score:h,part:p,position:[Math.trunc(c.x*i),Math.trunc(c.y*s)],positionRaw:[c.x/n,c.y/n]})),annotations:{}});return e.map((u,d)=>o(u,d))}var M5=class{constructor(t,r){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=r}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let r=2*t;if(r<this.numberOfElements&&this.less(r,r+1)&&r++,!this.less(t,r))break;this.exchange(t,r),t=r}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,r){return this.getValueAt(t)<this.getValueAt(r)}exchange(t,r){let n=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[r],this.priorityQueue[r]=n}};function F5(e,t,r,n){return{y:n.get(e,t,r),x:n.get(e,t,r+aAe)}}function $5(e,t,r){let{heatmapY:n,heatmapX:a,id:s}=e,{y:i,x:o}=F5(n,a,s,r);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function P5(e,t,r){return e<t?t:e>r?r:e}function f9(e,t,r,n){let a=r-e,s=n-t;return a*a+s*s}function _5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var va,oAe=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],q0=1,Ld=16,lAe=50**2;function m9(e,t,r,n,a,s,i=2){let o=y=>({y:s.get(y.y,y.x,e),x:s.get(y.y,y.x,s.shape[2]/2+e)}),l=(y,A,x)=>({y:P5(Math.round(y.y/Ld),0,A-1),x:P5(Math.round(y.x/Ld),0,x-1)}),[u,d]=n.shape,h=l(t.position,u,d),p=o(h),f=_5(t.position,p);for(let y=0;y<i;y++){let A=l(f,u,d),x=F5(A.y,A.x,r,a);f=_5({x:A.x*Ld,y:A.y*Ld},{x:x.x,y:x.y})}let m=l(f,u,d),g=n.get(m.y,m.x,r);return{position:f,part:Hh[r],score:g}}function uAe(e,t,r,n,a){let 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p2e=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],h2e=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],c2e=[33,133,362,263,1,78,308],Uke=p2e.map(e=>Bh[e]),Gke=h2e.map(e=>Bh[e]),jke=c2e.map(e=>Bh[e]);var Cd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],w0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],Gb=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],jb=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],RN=(e,t)=>{let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:r,endPoint:n,landmarks:e.landmarks,confidence:e.confidence}},Vb=(e,t,r)=>{let n=t.shape[1],a=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a],i=Ie.cropAndResize(t,[s],[0],r),o=pe(i,Qe.tf255);return re(i),o},k0=(e,t)=>{let r=w0(e),n=Cd(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},I0=e=>{let t=w0(e),r=Cd(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},MN=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},Ub=[[1,0,0],[0,1,0],[0,0,1]],f2e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),m2e=(e,t)=>f2e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var CN=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Pl=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},g2e=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},EN=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Pl(e[a],g2e(t,s)))}return r},FN=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=CN(t[0],t[1]),i=EN(s,a),o=CN(-t[0],-t[1]);return EN(i,o)},y2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Pl(t[0],r),-Pl(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},A2e=(e,t)=>[Pl(e,t[0]),Pl(e,t[1])];function $N(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function PN(e,t,r,n,a){let s=Cd(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?FN(r,[0,0]):Ub,u=o?i.map(c=>[...A2e(c,l),c[2]]):i,d=o?y2e(n):Ub,h=w0(t),p=[Pl(h,d[0]),Pl(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function _N(e,t,r,n){let a=t.landmarks.length>=Lb.count?Lb.symmetryLine:Lh.symmetryLine,s=0,i=Ub,o;if(e&&ce.kernels.includes("rotatewithoffset"))if(s=m2e(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=w0(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=FN(-s,u),o=Vb(t,h,[n,n]),re(h)}else o=Vb(t,r,[n,n]);else o=Vb(t,r,[n,n]);return[s,i,o]}var x2e=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},zN=(e,t)=>{let r=x2e(e),n=Cd(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var ON=6,b2e=1.2,_a,DN=null,Vi=0,Wh=null,S0=()=>Vi;async function LN(e){var t;return ce.initial&&(_a=null),_a?e.debug&&ie("cached model:",_a.modelUrl):_a=await je((t=e.face.detector)==null?void 0:t.modelPath),Vi=_a.inputs[0].shape?_a.inputs[0].shape[2]:0,Wh=Se(Vi,"int32"),DN=ua($N(Vi)),_a}function v2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,DN),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Wh),t.centersNormalized=pe(t.centers,Wh),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=he(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Wh),t.endNormalized=L(t.ends,Wh);let r=ad([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>re(t[n])),r}async function BN(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Vi,Vi]),r.div=pe(r.resized,Qe.tf127),r.normalized=he(r.div,Qe.tf05);let n=_a==null?void 0:_a.execute(r.normalized);if(Array.isArray(n)){let h=n.sort((p,c)=>p.size-c.size);r.concat384=kt([h[0],h[2]],2),r.concat512=kt([h[1],h[3]],2),r.concat=kt([r.concat512,r.concat384],1),r.batch=et(r.concat,0)}else r.batch=et(n);re(n),r.boxes=v2e(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Tr(r.logits),r.scores=et(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=Pe(r.boxes,[a[h],0],[1,-1]),c.slice=Pe(r.batch,[a[h],ON-1],[1,-1]),c.squeeze=et(c.slice),c.landmarks=G(c.squeeze,[ON,-1]);let f=await c.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await c.landmarks.array(),confidence:p},g=RN(m,[(e.shape[2]||0)/Vi,(e.shape[1]||0)/Vi]),y=k0(g,t.face.scale||b2e),A=I0(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(h=>re(r[h])),s}var T0={};gs(T0,{connected:()=>Kb,kpt:()=>qb});var qb=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Kb={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var VN=224,w2e,k2e=5,N0=[8,16,32,32,32];async function UN(){let e=[],t=0;for(;t<k2e;){let r=0,n=t;for(;n<N0.length&&N0[n]===N0[t];)r+=2,n++;let a=N0[t],s=Math.ceil(VN/a),i=Math.ceil(VN/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}w2e={x:St(e.map(r=>r.x)),y:St(e.map(r=>r.y))}}function as(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function GN(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function C0(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var qN={initial:!0},mn={detector:null,landmarks:null},Ed={detector:[224,224],landmarks:[256,256]},Xb=Number.MAX_SAFE_INTEGER,S2e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},R0=null,Vh,Ui=[[0,0],[0,0],[0,0],[0,0]],jN=0,HN=e=>1-1/(1+Math.exp(e));async function KN(e){if(qN.initial&&(mn.detector=null),!mn.detector&&e.body.detector&&e.body.detector.modelPath){mn.detector=await je(e.body.detector.modelPath);let t=Object.values(mn.detector.modelSignature.inputs);Ed.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ed.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&mn.detector&&ie("cached model:",mn.detector.modelUrl);return await UN(),mn.detector}async function XN(e){if(qN.initial&&(mn.landmarks=null),mn.landmarks)e.debug&&ie("cached model:",mn.landmarks.modelUrl);else{mn.landmarks=await je(e.body.modelPath);let t=Object.values(mn.landmarks.modelSignature.inputs);Ed.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ed.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return mn.landmarks}async function T2e(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Vh&&(r.cropped=Ie.cropAndResize(e,[Vh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Ui=[[0,0],a,s,[0,0]],r.pad=Gn(r.cropped||e,Ui),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>re(r[a])),n}function N2e(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Ui[2][0]+Ui[2][1])/t[0]-Ui[2][0]),Math.trunc(r.position[1]*(t[1]+Ui[1][0]+Ui[1][1])/t[1]-Ui[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Vh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Vh[1],r.positionRaw[1]+Vh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function C2e(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function E2e(e,t,r){var f;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(f=mn.landmarks)==null?void 0:f.execute(e,S2e.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>re(n[m]));let o=[],l=5;for(let m=0;m<s.length/l;m++){let g=HN(s[l*m+3]),y=HN(s[l*m+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*m+0]/Ed.landmarks[0],s[l*m+1]/Ed.landmarks[1],s[l*m+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],w=[i[l*m+0],i[l*m+1],i[l*m+2]+0];o.push({part:qb[m],positionRaw:x,position:b,distance:w,score:A})}if(a<(t.body.minConfidence||0))return null;C2e(o);let u=N2e(o,r),d=u.map(m=>m.position),h=as(d,[r[0],r[1]]),p={};for(let[m,g]of Object.entries(Kb)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(w=>w.part===g[A]),b=u.find(w=>w.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[m]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function Zb(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-jN,a=Xb<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&R0!==null)Xb++;else{let s={};s.landmarks=await T2e(e,256),R0=await E2e(s.landmarks,t,r),Object.keys(s).forEach(i=>re(s[i])),jN=oe(),Xb=0}return R0?[R0]:[]}var Rd=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var ss,_l=0,Yb=[],YN=0,Jb=Number.MAX_SAFE_INTEGER;async function JN(e){if(ce.initial&&(ss=null),ss)e.debug&&ie("cached model:",ss.modelUrl);else{ss=await je(e.object.modelPath);let t=Object.values(ss.modelSignature.inputs);_l=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return ss}async function R2e(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=et(e);let i=Kt(n.squeeze,6,1);n.stack=sr([i[1],i[0],i[3],i[2]],1),n.boxes=et(n.stack),n.scores=et(i[4]),n.classes=et(i[5]),re([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=Rd[h].label,[c,f]=[s[0][u][0]/_l,s[0][u][1]/_l],m=[c,f,s[0][u][2]/_l-c,s[0][u][3]/_l-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:m})}return Object.keys(n).forEach(u=>re(n[u])),a}async function Qb(e,t){let r=(t.object.skipTime||0)>oe()-YN,n=Jb<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&Yb.length>0?(Jb++,Yb):(Jb=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[_l,_l]),o=t.object.enabled?ss==null?void 0:ss.execute(i,["tower_0/detections"]):null;YN=oe(),re(i);let l=await R2e(o,s,t);Yb=l,a(l)}))}var M0={};gs(M0,{connected:()=>t5,kpt:()=>e5});var e5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],t5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Rr,eC=0,qr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},r5=Number.MAX_SAFE_INTEGER;async function tC(e){return ce.initial&&(Rr=null),Rr?e.debug&&ie("cached model:",Rr.modelUrl):Rr=await je(e.body.modelPath),Rr}async function M2e(e,t){let[r,n]=e.shape,a=G(e,[n*r]),s=fr(a,0),i=(await s.data())[0];if(re([a,s]),i>t){let o=Nn(a,0),l=od(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return re([l,d]),[u,h,i]}return[0,0,i]}async function n5(e,t){let r=(t.body.skipTime||0)>oe()-eC,n=r5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(qr.keypoints).length>0?(r5++,[qr]):(r5=0,new Promise(async a=>{var h;let s=K(()=>{if(!(Rr!=null&&Rr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Rr.inputs[0].shape[2],Rr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return he(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Rr==null?void 0:Rr.execute(s)),eC=oe(),re(s),i){qr.keypoints.length=0;let p=i.squeeze();re(i);let c=p.unstack(2);re(p);for(let f=0;f<c.length;f++){let[m,g,y]=await M2e(c[f],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&qr.keypoints.push({score:Math.round(100*y)/100,part:e5[f],positionRaw:[m/Rr.inputs[0].shape[2],g/Rr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Rr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Rr.inputs[0].shape[1])]})}c.forEach(f=>re(f))}qr.score=qr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=qr.keypoints.map(p=>p.position[0]),l=qr.keypoints.map(p=>p.position[1]);qr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=qr.keypoints.map(p=>p.positionRaw[0]),d=qr.keypoints.map(p=>p.positionRaw[1]);qr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(t5)){let f=[];for(let m=0;m<c.length-1;m++){let g=qr.keypoints.find(A=>A.part===c[m]),y=qr.keypoints.find(A=>A.part===c[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}qr.annotations[p]=f}a([qr])}))}var F2e=["angry","disgust","fear","happy","sad","surprise","neutral"],_n,F0=[],nC=0,aC=0,a5=Number.MAX_SAFE_INTEGER;async function sC(e){var t;return ce.initial&&(_n=null),_n?e.debug&&ie("cached model:",_n.modelUrl):_n=await je((t=e.face.emotion)==null?void 0:t.modelPath),_n}async function s5(e,t,r,n){var i,o;if(!_n)return[];let a=a5<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-aC;return t.skipAllowed&&s&&a&&nC===n&&F0[r]&&F0[r].length>0?(a5++,F0[r]):(a5=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=_n!=null&&_n.inputs[0].shape?_n.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=he(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=_n==null?void 0:_n.execute(p.grayscaleMul),aC=oe();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:F2e[m]});u.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>re(p[m]))}F0[r]=u,nC=n,l(u)}))}var gn,i5=[],oC=0,lC=0,uC=Number.MAX_SAFE_INTEGER;async function dC(e){return ce.initial&&(gn=null),gn?e.debug&&ie("cached 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t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=a),[i[0],i[1],l]})};async function gC(e,t,r,n){if(!is)return r.debug&&ie("face mesh iris detection requested, but model is not loaded"),e;let{box:a,boxSize:s,crop:i}=hC(e,t,Md.leftBounds[0],Md.leftBounds[1],n,!0),{box:o,boxSize:l,crop:u}=hC(e,t,Md.rightBounds[0],Md.rightBounds[1],n,!0),d=kt([i,u]);re(i),re(u);let h=is.execute(d);re(d);let p=await h.data();re(h);let c=p.slice(0,Fd.numCoordinates*3),{rawCoords:f,iris:m}=cC(c,a,s,!0),g=p.slice(Fd.numCoordinates*3),{rawCoords:y,iris:A}=cC(g,o,l),x=P2e(e);Math.abs(x)<30?($0(e,f,"left",null),$0(e,y,"right",null)):x<1?$0(e,f,"left",["EyeUpper0","EyeLower0"]):$0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=fC(e,m,"left"),w=fC(e,A,"right");return e.concat(b).concat(w)}var za={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},os=null,$d=0;async function AC(e,t){var o,l,u,d,h,p,c,f,m;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-za.timestamp,n=za.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||za.boxes.length===0?(za.boxes=await BN(e,t),za.timestamp=oe(),za.skipped=0):za.skipped++;let a=[],s=[],i=0;for(let g=0;g<za.boxes.length;g++){let y=za.boxes[g],A=0,x,b={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([A,x,b.tensor]=_N((u=t.face.detector)==null?void 0:u.rotation,y,e,(d=t.face.mesh)!=null&&d.enabled?$d:S0()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let w=await y0(b.tensor);re(b.tensor),b.tensor=w}if(b.boxScore=Math.round(100*y.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!os)t.debug&&ie("face mesh detection requested, but model is not loaded");else{let[w,T,S]=os.execute(b.tensor),E=await T.data();b.faceScore=Math.round(100*E[0])/100;let R=G(S,[-1,3]),_=await R.array();if(re([S,R,T,w]),b.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))y.confidence=b.faceScore;else{(f=t.face.iris)!=null&&f.enabled&&(_=await gC(_,b.tensor,t,$d)),b.mesh=PN(_,y,A,x,$d),b.meshRaw=b.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/$d]);for(let I of Object.keys(Zn))b.annotations[I]=Zn[I].map(O=>b.mesh[O]);b.score=b.faceScore;let M={...zN(b.mesh,y),confidence:y.confidence,landmarks:y.landmarks};b.box=Gb(M,e),b.boxRaw=jb(M,e),s.push(M)}}else{b.box=Gb(y,e),b.boxRaw=jb(y,e),b.score=b.boxScore,b.mesh=y.landmarks.map(w=>[(y.startPoint[0]+y.endPoint[0])/2+(y.endPoint[0]+y.startPoint[0])*w[0]/S0(),(y.startPoint[1]+y.endPoint[1])/2+(y.endPoint[1]+y.startPoint[1])*w[1]/S0()]),b.meshRaw=b.mesh.map(w=>[w[0]/(e.shape[2]||0),w[1]/(e.shape[1]||0),(w[2]||0)/$d]);for(let w of Object.keys(Lh))b.annotations[w]=[b.mesh[Lh[w]]]}b.score>(((m=t.face.detector)==null?void 0:m.minConfidence)||1)?a.push(b):re(b.tensor)}return za.boxes=s,a}async function xC(e){var t;return ce.initial&&(os=null),os?e.debug&&ie("cached model:",os.modelUrl):os=await je((t=e.face.mesh)==null?void 0:t.modelPath),$d=os.inputs[0].shape?os.inputs[0].shape[2]:0,os}var bC=$l,vC=Bh;var yn,P0=[],wC=0,kC=0,p5=Number.MAX_SAFE_INTEGER;async function IC(e){var t;return ce.initial&&(yn=null),yn?e.debug&&ie("cached model:",yn.modelUrl):yn=await je((t=e.face.description)==null?void 0:t.modelPath),yn}function h5(e){let t=e.image||e.tensor||e;if(!(yn!=null&&yn.inputs[0].shape))return t;let r=Ie.resizeBilinear(t,[yn.inputs[0].shape[2],yn.inputs[0].shape[1]],!1),n=L(r,Qe.tf255);return re(r),n}async function c5(e,t,r,n){var i,o,l,u;if(!yn)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let a=p5<(((i=t.face.description)==null?void 0:i.skipFrames)||0),s=(((o=t.face.description)==null?void 0:o.skipTime)||0)>oe()-wC;return t.skipAllowed&&a&&s&&kC===n&&((l=P0[r])==null?void 0:l.age)&&((u=P0[r])==null?void 0:u.age)>0?(p5++,P0[r]):(p5=0,new Promise(async d=>{var p,c;let h={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)!=null&&p.enabled){let f=h5(e),m=yn==null?void 0:yn.execute(f);wC=oe(),re(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((c=t.face.description)==null?void 0:c.minConfidence)||0)&&(h.gender=y[0]<=.5?"female":"male",h.genderScore=Math.min(.99,A));let x=Nn(m.find(R=>R.shape[1]===100),1),b=(await x.data())[0];re(x);let T=await m.find(R=>R.shape[1]===100).data();h.age=Math.round(T[b-1]>T[b+1]?10*b-100*T[b-1]:10*b+100*T[b+1])/10;let S=m.find(R=>R.shape[1]===1024),E=S?await S.data():[];h.descriptor=Array.from(E),m.forEach(R=>re(R))}P0[r]=h,kC=n,d(h)}))}function _0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Uh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function NC(e,t,r){let n=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a]];return Ie.cropAndResize(t,s,[0],r)}function CC(e,t){let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:r,endPoint:n,palmLandmarks:a,confidence:e.confidence}}function z0(e,t=1.5){let r=Uh(e),n=_0(e),a=[t*n[0]/2,t*n[1]/2],s=[r[0]-a[0],r[1]-a[1]],i=[r[0]+a[0],r[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function O0(e){let t=Uh(e),r=_0(e),a=Math.max(...r)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function _2e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function EC(e,t){let r=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return _2e(r)}var SC=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ji(e,t){let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r}function z2e(e,t){let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r}function TC(e,t){let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(ji(e[a],z2e(t,s)))}return r}function m5(e,t){let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=SC(t[0],t[1]),i=TC(s,a),o=SC(-t[0],-t[1]);return TC(i,o)}function RC(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-ji(t[0],r),-ji(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]}function g5(e,t){return[ji(e,t[0]),ji(e,t[1])]}var 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n={};n.reshape=G(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>re(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=he(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=et(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Tr(n.slice),n.scores=et(n.sigmoid);let a=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=Pe(n.norm,[o,0],[1,-1]),l.slice=Pe(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=G(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},f=CC(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(f),Object.keys(l).forEach(m=>re(l[m]))}return Object.keys(n).forEach(o=>re(n[o])),i}};var L2e=5,$C=1.65,PC=[0,5,9,13,17,1,2],B2e=0,W2e=2,_C=0,L0=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>g5([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return z0(O0(a),L2e)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=z0(O0(r),$C);n.palmLandmarks=[];for(let a=0;a<PC.length;a++)n.palmLandmarks.push(t[PC[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=_0(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=m5(n,[0,0]),u=o.map(c=>[...g5(c,l),c[2]]),d=RC(a),h=[...Uh(r),1],p=[ji(h,d[0]),ji(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-_C,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?EC(u.palmLandmarks[B2e],u.palmLandmarks[W2e]):0,h=Uh(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&ce.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),f=m5(-d,h),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=NC(m,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);_C=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let w=G(x,[-1,3]),T=await w.array();re(x),re(w);let S=this.transformRawCoords(T,m,d,f),E=this.getBoxForHandLandmarks(S);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:S,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let d=z0(O0(u),$C),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Kr={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Kr.nameMapping[e],getPoints:e=>Kr.pointsMapping[e]},qi={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>qi.nameMapping[e]},Dt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Dt.nameMapping[e]},Hi=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:Aa,index:ls,middle:us,ring:zl,pinky:Ol}=Kr,{none:xa,half:U2e,full:ba}=qi,{verticalUp:Pd,verticalDown:P7e,horizontalLeft:y5,horizontalRight:G2e,diagonalUpRight:j2e,diagonalUpLeft:_d,diagonalDownRight:_7e,diagonalDownLeft:z7e}=Dt,Ki=new Hi("thumbs up");Ki.curl(Aa,xa,1);Ki.direction(Aa,Pd,1);Ki.direction(Aa,_d,.25);Ki.direction(Aa,j2e,.25);for(let e of[Kr.index,Kr.middle,Kr.ring,Kr.pinky])Ki.curl(e,ba,1),Ki.direction(e,y5,1),Ki.direction(e,G2e,1);var Yt=new Hi("victory");Yt.curl(Aa,U2e,.5);Yt.curl(Aa,xa,.5);Yt.direction(Aa,Pd,1);Yt.direction(Aa,_d,1);Yt.curl(ls,xa,1);Yt.direction(ls,Pd,.75);Yt.direction(ls,_d,1);Yt.curl(us,xa,1);Yt.direction(us,Pd,1);Yt.direction(us,_d,.75);Yt.curl(zl,ba,1);Yt.direction(zl,Pd,.2);Yt.direction(zl,_d,1);Yt.direction(zl,y5,.2);Yt.curl(Ol,ba,1);Yt.direction(Ol,Pd,.2);Yt.direction(Ol,_d,1);Yt.direction(Ol,y5,.2);Yt.weight(ls,2);Yt.weight(us,2);var Xi=new Hi("point");Xi.curl(Aa,ba,1);Xi.curl(ls,xa,.5);Xi.curl(us,ba,.5);Xi.curl(zl,ba,.5);Xi.curl(Ol,ba,.5);Xi.weight(ls,2);Xi.weight(us,2);var Zi=new Hi("middle finger");Zi.curl(Aa,xa,1);Zi.curl(ls,ba,.5);Zi.curl(us,ba,.5);Zi.curl(zl,ba,.5);Zi.curl(Ol,ba,.5);Zi.weight(ls,2);Zi.weight(us,2);var zd=new Hi("open palm");zd.curl(Aa,xa,.75);zd.curl(ls,xa,.75);zd.curl(us,xa,.75);zd.curl(zl,xa,.75);zd.curl(Ol,xa,.75);var zC=[Ki,Yt,Xi,Zi,zd];var H2e=.7,Dl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function OC(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function LC(e,t){if(!e||!t)return[0,0];let r=OC(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=OC(e[1],e[2],t[1],t[2]);return[r,n]}function DC(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function q2e(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),f=Math.sqrt(s*s+l*l+h*h),m=(f*f+p*p-c*c)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Dl.NO_CURL_START_LIMIT?y=qi.none:g>Dl.HALF_CURL_START_LIMIT?y=qi.half:y=qi.full,y}function BC(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:n===Math.abs(t)?t>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:r>0?a=Dt.horizontalLeft:a=Dt.horizontalRight,a}function WC(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Dt.verticalDown:a=Dt.verticalUp:n===Math.abs(t)?t<0?a=Dt.verticalDown:a=Dt.verticalUp:r<0?a=Dt.verticalDown:a=Dt.verticalUp,a}function K2e(e,t,r,n,a,s,i,o){let l,u=WC(e,t,r,n),d=BC(a,s,i,o);return u===Dt.verticalUp?d===Dt.horizontalLeft?l=Dt.diagonalUpLeft:l=Dt.diagonalUpRight:d===Dt.horizontalLeft?l=Dt.diagonalDownLeft:l=Dt.diagonalDownRight,l}function X2e(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,f=0,m=h/(d+1e-5);m>1.5?p+=Dl.DISTANCE_VOTE_POWER:m>.66?c+=Dl.DISTANCE_VOTE_POWER:f+=Dl.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],w=e[1],T=r[0],S=r[1];x===g?(T=r[0],S=r[1]):x===A&&(b=t[0],w=t[1]);let _=LC([b,w],[T,S]),M=DC(_,Dl.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],f+=M[2];for(let O of n){let z=DC(O,Dl.SINGLE_ANGLE_VOTE_POWER);p+=z[0],c+=z[1],f+=z[2]}let I;return p===Math.max(p,c,f)?I=WC(l,o,u,h):f===Math.max(c,f)?I=BC(s,a,i,d):I=K2e(l,o,u,h,s,a,i,d),I}function VC(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Kr.all){let i=Kr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=LC(d,h),c=p[0],f=p[1];o.push(c),l.push(f)}t.push(o),r.push(l)}for(let s of Kr.all){let i=s===Kr.thumb?1:0,o=Kr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=q2e(l,u,d),p=X2e(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function B0(e){if(!e||e.length===0)return null;let t=VC(e),r={};for(let n of Kr.all)r[Kr.getName(n)]={curl:qi.getName(t.curls[n]),direction:Dt.getName(t.directions[n])};return r}function UC(e){let t=[];if(!e||e.length===0)return t;let r=VC(e);for(let n of zC){let a=n.matchAgainst(r.curls,r.directions);a>=H2e&&t.push({name:n.name,confidence:a})}return t}var GC={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Od,Dd,jC;async function x5(e,t){let r=await jC.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(GC))s[d]=GC[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=B0(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function b5(e){var r,n;ce.initial&&(Od=null,Dd=null),!Od||!Dd?[Od,Dd]=await Promise.all([e.hand.enabled?je((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?je((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&ie("cached model:",Od.modelUrl),e.debug&&ie("cached model:",Dd.modelUrl));let t=new D0(Od);return jC=new L0(t,Dd),[Od,Dd]}var lr=[null,null],Z2e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Yi=[[0,0],[0,0]],Y2e=["hand","fist","pinch","point","face","tip","pinchtip"],qC=4,KC=1.6,J2e=512,Q2e=1.4,W0=Number.MAX_SAFE_INTEGER,v5=0,ds=[0,0],Gt={boxes:[],hands:[]},XC={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function ZC(e){var t;if(ce.initial&&(lr[0]=null),lr[0])e.debug&&ie("cached model:",lr[0].modelUrl);else{V0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),lr[0]=await je((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(lr[0].modelSignature.inputs);Yi[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Yi[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[0]}async function YC(e){var t;if(ce.initial&&(lr[1]=null),lr[1])e.debug&&ie("cached model:",lr[1].modelUrl);else{lr[1]=await je((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(lr[1].modelSignature.inputs);Yi[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Yi[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[1]}async function eAe(e,t){let r=[];if(!e||!lr[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,J2e),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await lr[0].executeAsync(n.cast,Z2e),n.boxes=et(n.rawBoxes,[0,2]),n.scores=et(n.rawScores,[0]);let o=en(n.scores,1);re(o[qC]),o.splice(qC,1),n.filtered=sr(o,1),re(o),n.max=fr(n.filtered,1),n.argmax=Nn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=Pe(n.boxes,p,1),f=await c.data();re(c);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=C0(m,Q2e),y=[Math.trunc(m[0]*ds[0]),Math.trunc(m[1]*ds[1]),Math.trunc(m[2]*ds[0]),Math.trunc(m[3]*ds[1])],A=d[p],x=Y2e[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>re(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function w5(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&lr[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[Yi[1][0],Yi[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=lr[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=G(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/Yi[1][1],h[1]/Yi[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[ds[0]*(h[0]+t.boxRaw[0]),ds[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=B0(n.keypoints);for(let h of Object.keys(XC))n.annotations[h]=XC[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>re(a[l]))}return n}async function k5(e,t){var a,s;if(!lr[0]||!lr[1]||!((a=lr[0])!=null&&a.inputs[0].shape)||!((s=lr[1])!=null&&s.inputs[0].shape))return[];ds=[e.shape[2]||0,e.shape[1]||0],W0++;let r=(t.hand.skipTime||0)>oe()-v5,n=W0<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Gt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-v5,l=W0<3*(t.hand.skipFrames||0);t.skipAllowed&&Gt.hands.length===t.hand.maxDetected?Gt.hands=await Promise.all(Gt.boxes.map(d=>w5(e,d,t))):t.skipAllowed&&o&&l&&Gt.hands.length>0?Gt.hands=await Promise.all(Gt.boxes.map(d=>w5(e,d,t))):(Gt.boxes=await eAe(e,t),v5=oe(),Gt.hands=await Promise.all(Gt.boxes.map(d=>w5(e,d,t))),W0=0);let u=[...Gt.boxes];if(Gt.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Gt.hands.length;d++){let h=GN(Gt.hands[d].keypoints,ds);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&Gt.hands[d].fingerScore&&Gt.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=C0(h.box,KC),c=C0(h.boxRaw,KC);Gt.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Gt.hands.length;d++){let h=as(Gt.hands[d].keypoints,ds);Gt.hands[d].box=h.box,Gt.hands[d].boxRaw=h.boxRaw}i(Gt.hands)})}var Mr,U0=[],I5=Number.MAX_SAFE_INTEGER,QC=0,e9=0;async function t9(e){var t;return ce.initial&&(Mr=null),Mr?e.debug&&ie("cached model:",Mr.modelUrl):Mr=await je((t=e.face.liveness)==null?void 0:t.modelPath),Mr}async function S5(e,t,r,n){var i,o;if(!Mr)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-e9,s=I5<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&QC===n&&U0[r]?(I5++,U0[r]):(I5=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[Mr!=null&&Mr.inputs[0].shape?Mr.inputs[0].shape[2]:0,Mr!=null&&Mr.inputs[0].shape?Mr.inputs[0].shape[1]:0],!1),d=Mr==null?void 0:Mr.execute(u),h=(await d.data())[0];U0[r]=Math.round(100*h)/100,QC=n,e9=oe(),re([u,d]),l(U0[r])}))}var Gh={};gs(Gh,{connected:()=>j0,horizontal:()=>T5,kpt:()=>G0,relative:()=>C5,vertical:()=>N5});var G0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],T5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],N5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],C5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],j0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var n9=.005,An={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function E5(e){for(let t of T5){let r=e.keypoints.findIndex(a=>a.part===t[0]),n=e.keypoints.findIndex(a=>a.part===t[1]);if(e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[0]<e.keypoints[n].position[0]){let a=e.keypoints[r];e.keypoints[r]=e.keypoints[n],e.keypoints[n]=a}}for(let t of N5){let r=e.keypoints.findIndex(a=>a&&a.part===t[0]),n=e.keypoints.findIndex(a=>a&&a.part===t[1]);e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(r,1)}for(let[t,r]of C5){let n=e.keypoints.findIndex(u=>u&&u.part===t[0]),a=e.keypoints.findIndex(u=>u&&u.part===t[1]),s=e.keypoints.findIndex(u=>u&&u.part===r[0]),i=e.keypoints.findIndex(u=>u&&u.part===r[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[a]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[a].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[a].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[a],e.keypoints[a]=u}}}function a9(e){for(let t=0;t<e.length;t++)if(e[t]&&An.keypoints[t]){let r=[Math.abs(e[t].positionRaw[0]-An.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-An.keypoints[t].positionRaw[1])];r[0]<n9&&r[1]<n9?e[t]=An.keypoints[t]:An.keypoints[t]=e[t]}else An.keypoints[t]=e[t];return e}function s9(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;An.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],r.pad=Gn(e,An.padding),r.resize=Ie.resizeBilinear(r.pad,[t,t]);let n=me(r.resize,"int32");return Object.keys(r).forEach(a=>re(r[a])),n}function i9(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+An.padding[2][0]+An.padding[2][1])/t[0]-An.padding[2][0],n.position[1]*(t[1]+An.padding[1][0]+An.padding[1][1])/t[1]-An.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let r=as(e.keypoints.map(n=>n.position),t);return e.box=r.box,e.boxRaw=r.boxRaw,e}var xn,H0=0,R5=Number.MAX_SAFE_INTEGER,Ll={boxes:[],bodies:[],last:0};async function o9(e){return ce.initial&&(xn=null),xn?e.debug&&ie("cached model:",xn.modelUrl):(V0(["size"],e),xn=await je(e.body.modelPath)),H0=xn.inputs[0].shape?xn.inputs[0].shape[2]:0,H0<64&&(H0=256),xn}async function rAe(e,t,r){let n=e[0][0],a=[],s=0;for(let d=0;d<n.length;d++)if(s=n[d][2],s>t.body.minConfidence){let h=[n[d][1],n[d][0]];a.push({score:Math.round(100*s)/100,part:G0[d],positionRaw:h,position:[Math.round((r.shape[2]||0)*h[0]),Math.round((r.shape[1]||0)*h[1])]})}s=a.reduce((d,h)=>h.score>d?h.score:d,0);let i=[],o=as(a.map(d=>d.position),[r.shape[2],r.shape[1]]),l={};for(let[d,h]of Object.entries(j0)){let p=[];for(let c=0;c<h.length-1;c++){let f=a.find(g=>g.part===h[c]),m=a.find(g=>g.part===h[c+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}l[d]=p}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:a,annotations:l};return E5(u),i.push(u),i}async function nAe(e,t,r){let n=[];for(let a=0;a<e[0].length;a++){let s=e[0][a],i=Math.round(100*s[51+4])/100;if(i>t.body.minConfidence){let o=[];for(let h=0;h<17;h++){let p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:G0[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=as(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(j0)){let c=[];for(let f=0;f<p.length-1;f++){let m=o.find(y=>y.part===p[f]),g=o.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([m.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};E5(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function M5(e,t){if(!xn||!(xn!=null&&xn.inputs[0].shape))return[];t.skipAllowed||(Ll.boxes.length=0),R5++;let r=(t.body.skipTime||0)>oe()-Ll.last,n=R5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?Ll.bodies:new Promise(async a=>{let s={};R5=0,s.input=s9(e,H0),s.res=xn==null?void 0:xn.execute(s.input),Ll.last=oe();let i=await s.res.array();Ll.bodies=s.res.shape[2]===17?await rAe(i,t,e):await nAe(i,t,e);for(let o of Ll.bodies)i9(o,[e.shape[2]||1,e.shape[1]||1]),a9(o.keypoints);Object.keys(s).forEach(o=>re(s[o])),a(Ll.bodies)})}var Ld,q0=[],u9=0,F5=Number.MAX_SAFE_INTEGER,X0=0,K0=2.5;async function d9(e){if(!Ld||ce.initial){Ld=await je(e.object.modelPath);let t=Object.values(Ld.modelSignature.inputs);X0=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&ie("cached model:",Ld.modelUrl);return Ld}async function aAe(e,t,r){let n=0,a=[];for(let l of[1,2,4])K(async()=>{let u=l*13,d=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)===Rd.length)),h=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)<Rd.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),f=await d.array();for(let m=0;m<d.shape[0];m++)for(let g=0;g<d.shape[1];g++){let y=f[m][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(m%u))/u,x=(.5+Math.trunc(m/u))/u,b=c[m].map(I=>I*(u/l/X0)),[w,T]=[A-K0/l*b[0],x-K0/l*b[1]],[S,E]=[A+K0/l*b[2]-w,x+K0/l*b[3]-T],R=[w,T,S,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let _=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:n++,score:Math.round(100*y)/100,class:g+1,label:Rd[g].label,box:_.map(I=>Math.trunc(I)),boxRaw:R};a.push(M)}}});e.forEach(l=>re(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function $5(e,t){let r=(t.object.skipTime||0)>oe()-u9,n=F5<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&q0.length>0?(F5++,q0):(F5=0,!ce.kernels.includes("mod")||!ce.kernels.includes("sparsetodense")?q0:new Promise(async a=>{let 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Hh=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],sAe=Hh.length,jh=Hh.reduce((e,t,r)=>(e[t]=r,e),{}),iAe=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],i4e=iAe.map(([e,t])=>[jh[e],jh[t]]),h9=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function c9(e){let t=e.reduce(({maxX:r,maxY:n,minX:a,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(r,i),maxY:Math.max(n,o),minX:Math.min(a,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function f9(e,[t,r],[n,a]){let s=t/n,i=r/a,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/a,u.box[1]/n,u.box[2]/a,u.box[3]/n],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:h,part:p,position:c})=>({score:h,part:p,position:[Math.trunc(c.x*i),Math.trunc(c.y*s)],positionRaw:[c.x/n,c.y/n]})),annotations:{}});return e.map((u,d)=>o(u,d))}var Z0=class{constructor(t,r){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new 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P5(e,t,r,n){return{y:n.get(e,t,r),x:n.get(e,t,r+sAe)}}function _5(e,t,r){let{heatmapY:n,heatmapX:a,id:s}=e,{y:i,x:o}=P5(n,a,s,r);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function z5(e,t,r){return e<t?t:e>r?r:e}function m9(e,t,r,n){let a=r-e,s=n-t;return a*a+s*s}function O5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var va,lAe=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],Y0=1,Bd=16,uAe=50**2;function g9(e,t,r,n,a,s,i=2){let o=y=>({y:s.get(y.y,y.x,e),x:s.get(y.y,y.x,s.shape[2]/2+e)}),l=(y,A,x)=>({y:z5(Math.round(y.y/Bd),0,A-1),x:z5(Math.round(y.x/Bd),0,x-1)}),[u,d]=n.shape,h=l(t.position,u,d),p=o(h),f=O5(t.position,p);for(let y=0;y<i;y++){let A=l(f,u,d),x=P5(A.y,A.x,r,a);f=O5({x:A.x*Bd,y:A.y*Bd},{x:x.x,y:x.y})}let m=l(f,u,d),g=n.get(m.y,m.x,r);return{position:f,part:Hh[r],score:g}}function dAe(e,t,r,n,a){let 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h=Math.abs(d.annotations.leftEyeIris[3][0]-d.annotations.leftEyeIris[1][0])/2,p=Math.abs(d.annotations.leftEyeIris[4][1]-d.annotations.leftEyeIris[2][1])/2;a.ellipse(d.annotations.leftEyeIris[0][0],d.annotations.leftEyeIris[0][1],h,p,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.color,a.fill())}if(d.annotations&&d.annotations.rightEyeIris&&d.annotations.rightEyeIris[0]){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.color,a.beginPath();let h=Math.abs(d.annotations.rightEyeIris[3][0]-d.annotations.rightEyeIris[1][0])/2,p=Math.abs(d.annotations.rightEyeIris[4][1]-d.annotations.rightEyeIris[2][1])/2;a.ellipse(d.annotations.rightEyeIris[0][0],d.annotations.rightEyeIris[0][1],h,p,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.color,a.fill())}if(n.drawGaze&&((s=d.rotation)==null?void 0:s.angle)&&typeof Path2D!="undefined"){a.strokeStyle="pink";let h=d.box[0]+d.box[2]/2-d.box[3]*Wd(d.rotation.angle.yaw)/90,p=d.box[1]+d.box[3]/2+d.box[2]*Wd(d.rotation.angle.pitch)/90,c=new Path2D(`
             M ${d.box[0]+d.box[2]/2} ${d.box[1]}
             C
               ${h} ${d.box[1]},
@@ -7050,7 +7053,7 @@ return a / b;`,Uie=`
               ${d.box[0]} ${p},
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n=kr(Da,r);if(!t||!e)return;let a=Bl(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(qh(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=n.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 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t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,qh(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=n.useDepth?`rgba(${127.5+2*(i[2]||0)}, ${127.5-2*(i[2]||0)}, 255, 0.5)`:n.color,V5(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||0;a.fillStyle=n.useDepth?`rgba(${127.5+2*u}, ${127.5-2*u}, 255, 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n=oe(),a=kr(Da,r),s=Promise.all([Q0(e,t.face,a),eg(e,t.body,a),tg(e,t.hand,a),rg(e,t.object,a),J0(e,t.gesture,a)]);return W5=ce.perfadd?W5+Math.round(oe()-n):Math.round(oe()-n),t.performance.draw=W5,s}var Wd=.1,q5=.5;function AAe(e,t,r){let n=!1,a=r.length-1;for(let s=0;s<r.length;a=s++)r[s].y>t!=r[a].y>t&&e<(r[a].x-r[s].x)*(t-r[s].y)/(r[a].y-r[s].y)+r[s].x&&(n=!n);return n}async function S9(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,n=await e.tensor.buffer(),a=[];for(let i of Zn.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Wd&&Wd>0&&(a=a.map(i=>({x:i.x>.5?i.x+Wd:i.x-Wd,y:i.y>.5?i.y+Wd:i.y-Wd})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)AAe(i/t,o/t,a)||(n.set(q5*n.get(0,o,i,0),0,o,i,0),n.set(q5*n.get(0,o,i,1),0,o,i,1),n.set(q5*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return re(n),s}var bAe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=a?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},T9=(e,t)=>{let r=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},n=(m,g)=>{let y=m[0]-g[0],A=m[1]-g[1],x=m[2]-g[2];return[y,A,x]},a=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],A=m[2]*g[0]-m[0]*g[2],x=m[0]*g[1]-m[1]*g[0];return[y,A,x]},s=m=>{let[g,y,A,x,b,w,T,S,E]=m,R,_,M;return 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SSRNet:"),e.config.async?(n=(T=e.config.face.ssrnet)!=null&&T.enabled?Cb(p[V].tensor||ct([]),e.config,V,p.length):null,s=(S=e.config.face.ssrnet)!=null&&S.enabled?Mb(p[V].tensor||ct([]),e.config,V,p.length):null):(e.state="run:ssrnet",r=oe(),n=(E=e.config.face.ssrnet)!=null&&E.enabled?await Cb(p[V].tensor||ct([]),e.config,V,p.length):null,s=(R=e.config.face.ssrnet)!=null&&R.enabled?await Mb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.ssrnet=Math.trunc(oe()-r)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?o=(_=e.config.face.mobilefacenet)!=null&&_.enabled?r5(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:mobilefacenet",r=oe(),o=(M=e.config.face.mobilefacenet)!=null&&M.enabled?await r5(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.mobilefacenet=Math.trunc(oe()-r)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(I=e.config.face.description)!=null&&I.enabled?l5(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(O=e.config.face.description)!=null&&O.enabled?await l5(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.description=ce.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let J=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,se=(Q=e.config.face.detector)!=null&&Q.return?et(p[V].tensor):null;re(p[V].tensor),p[V].tensor&&delete p[V].tensor;let Z={...p[V],id:V};d!=null&&d.age&&(Z.age=d.age),d!=null&&d.gender&&(Z.gender=d.gender),d!=null&&d.genderScore&&(Z.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(Z.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(Z.race=d==null?void 0:d.race),i&&(Z.emotion=i),l&&(Z.real=l),u&&(Z.live=u),J&&J!==0&&(Z.iris=Math.trunc(500/J/11.7)/100),ee&&(Z.rotation=ee),se&&(Z.tensor=se),h.push(Z),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),h};var N9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},C9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},E9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],f=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(f<.01||c<.01||f>.022||c>.022)&&(u=!1),(f<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(f>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},R9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=VC(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ce={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},X5=0;function M9(e,t){var i,o,l,u,d,h,p,c,f,m,g,y,A,x,b,w,T,S,E,R,_,M,I,O,z,j,X;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ce.canvas=e.canvas),e.error&&(Ce.error=e.error),!Ce.body||e.body.length!==Ce.body.length)Ce.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let Q=e.body[D].box.map((Z,ae)=>((a-1)*Ce.body[D].box[ae]+Z)/a),V=e.body[D].boxRaw.map((Z,ae)=>((a-1)*Ce.body[D].boxRaw[ae]+Z)/a),ee=e.body[D].keypoints.map((Z,ae)=>{var de,Ae,be,Ee,Me,De,Be,Ze,ot;return{score:Z.score,part:Z.part,position:[Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[0]||0)+(Z.position[0]||0))/a:Z.position[0],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[1]||0)+(Z.position[1]||0))/a:Z.position[1],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[2]||0)+(Z.position[2]||0))/a:Z.position[2]],positionRaw:[Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[0]||0)+(Z.positionRaw[0]||0))/a:Z.positionRaw[0],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[1]||0)+(Z.positionRaw[1]||0))/a:Z.positionRaw[1],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[2]||0)+(Z.positionRaw[2]||0))/a:Z.positionRaw[2]],distance:[Ce.body[D].keypoints[ae]?((a-1)*(((de=Ce.body[D].keypoints[ae].distance)==null?void 0:de[0])||0)+(((Ae=Z.distance)==null?void 0:Ae[0])||0))/a:(be=Z.distance)==null?void 0:be[0],Ce.body[D].keypoints[ae]?((a-1)*(((Ee=Ce.body[D].keypoints[ae].distance)==null?void 0:Ee[1])||0)+(((Me=Z.distance)==null?void 0:Me[1])||0))/a:(De=Z.distance)==null?void 0:De[1],Ce.body[D].keypoints[ae]?((a-1)*(((Be=Ce.body[D].keypoints[ae].distance)==null?void 0:Be[2])||0)+(((Ze=Z.distance)==null?void 0:Ze[2])||0))/a:(ot=Z.distance)==null?void 0:ot[2]]}}),J={},se={connected:{}};(o=(i=t.body)==null?void 0:i.modelPath)!=null&&o.includes("efficientpose")?se=R0:(u=(l=t.body)==null?void 0:l.modelPath)!=null&&u.includes("blazepose")?se=S0:(h=(d=t.body)==null?void 0:d.modelPath)!=null&&h.includes("movenet")&&(se=Gh);for(let[Z,ae]of Object.entries(se.connected)){let de=[];for(let Ae=0;Ae<ae.length-1;Ae++){let be=ee.find(Me=>Me.part===ae[Ae]),Ee=ee.find(Me=>Me.part===ae[Ae+1]);be&&Ee&&de.push([be.position,Ee.position])}J[Z]=de}Ce.body[D]={...e.body[D],box:Q,boxRaw:V,keypoints:ee,annotations:J}}if(!Ce.hand||e.hand.length!==Ce.hand.length)Ce.hand=JSON.parse(JSON.stringify(e.hand));else for(let D=0;D<e.hand.length;D++){let Q=e.hand[D].box.map((se,Z)=>((a-1)*Ce.hand[D].box[Z]+se)/a),V=e.hand[D].boxRaw.map((se,Z)=>((a-1)*Ce.hand[D].boxRaw[Z]+se)/a);Ce.hand[D].keypoints.length!==e.hand[D].keypoints.length&&(Ce.hand[D].keypoints=e.hand[D].keypoints);let ee=e.hand[D].keypoints&&e.hand[D].keypoints.length>0?e.hand[D].keypoints.map((se,Z)=>se.map((ae,de)=>((a-1)*(Ce.hand[D].keypoints[Z][de]||1)+(ae||0))/a)):[],J={};if(Object.keys(Ce.hand[D].annotations).length!==Object.keys(e.hand[D].annotations).length)Ce.hand[D].annotations=e.hand[D].annotations,J=Ce.hand[D].annotations;else if(e.hand[D].annotations)for(let se of Object.keys(e.hand[D].annotations))J[se]=e.hand[D].annotations[se]&&e.hand[D].annotations[se][0]?e.hand[D].annotations[se].map((Z,ae)=>Z.map((de,Ae)=>((a-1)*Ce.hand[D].annotations[se][ae][Ae]+de)/a)):null;Ce.hand[D]={...e.hand[D],box:Q,boxRaw:V,keypoints:ee,annotations:J}}if(!Ce.face||e.face.length!==Ce.face.length)Ce.face=JSON.parse(JSON.stringify(e.face));else for(let D=0;D<e.face.length;D++){let Q=e.face[D].box.map((ee,J)=>((a-1)*Ce.face[D].box[J]+ee)/a),V=e.face[D].boxRaw.map((ee,J)=>((a-1)*Ce.face[D].boxRaw[J]+ee)/a);if(e.face[D].rotation){let ee={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};ee.matrix=(p=e.face[D].rotation)==null?void 0:p.matrix,ee.angle={roll:((a-1)*(((f=(c=Ce.face[D].rotation)==null?void 0:c.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[D].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/a,yaw:((a-1)*(((A=(y=Ce.face[D].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[D].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/a,pitch:((a-1)*(((T=(w=Ce.face[D].rotation)==null?void 0:w.angle)==null?void 0:T.pitch)||0)+(((E=(S=e.face[D].rotation)==null?void 0:S.angle)==null?void 0:E.pitch)||0))/a},ee.gaze={bearing:((a-1)*(((_=(R=Ce.face[D].rotation)==null?void 0:R.gaze)==null?void 0:_.bearing)||0)+(((I=(M=e.face[D].rotation)==null?void 0:M.gaze)==null?void 0:I.bearing)||0))/a,strength:((a-1)*(((z=(O=Ce.face[D].rotation)==null?void 0:O.gaze)==null?void 0:z.strength)||0)+(((X=(j=e.face[D].rotation)==null?void 0:j.gaze)==null?void 0:X.strength)||0))/a},Ce.face[D]={...e.face[D],rotation:ee,box:Q,boxRaw:V}}Ce.face[D]={...e.face[D],box:Q,boxRaw:V}}if(!Ce.object||e.object.length!==Ce.object.length)Ce.object=JSON.parse(JSON.stringify(e.object));else for(let D=0;D<e.object.length;D++){let Q=e.object[D].box.map((ee,J)=>((a-1)*Ce.object[D].box[J]+ee)/a),V=e.object[D].boxRaw.map((ee,J)=>((a-1)*Ce.object[D].boxRaw[J]+ee)/a);Ce.object[D]={...e.object[D],box:Q,boxRaw:V}}if(e.persons){let D=e.persons;if(!Ce.persons||D.length!==Ce.persons.length)Ce.persons=JSON.parse(JSON.stringify(D));else for(let Q=0;Q<D.length;Q++)Ce.persons[Q].box=D[Q].box.map((V,ee)=>((a-1)*Ce.persons[Q].box[ee]+V)/a)}e.gesture&&(Ce.gesture=e.gesture);let s=oe();return X5=ce.perfadd?X5+Math.round(s-r):Math.round(s-r),e.performance&&(Ce.performance={...e.performance,interpolate:X5}),Ce}var J5={};gs(J5,{distance:()=>Kh,match:()=>Y5,similarity:()=>Z5});function Kh(e,t,r={order:2,multiplier:25}){let n=0;for(let a=0;a<e.length;a++){let s=!r.order||r.order===2?e[a]-t[a]:Math.abs(e[a]-t[a]);n+=!r.order||r.order===2?s*s:s**r.order}return(r.multiplier||20)*n}var F9=(e,t,r,n)=>{if(e===0)return 1;let a=t===2?Math.sqrt(e):e**(1/t),s=(1-a/100-r)/(n-r);return Math.max(Math.min(s,1),0)};function Z5(e,t,r={order:2,multiplier:25,min:.2,max:.8}){let n=Kh(e,t,r);return F9(n,r.order||2,r.min||0,r.max||1)}function Y5(e,t,r={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,a=-1;for(let i=0;i<t.length;i++){let o=Kh(e,t[i],r);if(o<n&&(n=o,a=i),n<(r.threshold||0))break}let s=F9(n,r.order||2,r.min||0,r.max||1);return{index:a,distance:n,similarity:s}}function $9(e,t,r,n,a){var o,l,u,d,h,p,c,f,m,g,y,A,x,b,w,T;let s=0,i=[];for(let S of e){let E={id:s++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let z of t)S.box[0]>z.box[0]&&S.box[0]<z.box[0]+z.box[2]&&S.box[1]+S.box[3]>z.box[1]&&S.box[1]+S.box[3]<z.box[1]+z.box[3]&&(E.body=z);if(E.body)for(let z of r)z.box[0]+z.box[2]>E.body.box[0]&&z.box[0]+z.box[2]<E.body.box[0]+E.body.box[2]&&z.box[1]+z.box[3]>E.body.box[1]&&z.box[1]+z.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.left=z),z.box[0]<E.body.box[0]+E.body.box[2]&&z.box[0]>E.body.box[0]&&z.box[1]+z.box[3]>E.body.box[1]&&z.box[1]+z.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.right=z);for(let z of n)z.face!==void 0&&z.face===S.id?(o=E.gestures)==null||o.push(z):z.iris!==void 0&&z.iris===S.id?(l=E.gestures)==null||l.push(z):z.body!==void 0&&z.body===((u=E.body)==null?void 0:u.id)?(d=E.gestures)==null||d.push(z):z.hand!==void 0&&z.hand===((p=(h=E.hands)==null?void 0:h.left)==null?void 0:p.id)?(c=E.gestures)==null||c.push(z):z.hand!==void 0&&z.hand===((m=(f=E.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=E.gestures)==null||g.push(z));let R=[],_=[],M=z=>{z&&z.length===4&&(R.push(z[0],z[0]+z[2]),_.push(z[1],z[1]+z[3]))};M((y=E.face)==null?void 0:y.box),M((A=E.body)==null?void 0:A.box),M((b=(x=E.hands)==null?void 0:x.left)==null?void 0:b.box),M((T=(w=E.hands)==null?void 0:w.right)==null?void 0:T.box);let I=Math.min(...R),O=Math.min(..._);E.box=[I,O,Math.max(...R)-I,Math.max(..._)-O],a&&a[1]&&a[2]&&(E.boxRaw=[E.box[0]/a[2],E.box[1]/a[1],E.box[2]/a[2],E.box[3]/a[1]]),i.push(E)}return i}var ng=`
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n=kr(Da,r);if(!t||!e)return;let a=Bl(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(Kh(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=n.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:n.color,G5(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=n.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:n.color,a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)AAe(a,l,n)}}}async function ag(e,t,r){let n=kr(Da,r);if(!t||!e)return;let a=Bl(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,Kh(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=n.useDepth?`rgba(${127.5+2*(i[2]||0)}, ${127.5-2*(i[2]||0)}, 255, 0.5)`:n.color,G5(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||0;a.fillStyle=n.useDepth?`rgba(${127.5+2*u}, ${127.5-2*u}, 255, 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n=oe(),a=kr(Da,r),s=Promise.all([rg(e,t.face,a),ng(e,t.body,a),ag(e,t.hand,a),sg(e,t.object,a),tg(e,t.gesture,a)]);return U5=ce.perfadd?U5+Math.round(oe()-n):Math.round(oe()-n),t.performance.draw=U5,s}var Vd=.1,X5=.5;function xAe(e,t,r){let n=!1,a=r.length-1;for(let s=0;s<r.length;a=s++)r[s].y>t!=r[a].y>t&&e<(r[a].x-r[s].x)*(t-r[s].y)/(r[a].y-r[s].y)+r[s].x&&(n=!n);return n}async function T9(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,n=await e.tensor.buffer(),a=[];for(let i of Zn.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Vd&&Vd>0&&(a=a.map(i=>({x:i.x>.5?i.x+Vd:i.x-Vd,y:i.y>.5?i.y+Vd:i.y-Vd})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)xAe(i/t,o/t,a)||(n.set(X5*n.get(0,o,i,0),0,o,i,0),n.set(X5*n.get(0,o,i,1),0,o,i,1),n.set(X5*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return re(n),s}var vAe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=a?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},N9=(e,t)=>{let r=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},n=(m,g)=>{let y=m[0]-g[0],A=m[1]-g[1],x=m[2]-g[2];return[y,A,x]},a=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],A=m[2]*g[0]-m[0]*g[2],x=m[0]*g[1]-m[1]*g[0];return[y,A,x]},s=m=>{let[g,y,A,x,b,w,T,S,E]=m,R,_,M;return x<1?x>-1?(M=Math.asin(x),_=Math.atan2(-T,g),R=Math.atan2(-w,b)):(M=-Math.PI/2,_=-Math.atan2(S,E),R=0):(M=Math.PI/2,_=Math.atan2(S,E),R=0),isNaN(R)&&(R=0),isNaN(_)&&(_=0),isNaN(M)&&(M=0),{pitch:2*-R,yaw:2*-_,roll:2*-M}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let o=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[i[10],i[152],i[234],i[454]].map(m=>[m[0]*t[0]/o,m[1]*t[1]/o,m[2]]),u=r(n(l[1],l[0])),d=r(n(l[3],l[2])),h=r(a(d,u));d=a(u,h);let p=[d[0],d[1],d[2],u[0],u[1],u[2],h[0],h[1],h[2]],c=s(p),f=i.length===478?vAe(e):{bearing:0,strength:0};return{angle:c,matrix:p,gaze:f}};var Z5=async(e,t)=>{var c,f,m,g,y,A,x,b,w,T,S,E,R,_,M,I,O,z,j,X,D,Q;let r=oe(),n,a,s,i,o,l,u,d,h=[];e.state="run:face";let p=await AC(t,e.config);if(e.performance.face=ce.perfadd?(e.performance.face||0)+Math.trunc(oe()-r):Math.trunc(oe()-r),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let V=0;V<p.length;V++){if(e.analyze("Get Face"),!p[V].tensor||p[V].tensor.isDisposedInternal){ie("Face object is disposed:",p[V].tensor);continue}if((c=e.config.face.detector)!=null&&c.mask){let ae=await T9(p[V]);re(p[V].tensor),p[V].tensor=ae}let ee=p[V].mesh&&p[V].mesh.length>200?N9(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(f=e.config.face.emotion)!=null&&f.enabled?s5(p[V].tensor||ct([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=(m=e.config.face.emotion)!=null&&m.enabled?await s5(p[V].tensor||ct([]),e.config,V,p.length):[],e.performance.emotion=ce.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(g=e.config.face.antispoof)!=null&&g.enabled?Db(p[V].tensor||ct([]),e.config,V,p.length):0:(e.state="run:antispoof",r=oe(),l=(y=e.config.face.antispoof)!=null&&y.enabled?await Db(p[V].tensor||ct([]),e.config,V,p.length):0,e.performance.antispoof=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?u=(A=e.config.face.liveness)!=null&&A.enabled?S5(p[V].tensor||ct([]),e.config,V,p.length):0:(e.state="run:liveness",r=oe(),u=(x=e.config.face.liveness)!=null&&x.enabled?await S5(p[V].tensor||ct([]),e.config,V,p.length):0,e.performance.liveness=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?a=(b=e.config.face.gear)!=null&&b.enabled?Mb(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:gear",r=oe(),a=(w=e.config.face.gear)!=null&&w.enabled?await Mb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.gear=Math.trunc(oe()-r)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(T=e.config.face.ssrnet)!=null&&T.enabled?$b(p[V].tensor||ct([]),e.config,V,p.length):null,s=(S=e.config.face.ssrnet)!=null&&S.enabled?zb(p[V].tensor||ct([]),e.config,V,p.length):null):(e.state="run:ssrnet",r=oe(),n=(E=e.config.face.ssrnet)!=null&&E.enabled?await $b(p[V].tensor||ct([]),e.config,V,p.length):null,s=(R=e.config.face.ssrnet)!=null&&R.enabled?await zb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.ssrnet=Math.trunc(oe()-r)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?o=(_=e.config.face.mobilefacenet)!=null&&_.enabled?o5(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:mobilefacenet",r=oe(),o=(M=e.config.face.mobilefacenet)!=null&&M.enabled?await o5(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.mobilefacenet=Math.trunc(oe()-r)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(I=e.config.face.description)!=null&&I.enabled?c5(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(O=e.config.face.description)!=null&&O.enabled?await c5(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.description=ce.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let J=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,se=(Q=e.config.face.detector)!=null&&Q.return?et(p[V].tensor):null;re(p[V].tensor),p[V].tensor&&delete p[V].tensor;let Z={...p[V],id:V};d!=null&&d.age&&(Z.age=d.age),d!=null&&d.gender&&(Z.gender=d.gender),d!=null&&d.genderScore&&(Z.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(Z.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(Z.race=d==null?void 0:d.race),i&&(Z.emotion=i),l&&(Z.real=l),u&&(Z.live=u),J&&J!==0&&(Z.iris=Math.trunc(500/J/11.7)/100),ee&&(Z.rotation=ee),se&&(Z.tensor=se),h.push(Z),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),h};var C9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},E9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},R9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],f=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(f<.01||c<.01||f>.022||c>.022)&&(u=!1),(f<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(f>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},M9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=UC(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ce={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},Y5=0;function F9(e,t){var i,o,l,u,d,h,p,c,f,m,g,y,A,x,b,w,T,S,E,R,_,M,I,O,z,j,X;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ce.canvas=e.canvas),e.error&&(Ce.error=e.error),!Ce.body||e.body.length!==Ce.body.length)Ce.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let Q=e.body[D].box.map((Z,ae)=>((a-1)*Ce.body[D].box[ae]+Z)/a),V=e.body[D].boxRaw.map((Z,ae)=>((a-1)*Ce.body[D].boxRaw[ae]+Z)/a),ee=e.body[D].keypoints.map((Z,ae)=>{var 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K0,this.draw={options:Da,canvas:(r,n)=>G5(r,n),face:(r,n,a)=>Q0(r,n,a),body:(r,n,a)=>eg(r,n,a),hand:(r,n,a)=>tg(r,n,a),gesture:(r,n,a)=>J0(r,n,a),object:(r,n,a)=>rg(r,n,a),person:(r,n,a)=>U5(r,n,a),all:(r,n,a)=>j5(r,n,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=xC,this.faceUVMap=bC,this.gl=Ct,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ys)),this.config.backend=t}validate(t){return Oy(ys,t||this.config)}now(){return oe()}image(t,r=!0){return Td(t,this.config,r)}async segmentation(t,r){return x9(t,r,this.config)}enhance(t){return o5(t)}compare(t,r){return oN(this.config,t,r)}async init(){await Y0(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=kr(this.config,t)),this.env.initial&&(this.config.debug&&ie(`version: ${this.version}`),this.config.debug&&ie(`tfjs version: ${this.tf.version["tfjs-core"]}`),await Y0(this)||ie("error: backend check failed"),await rd(),this.env.browser&&(this.config.debug&&ie("configuration:",this.config),this.config.debug&&ie("environment:",this.env),this.config.debug&&ie("tf flags:",this.tf.ENV.flags))),await L5(this),this.env.initial&&this.config.debug&&ie("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(await B5(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return M9(t,this.config)}async warmup(t){let r=oe(),n=await P9(this,t),a=oe();return this.performance.warmup=Math.trunc(a-r),n}async profile(t,r){let n=await this.tf.profile(()=>this.detect(t,r)),a={};for(let o of n.kernels)a[o.name]?a[o.name]+=o.kernelTimeMs:a[o.name]=o.kernelTimeMs;let s=[];Object.entries(a).forEach(o=>s.push({name:o[0],ms:o[1]})),s.sort((o,l)=>l.ms-o.ms),s.length=20;let i={};for(let o of s)i[o.name]=o.ms;return i}async detect(t,r){return this.state="detect",new Promise(async n=>{var g,y,A,x,b,w,T,S,E,R,_,M,I,O,z,j,X,D,Q,V,ee,J;this.state="config";let a;this.config=kr(this.config,r),this.state="check";let s=tp(this,sg).call(this,t);s&&(ie(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:s}));let i=oe();await Y0(this),await this.load(),a=oe(),this.state="image";let o=await Td(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Get Image:"),!o.tensor){this.config.debug&&ie("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),a=oe(),this.config.skipAllowed=await iN(this.config,o.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Check Changed:");let l=[],u=[],d=[],h=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?K5(this,o.tensor):[],this.performance.face&&delete this.performance.face):(a=oe(),l=this.config.face.enabled?await K5(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?kr(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?z5(o.tensor,p):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?jb(o.tensor,p):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?Jb(o.tensor,p):[]:(x=this.config.body.modelPath)!=null&&x.includes("movenet")&&(u=this.config.body.enabled?C5(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(a=oe(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await z5(o.tensor,p):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await jb(o.tensor,p):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?u=this.config.body.enabled?await Jb(o.tensor,p):[]:(S=this.config.body.modelPath)!=null&&S.includes("movenet")&&(u=this.config.body.enabled?await C5(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?kr(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?g5(o.tensor,c):[]:(M=(_=this.config.hand.detector)==null?void 0:_.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?b5(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(O=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&O.includes("handdetect")?d=this.config.hand.enabled?await g5(o.tensor,c):[]:(j=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await b5(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?h=this.config.object.enabled?R5(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?Kb(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?h=this.config.object.enabled?await R5(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await Kb(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(a=oe(),f=[...C9(l),...N9(u),...R9(d),...E9(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let m=((J=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:J.shape)||[];this.result={face:l,body:u,hand:d,gesture:f,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return $9(l,u,d,f,m)}},re(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Vd=new WeakMap,Xh=new WeakMap,Zh=new WeakMap,sg=new WeakMap;return PE(MAe);})();
+2Q==`;async function NAe(e){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),r,n;switch(e.config.warmup){case"face":r=await t(ig);break;case"body":case"full":r=await t(og);break;default:r=null}if(r){let a=await createImageBitmap(r);n=await e.detect(a,e.config),a.close()}return n}async function CAe(e){return new Promise(t=>{let r;switch(e.config.warmup){case"face":r="data:image/jpeg;base64,"+ig;break;case"full":case"body":r="data:image/jpeg;base64,"+og;break;default:r=null}let n;if(typeof Image!="undefined")n=new Image;else if(ce.Image)n=new ce.Image;else return;n.onload=async()=>{let a=Hr(n.naturalWidth,n.naturalHeight);if(!a)ie("Warmup: Canvas not found"),t(void 0);else{let s=a.getContext("2d");s&&s.drawImage(n,0,0);let i=await e.image(a),o=await e.detect(i.tensor,e.config);t(o)}},r?n.src=r:t(void 0)})}async function EAe(e){let t=a=>Buffer.from(a,"base64"),r;e.config.warmup==="face"?r=t(ig):r=t(og);let n;if("node"in Ue){let a=(void 0).decodeJpeg(r),s=a.expandDims(0);e.tf.dispose(a),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&ie("Warmup tfjs-node not loaded");return n}async function RAe(e){let t;return typeof createImageBitmap=="function"?t=await NAe(e):typeof Image!="undefined"||ce.Canvas!==void 0?t=await CAe(e):t=await EAe(e),t}async function _9(e,t){let r=oe();return e.state="warmup",t&&(e.config=kr(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:oe(),persons:[],error:null}:new Promise(async n=>{let a=await RAe(e),s=oe();e.config.debug&&ie("warmup",e.config.warmup,Math.round(s-r),"ms"),e.emit("warmup"),n(a)})}var Ud,Zh,Yh,lg,t3=class{constructor(t){fe(this,"version");fe(this,"config");fe(this,"result");fe(this,"state");fe(this,"process");fe(this,"tf");fe(this,"env");fe(this,"draw");fe(this,"models");fe(this,"events");fe(this,"faceTriangulation");fe(this,"faceUVMap");fe(this,"performance");np(this,Ud,void 0);np(this,Zh,void 0);np(this,Yh,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!rp(this,Zh))return;let r=this.tf.engine().state.numTensors,n=rp(this,Ud);ap(this,Ud,r);let a=r-n;a!==0&&ie(...t,a)});np(this,lg,t=>{if(!rp(this,Yh))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof rt))return"input must be a tensor";try{this.tf.getBackend()}catch(r){return"backend not loaded"}return null});fe(this,"similarity",J5);fe(this,"distance",Xh);fe(this,"match",Q5);fe(this,"emit",t=>{var r;this.events&&this.events.dispatchEvent&&((r=this.events)==null||r.dispatchEvent(new Event(t)))});this.env=ce,ys.wasmPath=Oh["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${w2}/dist/`,ys.modelBasePath=ce.browser?"../models/":"file://models/",ys.backend=ce.browser?"humangl":"tensorflow",this.version=Cb,Object.defineProperty(this,"version",{value:Cb}),this.config=JSON.parse(JSON.stringify(ys)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=kr(this.config,t)),dN(this.config),this.tf=Ue,this.state="idle",ap(this,Ud,0),ap(this,Zh,!1),ap(this,Yh,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new qh,this.draw={options:Da,canvas:(r,n)=>H5(r,n),face:(r,n,a)=>rg(r,n,a),body:(r,n,a)=>ng(r,n,a),hand:(r,n,a)=>ag(r,n,a),gesture:(r,n,a)=>tg(r,n,a),object:(r,n,a)=>sg(r,n,a),person:(r,n,a)=>j5(r,n,a),all:(r,n,a)=>q5(r,n,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=bC,this.faceUVMap=vC,this.gl=Ct,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ys)),this.config.backend=t}validate(t){return By(ys,t||this.config)}now(){return oe()}image(t,r=!0){return Nd(t,this.config,r)}async segmentation(t,r){return b9(t,r,this.config)}enhance(t){return h5(t)}compare(t,r){return uN(this.config,t,r)}async init(){await eg(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=kr(this.config,t)),this.env.initial&&(this.config.debug&&ie(`version: ${this.version}`),this.config.debug&&ie(`tfjs version: ${this.tf.version["tfjs-core"]}`),await eg(this)||ie("error: backend check failed"),await nd(),this.env.browser&&(this.config.debug&&ie("configuration:",this.config),this.config.debug&&ie("environment:",this.env),this.config.debug&&ie("tf flags:",this.tf.ENV.flags))),await W5(this),this.env.initial&&this.config.debug&&ie("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(await V5(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return F9(t,this.config)}async warmup(t){let r=oe(),n=await _9(this,t),a=oe();return this.performance.warmup=Math.trunc(a-r),n}async profile(t,r){let n=await this.tf.profile(()=>this.detect(t,r)),a={};for(let o of n.kernels)a[o.name]?a[o.name]+=o.kernelTimeMs:a[o.name]=o.kernelTimeMs;let s=[];Object.entries(a).forEach(o=>s.push({name:o[0],ms:o[1]})),s.sort((o,l)=>l.ms-o.ms),s.length=20;let i={};for(let o of s)i[o.name]=o.ms;return i}async detect(t,r){return this.state="detect",new Promise(async n=>{var g,y,A,x,b,w,T,S,E,R,_,M,I,O,z,j,X,D,Q,V,ee,J;this.state="config";let a;this.config=kr(this.config,r),this.state="check";let s=rp(this,lg).call(this,t);s&&(ie(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:s}));let i=oe();await eg(this),await this.load(),a=oe(),this.state="image";let o=await Nd(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Get Image:"),!o.tensor){this.config.debug&&ie("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),a=oe(),this.config.skipAllowed=await lN(this.config,o.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Check Changed:");let l=[],u=[],d=[],h=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Z5(this,o.tensor):[],this.performance.face&&delete this.performance.face):(a=oe(),l=this.config.face.enabled?await Z5(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?kr(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?D5(o.tensor,p):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?Zb(o.tensor,p):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?n5(o.tensor,p):[]:(x=this.config.body.modelPath)!=null&&x.includes("movenet")&&(u=this.config.body.enabled?M5(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(a=oe(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await D5(o.tensor,p):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await Zb(o.tensor,p):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?u=this.config.body.enabled?await n5(o.tensor,p):[]:(S=this.config.body.modelPath)!=null&&S.includes("movenet")&&(u=this.config.body.enabled?await M5(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?kr(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?x5(o.tensor,c):[]:(M=(_=this.config.hand.detector)==null?void 0:_.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?k5(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(O=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&O.includes("handdetect")?d=this.config.hand.enabled?await x5(o.tensor,c):[]:(j=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await k5(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?h=this.config.object.enabled?$5(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?Qb(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?h=this.config.object.enabled?await $5(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await Qb(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(a=oe(),f=[...E9(l),...C9(u),...M9(d),...R9(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let m=((J=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:J.shape)||[];this.result={face:l,body:u,hand:d,gesture:f,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return P9(l,u,d,f,m)}},re(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Ud=new WeakMap,Zh=new WeakMap,Yh=new WeakMap,lg=new WeakMap;return PE(FAe);})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js
index 89e224d7..de8dd406 100644
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
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     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
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+`;var aA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +26,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,rA=`
+`,iA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
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     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
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   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
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     gl_FragColor += texture2D(texture, coord);
   }
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+`,yA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +74,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
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+`,xA=`
   precision highp float;
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     gl_FragColor.a = c22.a;
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a=g0.keypoints.map(x=>x.position[0]),y=g0.keypoints.map(x=>x.position[1]);g0.box=[Math.min(...a),Math.min(...y),Math.max(...a)-Math.min(...a),Math.max(...y)-Math.min(...y)];let c=g0.keypoints.map(x=>x.positionRaw[0]),i=g0.keypoints.map(x=>x.positionRaw[1]);g0.boxRaw=[Math.min(...c),Math.min(...i),Math.max(...c)-Math.min(...c),Math.max(...i)-Math.min(...i)];for(let[x,d]of Object.entries(X5)){let u=[];for(let f=0;f<d.length-1;f++){let M=g0.keypoints.find(P=>P.part===d[f]),h=g0.keypoints.find(P=>P.part===d[f+1]);M&&h&&M.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&u.push([M.position,h.position])}g0.annotations[x]=u}n([g0])}))}var G0=V(F());var I2=["angry","disgust","fear","happy","sad","surprise","neutral"],O0,Nt=[],an=0,ln=0,U5=Number.MAX_SAFE_INTEGER;async function yn(e){var t;return T.initial&&(O0=null),O0?e.debug&&b("cached model:",O0.modelUrl):O0=await B((t=e.face.emotion)==null?void 0:t.modelPath),O0}async function J5(e,t,o,A){var s,a;if(!O0)return[];let n=U5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-ln;return t.skipAllowed&&r&&n&&an===A&&Nt[o]&&Nt[o].length>0?(U5++,Nt[o]):(U5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)!=null&&i.enabled){let x={},d=O0!=null&&O0.inputs[0].shape?O0.inputs[0].shape[2]:0;x.resize=G0.image.resizeBilinear(e,[d,d],!1),x.channels=G0.mul(x.resize,H.rgb),x.grayscale=G0.sum(x.channels,3,!0),x.grayscaleSub=G0.sub(x.grayscale,H.tf05),x.grayscaleMul=G0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),ln=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:I2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>G0.dispose(x[f]))}Nt[o]=c,an=A,y(c)}))}var mn=V(F());var E0,Y5=[],cn=0,dn=0,fn=Number.MAX_SAFE_INTEGER;async function pn(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await B(e.face.mobilefacenet.modelPath),E0}async function K5(e,t,o,A){var s,a;if(!E0)return[];let n=fn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-dn;return t.skipAllowed&&r&&n&&cn===A&&Y5[o]?(fn++,Y5[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=mn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}Y5[o]=c,cn=A,dn=g(),y(c)})}var Ve=V(F());var B0=V(F());var Ae,fe=0,N2=2.3,Q5=L0.leftEyeLower0,_5=L0.rightEyeLower0,Be={leftBounds:[Q5[0],Q5[Q5.length-1]],rightBounds:[_5[0],_5[_5.length-1]]},He={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function Pn(e){var t;return T.initial&&(Ae=null),Ae?e.debug&&b("cached model:",Ae.modelUrl):Ae=await 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A=e[L0[`${o}EyeUpper0`][He.upperCenter]][2],n=e[L0[`${o}EyeLower0`][He.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function Mn(e,t,o,A){if(!Ae)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=hn(e,t,Be.leftBounds[0],Be.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=hn(e,t,Be.rightBounds[0],Be.rightBounds[1],A,!0),i=B0.concat([s,c]);B0.dispose(s),B0.dispose(c);let l=Ae.execute(i);B0.dispose(i);let x=await l.data();B0.dispose(l);let d=x.slice(0,He.numCoordinates*3),{rawCoords:u,iris:f}=bn(d,n,r,!0),M=x.slice(He.numCoordinates*3),{rawCoords:h,iris:P}=bn(M,a,y),m=L2(e);Math.abs(m)<30?(Lt(e,u,"left",null),Lt(e,h,"right",null)):m<1?Lt(e,u,"left",["EyeUpper0","EyeLower0"]):Lt(e,h,"right",["EyeUpper0","EyeLower0"]);let p=gn(e,f,"left"),k=gn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},ne=null,Fe=0;async function 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yt(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function On(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return Cn.image.cropAndResize(t,r,[0],o)}function In(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Ht(e,t=1.5){let o=yt(e),A=Bt(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Ft(e){let t=yt(e),o=Bt(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function G2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Nn(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return G2(o)}var Sn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function me(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function B2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function Wn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(me(e[n],B2(t,r)))}return o}function no(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Sn(t[0],t[1]),s=Wn(r,n),a=Sn(-t[0],-t[1]);return Wn(s,a)}function Ln(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-me(t[0],o),-me(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function ro(e,t){return[me(e,t[0]),me(e,t[1])]}var 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Object.keys(o).forEach(n=>G.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=G.reshape(t,[-1,7,2]),A.div=G.div(A.reshape,this.inputSizeTensor),A.landmarks=G.add(A.div,this.anchors[o]);let n=G.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>G.dispose(A[r])),n}async predict(t,o){let A={};A.resize=G.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=G.div(A.resize,H.tf127),A.image=G.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=G.squeeze(A.batched),A.slice=G.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=G.sigmoid(A.slice),A.scores=G.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=G.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await G.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=G.slice(A.norm,[a,0],[1,-1]),y.slice=G.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=G.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=In(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>G.dispose(y[f]))}return Object.keys(A).forEach(a=>G.dispose(A[a])),s}};var j0=V(F());var V2=5,Hn=1.65,Fn=[0,5,9,13,17,1,2],Z2=0,X2=2,Vn=0,ao=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),A=t.map(s=>s[1]),n=[Math.min(...o),Math.min(...A)],r=[Math.max(...o),Math.max(...A)];return{startPoint:n,endPoint:r}}getBoxForPalmLandmarks(t,o){let A=t.map(r=>ro([...r,1],o)),n=this.calculateLandmarksBoundingBox(A);return Ht(Ft(n),V2)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),A=Ht(Ft(o),Hn);A.palmLandmarks=[];for(let n=0;n<Fn.length;n++)A.palmLandmarks.push(t[Fn[n]].slice(0,2));return A}transformRawCoords(t,o,A,n){let r=Bt(o),s=[r[0]/this.inputSize,r[1]/this.inputSize,(r[0]+r[1])/this.inputSize/2],a=t.map(d=>[s[0]*(d[0]-this.inputSize/2),s[1]*(d[1]-this.inputSize/2),s[2]*d[2]]),y=no(A,[0,0]),c=a.map(d=>[...ro(d,y),d[2]]),i=Ln(n),l=[...yt(o),1],x=[me(l,i[0]),me(l,i[1])];return c.map(d=>[Math.trunc(d[0]+x[0]),Math.trunc(d[1]+x[1]),Math.trunc(d[2])])}async estimateHands(t,o){let A=!1,n,r=(o.hand.skipTime||0)>g()-Vn,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&r&&s&&(n=await 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A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&l0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},r=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=Y.image.cropAndResize(e,[r],[0],[ge[1][0],ge[1][1]],"bilinear"),n.div=Y.div(n.crop,H.tf255),[n.score,n.keypoints]=l0[1].execute(n.div,["Identity_1","Identity"]);let s=(await n.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){A.fingerScore=a,n.reshaped=Y.reshape(n.keypoints,[-1,3]);let i=(await n.reshaped.array()).map(l=>[l[0]/ge[1][1],l[1]/ge[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);A.keypoints=i.map(l=>[ae[0]*(l[0]+t.boxRaw[0]),ae[1]*(l[1]+t.boxRaw[1]),l[2]||0]),A.landmarks=Vt(A.keypoints);for(let l of Object.keys(or))A.annotations[l]=or[l].map(x=>A.landmarks&&A.keypoints[x]?A.keypoints[x]:null)}Object.keys(n).forEach(y=>Y.dispose(n[y]))}return A}async function mo(e,t){var n,r;if(!l0[0]||!l0[1]||!((n=l0[0])!=null&&n.inputs[0].shape)||!((r=l0[1])!=null&&r.inputs[0].shape))return[];ae=[e.shape[2]||0,e.shape[1]||0],Zt++;let o=(t.hand.skipTime||0)>g()-co,A=Zt<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?o0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>g()-co,y=Zt<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(i=>fo(e,i,t))):t.skipAllowed&&a&&y&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(i=>fo(e,i,t))):(o0.boxes=await As(e,t),co=g(),o0.hands=await Promise.all(o0.boxes.map(i=>fo(e,i,t))),Zt=0);let c=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let i=0;i<o0.hands.length;i++){let l=JA(o0.hands[i].keypoints,ae);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&o0.hands[i].fingerScore&&o0.hands[i].fingerScore>(t.hand.minConfidence||0)){let x=Wt(l.box,tr),d=Wt(l.boxRaw,tr);o0.boxes.push({...c[i],box:x,boxRaw:d})}}for(let i=0;i<o0.hands.length;i++){let l=te(o0.hands[i].keypoints,ae);o0.hands[i].box=l.box,o0.hands[i].boxRaw=l.boxRaw}s(o0.hands)})}var qt=V(F());var u0,Dt=[],po=Number.MAX_SAFE_INTEGER,sr=0,ar=0;async function ir(e){var t;return T.initial&&(u0=null),u0?e.debug&&b("cached model:",u0.modelUrl):u0=await B((t=e.face.liveness)==null?void 0:t.modelPath),u0}async function uo(e,t,o,A){var s,a;if(!u0)return 0;let n=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>g()-ar,r=po<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&n&&r&&sr===A&&Dt[o]?(po++,Dt[o]):(po=0,new Promise(async y=>{let 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Ut=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ho=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],bo=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],go=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Jt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var 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a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Ut[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=te(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(Jt)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};Po(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function vo(e,t){if(!W0||!(W0!=null&&W0.inputs[0].shape))return[];t.skipAllowed||(je.boxes.length=0),Mo++;let o=(t.body.skipTime||0)>g()-je.last,A=Mo<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?je.bodies:new Promise(async n=>{let r={};Mo=0,r.input=cr(e,Yt),r.res=W0==null?void 0:W0.execute(r.input),je.last=g();let s=await r.res.array();je.bodies=r.res.shape[2]===17?await rs(s,t,e):await ss(s,t,e);for(let a of je.bodies)dr(a,[e.shape[2]||1,e.shape[1]||1]),xr(a.keypoints);Object.keys(r).forEach(a=>fr.dispose(r[a])),n(je.bodies)})}var M0=V(F());var Je,Kt=[],ur=0,To=Number.MAX_SAFE_INTEGER,_t=0,Qt=2.5;async function hr(e){if(!Je||T.initial){Je=await B(e.object.modelPath);let t=Object.values(Je.modelSignature.inputs);_t=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&b("cached model:",Je.modelUrl);return Je}async function as(e,t,o){let A=0,n=[];for(let y of[1,2,4])M0.tidy(async()=>{let c=y*13,i=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)===Ge.length)),l=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)<Ge.length)),d=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await i.array();for(let f=0;f<i.shape[0];f++)for(let M=0;M<i.shape[1];M++){let h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/_t)),[k,C]=[P-Qt/y*p[0],m-Qt/y*p[1]],[O,j]=[P+Qt/y*p[2]-k,m+Qt/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Ge[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function wo(e,t){let o=(t.object.skipTime||0)>g()-ur,A=To<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Kt.length>0?(To++,Kt):(To=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?Kt:new Promise(async 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dt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],is=dt.length,ct=dt.reduce((e,t,o)=>(e[t]=o,e),{}),ls=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=ls.map(([e,t])=>[ct[e],ct[t]]),gr=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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i=Math.trunc(100*r[0][c][4])/100,l=r[0][c][5],x=Be[l].label,[d,u]=[r[0][c][0]/Re,r[0][c][1]/Re],f=[d,u,r[0][c][2]/Re-d,r[0][c][3]/Re-u],M=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];n.push({id:y++,score:i,class:l,label:x,box:M,boxRaw:f})}return Object.keys(A).forEach(c=>b0.dispose(A[c])),n}async function U5(e,t){let o=(t.object.skipTime||0)>g()-on,A=q5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&D5.length>0?(q5++,D5):(q5=0,new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=b0.image.resizeBilinear(e,[Re,Re]),a=t.object.enabled?oe==null?void 0:oe.execute(s,["tower_0/detections"]):null;on=g(),b0.dispose(s);let y=await C2(a,r,t);D5=y,n(y)}))}var K=V(F());var Nt={};ve(Nt,{connected:()=>Y5,kpt:()=>J5});var 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a=g0.keypoints.map(x=>x.position[0]),y=g0.keypoints.map(x=>x.position[1]);g0.box=[Math.min(...a),Math.min(...y),Math.max(...a)-Math.min(...a),Math.max(...y)-Math.min(...y)];let c=g0.keypoints.map(x=>x.positionRaw[0]),i=g0.keypoints.map(x=>x.positionRaw[1]);g0.boxRaw=[Math.min(...c),Math.min(...i),Math.max(...c)-Math.min(...c),Math.max(...i)-Math.min(...i)];for(let[x,d]of Object.entries(Y5)){let u=[];for(let f=0;f<d.length-1;f++){let M=g0.keypoints.find(P=>P.part===d[f]),h=g0.keypoints.find(P=>P.part===d[f+1]);M&&h&&M.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&u.push([M.position,h.position])}g0.annotations[x]=u}n([g0])}))}var B0=V(F());var I2=["angry","disgust","fear","happy","sad","surprise","neutral"],O0,Lt=[],ln=0,yn=0,_5=Number.MAX_SAFE_INTEGER;async function xn(e){var t;return T.initial&&(O0=null),O0?e.debug&&b("cached model:",O0.modelUrl):O0=await G((t=e.face.emotion)==null?void 0:t.modelPath),O0}async function $5(e,t,o,A){var s,a;if(!O0)return[];let n=_5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-yn;return t.skipAllowed&&r&&n&&ln===A&&Lt[o]&&Lt[o].length>0?(_5++,Lt[o]):(_5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)!=null&&i.enabled){let x={},d=O0!=null&&O0.inputs[0].shape?O0.inputs[0].shape[2]:0;x.resize=B0.image.resizeBilinear(e,[d,d],!1),x.channels=B0.mul(x.resize,H.rgb),x.grayscale=B0.sum(x.channels,3,!0),x.grayscaleSub=B0.sub(x.grayscale,H.tf05),x.grayscaleMul=B0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),yn=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:I2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>B0.dispose(x[f]))}Lt[o]=c,ln=A,y(c)}))}var pn=V(F());var E0,eo=[],dn=0,fn=0,mn=Number.MAX_SAFE_INTEGER;async function un(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await G(e.face.mobilefacenet.modelPath),E0}async function to(e,t,o,A){var s,a;if(!E0)return[];let n=mn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-fn;return t.skipAllowed&&r&&n&&dn===A&&eo[o]?(mn++,eo[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=pn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}eo[o]=c,dn=A,fn=g(),y(c)})}var Ve=V(F());var G0=V(F());var Ae,fe=0,N2=2.3,oo=L0.leftEyeLower0,Ao=L0.rightEyeLower0,Ge={leftBounds:[oo[0],oo[oo.length-1]],rightBounds:[Ao[0],Ao[Ao.length-1]]},He={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function Mn(e){var t;return T.initial&&(Ae=null),Ae?e.debug&&b("cached model:",Ae.modelUrl):Ae=await 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A=e[L0[`${o}EyeUpper0`][He.upperCenter]][2],n=e[L0[`${o}EyeLower0`][He.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function vn(e,t,o,A){if(!Ae)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=bn(e,t,Ge.leftBounds[0],Ge.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=bn(e,t,Ge.rightBounds[0],Ge.rightBounds[1],A,!0),i=G0.concat([s,c]);G0.dispose(s),G0.dispose(c);let l=Ae.execute(i);G0.dispose(i);let x=await l.data();G0.dispose(l);let d=x.slice(0,He.numCoordinates*3),{rawCoords:u,iris:f}=gn(d,n,r,!0),M=x.slice(He.numCoordinates*3),{rawCoords:h,iris:P}=gn(M,a,y),m=L2(e);Math.abs(m)<30?(Bt(e,u,"left",null),Bt(e,h,"right",null)):m<1?Bt(e,u,"left",["EyeUpper0","EyeLower0"]):Bt(e,h,"right",["EyeUpper0","EyeLower0"]);let p=Pn(e,f,"left"),k=Pn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},ne=null,Fe=0;async function wn(e,t){var a,y,c,i,l,x,d,u,f;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>g()-U0.timestamp,A=U0.skipped<(((y=t.face.detector)==null?void 0:y.skipFrames)||0);!t.skipAllowed||!o||!A||U0.boxes.length===0?(U0.boxes=await DA(e,t),U0.timestamp=g(),U0.skipped=0):U0.skipped++;let n=[],r=[],s=0;for(let M=0;M<U0.boxes.length;M++){let h=U0.boxes[M],P=0,m,p={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,m,p.tensor]=HA((c=t.face.detector)==null?void 0:c.rotation,h,e,(i=t.face.mesh)!=null&&i.enabled?Fe:jt()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let k=await bt(p.tensor);Ve.dispose(p.tensor),p.tensor=k}if(p.boxScore=Math.round(100*h.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!ne)t.debug&&b("face mesh detection requested, but model is not loaded");else{let[k,C,O]=ne.execute(p.tensor),j=await C.data();p.faceScore=Math.round(100*j[0])/100;let L=Ve.reshape(O,[-1,3]),J=await 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yt(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function In(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return On.image.cropAndResize(t,r,[0],o)}function Nn(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Ft(e,t=1.5){let o=yt(e),A=Ht(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Vt(e){let t=yt(e),o=Ht(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function B2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Ln(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return B2(o)}var Wn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function me(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function G2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function Cn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(me(e[n],G2(t,r)))}return o}function lo(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Wn(t[0],t[1]),s=Cn(r,n),a=Wn(-t[0],-t[1]);return Cn(s,a)}function Bn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-me(t[0],o),-me(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function yo(e,t){return[me(e,t[0]),me(e,t[1])]}var 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Object.keys(o).forEach(n=>B.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=B.reshape(t,[-1,7,2]),A.div=B.div(A.reshape,this.inputSizeTensor),A.landmarks=B.add(A.div,this.anchors[o]);let n=B.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>B.dispose(A[r])),n}async predict(t,o){let A={};A.resize=B.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=B.div(A.resize,H.tf127),A.image=B.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=B.squeeze(A.batched),A.slice=B.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=B.sigmoid(A.slice),A.scores=B.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=B.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await B.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=B.slice(A.norm,[a,0],[1,-1]),y.slice=B.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=B.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=Nn(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>B.dispose(y[f]))}return Object.keys(A).forEach(a=>B.dispose(A[a])),s}};var j0=V(F());var V2=5,Fn=1.65,Vn=[0,5,9,13,17,1,2],Z2=0,X2=2,Zn=0,Xt=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),A=t.map(s=>s[1]),n=[Math.min(...o),Math.min(...A)],r=[Math.max(...o),Math.max(...A)];return{startPoint:n,endPoint:r}}getBoxForPalmLandmarks(t,o){let A=t.map(r=>yo([...r,1],o)),n=this.calculateLandmarksBoundingBox(A);return Ft(Vt(n),V2)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),A=Ft(Vt(o),Fn);A.palmLandmarks=[];for(let n=0;n<Vn.length;n++)A.palmLandmarks.push(t[Vn[n]].slice(0,2));return A}transformRawCoords(t,o,A,n){let r=Ht(o),s=[r[0]/this.inputSize,r[1]/this.inputSize,(r[0]+r[1])/this.inputSize/2],a=t.map(d=>[s[0]*(d[0]-this.inputSize/2),s[1]*(d[1]-this.inputSize/2),s[2]*d[2]]),y=lo(A,[0,0]),c=a.map(d=>[...yo(d,y),d[2]]),i=Bn(n),l=[...yt(o),1],x=[me(l,i[0]),me(l,i[1])];return c.map(d=>[Math.trunc(d[0]+x[0]),Math.trunc(d[1]+x[1]),Math.trunc(d[2])])}async estimateHands(t,o){let A=!1,n,r=(o.hand.skipTime||0)>g()-Zn,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&r&&s&&(n=await 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A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&l0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},r=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=Y.image.cropAndResize(e,[r],[0],[Pe[1][0],Pe[1][1]],"bilinear"),n.div=Y.div(n.crop,H.tf255),[n.score,n.keypoints]=l0[1].execute(n.div,["Identity_1","Identity"]);let s=(await n.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){A.fingerScore=a,n.reshaped=Y.reshape(n.keypoints,[-1,3]);let i=(await n.reshaped.array()).map(l=>[l[0]/Pe[1][1],l[1]/Pe[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);A.keypoints=i.map(l=>[ae[0]*(l[0]+t.boxRaw[0]),ae[1]*(l[1]+t.boxRaw[1]),l[2]||0]),A.landmarks=Dt(A.keypoints);for(let l of Object.keys(Ar))A.annotations[l]=Ar[l].map(x=>A.landmarks&&A.keypoints[x]?A.keypoints[x]:null)}Object.keys(n).forEach(y=>Y.dispose(n[y]))}return A}async function ho(e,t){var n,r;if(!l0[0]||!l0[1]||!((n=l0[0])!=null&&n.inputs[0].shape)||!((r=l0[1])!=null&&r.inputs[0].shape))return[];ae=[e.shape[2]||0,e.shape[1]||0],qt++;let o=(t.hand.skipTime||0)>g()-po,A=qt<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?o0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>g()-po,y=qt<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(i=>uo(e,i,t))):t.skipAllowed&&a&&y&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(i=>uo(e,i,t))):(o0.boxes=await As(e,t),po=g(),o0.hands=await Promise.all(o0.boxes.map(i=>uo(e,i,t))),qt=0);let c=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let i=0;i<o0.hands.length;i++){let 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dt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],is=dt.length,ct=dt.reduce((e,t,o)=>(e[t]=o,e),{}),ls=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=ls.map(([e,t])=>[ct[e],ct[t]]),Pr=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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c=a[1]>0?`#${a[1]}`:"",i=`${a[0]} ${c}: ${y[1]}`;A.shadowColor&&A.shadowColor!==""&&(n.fillStyle=A.shadowColor,n.fillText(i,8,2+r*A.lineHeight)),n.fillStyle=A.labelColor,n.fillText(i,6,0+r*A.lineHeight),r+=1}}}}async function i5(e,t,o){var r,s,a,y,c;let A=c0(Y0,o);if(!t||!e)return;let n=Se(e);if(!!n)for(let i of t){if(n.font=A.font,n.strokeStyle=A.color,n.fillStyle=A.color,A.drawBoxes&&mt(n,i.box[0],i.box[1],i.box[2],i.box[3],A),A.drawLabels){let l=[];if(l.push(`face: ${Math.trunc(100*i.score)}%`),i.genderScore&&l.push(`${i.gender||""} ${Math.trunc(100*i.genderScore)}%`),i.age&&l.push(`age: ${i.age||""}`),i.iris&&l.push(`distance: ${i.iris}`),i.real&&l.push(`real: ${Math.trunc(100*i.real)}%`),i.live&&l.push(`live: ${Math.trunc(100*i.live)}%`),i.emotion&&i.emotion.length>0){let x=i.emotion.map(d=>`${Math.trunc(100*d.score)}% ${d.emotion}`);x.length>3&&(x.length=3),l.push(x.join(" "))}i.rotation&&i.rotation.angle&&i.rotation.gaze&&(i.rotation.angle.roll&&l.push(`roll: 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l=Math.abs(i.annotations.leftEyeIris[3][0]-i.annotations.leftEyeIris[1][0])/2,x=Math.abs(i.annotations.leftEyeIris[4][1]-i.annotations.leftEyeIris[2][1])/2;n.ellipse(i.annotations.leftEyeIris[0][0],i.annotations.leftEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(i.annotations&&i.annotations.rightEyeIris&&i.annotations.rightEyeIris[0]){n.strokeStyle=A.useDepth?"rgba(255, 200, 255, 0.3)":A.color,n.beginPath();let l=Math.abs(i.annotations.rightEyeIris[3][0]-i.annotations.rightEyeIris[1][0])/2,x=Math.abs(i.annotations.rightEyeIris[4][1]-i.annotations.rightEyeIris[2][1])/2;n.ellipse(i.annotations.rightEyeIris[0][0],i.annotations.rightEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(A.drawGaze&&((r=i.rotation)==null?void 0:r.angle)&&typeof Path2D!="undefined"){n.strokeStyle="pink";let 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             C
               ${l} ${i.box[1]},
@@ -108,7 +108,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
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Description:"),e.config.async?i=(q=e.config.face.description)!=null&&q.enabled?oo(x[R].tensor||A0.tensor([]),e.config,R,x.length):null:(e.state="run:description",o=g(),i=(x0=e.config.face.description)!=null&&x0.enabled?await oo(x[R].tensor||A0.tensor([]),e.config,R,x.length):null,e.performance.description=T.perfadd?(e.performance.description||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End Description:"),e.config.async&&([A,r,s,a,i,n,y,c]=await Promise.all([A,r,s,a,i,n,y,c])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&A&&r&&(i={...i,age:A.age,gender:r.gender,genderScore:r.genderScore}),((C0=e.config.face.gear)==null?void 0:C0.enabled)&&n&&(i={...i,age:n.age,gender:n.gender,genderScore:n.genderScore,race:n.race}),((le=e.config.face.mobilefacenet)==null?void 0:le.enabled)&&a&&(i.descriptor=a),(v=e.config.face.iris)!=null&&v.enabled;let n0=x[R].annotations&&x[R].annotations.leftEyeIris&&x[R].annotations.leftEyeIris[0]&&x[R].annotations.rightEyeIris&&x[R].annotations.rightEyeIris[0]&&x[R].annotations.leftEyeIris.length>0&&x[R].annotations.rightEyeIris.length>0&&x[R].annotations.leftEyeIris[0]!==null&&x[R].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[R].annotations.leftEyeIris[3][0]-x[R].annotations.leftEyeIris[1][0]),Math.abs(x[R].annotations.rightEyeIris[4][1]-x[R].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y0=(a0=e.config.face.detector)!=null&&a0.return?A0.squeeze(x[R].tensor):null;A0.dispose(x[R].tensor),x[R].tensor&&delete x[R].tensor;let I={...x[R],id:R};i!=null&&i.age&&(I.age=i.age),i!=null&&i.gender&&(I.gender=i.gender),i!=null&&i.genderScore&&(I.genderScore=i==null?void 0:i.genderScore),i!=null&&i.descriptor&&(I.embedding=i==null?void 0:i.descriptor),i!=null&&i.race&&(I.race=i==null?void 0:i.race),s&&(I.emotion=s),y&&(I.real=y),c&&(I.live=c),n0&&n0!==0&&(I.iris=Math.trunc(500/n0/11.7)/100),Q&&(I.rotation=Q),y0&&(I.tensor=y0),l.push(I),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Lr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=e[o].keypoints.find(y=>y.part==="leftWrist"),n=e[o].keypoints.find(y=>y.part==="rightWrist"),r=e[o].keypoints.find(y=>y.part==="nose");r&&A&&n&&A.position[1]<r.position[1]&&n.position[1]<r.position[1]?t.push({body:o,gesture:"i give up"}):r&&A&&A.position[1]<r.position[1]?t.push({body:o,gesture:"raise left hand"}):r&&n&&n.position[1]<r.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(y=>y.part==="leftShoulder"),a=e[o].keypoints.find(y=>y.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},Gr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let y=e[o].mesh[152][2]||0;Math.abs(y)>10&&t.push({face:o,gesture:`head ${y<0?"up":"down"}`})}return t},Br=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let A=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],n=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],r=Math.abs(A*n),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],y=Math.abs(s*a),c=!1;Math.abs(r-y)/Math.max(r,y)<.25&&(c=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(c=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let d=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||d<.01||u>.022||d>.022)&&(c=!1),(u<.01||d<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||d>.022)&&t.push({iris:o,gesture:"looking up"}),c&&t.push({iris:o,gesture:"looking center"})}return t},Hr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=[];if(e[o].annotations)for(let[n,r]of Object.entries(e[o].annotations))n!=="palmBase"&&Array.isArray(r)&&r[0]&&A.push({name:n.toLowerCase(),position:r[0]});if(A&&A.length>0){let n=A.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${n.name} forward`});let r=A.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${r.name} up`})}if(e[o].keypoints){let n=Kn(e[o].keypoints);for(let r of n)t.push({hand:o,gesture:r.name})}}return t};var 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v=0;v<e.face.length;v++){let a0=e.face[v].box.map((Q,n0)=>((n-1)*z.face[v].box[n0]+Q)/n),R=e.face[v].boxRaw.map((Q,n0)=>((n-1)*z.face[v].boxRaw[n0]+Q)/n);if(e.face[v].rotation){let Q={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Q.matrix=(x=e.face[v].rotation)==null?void 0:x.matrix,Q.angle={roll:((n-1)*(((u=(d=z.face[v].rotation)==null?void 0:d.angle)==null?void 0:u.roll)||0)+(((M=(f=e.face[v].rotation)==null?void 0:f.angle)==null?void 0:M.roll)||0))/n,yaw:((n-1)*(((P=(h=z.face[v].rotation)==null?void 0:h.angle)==null?void 0:P.yaw)||0)+(((p=(m=e.face[v].rotation)==null?void 0:m.angle)==null?void 0:p.yaw)||0))/n,pitch:((n-1)*(((C=(k=z.face[v].rotation)==null?void 0:k.angle)==null?void 0:C.pitch)||0)+(((j=(O=e.face[v].rotation)==null?void 0:O.angle)==null?void 0:j.pitch)||0))/n},Q.gaze={bearing:((n-1)*(((J=(L=z.face[v].rotation)==null?void 0:L.gaze)==null?void 0:J.bearing)||0)+(((q=(Z=e.face[v].rotation)==null?void 0:Z.gaze)==null?void 0:q.bearing)||0))/n,strength:((n-1)*(((E=(x0=z.face[v].rotation)==null?void 0:x0.gaze)==null?void 0:E.strength)||0)+(((le=(C0=e.face[v].rotation)==null?void 0:C0.gaze)==null?void 0:le.strength)||0))/n},z.face[v]={...e.face[v],rotation:Q,box:a0,boxRaw:R}}z.face[v]={...e.face[v],box:a0,boxRaw:R}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let v=0;v<e.object.length;v++){let a0=e.object[v].box.map((Q,n0)=>((n-1)*z.object[v].box[n0]+Q)/n),R=e.object[v].boxRaw.map((Q,n0)=>((n-1)*z.object[v].boxRaw[n0]+Q)/n);z.object[v]={...e.object[v],box:a0,boxRaw:R}}if(e.persons){let v=e.persons;if(!z.persons||v.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(v));else for(let a0=0;a0<v.length;a0++)z.persons[a0].box=v[a0].box.map((R,Q)=>((n-1)*z.persons[a0].box[Q]+R)/n)}e.gesture&&(z.gesture=e.gesture);let r=g();return Xo=T.perfadd?Xo+Math.round(r-o):Math.round(r-o),e.performance&&(z.performance={...e.performance,interpolate:Xo}),z}var Uo={};Me(Uo,{distance:()=>mt,match:()=>qo,similarity:()=>Do});function mt(e,t,o={order:2,multiplier:25}){let A=0;for(let n=0;n<e.length;n++){let r=!o.order||o.order===2?e[n]-t[n]:Math.abs(e[n]-t[n]);A+=!o.order||o.order===2?r*r:r**o.order}return(o.multiplier||20)*A}var Vr=(e,t,o,A)=>{if(e===0)return 1;let n=t===2?Math.sqrt(e):e**(1/t),r=(1-n/100-o)/(A-o);return Math.max(Math.min(r,1),0)};function Do(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let A=mt(e,t,o);return Vr(A,o.order||2,o.min||0,o.max||1)}function qo(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let A=Number.MAX_SAFE_INTEGER,n=-1;for(let s=0;s<t.length;s++){let a=mt(e,t[s],o);if(a<A&&(A=a,n=s),A<(o.threshold||0))break}let r=Vr(A,o.order||2,o.min||0,o.max||1);return{index:n,distance:A,similarity:r}}function Zr(e,t,o,A,n){var a,y,c,i,l,x,d,u,f,M,h,P,m,p,k,C;let 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_e,ut,ht,f5,Ko=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env");w(this,"draw");w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");At(this,_e,void 0);At(this,ut,void 0);At(this,ht,void 0);w(this,"gl");w(this,"analyze",(...t)=>{if(!ot(this,ut))return;let o=this.tf.engine().state.numTensors,A=ot(this,_e);nt(this,_e,o);let n=o-A;n!==0&&b(...t,n)});At(this,f5,t=>{if(!ot(this,ht))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof D0.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});w(this,"similarity",Uo);w(this,"distance",pt);w(this,"match",Jo);w(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new 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0:J.modelPath)!=null&&Z.includes("handtrack")&&(i=this.config.hand.enabled?ho(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(n=g(),(x0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&x0.includes("handdetect")?i=this.config.hand.enabled?await fo(a.tensor,d):[]:(C0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&C0.includes("handtrack")&&(i=this.config.hand.enabled?await ho(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((le=this.config.object.modelPath)!=null&&le.includes("nanodet")?l=this.config.object.enabled?Eo(a.tensor,this.config):[]:(v=this.config.object.modelPath)!=null&&v.includes("centernet")&&(l=this.config.object.enabled?U5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=g(),(a0=this.config.object.modelPath)!=null&&a0.includes("nanodet")?l=this.config.object.enabled?await Eo(a.tensor,this.config):[]:(R=this.config.object.modelPath)!=null&&R.includes("centernet")&&(l=this.config.object.enabled?await U5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([y,c,i,l]=await Promise.all([y,c,i,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(n=g(),u=[...Gr(y),...Br(c),...Fr(i),...Hr(y)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-s):Math.trunc(g()-s);let f=((n0=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:n0.shape)||[];this.result={face:y,body:c,hand:i,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Xr(y,c,i,u,f)}},D0.dispose(a.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};_e=new WeakMap,ut=new WeakMap,ht=new WeakMap,f5=new WeakMap;0&&(module.exports={Human,defaults,draw,env,match,models});
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js
index 2a6a80f2..2ea575f1 100644
--- a/dist/human.node-wasm.js
+++ b/dist/human.node-wasm.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
-var Ur=Object.create;var tt=Object.defineProperty;var Jr=Object.getOwnPropertyDescriptor;var Yr=Object.getOwnPropertyNames;var Kr=Object.getPrototypeOf,Qr=Object.prototype.hasOwnProperty;var _r=(e,t,o)=>t in e?tt(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var $r=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Me=(e,t)=>{for(var o in t)tt(e,o,{get:t[o],enumerable:!0})},$o=(e,t,o,A)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of Yr(t))!Qr.call(e,n)&&n!==o&&tt(e,n,{get:()=>t[n],enumerable:!(A=Jr(t,n))||A.enumerable});return e};var V=(e,t,o)=>(o=e!=null?Ur(Kr(e)):{},$o(t||!e||!e.__esModule?tt(o,"default",{value:e,enumerable:!0}):o,e)),e2=e=>$o(tt({},"__esModule",{value:!0}),e);var w=(e,t,o)=>(_r(e,typeof t!="symbol"?t+"":t,o),o),eA=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var ot=(e,t,o)=>(eA(e,t,"read from private field"),o?o.call(e):t.get(e)),At=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},nt=(e,t,o,A)=>(eA(e,t,"write to private field"),A?A.call(e,o):t.set(e,o),o);var F=$r((Bs,ht)=>{var oA=Object.defineProperty,t2=Object.getOwnPropertyDescriptor,o2=Object.getOwnPropertyNames,A2=Object.prototype.hasOwnProperty,f5=(e,t,o,A)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of o2(t))!A2.call(e,n)&&n!==o&&oA(e,n,{get:()=>t[n],enumerable:!(A=t2(t,n))||A.enumerable});return e},AA=(e,t,o)=>(f5(e,t,"default"),o&&f5(o,t,"default")),n2=e=>f5(oA({},"__esModule",{value:!0}),e),m5={};ht.exports=n2(m5);AA(m5,require("@tensorflow/tfjs"),ht.exports);AA(m5,require("@tensorflow/tfjs-backend-wasm"),ht.exports)});var Is={};Me(Is,{Human:()=>qr,default:()=>qr,defaults:()=>ye,draw:()=>Vo,env:()=>T,match:()=>Jo,models:()=>A5});module.exports=e2(Is);function b(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function tA(e,t){let o=e.endsWith("/")?"":"/",n=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!n.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${n}`);return n}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function d5(e,t,o="config",A=[]){for(let n of Object.keys(t))if(typeof t[n]=="object")d5(e[n],t[n],n,A);else{let r=e&&typeof e[n]!="undefined";r||A.push({reason:"unknown property",where:`${o}.${n} = ${t[n]}`});let s=e&&typeof e[n]==typeof t[n];r&&!s&&A.push({reason:"property type mismatch",where:`${o}.${n} = ${t[n]}`,expected:typeof e[n]})}return t.debug&&o==="config"&&A.length>0&&b("invalid configuration",A),A}function c0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,A)=>(Object.keys(A||{}).forEach(n=>{let r=o[n],s=A[n];Array.isArray(r)&&Array.isArray(s)?o[n]=r.concat(...s):t(r)&&t(s)?o[n]=c0(r,s):o[n]=s}),o),{})}var ye={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var d0=V(F());var W=V(F());var nA=`
+var Ur=Object.create;var tt=Object.defineProperty;var Jr=Object.getOwnPropertyDescriptor;var Yr=Object.getOwnPropertyNames;var Kr=Object.getPrototypeOf,Qr=Object.prototype.hasOwnProperty;var _r=(e,t,o)=>t in e?tt(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var $r=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ve=(e,t)=>{for(var o in t)tt(e,o,{get:t[o],enumerable:!0})},oA=(e,t,o,A)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of Yr(t))!Qr.call(e,n)&&n!==o&&tt(e,n,{get:()=>t[n],enumerable:!(A=Jr(t,n))||A.enumerable});return e};var V=(e,t,o)=>(o=e!=null?Ur(Kr(e)):{},oA(t||!e||!e.__esModule?tt(o,"default",{value:e,enumerable:!0}):o,e)),e2=e=>oA(tt({},"__esModule",{value:!0}),e);var w=(e,t,o)=>(_r(e,typeof t!="symbol"?t+"":t,o),o),AA=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var ot=(e,t,o)=>(AA(e,t,"read from private field"),o?o.call(e):t.get(e)),At=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},nt=(e,t,o,A)=>(AA(e,t,"write to private field"),A?A.call(e,o):t.set(e,o),o);var F=$r((Gs,bt)=>{var rA=Object.defineProperty,t2=Object.getOwnPropertyDescriptor,o2=Object.getOwnPropertyNames,A2=Object.prototype.hasOwnProperty,u5=(e,t,o,A)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of o2(t))!A2.call(e,n)&&n!==o&&rA(e,n,{get:()=>t[n],enumerable:!(A=t2(t,n))||A.enumerable});return e},sA=(e,t,o)=>(u5(e,t,"default"),o&&u5(o,t,"default")),n2=e=>u5(rA({},"__esModule",{value:!0}),e),h5={};bt.exports=n2(h5);sA(h5,require("@tensorflow/tfjs"),bt.exports);sA(h5,require("@tensorflow/tfjs-backend-wasm"),bt.exports)});var Is={};ve(Is,{Human:()=>Qo,default:()=>Qo,defaults:()=>ye,draw:()=>Xo,env:()=>T,match:()=>Ko,models:()=>s5});module.exports=e2(Is);function b(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function nA(e,t){let o=e.endsWith("/")?"":"/",n=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!n.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${n}`);return n}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function p5(e,t,o="config",A=[]){for(let n of Object.keys(t))if(typeof t[n]=="object")p5(e[n],t[n],n,A);else{let r=e&&typeof e[n]!="undefined";r||A.push({reason:"unknown property",where:`${o}.${n} = ${t[n]}`});let s=e&&typeof e[n]==typeof t[n];r&&!s&&A.push({reason:"property type mismatch",where:`${o}.${n} = ${t[n]}`,expected:typeof e[n]})}return t.debug&&o==="config"&&A.length>0&&b("invalid configuration",A),A}function c0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,A)=>(Object.keys(A||{}).forEach(n=>{let r=o[n],s=A[n];Array.isArray(r)&&Array.isArray(s)?o[n]=r.concat(...s):t(r)&&t(s)?o[n]=c0(r,s):o[n]=s}),o),{})}var ye={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var d0=V(F());var W=V(F());var aA=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +14,7 @@ var Ur=Object.create;var tt=Object.defineProperty;var Jr=Object.getOwnPropertyDe
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var rA=`
+`;var iA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +26,7 @@ var Ur=Object.create;var tt=Object.defineProperty;var Jr=Object.getOwnPropertyDe
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,sA=`
+`,lA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +38,7 @@ var Ur=Object.create;var tt=Object.defineProperty;var Jr=Object.getOwnPropertyDe
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,aA=`
+`,yA=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +51,7 @@ var Ur=Object.create;var tt=Object.defineProperty;var Jr=Object.getOwnPropertyDe
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,iA=`
+`,xA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +74,7 @@ var Ur=Object.create;var tt=Object.defineProperty;var Jr=Object.getOwnPropertyDe
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,lA=`
+`,cA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,7 +96,7 @@ var Ur=Object.create;var tt=Object.defineProperty;var Jr=Object.getOwnPropertyDe
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
-`;var p5=(e,t,o)=>{let A=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(A,(n,r)=>(o[r]=0,n))},yA=class{constructor(t,o,A){w(this,"uniform",{});w(this,"attribute",{});w(this,"gl");w(this,"id");w(this,"compile",(t,o)=>{let A=this.gl.createShader(o);return A?(this.gl.shaderSource(A,t),this.gl.compileShader(A),this.gl.getShaderParameter(A,this.gl.COMPILE_STATUS)?A:(b(`filter: gl compile failed: ${this.gl.getShaderInfoLog(A)}`),null)):(b("filter: could not create shader"),null)});this.gl=t;let n=this.compile(o,this.gl.VERTEX_SHADER),r=this.compile(A,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!n||!r)){if(!this.id){b("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,n),this.gl.attachShader(this.id,r),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){b(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),p5(o,"attribute",this.attribute);for(let s in 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a=g0.keypoints.map(x=>x.position[0]),y=g0.keypoints.map(x=>x.position[1]);g0.box=[Math.min(...a),Math.min(...y),Math.max(...a)-Math.min(...a),Math.max(...y)-Math.min(...y)];let c=g0.keypoints.map(x=>x.positionRaw[0]),i=g0.keypoints.map(x=>x.positionRaw[1]);g0.boxRaw=[Math.min(...c),Math.min(...i),Math.max(...c)-Math.min(...c),Math.max(...i)-Math.min(...i)];for(let[x,d]of Object.entries(D5)){let u=[];for(let f=0;f<d.length-1;f++){let M=g0.keypoints.find(P=>P.part===d[f]),h=g0.keypoints.find(P=>P.part===d[f+1]);M&&h&&M.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&u.push([M.position,h.position])}g0.annotations[x]=u}n([g0])}))}var G0=V(F());var I2=["angry","disgust","fear","happy","sad","surprise","neutral"],O0,Lt=[],ln=0,yn=0,J5=Number.MAX_SAFE_INTEGER;async function xn(e){var t;return T.initial&&(O0=null),O0?e.debug&&b("cached model:",O0.modelUrl):O0=await B((t=e.face.emotion)==null?void 0:t.modelPath),O0}async function Y5(e,t,o,A){var s,a;if(!O0)return[];let 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T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await B(e.face.mobilefacenet.modelPath),E0}async function Q5(e,t,o,A){var s,a;if(!E0)return[];let n=mn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-fn;return t.skipAllowed&&r&&n&&dn===A&&K5[o]?(mn++,K5[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=pn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}K5[o]=c,dn=A,fn=g(),y(c)})}var Ve=V(F());var B0=V(F());var Ae,fe=0,N2=2.3,_5=L0.leftEyeLower0,$5=L0.rightEyeLower0,Be={leftBounds:[_5[0],_5[_5.length-1]],rightBounds:[$5[0],$5[$5.length-1]]},He={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function Mn(e){var t;return T.initial&&(Ae=null),Ae?e.debug&&b("cached model:",Ae.modelUrl):Ae=await 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A=e[L0[`${o}EyeUpper0`][He.upperCenter]][2],n=e[L0[`${o}EyeLower0`][He.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function vn(e,t,o,A){if(!Ae)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=bn(e,t,Be.leftBounds[0],Be.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=bn(e,t,Be.rightBounds[0],Be.rightBounds[1],A,!0),i=B0.concat([s,c]);B0.dispose(s),B0.dispose(c);let l=Ae.execute(i);B0.dispose(i);let x=await l.data();B0.dispose(l);let d=x.slice(0,He.numCoordinates*3),{rawCoords:u,iris:f}=gn(d,n,r,!0),M=x.slice(He.numCoordinates*3),{rawCoords:h,iris:P}=gn(M,a,y),m=L2(e);Math.abs(m)<30?(Gt(e,u,"left",null),Gt(e,h,"right",null)):m<1?Gt(e,u,"left",["EyeUpper0","EyeLower0"]):Gt(e,h,"right",["EyeUpper0","EyeLower0"]);let p=Pn(e,f,"left"),k=Pn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},ne=null,Fe=0;async function wn(e,t){var a,y,c,i,l,x,d,u,f;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>g()-U0.timestamp,A=U0.skipped<(((y=t.face.detector)==null?void 0:y.skipFrames)||0);!t.skipAllowed||!o||!A||U0.boxes.length===0?(U0.boxes=await DA(e,t),U0.timestamp=g(),U0.skipped=0):U0.skipped++;let n=[],r=[],s=0;for(let M=0;M<U0.boxes.length;M++){let h=U0.boxes[M],P=0,m,p={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,m,p.tensor]=HA((c=t.face.detector)==null?void 0:c.rotation,h,e,(i=t.face.mesh)!=null&&i.enabled?Fe:jt()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let k=await bt(p.tensor);Ve.dispose(p.tensor),p.tensor=k}if(p.boxScore=Math.round(100*h.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!ne)t.debug&&b("face mesh detection requested, but model is not loaded");else{let[k,C,O]=ne.execute(p.tensor),j=await C.data();p.faceScore=Math.round(100*j[0])/100;let L=Ve.reshape(O,[-1,3]),J=await 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yt(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function In(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return On.image.cropAndResize(t,r,[0],o)}function Nn(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Ft(e,t=1.5){let o=yt(e),A=Ht(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Vt(e){let t=yt(e),o=Ht(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function G2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Ln(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return G2(o)}var Wn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function me(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function B2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function Cn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(me(e[n],B2(t,r)))}return o}function ro(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Wn(t[0],t[1]),s=Cn(r,n),a=Wn(-t[0],-t[1]);return Cn(s,a)}function Gn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-me(t[0],o),-me(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function so(e,t){return[me(e,t[0]),me(e,t[1])]}var 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Object.keys(o).forEach(n=>G.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=G.reshape(t,[-1,7,2]),A.div=G.div(A.reshape,this.inputSizeTensor),A.landmarks=G.add(A.div,this.anchors[o]);let n=G.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>G.dispose(A[r])),n}async predict(t,o){let A={};A.resize=G.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=G.div(A.resize,H.tf127),A.image=G.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=G.squeeze(A.batched),A.slice=G.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=G.sigmoid(A.slice),A.scores=G.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=G.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await G.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=G.slice(A.norm,[a,0],[1,-1]),y.slice=G.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=G.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=Nn(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>G.dispose(y[f]))}return Object.keys(A).forEach(a=>G.dispose(A[a])),s}};var j0=V(F());var V2=5,Fn=1.65,Vn=[0,5,9,13,17,1,2],Z2=0,X2=2,Zn=0,io=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),A=t.map(s=>s[1]),n=[Math.min(...o),Math.min(...A)],r=[Math.max(...o),Math.max(...A)];return{startPoint:n,endPoint:r}}getBoxForPalmLandmarks(t,o){let A=t.map(r=>so([...r,1],o)),n=this.calculateLandmarksBoundingBox(A);return Ft(Vt(n),V2)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),A=Ft(Vt(o),Fn);A.palmLandmarks=[];for(let n=0;n<Vn.length;n++)A.palmLandmarks.push(t[Vn[n]].slice(0,2));return A}transformRawCoords(t,o,A,n){let r=Ht(o),s=[r[0]/this.inputSize,r[1]/this.inputSize,(r[0]+r[1])/this.inputSize/2],a=t.map(d=>[s[0]*(d[0]-this.inputSize/2),s[1]*(d[1]-this.inputSize/2),s[2]*d[2]]),y=ro(A,[0,0]),c=a.map(d=>[...so(d,y),d[2]]),i=Gn(n),l=[...yt(o),1],x=[me(l,i[0]),me(l,i[1])];return c.map(d=>[Math.trunc(d[0]+x[0]),Math.trunc(d[1]+x[1]),Math.trunc(d[2])])}async estimateHands(t,o){let A=!1,n,r=(o.hand.skipTime||0)>g()-Zn,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&r&&s&&(n=await 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A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&l0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},r=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=Y.image.cropAndResize(e,[r],[0],[ge[1][0],ge[1][1]],"bilinear"),n.div=Y.div(n.crop,H.tf255),[n.score,n.keypoints]=l0[1].execute(n.div,["Identity_1","Identity"]);let s=(await n.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){A.fingerScore=a,n.reshaped=Y.reshape(n.keypoints,[-1,3]);let i=(await n.reshaped.array()).map(l=>[l[0]/ge[1][1],l[1]/ge[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);A.keypoints=i.map(l=>[ae[0]*(l[0]+t.boxRaw[0]),ae[1]*(l[1]+t.boxRaw[1]),l[2]||0]),A.landmarks=Zt(A.keypoints);for(let l of Object.keys(Ar))A.annotations[l]=Ar[l].map(x=>A.landmarks&&A.keypoints[x]?A.keypoints[x]:null)}Object.keys(n).forEach(y=>Y.dispose(n[y]))}return A}async function po(e,t){var n,r;if(!l0[0]||!l0[1]||!((n=l0[0])!=null&&n.inputs[0].shape)||!((r=l0[1])!=null&&r.inputs[0].shape))return[];ae=[e.shape[2]||0,e.shape[1]||0],Xt++;let o=(t.hand.skipTime||0)>g()-fo,A=Xt<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?o0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>g()-fo,y=Xt<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(i=>mo(e,i,t))):t.skipAllowed&&a&&y&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(i=>mo(e,i,t))):(o0.boxes=await As(e,t),fo=g(),o0.hands=await Promise.all(o0.boxes.map(i=>mo(e,i,t))),Xt=0);let c=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let i=0;i<o0.hands.length;i++){let l=YA(o0.hands[i].keypoints,ae);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&o0.hands[i].fingerScore&&o0.hands[i].fingerScore>(t.hand.minConfidence||0)){let x=Ct(l.box,or),d=Ct(l.boxRaw,or);o0.boxes.push({...c[i],box:x,boxRaw:d})}}for(let i=0;i<o0.hands.length;i++){let l=te(o0.hands[i].keypoints,ae);o0.hands[i].box=l.box,o0.hands[i].boxRaw=l.boxRaw}s(o0.hands)})}var Ut=V(F());var u0,qt=[],uo=Number.MAX_SAFE_INTEGER,ar=0,ir=0;async function lr(e){var t;return T.initial&&(u0=null),u0?e.debug&&b("cached model:",u0.modelUrl):u0=await B((t=e.face.liveness)==null?void 0:t.modelPath),u0}async function ho(e,t,o,A){var s,a;if(!u0)return 0;let n=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>g()-ir,r=uo<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&n&&r&&ar===A&&qt[o]?(uo++,qt[o]):(uo=0,new Promise(async y=>{let c=Ut.image.resizeBilinear(e,[u0!=null&&u0.inputs[0].shape?u0.inputs[0].shape[2]:0,u0!=null&&u0.inputs[0].shape?u0.inputs[0].shape[1]:0],!1),i=u0==null?void 0:u0.execute(c),l=(await i.data())[0];qt[o]=Math.round(100*l)/100,ar=A,ir=g(),Ut.dispose([c,i]),y(qt[o])}))}var mr=V(F());var xt={};Me(xt,{connected:()=>Yt,horizontal:()=>bo,kpt:()=>Jt,relative:()=>Po,vertical:()=>go});var Jt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],bo=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],go=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Po=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Yt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Pe=V(F()),xr=.005,S0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Mo(e){for(let t of bo){let o=e.keypoints.findIndex(n=>n.part===t[0]),A=e.keypoints.findIndex(n=>n.part===t[1]);if(e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[0]<e.keypoints[A].position[0]){let n=e.keypoints[o];e.keypoints[o]=e.keypoints[A],e.keypoints[A]=n}}for(let t of go){let o=e.keypoints.findIndex(n=>n&&n.part===t[0]),A=e.keypoints.findIndex(n=>n&&n.part===t[1]);e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[1]<e.keypoints[A].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of Po){let A=e.keypoints.findIndex(c=>c&&c.part===t[0]),n=e.keypoints.findIndex(c=>c&&c.part===t[1]),r=e.keypoints.findIndex(c=>c&&c.part===o[0]),s=e.keypoints.findIndex(c=>c&&c.part===o[1]);if(!e.keypoints[r]||!e.keypoints[s])continue;let a=e.keypoints[A]?[Math.abs(e.keypoints[r].position[0]-e.keypoints[A].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[A].position[0])]:[0,0],y=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[r].position[0]-e.keypoints[n].position[0])]:[0,0];if(a[0]>a[1]||y[0]>y[1]){let c=e.keypoints[A];e.keypoints[A]=e.keypoints[n],e.keypoints[n]=c}}}function cr(e){for(let t=0;t<e.length;t++)if(e[t]&&S0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-S0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-S0.keypoints[t].positionRaw[1])];o[0]<xr&&o[1]<xr?e[t]=S0.keypoints[t]:S0.keypoints[t]=e[t]}else S0.keypoints[t]=e[t];return e}function dr(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;S0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=Pe.pad(e,S0.padding),o.resize=Pe.image.resizeBilinear(o.pad,[t,t]);let A=Pe.cast(o.resize,"int32");return Object.keys(o).forEach(n=>Pe.dispose(o[n])),A}function fr(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+S0.padding[2][0]+S0.padding[2][1])/t[0]-S0.padding[2][0],A.position[1]*(t[1]+S0.padding[1][0]+S0.padding[1][1])/t[1]-S0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=te(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var W0,Kt=0,vo=Number.MAX_SAFE_INTEGER,je={boxes:[],bodies:[],last:0};async function pr(e){return T.initial&&(W0=null),W0?e.debug&&b("cached model:",W0.modelUrl):(Dt(["size"],e),W0=await B(e.body.modelPath)),Kt=W0.inputs[0].shape?W0.inputs[0].shape[2]:0,Kt<64&&(Kt=256),W0}async function rs(e,t,o){let A=e[0][0],n=[],r=0;for(let i=0;i<A.length;i++)if(r=A[i][2],r>t.body.minConfidence){let l=[A[i][1],A[i][0]];n.push({score:Math.round(100*r)/100,part:Jt[i],positionRaw:l,position:[Math.round((o.shape[2]||0)*l[0]),Math.round((o.shape[1]||0)*l[1])]})}r=n.reduce((i,l)=>l.score>i?l.score:i,0);let s=[],a=te(n.map(i=>i.position),[o.shape[2],o.shape[1]]),y={};for(let[i,l]of Object.entries(Yt)){let x=[];for(let d=0;d<l.length-1;d++){let u=n.find(M=>M.part===l[d]),f=n.find(M=>M.part===l[d+1]);u&&f&&u.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&x.push([u.position,f.position])}y[i]=x}let c={id:0,score:r,box:a.box,boxRaw:a.boxRaw,keypoints:n,annotations:y};return Mo(c),s.push(c),s}async function ss(e,t,o){let A=[];for(let n=0;n<e[0].length;n++){let r=e[0][n],s=Math.round(100*r[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Jt[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=te(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(Yt)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};Mo(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function To(e,t){if(!W0||!(W0!=null&&W0.inputs[0].shape))return[];t.skipAllowed||(je.boxes.length=0),vo++;let o=(t.body.skipTime||0)>g()-je.last,A=vo<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?je.bodies:new Promise(async n=>{let r={};vo=0,r.input=dr(e,Kt),r.res=W0==null?void 0:W0.execute(r.input),je.last=g();let s=await r.res.array();je.bodies=r.res.shape[2]===17?await rs(s,t,e):await ss(s,t,e);for(let a of je.bodies)fr(a,[e.shape[2]||1,e.shape[1]||1]),cr(a.keypoints);Object.keys(r).forEach(a=>mr.dispose(r[a])),n(je.bodies)})}var M0=V(F());var Je,Qt=[],hr=0,wo=Number.MAX_SAFE_INTEGER,$t=0,_t=2.5;async function br(e){if(!Je||T.initial){Je=await B(e.object.modelPath);let t=Object.values(Je.modelSignature.inputs);$t=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&b("cached model:",Je.modelUrl);return Je}async function as(e,t,o){let A=0,n=[];for(let y of[1,2,4])M0.tidy(async()=>{let c=y*13,i=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)===Ge.length)),l=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)<Ge.length)),d=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await i.array();for(let f=0;f<i.shape[0];f++)for(let M=0;M<i.shape[1];M++){let h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/$t)),[k,C]=[P-_t/y*p[0],m-_t/y*p[1]],[O,j]=[P+_t/y*p[2]-k,m+_t/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Ge[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function Ro(e,t){let o=(t.object.skipTime||0)>g()-hr,A=wo<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Qt.length>0?(wo++,Qt):(wo=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?Qt:new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=M0.image.resizeBilinear(e,[$t,$t],!1),a=M0.div(s,H.tf255),y=a.transpose([0,3,1,2]);M0.dispose(a),M0.dispose(s);let c;t.object.enabled&&(c=Je.execute(y)),hr=g(),M0.dispose(y);let i=await as(c,r,t);Qt=i,n(i)}))}var v0=V(F());var dt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],is=dt.length,ct=dt.reduce((e,t,o)=>(e[t]=o,e),{}),ls=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=ls.map(([e,t])=>[ct[e],ct[t]]),Pr=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Mr(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:r},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(A,a),minX:Math.min(n,s),minY:Math.min(r,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function vr(e,[t,o],[A,n]){let r=t/A,s=o/n,a=(c,i)=>({id:i,score:c.score,boxRaw:[c.box[0]/n,c.box[1]/A,c.box[2]/n,c.box[3]/A],box:[Math.trunc(c.box[0]*s),Math.trunc(c.box[1]*r),Math.trunc(c.box[2]*s),Math.trunc(c.box[3]*r)],keypoints:c.keypoints.map(({score:l,part:x,position:d})=>({score:l,part:x,position:[Math.trunc(d.x*s),Math.trunc(d.y*r)],positionRaw:[d.x/A,d.y/A]})),annotations:{}});return e.map((c,i)=>a(c,i))}var ko=class{constructor(t,o){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=o}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let o=2*t;if(o<this.numberOfElements&&this.less(o,o+1)&&o++,!this.less(t,o))break;this.exchange(t,o),t=o}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,o){return this.getValueAt(t)<this.getValueAt(o)}exchange(t,o){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[o],this.priorityQueue[o]=A}};function 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a=g0.keypoints.map(x=>x.position[0]),y=g0.keypoints.map(x=>x.position[1]);g0.box=[Math.min(...a),Math.min(...y),Math.max(...a)-Math.min(...a),Math.max(...y)-Math.min(...y)];let c=g0.keypoints.map(x=>x.positionRaw[0]),i=g0.keypoints.map(x=>x.positionRaw[1]);g0.boxRaw=[Math.min(...c),Math.min(...i),Math.max(...c)-Math.min(...c),Math.max(...i)-Math.min(...i)];for(let[x,d]of Object.entries(K5)){let u=[];for(let f=0;f<d.length-1;f++){let M=g0.keypoints.find(P=>P.part===d[f]),h=g0.keypoints.find(P=>P.part===d[f+1]);M&&h&&M.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&u.push([M.position,h.position])}g0.annotations[x]=u}n([g0])}))}var B0=V(F());var I2=["angry","disgust","fear","happy","sad","surprise","neutral"],O0,Bt=[],yn=0,xn=0,$5=Number.MAX_SAFE_INTEGER;async function cn(e){var t;return T.initial&&(O0=null),O0?e.debug&&b("cached model:",O0.modelUrl):O0=await G((t=e.face.emotion)==null?void 0:t.modelPath),O0}async function eo(e,t,o,A){var s,a;if(!O0)return[];let n=$5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-xn;return t.skipAllowed&&r&&n&&yn===A&&Bt[o]&&Bt[o].length>0?($5++,Bt[o]):($5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)!=null&&i.enabled){let x={},d=O0!=null&&O0.inputs[0].shape?O0.inputs[0].shape[2]:0;x.resize=B0.image.resizeBilinear(e,[d,d],!1),x.channels=B0.mul(x.resize,H.rgb),x.grayscale=B0.sum(x.channels,3,!0),x.grayscaleSub=B0.sub(x.grayscale,H.tf05),x.grayscaleMul=B0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),xn=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:I2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>B0.dispose(x[f]))}Bt[o]=c,yn=A,y(c)}))}var un=V(F());var E0,to=[],fn=0,mn=0,pn=Number.MAX_SAFE_INTEGER;async function hn(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await G(e.face.mobilefacenet.modelPath),E0}async function oo(e,t,o,A){var s,a;if(!E0)return[];let n=pn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-mn;return t.skipAllowed&&r&&n&&fn===A&&to[o]?(pn++,to[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=un.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}to[o]=c,fn=A,mn=g(),y(c)})}var Ve=V(F());var G0=V(F());var Ae,fe=0,N2=2.3,Ao=L0.leftEyeLower0,no=L0.rightEyeLower0,Ge={leftBounds:[Ao[0],Ao[Ao.length-1]],rightBounds:[no[0],no[no.length-1]]},He={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function vn(e){var t;return T.initial&&(Ae=null),Ae?e.debug&&b("cached model:",Ae.modelUrl):Ae=await 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A=e[L0[`${o}EyeUpper0`][He.upperCenter]][2],n=e[L0[`${o}EyeLower0`][He.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function Tn(e,t,o,A){if(!Ae)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=gn(e,t,Ge.leftBounds[0],Ge.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=gn(e,t,Ge.rightBounds[0],Ge.rightBounds[1],A,!0),i=G0.concat([s,c]);G0.dispose(s),G0.dispose(c);let l=Ae.execute(i);G0.dispose(i);let x=await l.data();G0.dispose(l);let d=x.slice(0,He.numCoordinates*3),{rawCoords:u,iris:f}=Pn(d,n,r,!0),M=x.slice(He.numCoordinates*3),{rawCoords:h,iris:P}=Pn(M,a,y),m=L2(e);Math.abs(m)<30?(Gt(e,u,"left",null),Gt(e,h,"right",null)):m<1?Gt(e,u,"left",["EyeUpper0","EyeLower0"]):Gt(e,h,"right",["EyeUpper0","EyeLower0"]);let p=Mn(e,f,"left"),k=Mn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},ne=null,Fe=0;async function 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yt(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Nn(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return In.image.cropAndResize(t,r,[0],o)}function Ln(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Vt(e,t=1.5){let o=yt(e),A=Ft(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Zt(e){let t=yt(e),o=Ft(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function B2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Bn(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return B2(o)}var Cn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function me(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function G2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function On(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(me(e[n],G2(t,r)))}return o}function yo(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Cn(t[0],t[1]),s=On(r,n),a=Cn(-t[0],-t[1]);return On(s,a)}function Gn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-me(t[0],o),-me(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function xo(e,t){return[me(e,t[0]),me(e,t[1])]}var 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Object.keys(o).forEach(n=>B.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=B.reshape(t,[-1,7,2]),A.div=B.div(A.reshape,this.inputSizeTensor),A.landmarks=B.add(A.div,this.anchors[o]);let n=B.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>B.dispose(A[r])),n}async predict(t,o){let A={};A.resize=B.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=B.div(A.resize,H.tf127),A.image=B.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=B.squeeze(A.batched),A.slice=B.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=B.sigmoid(A.slice),A.scores=B.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=B.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await B.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=B.slice(A.norm,[a,0],[1,-1]),y.slice=B.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=B.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=Ln(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>B.dispose(y[f]))}return Object.keys(A).forEach(a=>B.dispose(A[a])),s}};var j0=V(F());var V2=5,Vn=1.65,Zn=[0,5,9,13,17,1,2],Z2=0,X2=2,Xn=0,Dt=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let 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A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&l0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},r=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=Y.image.cropAndResize(e,[r],[0],[Pe[1][0],Pe[1][1]],"bilinear"),n.div=Y.div(n.crop,H.tf255),[n.score,n.keypoints]=l0[1].execute(n.div,["Identity_1","Identity"]);let s=(await n.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){A.fingerScore=a,n.reshaped=Y.reshape(n.keypoints,[-1,3]);let i=(await n.reshaped.array()).map(l=>[l[0]/Pe[1][1],l[1]/Pe[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);A.keypoints=i.map(l=>[ae[0]*(l[0]+t.boxRaw[0]),ae[1]*(l[1]+t.boxRaw[1]),l[2]||0]),A.landmarks=qt(A.keypoints);for(let l of Object.keys(nr))A.annotations[l]=nr[l].map(x=>A.landmarks&&A.keypoints[x]?A.keypoints[x]:null)}Object.keys(n).forEach(y=>Y.dispose(n[y]))}return A}async function bo(e,t){var n,r;if(!l0[0]||!l0[1]||!((n=l0[0])!=null&&n.inputs[0].shape)||!((r=l0[1])!=null&&r.inputs[0].shape))return[];ae=[e.shape[2]||0,e.shape[1]||0],Ut++;let o=(t.hand.skipTime||0)>g()-uo,A=Ut<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?o0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>g()-uo,y=Ut<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(i=>ho(e,i,t))):t.skipAllowed&&a&&y&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(i=>ho(e,i,t))):(o0.boxes=await As(e,t),uo=g(),o0.hands=await Promise.all(o0.boxes.map(i=>ho(e,i,t))),Ut=0);let c=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let i=0;i<o0.hands.length;i++){let 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a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Qt[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=te(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(_t)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};wo(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function ko(e,t){if(!W0||!(W0!=null&&W0.inputs[0].shape))return[];t.skipAllowed||(je.boxes.length=0),Ro++;let o=(t.body.skipTime||0)>g()-je.last,A=Ro<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?je.bodies:new Promise(async n=>{let 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h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/o5)),[k,C]=[P-t5/y*p[0],m-t5/y*p[1]],[O,j]=[P+t5/y*p[2]-k,m+t5/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Be[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function zo(e,t){let o=(t.object.skipTime||0)>g()-br,A=Eo<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&e5.length>0?(Eo++,e5):(Eo=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?e5:new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=M0.image.resizeBilinear(e,[o5,o5],!1),a=M0.div(s,H.tf255),y=a.transpose([0,3,1,2]);M0.dispose(a),M0.dispose(s);let c;t.object.enabled&&(c=Je.execute(y)),br=g(),M0.dispose(y);let i=await as(c,r,t);e5=i,n(i)}))}var v0=V(F());var dt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],is=dt.length,ct=dt.reduce((e,t,o)=>(e[t]=o,e),{}),ls=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=ls.map(([e,t])=>[ct[e],ct[t]]),Mr=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function vr(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:r},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(A,a),minX:Math.min(n,s),minY:Math.min(r,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function Tr(e,[t,o],[A,n]){let r=t/A,s=o/n,a=(c,i)=>({id:i,score:c.score,boxRaw:[c.box[0]/n,c.box[1]/A,c.box[2]/n,c.box[3]/A],box:[Math.trunc(c.box[0]*s),Math.trunc(c.box[1]*r),Math.trunc(c.box[2]*s),Math.trunc(c.box[3]*r)],keypoints:c.keypoints.map(({score:l,part:x,position:d})=>({score:l,part:x,position:[Math.trunc(d.x*s),Math.trunc(d.y*r)],positionRaw:[d.x/A,d.y/A]})),annotations:{}});return e.map((c,i)=>a(c,i))}var A5=class{constructor(t,o){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new 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c=a[1]>0?`#${a[1]}`:"",i=`${a[0]} ${c}: ${y[1]}`;A.shadowColor&&A.shadowColor!==""&&(n.fillStyle=A.shadowColor,n.fillText(i,8,2+r*A.lineHeight)),n.fillStyle=A.labelColor,n.fillText(i,6,0+r*A.lineHeight),r+=1}}}}async function l5(e,t,o){var r,s,a,y,c;let A=c0(Y0,o);if(!t||!e)return;let n=Se(e);if(!!n)for(let i of t){if(n.font=A.font,n.strokeStyle=A.color,n.fillStyle=A.color,A.drawBoxes&&mt(n,i.box[0],i.box[1],i.box[2],i.box[3],A),A.drawLabels){let l=[];if(l.push(`face: ${Math.trunc(100*i.score)}%`),i.genderScore&&l.push(`${i.gender||""} ${Math.trunc(100*i.genderScore)}%`),i.age&&l.push(`age: ${i.age||""}`),i.iris&&l.push(`distance: ${i.iris}`),i.real&&l.push(`real: ${Math.trunc(100*i.real)}%`),i.live&&l.push(`live: ${Math.trunc(100*i.live)}%`),i.emotion&&i.emotion.length>0){let x=i.emotion.map(d=>`${Math.trunc(100*d.score)}% ${d.emotion}`);x.length>3&&(x.length=3),l.push(x.join(" "))}i.rotation&&i.rotation.angle&&i.rotation.gaze&&(i.rotation.angle.roll&&l.push(`roll: 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@@ -108,7 +108,7 @@ var Ur=Object.create;var tt=Object.defineProperty;var Jr=Object.getOwnPropertyDe
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Description:"),e.config.async?i=(q=e.config.face.description)!=null&&q.enabled?Ao(x[R].tensor||A0.tensor([]),e.config,R,x.length):null:(e.state="run:description",o=g(),i=(x0=e.config.face.description)!=null&&x0.enabled?await Ao(x[R].tensor||A0.tensor([]),e.config,R,x.length):null,e.performance.description=T.perfadd?(e.performance.description||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End Description:"),e.config.async&&([A,r,s,a,i,n,y,c]=await Promise.all([A,r,s,a,i,n,y,c])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&A&&r&&(i={...i,age:A.age,gender:r.gender,genderScore:r.genderScore}),((C0=e.config.face.gear)==null?void 0:C0.enabled)&&n&&(i={...i,age:n.age,gender:n.gender,genderScore:n.genderScore,race:n.race}),((le=e.config.face.mobilefacenet)==null?void 0:le.enabled)&&a&&(i.descriptor=a),(v=e.config.face.iris)!=null&&v.enabled;let n0=x[R].annotations&&x[R].annotations.leftEyeIris&&x[R].annotations.leftEyeIris[0]&&x[R].annotations.rightEyeIris&&x[R].annotations.rightEyeIris[0]&&x[R].annotations.leftEyeIris.length>0&&x[R].annotations.rightEyeIris.length>0&&x[R].annotations.leftEyeIris[0]!==null&&x[R].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[R].annotations.leftEyeIris[3][0]-x[R].annotations.leftEyeIris[1][0]),Math.abs(x[R].annotations.rightEyeIris[4][1]-x[R].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y0=(a0=e.config.face.detector)!=null&&a0.return?A0.squeeze(x[R].tensor):null;A0.dispose(x[R].tensor),x[R].tensor&&delete x[R].tensor;let I={...x[R],id:R};i!=null&&i.age&&(I.age=i.age),i!=null&&i.gender&&(I.gender=i.gender),i!=null&&i.genderScore&&(I.genderScore=i==null?void 0:i.genderScore),i!=null&&i.descriptor&&(I.embedding=i==null?void 0:i.descriptor),i!=null&&i.race&&(I.race=i==null?void 0:i.race),s&&(I.emotion=s),y&&(I.real=y),c&&(I.live=c),n0&&n0!==0&&(I.iris=Math.trunc(500/n0/11.7)/100),Q&&(I.rotation=Q),y0&&(I.tensor=y0),l.push(I),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Gr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=e[o].keypoints.find(y=>y.part==="leftWrist"),n=e[o].keypoints.find(y=>y.part==="rightWrist"),r=e[o].keypoints.find(y=>y.part==="nose");r&&A&&n&&A.position[1]<r.position[1]&&n.position[1]<r.position[1]?t.push({body:o,gesture:"i give up"}):r&&A&&A.position[1]<r.position[1]?t.push({body:o,gesture:"raise left hand"}):r&&n&&n.position[1]<r.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(y=>y.part==="leftShoulder"),a=e[o].keypoints.find(y=>y.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},Br=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let y=e[o].mesh[152][2]||0;Math.abs(y)>10&&t.push({face:o,gesture:`head ${y<0?"up":"down"}`})}return t},Hr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let A=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],n=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],r=Math.abs(A*n),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],y=Math.abs(s*a),c=!1;Math.abs(r-y)/Math.max(r,y)<.25&&(c=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(c=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let d=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||d<.01||u>.022||d>.022)&&(c=!1),(u<.01||d<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||d>.022)&&t.push({iris:o,gesture:"looking up"}),c&&t.push({iris:o,gesture:"looking center"})}return t},Fr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=[];if(e[o].annotations)for(let[n,r]of Object.entries(e[o].annotations))n!=="palmBase"&&Array.isArray(r)&&r[0]&&A.push({name:n.toLowerCase(),position:r[0]});if(A&&A.length>0){let n=A.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${n.name} forward`});let r=A.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${r.name} up`})}if(e[o].keypoints){let n=Qn(e[o].keypoints);for(let r of n)t.push({hand:o,gesture:r.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},Do=0;function Vr(e,t){var s,a,y,c,i,l,x,d,u,f,M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,le;let o=g();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let A=Date.now()-e.timestamp,n=A<1e3?8-Math.log(A+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let a0=e.body[v].box.map((I,U)=>((n-1)*z.body[v].box[U]+I)/n),R=e.body[v].boxRaw.map((I,U)=>((n-1)*z.body[v].boxRaw[U]+I)/n),Q=e.body[v].keypoints.map((I,U)=>{var 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Jo={};Me(Jo,{distance:()=>mt,match:()=>Uo,similarity:()=>qo});function mt(e,t,o={order:2,multiplier:25}){let A=0;for(let n=0;n<e.length;n++){let r=!o.order||o.order===2?e[n]-t[n]:Math.abs(e[n]-t[n]);A+=!o.order||o.order===2?r*r:r**o.order}return(o.multiplier||20)*A}var Zr=(e,t,o,A)=>{if(e===0)return 1;let n=t===2?Math.sqrt(e):e**(1/t),r=(1-n/100-o)/(A-o);return Math.max(Math.min(r,1),0)};function qo(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let A=mt(e,t,o);return Zr(A,o.order||2,o.min||0,o.max||1)}function Uo(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let A=Number.MAX_SAFE_INTEGER,n=-1;for(let s=0;s<t.length;s++){let a=mt(e,t[s],o);if(a<A&&(A=a,n=s),A<(o.threshold||0))break}let r=Zr(A,o.order||2,o.min||0,o.max||1);return{index:n,distance:A,similarity:r}}function Xr(e,t,o,A,n){var a,y,c,i,l,x,d,u,f,M,h,P,m,p,k,C;let 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_e,ut,ht,m5,Qo=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env");w(this,"draw");w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");At(this,_e,void 0);At(this,ut,void 0);At(this,ht,void 0);w(this,"gl");w(this,"analyze",(...t)=>{if(!ot(this,ut))return;let o=this.tf.engine().state.numTensors,A=ot(this,_e);nt(this,_e,o);let n=o-A;n!==0&&b(...t,n)});At(this,m5,t=>{if(!ot(this,ht))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof D0.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});w(this,"similarity",Jo);w(this,"distance",pt);w(this,"match",Yo);w(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new 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0:J.modelPath)!=null&&Z.includes("handtrack")&&(i=this.config.hand.enabled?bo(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(n=g(),(x0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&x0.includes("handdetect")?i=this.config.hand.enabled?await mo(a.tensor,d):[]:(C0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&C0.includes("handtrack")&&(i=this.config.hand.enabled?await bo(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((le=this.config.object.modelPath)!=null&&le.includes("nanodet")?l=this.config.object.enabled?zo(a.tensor,this.config):[]:(v=this.config.object.modelPath)!=null&&v.includes("centernet")&&(l=this.config.object.enabled?J5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=g(),(a0=this.config.object.modelPath)!=null&&a0.includes("nanodet")?l=this.config.object.enabled?await zo(a.tensor,this.config):[]:(R=this.config.object.modelPath)!=null&&R.includes("centernet")&&(l=this.config.object.enabled?await J5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([y,c,i,l]=await Promise.all([y,c,i,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(n=g(),u=[...Hr(y),...Gr(c),...Vr(i),...Fr(y)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-s):Math.trunc(g()-s);let f=((n0=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:n0.shape)||[];this.result={face:y,body:c,hand:i,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Dr(y,c,i,u,f)}},D0.dispose(a.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};_e=new WeakMap,ut=new WeakMap,ht=new WeakMap,m5=new WeakMap;0&&(module.exports={Human,defaults,draw,env,match,models});
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/human.node.js b/dist/human.node.js
index 8bfc17fe..147d9528 100644
--- a/dist/human.node.js
+++ b/dist/human.node.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
-var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDescriptor;var Jr=Object.getOwnPropertyNames;var Yr=Object.getPrototypeOf,Kr=Object.prototype.hasOwnProperty;var Qr=(e,t,o)=>t in e?tt(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var _r=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Me=(e,t)=>{for(var o in t)tt(e,o,{get:t[o],enumerable:!0})},_o=(e,t,o,A)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of Jr(t))!Kr.call(e,n)&&n!==o&&tt(e,n,{get:()=>t[n],enumerable:!(A=Ur(t,n))||A.enumerable});return e};var V=(e,t,o)=>(o=e!=null?qr(Yr(e)):{},_o(t||!e||!e.__esModule?tt(o,"default",{value:e,enumerable:!0}):o,e)),$r=e=>_o(tt({},"__esModule",{value:!0}),e);var w=(e,t,o)=>(Qr(e,typeof t!="symbol"?t+"":t,o),o),$o=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var ot=(e,t,o)=>($o(e,t,"read from private field"),o?o.call(e):t.get(e)),At=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},nt=(e,t,o,A)=>($o(e,t,"write to private field"),A?A.call(e,o):t.set(e,o),o);var F=_r((Bs,f5)=>{var tA=Object.defineProperty,e2=Object.getOwnPropertyDescriptor,t2=Object.getOwnPropertyNames,o2=Object.prototype.hasOwnProperty,d5=(e,t,o,A)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of t2(t))!o2.call(e,n)&&n!==o&&tA(e,n,{get:()=>t[n],enumerable:!(A=e2(t,n))||A.enumerable});return e},A2=(e,t,o)=>(d5(e,t,"default"),o&&d5(o,t,"default")),n2=e=>d5(tA({},"__esModule",{value:!0}),e),oA={};f5.exports=n2(oA);A2(oA,require("@tensorflow/tfjs-node"),f5.exports)});var Is={};Me(Is,{Human:()=>Dr,default:()=>Dr,defaults:()=>ye,draw:()=>Fo,env:()=>T,match:()=>Uo,models:()=>o5});module.exports=$r(Is);function b(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function eA(e,t){let o=e.endsWith("/")?"":"/",n=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!n.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${n}`);return n}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function c5(e,t,o="config",A=[]){for(let n of Object.keys(t))if(typeof t[n]=="object")c5(e[n],t[n],n,A);else{let r=e&&typeof e[n]!="undefined";r||A.push({reason:"unknown property",where:`${o}.${n} = ${t[n]}`});let s=e&&typeof e[n]==typeof t[n];r&&!s&&A.push({reason:"property type mismatch",where:`${o}.${n} = ${t[n]}`,expected:typeof e[n]})}return t.debug&&o==="config"&&A.length>0&&b("invalid configuration",A),A}function c0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,A)=>(Object.keys(A||{}).forEach(n=>{let r=o[n],s=A[n];Array.isArray(r)&&Array.isArray(s)?o[n]=r.concat(...s):t(r)&&t(s)?o[n]=c0(r,s):o[n]=s}),o),{})}var ye={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var d0=V(F());var W=V(F());var AA=`
+var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDescriptor;var Jr=Object.getOwnPropertyNames;var Yr=Object.getPrototypeOf,Kr=Object.prototype.hasOwnProperty;var Qr=(e,t,o)=>t in e?tt(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var _r=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ve=(e,t)=>{for(var o in t)tt(e,o,{get:t[o],enumerable:!0})},tA=(e,t,o,A)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of Jr(t))!Kr.call(e,n)&&n!==o&&tt(e,n,{get:()=>t[n],enumerable:!(A=Ur(t,n))||A.enumerable});return e};var V=(e,t,o)=>(o=e!=null?qr(Yr(e)):{},tA(t||!e||!e.__esModule?tt(o,"default",{value:e,enumerable:!0}):o,e)),$r=e=>tA(tt({},"__esModule",{value:!0}),e);var w=(e,t,o)=>(Qr(e,typeof t!="symbol"?t+"":t,o),o),oA=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var ot=(e,t,o)=>(oA(e,t,"read from private field"),o?o.call(e):t.get(e)),At=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},nt=(e,t,o,A)=>(oA(e,t,"write to private field"),A?A.call(e,o):t.set(e,o),o);var F=_r((Gs,u5)=>{var nA=Object.defineProperty,e2=Object.getOwnPropertyDescriptor,t2=Object.getOwnPropertyNames,o2=Object.prototype.hasOwnProperty,p5=(e,t,o,A)=>{if(t&&typeof t=="object"||typeof t=="function")for(let n of t2(t))!o2.call(e,n)&&n!==o&&nA(e,n,{get:()=>t[n],enumerable:!(A=e2(t,n))||A.enumerable});return e},A2=(e,t,o)=>(p5(e,t,"default"),o&&p5(o,t,"default")),n2=e=>p5(nA({},"__esModule",{value:!0}),e),rA={};u5.exports=n2(rA);A2(rA,require("@tensorflow/tfjs-node"),u5.exports)});var Is={};ve(Is,{Human:()=>Ko,default:()=>Ko,defaults:()=>ye,draw:()=>Zo,env:()=>T,match:()=>Yo,models:()=>r5});module.exports=$r(Is);function b(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function AA(e,t){let o=e.endsWith("/")?"":"/",n=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!n.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${n}`);return n}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function m5(e,t,o="config",A=[]){for(let n of Object.keys(t))if(typeof t[n]=="object")m5(e[n],t[n],n,A);else{let r=e&&typeof e[n]!="undefined";r||A.push({reason:"unknown property",where:`${o}.${n} = ${t[n]}`});let s=e&&typeof e[n]==typeof t[n];r&&!s&&A.push({reason:"property type mismatch",where:`${o}.${n} = ${t[n]}`,expected:typeof e[n]})}return t.debug&&o==="config"&&A.length>0&&b("invalid configuration",A),A}function c0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,A)=>(Object.keys(A||{}).forEach(n=>{let r=o[n],s=A[n];Array.isArray(r)&&Array.isArray(s)?o[n]=r.concat(...s):t(r)&&t(s)?o[n]=c0(r,s):o[n]=s}),o),{})}var ye={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var d0=V(F());var W=V(F());var sA=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +14,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var nA=`
+`;var aA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +26,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,rA=`
+`,iA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +38,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,sA=`
+`,lA=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +51,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,aA=`
+`,yA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +74,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,iA=`
+`,xA=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,7 +96,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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a=g0.keypoints.map(x=>x.position[0]),y=g0.keypoints.map(x=>x.position[1]);g0.box=[Math.min(...a),Math.min(...y),Math.max(...a)-Math.min(...a),Math.max(...y)-Math.min(...y)];let c=g0.keypoints.map(x=>x.positionRaw[0]),i=g0.keypoints.map(x=>x.positionRaw[1]);g0.boxRaw=[Math.min(...c),Math.min(...i),Math.max(...c)-Math.min(...c),Math.max(...i)-Math.min(...i)];for(let[x,d]of Object.entries(X5)){let u=[];for(let f=0;f<d.length-1;f++){let M=g0.keypoints.find(P=>P.part===d[f]),h=g0.keypoints.find(P=>P.part===d[f+1]);M&&h&&M.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&u.push([M.position,h.position])}g0.annotations[x]=u}n([g0])}))}var G0=V(F());var I2=["angry","disgust","fear","happy","sad","surprise","neutral"],O0,Nt=[],an=0,ln=0,U5=Number.MAX_SAFE_INTEGER;async function yn(e){var t;return T.initial&&(O0=null),O0?e.debug&&b("cached model:",O0.modelUrl):O0=await B((t=e.face.emotion)==null?void 0:t.modelPath),O0}async function J5(e,t,o,A){var s,a;if(!O0)return[];let n=U5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-ln;return t.skipAllowed&&r&&n&&an===A&&Nt[o]&&Nt[o].length>0?(U5++,Nt[o]):(U5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)!=null&&i.enabled){let x={},d=O0!=null&&O0.inputs[0].shape?O0.inputs[0].shape[2]:0;x.resize=G0.image.resizeBilinear(e,[d,d],!1),x.channels=G0.mul(x.resize,H.rgb),x.grayscale=G0.sum(x.channels,3,!0),x.grayscaleSub=G0.sub(x.grayscale,H.tf05),x.grayscaleMul=G0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),ln=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:I2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>G0.dispose(x[f]))}Nt[o]=c,an=A,y(c)}))}var mn=V(F());var E0,Y5=[],cn=0,dn=0,fn=Number.MAX_SAFE_INTEGER;async function pn(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await B(e.face.mobilefacenet.modelPath),E0}async function K5(e,t,o,A){var s,a;if(!E0)return[];let n=fn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-dn;return t.skipAllowed&&r&&n&&cn===A&&Y5[o]?(fn++,Y5[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=mn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}Y5[o]=c,cn=A,dn=g(),y(c)})}var Ve=V(F());var B0=V(F());var Ae,fe=0,N2=2.3,Q5=L0.leftEyeLower0,_5=L0.rightEyeLower0,Be={leftBounds:[Q5[0],Q5[Q5.length-1]],rightBounds:[_5[0],_5[_5.length-1]]},He={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function Pn(e){var t;return T.initial&&(Ae=null),Ae?e.debug&&b("cached model:",Ae.modelUrl):Ae=await 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A=e[L0[`${o}EyeUpper0`][He.upperCenter]][2],n=e[L0[`${o}EyeLower0`][He.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function Mn(e,t,o,A){if(!Ae)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=hn(e,t,Be.leftBounds[0],Be.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=hn(e,t,Be.rightBounds[0],Be.rightBounds[1],A,!0),i=B0.concat([s,c]);B0.dispose(s),B0.dispose(c);let l=Ae.execute(i);B0.dispose(i);let x=await l.data();B0.dispose(l);let d=x.slice(0,He.numCoordinates*3),{rawCoords:u,iris:f}=bn(d,n,r,!0),M=x.slice(He.numCoordinates*3),{rawCoords:h,iris:P}=bn(M,a,y),m=L2(e);Math.abs(m)<30?(Lt(e,u,"left",null),Lt(e,h,"right",null)):m<1?Lt(e,u,"left",["EyeUpper0","EyeLower0"]):Lt(e,h,"right",["EyeUpper0","EyeLower0"]);let p=gn(e,f,"left"),k=gn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},ne=null,Fe=0;async function 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yt(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function On(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return Cn.image.cropAndResize(t,r,[0],o)}function In(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Ht(e,t=1.5){let o=yt(e),A=Bt(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Ft(e){let t=yt(e),o=Bt(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function G2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Nn(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return G2(o)}var Sn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function me(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function B2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function Wn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(me(e[n],B2(t,r)))}return o}function no(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Sn(t[0],t[1]),s=Wn(r,n),a=Sn(-t[0],-t[1]);return Wn(s,a)}function Ln(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-me(t[0],o),-me(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function ro(e,t){return[me(e,t[0]),me(e,t[1])]}var 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Object.keys(o).forEach(n=>G.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=G.reshape(t,[-1,7,2]),A.div=G.div(A.reshape,this.inputSizeTensor),A.landmarks=G.add(A.div,this.anchors[o]);let n=G.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>G.dispose(A[r])),n}async predict(t,o){let A={};A.resize=G.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=G.div(A.resize,H.tf127),A.image=G.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=G.squeeze(A.batched),A.slice=G.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=G.sigmoid(A.slice),A.scores=G.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=G.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await G.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=G.slice(A.norm,[a,0],[1,-1]),y.slice=G.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=G.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=In(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>G.dispose(y[f]))}return Object.keys(A).forEach(a=>G.dispose(A[a])),s}};var j0=V(F());var V2=5,Hn=1.65,Fn=[0,5,9,13,17,1,2],Z2=0,X2=2,Vn=0,ao=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),A=t.map(s=>s[1]),n=[Math.min(...o),Math.min(...A)],r=[Math.max(...o),Math.max(...A)];return{startPoint:n,endPoint:r}}getBoxForPalmLandmarks(t,o){let A=t.map(r=>ro([...r,1],o)),n=this.calculateLandmarksBoundingBox(A);return Ht(Ft(n),V2)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),A=Ht(Ft(o),Hn);A.palmLandmarks=[];for(let n=0;n<Fn.length;n++)A.palmLandmarks.push(t[Fn[n]].slice(0,2));return A}transformRawCoords(t,o,A,n){let r=Bt(o),s=[r[0]/this.inputSize,r[1]/this.inputSize,(r[0]+r[1])/this.inputSize/2],a=t.map(d=>[s[0]*(d[0]-this.inputSize/2),s[1]*(d[1]-this.inputSize/2),s[2]*d[2]]),y=no(A,[0,0]),c=a.map(d=>[...ro(d,y),d[2]]),i=Ln(n),l=[...yt(o),1],x=[me(l,i[0]),me(l,i[1])];return c.map(d=>[Math.trunc(d[0]+x[0]),Math.trunc(d[1]+x[1]),Math.trunc(d[2])])}async estimateHands(t,o){let A=!1,n,r=(o.hand.skipTime||0)>g()-Vn,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&r&&s&&(n=await 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A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&l0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},r=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=Y.image.cropAndResize(e,[r],[0],[ge[1][0],ge[1][1]],"bilinear"),n.div=Y.div(n.crop,H.tf255),[n.score,n.keypoints]=l0[1].execute(n.div,["Identity_1","Identity"]);let s=(await n.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){A.fingerScore=a,n.reshaped=Y.reshape(n.keypoints,[-1,3]);let i=(await n.reshaped.array()).map(l=>[l[0]/ge[1][1],l[1]/ge[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);A.keypoints=i.map(l=>[ae[0]*(l[0]+t.boxRaw[0]),ae[1]*(l[1]+t.boxRaw[1]),l[2]||0]),A.landmarks=Vt(A.keypoints);for(let l of Object.keys(or))A.annotations[l]=or[l].map(x=>A.landmarks&&A.keypoints[x]?A.keypoints[x]:null)}Object.keys(n).forEach(y=>Y.dispose(n[y]))}return A}async function mo(e,t){var n,r;if(!l0[0]||!l0[1]||!((n=l0[0])!=null&&n.inputs[0].shape)||!((r=l0[1])!=null&&r.inputs[0].shape))return[];ae=[e.shape[2]||0,e.shape[1]||0],Zt++;let o=(t.hand.skipTime||0)>g()-co,A=Zt<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?o0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>g()-co,y=Zt<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(i=>fo(e,i,t))):t.skipAllowed&&a&&y&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(i=>fo(e,i,t))):(o0.boxes=await As(e,t),co=g(),o0.hands=await Promise.all(o0.boxes.map(i=>fo(e,i,t))),Zt=0);let c=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let i=0;i<o0.hands.length;i++){let l=JA(o0.hands[i].keypoints,ae);if(l.box[2]/(e.shape[2]||1)>.05&&l.box[3]/(e.shape[1]||1)>.05&&o0.hands[i].fingerScore&&o0.hands[i].fingerScore>(t.hand.minConfidence||0)){let x=Wt(l.box,tr),d=Wt(l.boxRaw,tr);o0.boxes.push({...c[i],box:x,boxRaw:d})}}for(let i=0;i<o0.hands.length;i++){let l=te(o0.hands[i].keypoints,ae);o0.hands[i].box=l.box,o0.hands[i].boxRaw=l.boxRaw}s(o0.hands)})}var qt=V(F());var u0,Dt=[],po=Number.MAX_SAFE_INTEGER,sr=0,ar=0;async function ir(e){var t;return T.initial&&(u0=null),u0?e.debug&&b("cached model:",u0.modelUrl):u0=await B((t=e.face.liveness)==null?void 0:t.modelPath),u0}async function uo(e,t,o,A){var s,a;if(!u0)return 0;let n=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>g()-ar,r=po<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&n&&r&&sr===A&&Dt[o]?(po++,Dt[o]):(po=0,new Promise(async y=>{let 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Ut=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ho=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],bo=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],go=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Jt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var 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a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Ut[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=te(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(Jt)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};Po(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function vo(e,t){if(!W0||!(W0!=null&&W0.inputs[0].shape))return[];t.skipAllowed||(je.boxes.length=0),Mo++;let o=(t.body.skipTime||0)>g()-je.last,A=Mo<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?je.bodies:new Promise(async n=>{let r={};Mo=0,r.input=cr(e,Yt),r.res=W0==null?void 0:W0.execute(r.input),je.last=g();let s=await r.res.array();je.bodies=r.res.shape[2]===17?await rs(s,t,e):await ss(s,t,e);for(let a of je.bodies)dr(a,[e.shape[2]||1,e.shape[1]||1]),xr(a.keypoints);Object.keys(r).forEach(a=>fr.dispose(r[a])),n(je.bodies)})}var M0=V(F());var Je,Kt=[],ur=0,To=Number.MAX_SAFE_INTEGER,_t=0,Qt=2.5;async function hr(e){if(!Je||T.initial){Je=await B(e.object.modelPath);let t=Object.values(Je.modelSignature.inputs);_t=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&b("cached model:",Je.modelUrl);return Je}async function as(e,t,o){let A=0,n=[];for(let y of[1,2,4])M0.tidy(async()=>{let c=y*13,i=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)===Ge.length)),l=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)<Ge.length)),d=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await i.array();for(let f=0;f<i.shape[0];f++)for(let M=0;M<i.shape[1];M++){let h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/_t)),[k,C]=[P-Qt/y*p[0],m-Qt/y*p[1]],[O,j]=[P+Qt/y*p[2]-k,m+Qt/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Ge[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function wo(e,t){let o=(t.object.skipTime||0)>g()-ur,A=To<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Kt.length>0?(To++,Kt):(To=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?Kt:new Promise(async 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dt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],is=dt.length,ct=dt.reduce((e,t,o)=>(e[t]=o,e),{}),ls=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=ls.map(([e,t])=>[ct[e],ct[t]]),gr=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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i=Math.trunc(100*r[0][c][4])/100,l=r[0][c][5],x=Be[l].label,[d,u]=[r[0][c][0]/Re,r[0][c][1]/Re],f=[d,u,r[0][c][2]/Re-d,r[0][c][3]/Re-u],M=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];n.push({id:y++,score:i,class:l,label:x,box:M,boxRaw:f})}return Object.keys(A).forEach(c=>b0.dispose(A[c])),n}async function U5(e,t){let o=(t.object.skipTime||0)>g()-on,A=q5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&D5.length>0?(q5++,D5):(q5=0,new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=b0.image.resizeBilinear(e,[Re,Re]),a=t.object.enabled?oe==null?void 0:oe.execute(s,["tower_0/detections"]):null;on=g(),b0.dispose(s);let y=await C2(a,r,t);D5=y,n(y)}))}var K=V(F());var Nt={};ve(Nt,{connected:()=>Y5,kpt:()=>J5});var 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a=g0.keypoints.map(x=>x.position[0]),y=g0.keypoints.map(x=>x.position[1]);g0.box=[Math.min(...a),Math.min(...y),Math.max(...a)-Math.min(...a),Math.max(...y)-Math.min(...y)];let c=g0.keypoints.map(x=>x.positionRaw[0]),i=g0.keypoints.map(x=>x.positionRaw[1]);g0.boxRaw=[Math.min(...c),Math.min(...i),Math.max(...c)-Math.min(...c),Math.max(...i)-Math.min(...i)];for(let[x,d]of Object.entries(Y5)){let u=[];for(let f=0;f<d.length-1;f++){let M=g0.keypoints.find(P=>P.part===d[f]),h=g0.keypoints.find(P=>P.part===d[f+1]);M&&h&&M.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&u.push([M.position,h.position])}g0.annotations[x]=u}n([g0])}))}var B0=V(F());var I2=["angry","disgust","fear","happy","sad","surprise","neutral"],O0,Lt=[],ln=0,yn=0,_5=Number.MAX_SAFE_INTEGER;async function xn(e){var t;return T.initial&&(O0=null),O0?e.debug&&b("cached model:",O0.modelUrl):O0=await G((t=e.face.emotion)==null?void 0:t.modelPath),O0}async function $5(e,t,o,A){var s,a;if(!O0)return[];let n=_5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-yn;return t.skipAllowed&&r&&n&&ln===A&&Lt[o]&&Lt[o].length>0?(_5++,Lt[o]):(_5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)!=null&&i.enabled){let x={},d=O0!=null&&O0.inputs[0].shape?O0.inputs[0].shape[2]:0;x.resize=B0.image.resizeBilinear(e,[d,d],!1),x.channels=B0.mul(x.resize,H.rgb),x.grayscale=B0.sum(x.channels,3,!0),x.grayscaleSub=B0.sub(x.grayscale,H.tf05),x.grayscaleMul=B0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),yn=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:I2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>B0.dispose(x[f]))}Lt[o]=c,ln=A,y(c)}))}var pn=V(F());var E0,eo=[],dn=0,fn=0,mn=Number.MAX_SAFE_INTEGER;async function un(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await G(e.face.mobilefacenet.modelPath),E0}async function to(e,t,o,A){var s,a;if(!E0)return[];let n=mn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-fn;return t.skipAllowed&&r&&n&&dn===A&&eo[o]?(mn++,eo[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=pn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}eo[o]=c,dn=A,fn=g(),y(c)})}var Ve=V(F());var G0=V(F());var Ae,fe=0,N2=2.3,oo=L0.leftEyeLower0,Ao=L0.rightEyeLower0,Ge={leftBounds:[oo[0],oo[oo.length-1]],rightBounds:[Ao[0],Ao[Ao.length-1]]},He={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function Mn(e){var t;return T.initial&&(Ae=null),Ae?e.debug&&b("cached model:",Ae.modelUrl):Ae=await 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A=e[L0[`${o}EyeUpper0`][He.upperCenter]][2],n=e[L0[`${o}EyeLower0`][He.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function vn(e,t,o,A){if(!Ae)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=bn(e,t,Ge.leftBounds[0],Ge.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=bn(e,t,Ge.rightBounds[0],Ge.rightBounds[1],A,!0),i=G0.concat([s,c]);G0.dispose(s),G0.dispose(c);let l=Ae.execute(i);G0.dispose(i);let x=await l.data();G0.dispose(l);let d=x.slice(0,He.numCoordinates*3),{rawCoords:u,iris:f}=gn(d,n,r,!0),M=x.slice(He.numCoordinates*3),{rawCoords:h,iris:P}=gn(M,a,y),m=L2(e);Math.abs(m)<30?(Bt(e,u,"left",null),Bt(e,h,"right",null)):m<1?Bt(e,u,"left",["EyeUpper0","EyeLower0"]):Bt(e,h,"right",["EyeUpper0","EyeLower0"]);let p=Pn(e,f,"left"),k=Pn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},ne=null,Fe=0;async function wn(e,t){var a,y,c,i,l,x,d,u,f;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>g()-U0.timestamp,A=U0.skipped<(((y=t.face.detector)==null?void 0:y.skipFrames)||0);!t.skipAllowed||!o||!A||U0.boxes.length===0?(U0.boxes=await DA(e,t),U0.timestamp=g(),U0.skipped=0):U0.skipped++;let n=[],r=[],s=0;for(let M=0;M<U0.boxes.length;M++){let h=U0.boxes[M],P=0,m,p={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,m,p.tensor]=HA((c=t.face.detector)==null?void 0:c.rotation,h,e,(i=t.face.mesh)!=null&&i.enabled?Fe:jt()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let k=await bt(p.tensor);Ve.dispose(p.tensor),p.tensor=k}if(p.boxScore=Math.round(100*h.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!ne)t.debug&&b("face mesh detection requested, but model is not loaded");else{let[k,C,O]=ne.execute(p.tensor),j=await C.data();p.faceScore=Math.round(100*j[0])/100;let L=Ve.reshape(O,[-1,3]),J=await 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yt(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function In(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return On.image.cropAndResize(t,r,[0],o)}function Nn(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Ft(e,t=1.5){let o=yt(e),A=Ht(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Vt(e){let t=yt(e),o=Ht(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function B2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Ln(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return B2(o)}var Wn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function me(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function G2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function Cn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(me(e[n],G2(t,r)))}return o}function lo(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Wn(t[0],t[1]),s=Cn(r,n),a=Wn(-t[0],-t[1]);return Cn(s,a)}function Bn(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-me(t[0],o),-me(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function yo(e,t){return[me(e,t[0]),me(e,t[1])]}var 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Object.keys(o).forEach(n=>B.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=B.reshape(t,[-1,7,2]),A.div=B.div(A.reshape,this.inputSizeTensor),A.landmarks=B.add(A.div,this.anchors[o]);let n=B.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>B.dispose(A[r])),n}async predict(t,o){let A={};A.resize=B.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=B.div(A.resize,H.tf127),A.image=B.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=B.squeeze(A.batched),A.slice=B.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=B.sigmoid(A.slice),A.scores=B.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=B.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await B.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=B.slice(A.norm,[a,0],[1,-1]),y.slice=B.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=B.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=Nn(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>B.dispose(y[f]))}return Object.keys(A).forEach(a=>B.dispose(A[a])),s}};var j0=V(F());var V2=5,Fn=1.65,Vn=[0,5,9,13,17,1,2],Z2=0,X2=2,Zn=0,Xt=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),A=t.map(s=>s[1]),n=[Math.min(...o),Math.min(...A)],r=[Math.max(...o),Math.max(...A)];return{startPoint:n,endPoint:r}}getBoxForPalmLandmarks(t,o){let A=t.map(r=>yo([...r,1],o)),n=this.calculateLandmarksBoundingBox(A);return Ft(Vt(n),V2)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),A=Ft(Vt(o),Fn);A.palmLandmarks=[];for(let n=0;n<Vn.length;n++)A.palmLandmarks.push(t[Vn[n]].slice(0,2));return A}transformRawCoords(t,o,A,n){let r=Ht(o),s=[r[0]/this.inputSize,r[1]/this.inputSize,(r[0]+r[1])/this.inputSize/2],a=t.map(d=>[s[0]*(d[0]-this.inputSize/2),s[1]*(d[1]-this.inputSize/2),s[2]*d[2]]),y=lo(A,[0,0]),c=a.map(d=>[...yo(d,y),d[2]]),i=Bn(n),l=[...yt(o),1],x=[me(l,i[0]),me(l,i[1])];return c.map(d=>[Math.trunc(d[0]+x[0]),Math.trunc(d[1]+x[1]),Math.trunc(d[2])])}async estimateHands(t,o){let A=!1,n,r=(o.hand.skipTime||0)>g()-Zn,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&r&&s&&(n=await 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A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&l0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},r=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=Y.image.cropAndResize(e,[r],[0],[Pe[1][0],Pe[1][1]],"bilinear"),n.div=Y.div(n.crop,H.tf255),[n.score,n.keypoints]=l0[1].execute(n.div,["Identity_1","Identity"]);let s=(await n.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){A.fingerScore=a,n.reshaped=Y.reshape(n.keypoints,[-1,3]);let i=(await n.reshaped.array()).map(l=>[l[0]/Pe[1][1],l[1]/Pe[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);A.keypoints=i.map(l=>[ae[0]*(l[0]+t.boxRaw[0]),ae[1]*(l[1]+t.boxRaw[1]),l[2]||0]),A.landmarks=Dt(A.keypoints);for(let l of Object.keys(Ar))A.annotations[l]=Ar[l].map(x=>A.landmarks&&A.keypoints[x]?A.keypoints[x]:null)}Object.keys(n).forEach(y=>Y.dispose(n[y]))}return A}async function ho(e,t){var n,r;if(!l0[0]||!l0[1]||!((n=l0[0])!=null&&n.inputs[0].shape)||!((r=l0[1])!=null&&r.inputs[0].shape))return[];ae=[e.shape[2]||0,e.shape[1]||0],qt++;let o=(t.hand.skipTime||0)>g()-po,A=qt<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?o0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>g()-po,y=qt<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(i=>uo(e,i,t))):t.skipAllowed&&a&&y&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(i=>uo(e,i,t))):(o0.boxes=await As(e,t),po=g(),o0.hands=await Promise.all(o0.boxes.map(i=>uo(e,i,t))),qt=0);let c=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let i=0;i<o0.hands.length;i++){let 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dt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],is=dt.length,ct=dt.reduce((e,t,o)=>(e[t]=o,e),{}),ls=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=ls.map(([e,t])=>[ct[e],ct[t]]),Pr=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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c=a[1]>0?`#${a[1]}`:"",i=`${a[0]} ${c}: ${y[1]}`;A.shadowColor&&A.shadowColor!==""&&(n.fillStyle=A.shadowColor,n.fillText(i,8,2+r*A.lineHeight)),n.fillStyle=A.labelColor,n.fillText(i,6,0+r*A.lineHeight),r+=1}}}}async function i5(e,t,o){var r,s,a,y,c;let A=c0(Y0,o);if(!t||!e)return;let n=Se(e);if(!!n)for(let i of t){if(n.font=A.font,n.strokeStyle=A.color,n.fillStyle=A.color,A.drawBoxes&&mt(n,i.box[0],i.box[1],i.box[2],i.box[3],A),A.drawLabels){let l=[];if(l.push(`face: ${Math.trunc(100*i.score)}%`),i.genderScore&&l.push(`${i.gender||""} ${Math.trunc(100*i.genderScore)}%`),i.age&&l.push(`age: ${i.age||""}`),i.iris&&l.push(`distance: ${i.iris}`),i.real&&l.push(`real: ${Math.trunc(100*i.real)}%`),i.live&&l.push(`live: ${Math.trunc(100*i.live)}%`),i.emotion&&i.emotion.length>0){let x=i.emotion.map(d=>`${Math.trunc(100*d.score)}% ${d.emotion}`);x.length>3&&(x.length=3),l.push(x.join(" "))}i.rotation&&i.rotation.angle&&i.rotation.gaze&&(i.rotation.angle.roll&&l.push(`roll: 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l=Math.abs(i.annotations.leftEyeIris[3][0]-i.annotations.leftEyeIris[1][0])/2,x=Math.abs(i.annotations.leftEyeIris[4][1]-i.annotations.leftEyeIris[2][1])/2;n.ellipse(i.annotations.leftEyeIris[0][0],i.annotations.leftEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(i.annotations&&i.annotations.rightEyeIris&&i.annotations.rightEyeIris[0]){n.strokeStyle=A.useDepth?"rgba(255, 200, 255, 0.3)":A.color,n.beginPath();let l=Math.abs(i.annotations.rightEyeIris[3][0]-i.annotations.rightEyeIris[1][0])/2,x=Math.abs(i.annotations.rightEyeIris[4][1]-i.annotations.rightEyeIris[2][1])/2;n.ellipse(i.annotations.rightEyeIris[0][0],i.annotations.rightEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(A.drawGaze&&((r=i.rotation)==null?void 0:r.angle)&&typeof Path2D!="undefined"){n.strokeStyle="pink";let 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             C
               ${l} ${i.box[1]},
@@ -108,7 +108,7 @@ var qr=Object.create;var tt=Object.defineProperty;var Ur=Object.getOwnPropertyDe
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Description:"),e.config.async?i=(q=e.config.face.description)!=null&&q.enabled?oo(x[R].tensor||A0.tensor([]),e.config,R,x.length):null:(e.state="run:description",o=g(),i=(x0=e.config.face.description)!=null&&x0.enabled?await oo(x[R].tensor||A0.tensor([]),e.config,R,x.length):null,e.performance.description=T.perfadd?(e.performance.description||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End Description:"),e.config.async&&([A,r,s,a,i,n,y,c]=await Promise.all([A,r,s,a,i,n,y,c])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&A&&r&&(i={...i,age:A.age,gender:r.gender,genderScore:r.genderScore}),((C0=e.config.face.gear)==null?void 0:C0.enabled)&&n&&(i={...i,age:n.age,gender:n.gender,genderScore:n.genderScore,race:n.race}),((le=e.config.face.mobilefacenet)==null?void 0:le.enabled)&&a&&(i.descriptor=a),(v=e.config.face.iris)!=null&&v.enabled;let n0=x[R].annotations&&x[R].annotations.leftEyeIris&&x[R].annotations.leftEyeIris[0]&&x[R].annotations.rightEyeIris&&x[R].annotations.rightEyeIris[0]&&x[R].annotations.leftEyeIris.length>0&&x[R].annotations.rightEyeIris.length>0&&x[R].annotations.leftEyeIris[0]!==null&&x[R].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[R].annotations.leftEyeIris[3][0]-x[R].annotations.leftEyeIris[1][0]),Math.abs(x[R].annotations.rightEyeIris[4][1]-x[R].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y0=(a0=e.config.face.detector)!=null&&a0.return?A0.squeeze(x[R].tensor):null;A0.dispose(x[R].tensor),x[R].tensor&&delete x[R].tensor;let I={...x[R],id:R};i!=null&&i.age&&(I.age=i.age),i!=null&&i.gender&&(I.gender=i.gender),i!=null&&i.genderScore&&(I.genderScore=i==null?void 0:i.genderScore),i!=null&&i.descriptor&&(I.embedding=i==null?void 0:i.descriptor),i!=null&&i.race&&(I.race=i==null?void 0:i.race),s&&(I.emotion=s),y&&(I.real=y),c&&(I.live=c),n0&&n0!==0&&(I.iris=Math.trunc(500/n0/11.7)/100),Q&&(I.rotation=Q),y0&&(I.tensor=y0),l.push(I),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Lr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=e[o].keypoints.find(y=>y.part==="leftWrist"),n=e[o].keypoints.find(y=>y.part==="rightWrist"),r=e[o].keypoints.find(y=>y.part==="nose");r&&A&&n&&A.position[1]<r.position[1]&&n.position[1]<r.position[1]?t.push({body:o,gesture:"i give up"}):r&&A&&A.position[1]<r.position[1]?t.push({body:o,gesture:"raise left hand"}):r&&n&&n.position[1]<r.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(y=>y.part==="leftShoulder"),a=e[o].keypoints.find(y=>y.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},Gr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let y=e[o].mesh[152][2]||0;Math.abs(y)>10&&t.push({face:o,gesture:`head ${y<0?"up":"down"}`})}return t},Br=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let A=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],n=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],r=Math.abs(A*n),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],y=Math.abs(s*a),c=!1;Math.abs(r-y)/Math.max(r,y)<.25&&(c=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(c=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let d=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||d<.01||u>.022||d>.022)&&(c=!1),(u<.01||d<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||d>.022)&&t.push({iris:o,gesture:"looking up"}),c&&t.push({iris:o,gesture:"looking center"})}return t},Hr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=[];if(e[o].annotations)for(let[n,r]of Object.entries(e[o].annotations))n!=="palmBase"&&Array.isArray(r)&&r[0]&&A.push({name:n.toLowerCase(),position:r[0]});if(A&&A.length>0){let n=A.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${n.name} forward`});let r=A.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${r.name} up`})}if(e[o].keypoints){let n=Kn(e[o].keypoints);for(let r of n)t.push({hand:o,gesture:r.name})}}return t};var 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v=0;v<e.face.length;v++){let a0=e.face[v].box.map((Q,n0)=>((n-1)*z.face[v].box[n0]+Q)/n),R=e.face[v].boxRaw.map((Q,n0)=>((n-1)*z.face[v].boxRaw[n0]+Q)/n);if(e.face[v].rotation){let Q={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Q.matrix=(x=e.face[v].rotation)==null?void 0:x.matrix,Q.angle={roll:((n-1)*(((u=(d=z.face[v].rotation)==null?void 0:d.angle)==null?void 0:u.roll)||0)+(((M=(f=e.face[v].rotation)==null?void 0:f.angle)==null?void 0:M.roll)||0))/n,yaw:((n-1)*(((P=(h=z.face[v].rotation)==null?void 0:h.angle)==null?void 0:P.yaw)||0)+(((p=(m=e.face[v].rotation)==null?void 0:m.angle)==null?void 0:p.yaw)||0))/n,pitch:((n-1)*(((C=(k=z.face[v].rotation)==null?void 0:k.angle)==null?void 0:C.pitch)||0)+(((j=(O=e.face[v].rotation)==null?void 0:O.angle)==null?void 0:j.pitch)||0))/n},Q.gaze={bearing:((n-1)*(((J=(L=z.face[v].rotation)==null?void 0:L.gaze)==null?void 0:J.bearing)||0)+(((q=(Z=e.face[v].rotation)==null?void 0:Z.gaze)==null?void 0:q.bearing)||0))/n,strength:((n-1)*(((E=(x0=z.face[v].rotation)==null?void 0:x0.gaze)==null?void 0:E.strength)||0)+(((le=(C0=e.face[v].rotation)==null?void 0:C0.gaze)==null?void 0:le.strength)||0))/n},z.face[v]={...e.face[v],rotation:Q,box:a0,boxRaw:R}}z.face[v]={...e.face[v],box:a0,boxRaw:R}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let v=0;v<e.object.length;v++){let a0=e.object[v].box.map((Q,n0)=>((n-1)*z.object[v].box[n0]+Q)/n),R=e.object[v].boxRaw.map((Q,n0)=>((n-1)*z.object[v].boxRaw[n0]+Q)/n);z.object[v]={...e.object[v],box:a0,boxRaw:R}}if(e.persons){let v=e.persons;if(!z.persons||v.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(v));else for(let a0=0;a0<v.length;a0++)z.persons[a0].box=v[a0].box.map((R,Q)=>((n-1)*z.persons[a0].box[Q]+R)/n)}e.gesture&&(z.gesture=e.gesture);let r=g();return Xo=T.perfadd?Xo+Math.round(r-o):Math.round(r-o),e.performance&&(z.performance={...e.performance,interpolate:Xo}),z}var Uo={};Me(Uo,{distance:()=>mt,match:()=>qo,similarity:()=>Do});function mt(e,t,o={order:2,multiplier:25}){let A=0;for(let n=0;n<e.length;n++){let r=!o.order||o.order===2?e[n]-t[n]:Math.abs(e[n]-t[n]);A+=!o.order||o.order===2?r*r:r**o.order}return(o.multiplier||20)*A}var Vr=(e,t,o,A)=>{if(e===0)return 1;let n=t===2?Math.sqrt(e):e**(1/t),r=(1-n/100-o)/(A-o);return Math.max(Math.min(r,1),0)};function Do(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let A=mt(e,t,o);return Vr(A,o.order||2,o.min||0,o.max||1)}function qo(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let A=Number.MAX_SAFE_INTEGER,n=-1;for(let s=0;s<t.length;s++){let a=mt(e,t[s],o);if(a<A&&(A=a,n=s),A<(o.threshold||0))break}let r=Vr(A,o.order||2,o.min||0,o.max||1);return{index:n,distance:A,similarity:r}}function Zr(e,t,o,A,n){var a,y,c,i,l,x,d,u,f,M,h,P,m,p,k,C;let 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_e,ut,ht,f5,Ko=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env");w(this,"draw");w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");At(this,_e,void 0);At(this,ut,void 0);At(this,ht,void 0);w(this,"gl");w(this,"analyze",(...t)=>{if(!ot(this,ut))return;let o=this.tf.engine().state.numTensors,A=ot(this,_e);nt(this,_e,o);let n=o-A;n!==0&&b(...t,n)});At(this,f5,t=>{if(!ot(this,ht))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof D0.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});w(this,"similarity",Uo);w(this,"distance",pt);w(this,"match",Jo);w(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new 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0:n0.shape)||[];this.result={face:y,body:c,hand:i,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Xr(y,c,i,u,f)}},D0.dispose(a.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};_e=new WeakMap,ut=new WeakMap,ht=new WeakMap,f5=new WeakMap;0&&(module.exports={Human,defaults,draw,env,match,models});
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
index d2581631..0b56b416 100644
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@@ -4,63 +4,63 @@
   author: <https://github.com/vladmandic>'
 */
 
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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new 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s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Of(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Of(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Zf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:T,dtype:N}=x;return this.makeTensorFromDataId(k,T,N)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Of(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=nx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Xf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof gd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Xf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of 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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return tm.nextTensorId++}nextVariableId(){return tm.nextVariableId++}clone(e){let t=M.runKernel(Ba,{x:e}),n={x:e},s=a=>({x:()=>{let i="float32",o={x:a},u={dtype:i};return M.runKernel(Na,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Zf(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Of(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Of(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Zf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:T,dtype:N}=x;return this.makeTensorFromDataId(k,T,N)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Of(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=nx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&ir(e[0])&&(r=e.map(o=>Dl(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n==="string"){let o=this.state.tensorInfo.get(a),u=Xw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new gd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Xf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof gd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Xf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=nx(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=Hd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Rg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(O(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));O(r instanceof et,()=>"The result y returned by f() must be a tensor.");let a=H$(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[r.id]=n==null?r_(r.shape):n,q$(i,a,u=>this.tidy(u),a_);let o=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let l of u.saved)l.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return O(hr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{O(t.every(i=>i instanceof et),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,s={};t.forEach((i,o)=>{s[o]=i});let r=(i,o)=>(n=e(...t,o),O(n.value instanceof et,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),O(hr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(i,o)=>{let u=n.gradFunc(i,o),l=Array.isArray(u)?u:[u];O(l.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),O(l.every(p=>p instanceof et),()=>"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 c={};return l.forEach((p,d)=>{c[d]=()=>p}),c};return this.runKernelFunc({forwardFunc:r,backwardsFunc:a,inputs:s})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Uu(),n=await this.backend.time(e);return n.wallMs=Uu()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new ix;for(let e in 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2 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),ju(i,o,u,c,l,a)}var $E=L({batchNorm2d_:TE});function _E(e,t,n,s,r,a){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),O(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),O(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),ju(i,o,u,c,l,a)}var AE=L({batchNorm3d_:_E});function 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BO=L({stringToHashBucketFast_:LO}),kpe={fft:lb,ifft:Id,rfft:cb,irfft:xI},Ipe={hammingWindow:mF,hannWindow:AI,frame:EI,stft:vF},qn={flipLeftRight:IF,grayscaleToRGB:CF,resizeNearestNeighbor:XF,resizeBilinear:jF,rotateWithOffset:TF,cropAndResize:wF,nonMaxSuppression:_F,nonMaxSuppressionAsync:zF,nonMaxSuppressionWithScore:LF,nonMaxSuppressionWithScoreAsync:VF,nonMaxSuppressionPadded:UF,nonMaxSuppressionPaddedAsync:HF,threshold:ZF,transform:eO},VO={bandPart:nO,gramSchmidt:rO,qr:iO},Spe={absoluteDifference:cO,computeWeightedLoss:Ys,cosineDistance:pO,hingeLoss:fO,huberLoss:gO,logLoss:yO,meanSquaredError:xO,sigmoidCrossEntropy:IO,softmaxCrossEntropy:NO},Wc={sparseFillEmptyRows:$O,sparseReshape:AO,sparseSegmentMean:RO,sparseSegmentSum:FO},Mf={stringNGrams:PO,stringSplit:MO,stringToHashBucketFast:BO},_r=class extends Vk{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(a)}else 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_r{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>je(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;j(()=>{let l=ie(V(o,this.rho),V(ct(i),1-this.rho)),c=V(xe(dn(ie(u,this.epsilon)),dn(ie(o,this.epsilon))),i),p=ie(V(u,this.rho),V(ct(c),1-this.rho));o.assign(l),u.assign(p);let d=ie(V(c,-this.learningRate),r);r.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Re(this.accumulatedGrads.map(e=>e.variable)),Re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};fb.className="Adadelta";Tr(fb);var mb=class extends _r{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>Ol(r.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[s].variable;j(()=>{let o=ie(i,ct(a));i.assign(o);let u=ie(V(xe(a,dn(ie(o,M.backend.epsilon()))),-this.learningRate),r);r.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Re(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};mb.className="Adagrad";Tr(mb);var gb=class extends _r{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=we(t).variable(),this.accBeta2=we(n).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>je(i).variable(o))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>je(i).variable(o))});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=ie(V(c,this.beta2),V(ct(u),1-this.beta2)),h=xe(p,n),f=xe(d,s);l.assign(p),c.assign(d);let m=ie(V(xe(h,ie(dn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(pa(this.beta1,this.iterations_+1)),this.accBeta2.assign(pa(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};gb.className="Adam";Tr(gb);var bb=class extends _r{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=$r(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};bb.className="Adamax";Tr(bb);var Ip=class extends _r{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};Ip.className="SGD";Tr(Ip);var yb=class extends Ip{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=we(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulations[s]==null&&(this.accumulations[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(!1))});let a=this.accumulations[s].variable,i=Array.isArray(e)?e[s].tensor:e[n];i!=null&&j(()=>{let o,u=ie(V(this.m,a),i);this.useNesterov?o=ie(V(this.c,ie(i,V(u,this.m))),r):o=ie(V(this.c,u),r),a.assign(u),r.assign(o)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Re(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};yb.className="Momentum";Tr(yb);var vb=class extends _r{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),dn(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),dn(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};vb.className="RMSProp";Tr(vb);var Wr=class{static sgd(e){return new Ip(e)}static momentum(e,t,n=!1){return new yb(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new vb(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new gb(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new fb(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new bb(e,t,n,s,r)}static adagrad(e,t=.1){return new mb(e,t)}},Pi={sgd:Wr.sgd,momentum:Wr.momentum,adadelta:Wr.adadelta,adagrad:Wr.adagrad,rmsprop:Wr.rmsprop,adamax:Wr.adamax,adam:Wr.adam},WO=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function OI(){return new Promise(e=>WO(()=>e()))}var 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UO(e,t){let n=e[0].length;e.forEach((r,a)=>{O(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),O(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let i=0;i<n;i++)O(i===t||r[i]===s[i],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function GO(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var xb=30;function HO(e){return e<=xb?e:hd(e,Math.floor(Math.sqrt(e)))}function qO(e,t,n){let s=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[s,r]}function jO(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let i=0;i<a;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(a+1))}return r}function KO(e,t,n=!0){let s=[];if(n){s.push(t);for(let 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BO=L({stringToHashBucketFast_:LO}),Ipe={fft:lb,ifft:Id,rfft:cb,irfft:xI},Spe={hammingWindow:mF,hannWindow:AI,frame:EI,stft:vF},qn={flipLeftRight:IF,grayscaleToRGB:CF,resizeNearestNeighbor:XF,resizeBilinear:jF,rotateWithOffset:TF,cropAndResize:wF,nonMaxSuppression:_F,nonMaxSuppressionAsync:zF,nonMaxSuppressionWithScore:LF,nonMaxSuppressionWithScoreAsync:VF,nonMaxSuppressionPadded:UF,nonMaxSuppressionPaddedAsync:HF,threshold:ZF,transform:eO},VO={bandPart:nO,gramSchmidt:rO,qr:iO},Cpe={absoluteDifference:cO,computeWeightedLoss:Ys,cosineDistance:pO,hingeLoss:fO,huberLoss:gO,logLoss:yO,meanSquaredError:xO,sigmoidCrossEntropy:IO,softmaxCrossEntropy:NO},Wc={sparseFillEmptyRows:$O,sparseReshape:AO,sparseSegmentMean:RO,sparseSegmentSum:FO},Mf={stringNGrams:PO,stringSplit:MO,stringToHashBucketFast:BO},_r=class extends Vk{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(a)}else this.applyGradients(r);return Re(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return XR(e,t)}dispose(){this.iterations_!=null&&Re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:we(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(_r,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var fb=class extends _r{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>je(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;j(()=>{let l=ie(V(o,this.rho),V(ct(i),1-this.rho)),c=V(xe(dn(ie(u,this.epsilon)),dn(ie(o,this.epsilon))),i),p=ie(V(u,this.rho),V(ct(c),1-this.rho));o.assign(l),u.assign(p);let d=ie(V(c,-this.learningRate),r);r.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Re(this.accumulatedGrads.map(e=>e.variable)),Re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};fb.className="Adadelta";Tr(fb);var mb=class extends _r{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>Pl(r.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[s].variable;j(()=>{let o=ie(i,ct(a));i.assign(o);let u=ie(V(xe(a,dn(ie(o,M.backend.epsilon()))),-this.learningRate),r);r.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Re(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};mb.className="Adagrad";Tr(mb);var gb=class extends _r{constructor(e,t,n,s=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=we(t).variable(),this.accBeta2=we(n).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>je(i).variable(o))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>je(i).variable(o))});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=ie(V(c,this.beta2),V(ct(u),1-this.beta2)),h=xe(p,n),f=xe(d,s);l.assign(p),c.assign(d);let m=ie(V(xe(h,ie(dn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(pa(this.beta1,this.iterations_+1)),this.accBeta2.assign(pa(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};gb.className="Adam";Tr(gb);var bb=class extends _r{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=$r(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};bb.className="Adamax";Tr(bb);var Ip=class extends _r{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};yb.className="Momentum";Tr(yb);var vb=class extends _r{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),dn(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),dn(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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this.assertNotDisposed(),EL(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function EL(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function vm(e){return e.map(t=>t.read())}function Pb(e){e.forEach(t=>{t[0].write(t[1])})}var Dt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},$s=class{constructor(e,t,n,s,r,a,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=i,this.id=YI(),a!=null&&(this.originalName=HI(a),this.name=qI(this.originalName)),this.rank=t.length}},RL=0,Fp=class{constructor(e,t){this.callArgs=t,this.id=RL++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},DL=0,Ge=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=DL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+"_"+Dp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new hs(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return bn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return bn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return ea(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=FL(e),i=this.computeOutputShape(a),o,u=OL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`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 hs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Cd(this.weights)}build(e){this.built=!0}getWeights(e=!1){return vm(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=vm(t);for(let r=0;r<s.length;++r){let a=s[r],i=t[r],o=e[r];if(!w.arraysEqual(a.shape,o.shape))throw new U(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Pb(n)})}addWeight(e,t,n,s,r,a,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new U(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=o!=null?o():ht("zeros"));let u=s.apply(t,n),l=new AL(u,n,e,a,i);return u.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),a==null&&(a=!0),a?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=dt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,i=null){let o=dt(e);t=dt(t),n=dt(n),s=dt(s),r=Sd(r),a=Sd(a);let u=[],l=[],c=[];for(let p of o)u.push(p.sourceLayer),l.push(p.nodeIndex),c.push(p.tensorIndex);new Fp({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function FL(e){e=dt(e);let t=[];for(let n of e)t.push(n.shape);return bn(t)}function OL(e){return"float32"}function QI(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let i=s.inputTensors[a],o=s.inboundLayers[a],u=s.nodeIndices[a],l=QI(i,o,u);for(let c of l)r.indexOf(c)===-1&&r.push(c)}return r}}}var Yo=class extends Ge{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Dp("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new U("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new U("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new U("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new $s(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new U(`Cannot pass any input to an InputLayer's apply() method. 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function mB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Dp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(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 y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,"input layer has >1 nodes"),Cs(x===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Yo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.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 t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,T)=>{(x==null||k==null||T==null)&&(x=b.sourceLayer,k=b.nodeIndex,T=b.tensorIndex);let N=x.inboundNodes[k];if(v.indexOf(N)!==-1)throw new hs(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(N)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(N)===-1&&v.push(N);let E=N.inboundLayers.length;for(let A=0;A<E;A++){let P=N.inputTensors[A],R=N.inboundLayers[A],F=N.nodeIndices[A],$=N.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(N);v.indexOf(N)>=0;)v.splice(v.indexOf(N),1);i.push(N)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],T=b.nodeIndices[x],N=k.inboundNodes[T],E=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(y+1,E),n[N.id]=N}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Uc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],T=a[x.id];return k<T?-1:k>T?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Uc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new hs(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new hs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Pb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${uS}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=wm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Qr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return _u(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=fa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=fa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let N=r[x];if(N.inboundNodes.length<=k){i(m,g);return}let E=N.inboundNodes[k];b.push(E.outputTensors[T])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=ms(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!BM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function lS(e,t){return gB(e,t,"classWeight")}async function cS(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Gu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Zt(i,"float32")}else return null}function bB(e,t){return V(e,t)}var yB=32;function dS(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=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 a=Px("input",e.inputNames,n),i=Px("output",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function Px(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function vB(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function xB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. 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Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Td?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Lx(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Lx(t,this.feedOutputNames,r,!1,"target"),TB(e,t,null),$B(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[i,o]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let u=null;if(s!=null){let l=lS(s,this.outputNames);u=[];for(let c=0;c<l.length;++c)u.push(await cS(o[c],null,l[c]))}return[i,o,u]}testLoop(e,t,n,s=0,r){return j(()=>{let a=this.checkNumSamples(t,n,r,"steps"),i=[];if(s>0)throw new Fe("Verbose mode is not implemented yet.");if(r!=null)throw new Fe("steps mode in testLoop() is not implemented yet");{let o=Im(a,n),u=Zt(ys(0,a));for(let l=0;l<o.length;++l){let c=o[l][0],p=o[l][1],d=ta(u,c,p-c),h=Wb(t,d),f=e(h);if(l===0)for(let m=0;m<f.length;++m)i.push(we(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=ie(i[m],V(p-c,g))}}for(let l=0;l<i.length;++l)i[l]=xe(i[l],a)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;Sx(e,s)>1&&(r+=`_${Sx(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let p=new Qr(c),d=_u(this.outputs,p,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],d[f]);r[f]!=null&&(g=bB(g,r[f]));let b=St(g);t.push(b),f===0?h=g:h=ie(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],b=this.metricsTensors[f][1];m=St(g(s[b],d[b]))}Ht(m),a.push(m)}return h=St(h),this.calculateLosses().forEach(f=>{h=ie(h,f)}),h},o=this.collectedTrainableWeights.map(c=>c.read()),u=!0;return[this.optimizer_.minimize(i,u,o)].concat(a)}}makeTestFunction(){this.testFunction=e=>j(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Qr(a),o=_u(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=St(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=St(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return CB(this,e,t,n)}async fitDataset(e,t){return xB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return Re(i),ds(n[0],e),ds(n[1],t),bn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=dm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-dm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=Ws(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Ws(qc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(qc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(qc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Qu(e.optimizer_config),n=ms(t),s;if(typeof e.loss=="string")s=Kr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>Kr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=Kr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>Kr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Kr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=En.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await En.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:AB,generatedBy:`TensorFlow.js tfjs-layers v${uS}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:l,specs:c}=await En.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=En.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Dx(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Dx(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className="Model";ae.registerClass(pr);var hS=class extends pr{};hS.className="Functional";ae.registerClass(hS);async function EB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Qu(n),r=ms(s,t);if(e.weightsManifest!=null){let a=await En.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function RB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return DB(e,void 0,t)}async function DB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=ms(Qu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:c}=FB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function FB(e,t){let n=En.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Cm=class extends pr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Cm||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("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 U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=ZI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=QI(this.outputs[0])}this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:fa(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Cm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=ms(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Ub=Cm;Ub.className="Sequential";ae.registerClass(Ub);function Cpe(e){return new pr(e)}function Npe(e){return new Ub(e)}function Tpe(e,t){return t==null&&(t={}),RB(e,t)}function OB(e){return ZI(e)}function $pe(e,t){zb.registerCallbackConstructor(e,t)}var In=class extends ae.Serializable{getConfig(){return{}}},fS=class extends In{apply(e,t=1){return aL(e,t)}};fS.className="elu";ae.registerClass(fS);var mS=class extends In{apply(e){return gI(e)}};mS.className="selu";ae.registerClass(mS);var gS=class extends In{apply(e){return Xs(e)}};gS.className="relu";ae.registerClass(gS);var bS=class extends In{apply(e){return j(()=>bp(6,Xs(e)))}};bS.className="relu6";ae.registerClass(bS);var yS=class extends In{apply(e){return e}};yS.className="linear";ae.registerClass(yS);var vS=class extends In{apply(e){return qs(e)}};vS.className="sigmoid";ae.registerClass(vS);var xS=class extends In{apply(e){return oL(e)}};xS.className="hardSigmoid";ae.registerClass(xS);var wS=class extends In{apply(e){return Pl(e)}};wS.className="softplus";ae.registerClass(wS);var kS=class extends In{apply(e){return iL(e)}};kS.className="softsign";ae.registerClass(kS);var IS=class extends In{apply(e){return Hu(e)}};IS.className="tanh";ae.registerClass(IS);var Gb=class extends In{apply(e,t=-1){return ub(e,t)}};Gb.className="softmax";ae.registerClass(Gb);var SS=class extends In{apply(e,t=-1){return iI(e,t)}};SS.className="logSoftmax";ae.registerClass(SS);var CS=class extends In{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};CS.className="swish";ae.registerClass(CS);var NS=class extends In{apply(e){return j(()=>V(e,Hu(Pl(e))))}};NS.className="mish";ae.registerClass(NS);function br(e){return e.getClassName()}function Gf(e,t={}){return Ml(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function yr(e){if(e==null){let t={};return t.className="linear",t.config={},Gf(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Gf(t)}else return e instanceof In?e:Gf(e)}function Hb(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var TS=class extends ae.Serializable{},Ul=class extends TS{constructor(e){super();Hb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Vl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ul.className="L1L2";ae.registerClass(Ul);function PB(e){return Hb(e),new Ul({l1:e!=null?e.l1:null,l2:0})}function zB(e){return Hb(e),new Ul({l2:e!=null?e.l2:null,l1:0})}var Vx={l1l2:"L1L2"};function it(e){return wb(e)}function Wx(e,t={}){return Ml(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Vx?Vx[e]:e,config:{}};return Wx(n)}else return e instanceof TS?e:Wx(e)}var qb=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Vn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};qb.className="ReLU";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Xg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jb.className="LeakyReLU";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),sb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:it(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Kb.className="PReLU";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return fp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Xb.className="ELU";ae.registerClass(Xb);var Yb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Yb.className="ThresholdedReLU";ae.registerClass(Yb);var Qb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Qb.className="Softmax";ae.registerClass(Qb);function Xi(e,t,n){if(typeof e=="number")return fa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!tL(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(a==="channelsFirst"&&(e=qe(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Zk(e,t,s,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ws(o,n)),o})}function Ux(e,t,n,s=[1,1],r="valid",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Zb(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=ha.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),a==="channelsFirst"&&(u=qe(u,[0,3,1,2])),u})}function LB(e,t,n,s=[1,1,1],r="valid",a,i){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=$S(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=eI(o,t,s,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ws(o,n)),a==="channelsFirst"&&(o=qe(o,[0,4,1,2,3])),o})}var Jb=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Jb.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xi(t.kernelSize,e,"kernelSize"),this.strides=Xi(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Xi(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`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 U(`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 U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!kb(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Gl=class extends Jb{constructor(e,t){super(e,t);this.kernel=null,Gl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=UI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=MB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=LB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},_S=class extends Gl{constructor(e){super(2,e);_S.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!kb(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},zp=_S;zp.className="Conv2D";ae.registerClass(zp);var AS=class extends Gl{constructor(e){super(3,e);AS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Mp=AS;Mp.className="Conv3D";ae.registerClass(Mp);var ey=class extends zp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,1]));let g=Jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ey.className="Conv2DTranspose";ae.registerClass(ey);var ty=class extends Mp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat==="channelsFirst"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,4,1]));let k=tR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ty.className="Conv3DTranspose";ae.registerClass(ty);var ES=class extends Gl{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=qe(e,[0,2,3,1])),n=c3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};ES.className="SeparableConv";var ny=class extends ES{constructor(e){super(2,e)}};ny.className="SeparableConv2D";ae.registerClass(ny);var RS=class extends Gl{constructor(e){super(1,e);RS.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!kb(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},sy=RS;sy.className="Conv1D";ae.registerClass(sy);var ry=class extends Ge{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Gc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ry.className="Cropping2D";ae.registerClass(ry);var ay=class extends Ge{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,ZM(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation==="nearest"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ay.className="UpSampling2D";ae.registerClass(ay);function BB(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=bs()),Ct(r);let i=Zb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=hp(i,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}var iy=class extends Jb{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=BB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=gs(t,this.kernelSize[0],this.padding,this.strides[0]),a=gs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};iy.className="DepthwiseConv2D";ae.registerClass(iy);function DS(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function FS(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe("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."),r!=null&&(r=ce(ce(r,"bool"),"float32"),r.rank===u-1&&(r=Pn(r,-1)),r=qe(r,l)),s&&(t=Zn(t,0),r!=null&&(r=Zn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],T=ge(Qn(k),k),N=ie(V(v[0],k),V(d[0],T)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,T)));return{output:N,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Jn(c,1)),[p,g,d]})}var OS=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Vp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ym(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");ym(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=DS(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},u=FS((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Bl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?gm(t,[1,n]):t):this.cell.stateSize>1?[gm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===OS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=ms(s,n);return new e(Object.assign(t,{cell:r}))}},Ar=OS;Ar.className="RNN";ae.registerClass(Ar);var Hl=class extends Ge{},Lp=class extends Hl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Lp.className="SimpleRNNCell";ae.registerClass(Lp);var oy=class extends Ar{constructor(e){e.cell=new Lp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};oy.className="SimpleRNN";ae.registerClass(oy);var Bp=class extends Hl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Bp.className="GRUCell";ae.registerClass(Bp);var uy=class extends Ar{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Bp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};uy.className="GRU";ae.registerClass(uy);var ql=class extends Hl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new Np().apply([a]),c=r.apply([a*2]);return $x($x(u,l),c)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};ql.className="LSTMCell";ae.registerClass(ql);var ly=class extends Ar{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new ql(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ly.className="LSTM";ae.registerClass(ly);var Vp=class extends Hl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){ym(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ea(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(ms(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return vm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Pb(t)}};Vp.className="StackedRNNCells";ae.registerClass(Vp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):XI(t(),n),o=()=>Wl(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var PS=class extends Ar{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],c=gs(u,s[0],r,a[0],i[0]),p=gs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};PS.className="ConvRNN2D";var Wp=class extends ql{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t});this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Xi(n,2,"kernelSize"),this.kernelSize.forEach(o=>Bt(o,"kernelSize")),this.strides=Xi(s||1,2,"strides"),this.strides.forEach(o=>Bt(o,"strides")),this.padding=r||"valid",Un(this.padding),this.dataFormat=a||"channelsLast",Ct(this.dataFormat),this.dilationRate=Xi(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Bt(o,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return $b([d,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,T]=Bn(this.kernel.read(),i,y),[N,E,A,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,N,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,T,P,this.padding);let[R,F,$,z]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ca(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ca(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Wp.className="ConvLSTM2DCell";ae.registerClass(Wp);var cy=class extends PS{constructor(e){let t=new Wp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};cy.className="ConvLSTM2D";ae.registerClass(cy);var Up=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Wl(()=>XI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Up.className="Dropout";ae.registerClass(Up);var dy=class extends Up{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};dy.className="SpatialDropout1D";ae.registerClass(dy);var py=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=UI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};py.className="Dense";ae.registerClass(py);var hy=class extends Ge{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return rL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};hy.className="Flatten";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};fy.className="Activation";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),nL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};my.className="RepeatVector";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};gy.className="Reshape";ae.registerClass(gy);var by=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};by.className="Permute";ae.registerClass(by);var yy=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return pm(Ku(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=pm(Ku(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};yy.className="Masking";ae.registerClass(yy);var vy=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Ku(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Sp(n,"int32"));let s=KI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};vy.className="Embedding";ae.registerClass(vy);var bi=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Bl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},xy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};xy.className="Add";ae.registerClass(xy);var wy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};wy.className="Multiply";ae.registerClass(wy);var ky=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};ky.className="Average";ae.registerClass(ky);var Iy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$r(t,e[n]);return t})}};Iy.className="Maximum";ae.registerClass(Iy);var Sy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=bp(t,e[n]);return t})}};Sy.className="Minimum";ae.registerClass(Sy);var Cy=class extends bi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>$b(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Qn(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Pn(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return qk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="Concatenate";ae.registerClass(Cy);function Su(e,t){for(;e<0;)e+=t;return e}function VB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=Pn(o,1)),o})}var Ny=class extends bi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Su(r,e[a].shape.length)):s=[Su(this.axes,t.shape.length),Su(this.axes,n.shape.length)],this.normalize&&(t=Nd(t,s[0]),n=Nd(n,s[1])),VB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Su(this.axes,e.length),Su(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="Dot";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Wl(()=>ie(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Ty.className="GaussianNoise";ae.registerClass(Ty);var $y=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Wl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};$y.className="GaussianDropout";ae.registerClass($y);var _y=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Wl(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=jo(zl(n),this.rate);u=Sp(u,"float32");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};_y.className="AlphaDropout";ae.registerClass(_y);function Zu(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=$E(e,t,n,s,r,a);else if(e.rank===3)i=AE(e,t,n,s,r,a);else if(e.rank===4)i=RE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function WB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance;return[Zu(e,i,o,n,t,r),i,o]})}function UB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Zu(e,l,c,d,p,r),i,o]})}function GB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?WB(e,t,n,s,r):UB(e,t,n,s,r)}var Ay=class extends Ge{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.movingMeanInitializer=ht(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=fa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Zu(s,b,y,v,x,this.epsilon)}else return Zu(s,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 p();let[d,h,f]=GB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),T=V(ge(k,y),x);b.write(ge(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="BatchNormalization";ae.registerClass(Ay);var Ey=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=tb(n,this.axis,!0),u=fa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=ps(i,d),o=ps(o,d),c=ps(c,h),p=ps(p,h),Zu(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="LayerNormalization";ae.registerClass(Ey);function HB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=bs()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],fi(e,s)})}var Ry=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>HB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ry.className="ZeroPadding2D";ae.registerClass(Ry);function Gp(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=Zb(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=eb(e,t,n,o):i=Gg(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}function zS(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=$S(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=dI(e,t,n,o):i=Yk(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,4,1,2,3])),i})}var MS=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=gs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Bl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Dy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"max")}};Dy.className="MaxPooling1D";ae.registerClass(Dy);var Fy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"avg")}};Fy.className="AveragePooling1D";ae.registerClass(Fy);var LS=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"max")}};Oy.className="MaxPooling2D";ae.registerClass(Oy);var Py=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"avg")}};Py.className="AveragePooling2D";ae.registerClass(Py);var BS=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),s=gs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,"max")}};zy.className="MaxPooling3D";ae.registerClass(zy);var My=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,"avg")}};My.className="AveragePooling3D";ae.registerClass(My);var VS=class extends Ge{constructor(e){super(e);this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Ly=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return St(n,1)})}};Ly.className="GlobalAveragePooling1D";ae.registerClass(Ly);var By=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};By.className="GlobalMaxPooling1D";ae.registerClass(By);var WS=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Vy=class extends WS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};Vy.className="GlobalAveragePooling2D";ae.registerClass(Vy);var Wy=class extends WS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};Wy.className="GlobalMaxPooling2D";ae.registerClass(Wy);var US=class extends Ge{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=ms(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Uy=class extends US{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Oe(e),FS((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Uy.className="TimeDistributed";ae.registerClass(Uy);function qB(e){mi(QM,"BidirectionalMergeMode",e)}var jB="concat",Gy=class extends US{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=ms(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=ms(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?jB:e.mergeMode,qB(this.mergeMode),e.weights)throw new Fe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=DS(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(s!=null)throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");let o=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==o)throw new U("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Zn(r,1));let 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Dt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},$s=class{constructor(e,t,n,s,r,a,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=i,this.id=YI(),a!=null&&(this.originalName=HI(a),this.name=qI(this.originalName)),this.rank=t.length}},RL=0,Fp=class{constructor(e,t){this.callArgs=t,this.id=RL++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},DL=0,Ge=class extends ae.Serializable{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=DL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+"_"+Dp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new hs(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return bn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return bn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Use \`getOutputAt(nodeIndex)\` instead.`);return bn(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=dt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=dt(this.inputSpec);if(e.length!==t.length)throw new U(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return ea(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=FL(e),i=this.computeOutputShape(a),o,u=OL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`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 hs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Cd(this.weights)}build(e){this.built=!0}getWeights(e=!1){return vm(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=vm(t);for(let r=0;r<s.length;++r){let a=s[r],i=t[r],o=e[r];if(!w.arraysEqual(a.shape,o.shape))throw new U(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Pb(n)})}addWeight(e,t,n,s,r,a,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new U(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=o!=null?o():ht("zeros"));let u=s.apply(t,n),l=new AL(u,n,e,a,i);return u.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),a==null&&(a=!0),a?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=dt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,i=null){let o=dt(e);t=dt(t),n=dt(n),s=dt(s),r=Sd(r),a=Sd(a);let u=[],l=[],c=[];for(let p of o)u.push(p.sourceLayer),l.push(p.nodeIndex),c.push(p.tensorIndex);new Fp({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function FL(e){e=dt(e);let t=[];for(let n of e)t.push(n.shape);return bn(t)}function OL(e){return"float32"}function QI(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let i=s.inputTensors[a],o=s.inboundLayers[a],u=s.nodeIndices[a],l=QI(i,o,u);for(let c of l)r.indexOf(c)===-1&&r.push(c)}return r}}}var Qo=class extends Ge{constructor(e){if(super({dtype:e.dtype,name:e.name!=null?e.name:Dp("input").toString()}),e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new U("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new U("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new U("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new $s(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new U(`Cannot pass any input to an InputLayer's apply() method. 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function mB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Dp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(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 y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,"input layer has >1 nodes"),Cs(x===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Qo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.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 t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,T)=>{(x==null||k==null||T==null)&&(x=b.sourceLayer,k=b.nodeIndex,T=b.tensorIndex);let N=x.inboundNodes[k];if(v.indexOf(N)!==-1)throw new hs(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(N)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(N)===-1&&v.push(N);let E=N.inboundLayers.length;for(let A=0;A<E;A++){let P=N.inputTensors[A],R=N.inboundLayers[A],F=N.nodeIndices[A],$=N.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(N);v.indexOf(N)>=0;)v.splice(v.indexOf(N),1);i.push(N)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],T=b.nodeIndices[x],N=k.inboundNodes[T],E=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(y+1,E),n[N.id]=N}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Uc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],T=a[x.id];return k<T?-1:k>T?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Uc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new hs(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new hs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Pb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${uS}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=wm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Qr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Au(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=fa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=fa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let N=r[x];if(N.inboundNodes.length<=k){i(m,g);return}let E=N.inboundNodes[k];b.push(E.outputTensors[T])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=ms(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!BM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function lS(e,t){return gB(e,t,"classWeight")}async function cS(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Hu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Zt(i,"float32")}else return null}function bB(e,t){return V(e,t)}var yB=32;function dS(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=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 a=Px("input",e.inputNames,n),i=Px("output",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function Px(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function vB(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function xB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. 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Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Td?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Lx(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Lx(t,this.feedOutputNames,r,!1,"target"),TB(e,t,null),$B(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[i,o]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let u=null;if(s!=null){let l=lS(s,this.outputNames);u=[];for(let c=0;c<l.length;++c)u.push(await cS(o[c],null,l[c]))}return[i,o,u]}testLoop(e,t,n,s=0,r){return j(()=>{let a=this.checkNumSamples(t,n,r,"steps"),i=[];if(s>0)throw new Fe("Verbose mode is not implemented yet.");if(r!=null)throw new Fe("steps mode in testLoop() is not implemented yet");{let o=Im(a,n),u=Zt(ys(0,a));for(let l=0;l<o.length;++l){let c=o[l][0],p=o[l][1],d=ta(u,c,p-c),h=Wb(t,d),f=e(h);if(l===0)for(let m=0;m<f.length;++m)i.push(we(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=ie(i[m],V(p-c,g))}}for(let l=0;l<i.length;++l)i[l]=xe(i[l],a)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;Sx(e,s)>1&&(r+=`_${Sx(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let p=new Qr(c),d=Au(this.outputs,p,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],d[f]);r[f]!=null&&(g=bB(g,r[f]));let b=St(g);t.push(b),f===0?h=g:h=ie(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],b=this.metricsTensors[f][1];m=St(g(s[b],d[b]))}Ht(m),a.push(m)}return h=St(h),this.calculateLosses().forEach(f=>{h=ie(h,f)}),h},o=this.collectedTrainableWeights.map(c=>c.read()),u=!0;return[this.optimizer_.minimize(i,u,o)].concat(a)}}makeTestFunction(){this.testFunction=e=>j(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Qr(a),o=Au(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=St(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=St(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return CB(this,e,t,n)}async fitDataset(e,t){return xB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return Re(i),ds(n[0],e),ds(n[1],t),bn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=dm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-dm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=Ws(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Ws(qc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(qc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(qc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Zu(e.optimizer_config),n=ms(t),s;if(typeof e.loss=="string")s=Kr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>Kr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=Kr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>Kr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Kr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=En.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await En.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:AB,generatedBy:`TensorFlow.js tfjs-layers v${uS}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:l,specs:c}=await En.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=En.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Dx(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Dx(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className="Model";ae.registerClass(pr);var hS=class extends pr{};hS.className="Functional";ae.registerClass(hS);async function EB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Zu(n),r=ms(s,t);if(e.weightsManifest!=null){let a=await En.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function RB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return DB(e,void 0,t)}async function DB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=ms(Zu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:c}=FB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function FB(e,t){let n=En.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Cm=class extends pr{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Cm||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("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 U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=ZI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=QI(this.outputs[0])}this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:fa(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Cm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=ms(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Ub=Cm;Ub.className="Sequential";ae.registerClass(Ub);function Npe(e){return new pr(e)}function Tpe(e){return new Ub(e)}function $pe(e,t){return t==null&&(t={}),RB(e,t)}function OB(e){return ZI(e)}function _pe(e,t){zb.registerCallbackConstructor(e,t)}var In=class extends ae.Serializable{getConfig(){return{}}},fS=class extends In{apply(e,t=1){return aL(e,t)}};fS.className="elu";ae.registerClass(fS);var mS=class extends In{apply(e){return gI(e)}};mS.className="selu";ae.registerClass(mS);var gS=class extends In{apply(e){return Xs(e)}};gS.className="relu";ae.registerClass(gS);var bS=class extends In{apply(e){return j(()=>bp(6,Xs(e)))}};bS.className="relu6";ae.registerClass(bS);var yS=class extends In{apply(e){return e}};yS.className="linear";ae.registerClass(yS);var vS=class extends In{apply(e){return qs(e)}};vS.className="sigmoid";ae.registerClass(vS);var xS=class extends In{apply(e){return oL(e)}};xS.className="hardSigmoid";ae.registerClass(xS);var wS=class extends In{apply(e){return zl(e)}};wS.className="softplus";ae.registerClass(wS);var kS=class extends In{apply(e){return iL(e)}};kS.className="softsign";ae.registerClass(kS);var IS=class extends In{apply(e){return qu(e)}};IS.className="tanh";ae.registerClass(IS);var Gb=class extends In{apply(e,t=-1){return ub(e,t)}};Gb.className="softmax";ae.registerClass(Gb);var SS=class extends In{apply(e,t=-1){return iI(e,t)}};SS.className="logSoftmax";ae.registerClass(SS);var CS=class extends In{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};CS.className="swish";ae.registerClass(CS);var NS=class extends In{apply(e){return j(()=>V(e,qu(zl(e))))}};NS.className="mish";ae.registerClass(NS);function br(e){return e.getClassName()}function Gf(e,t={}){return Ll(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function yr(e){if(e==null){let t={};return t.className="linear",t.config={},Gf(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Gf(t)}else return e instanceof In?e:Gf(e)}function Hb(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var TS=class extends ae.Serializable{},Gl=class extends TS{constructor(e){super(),Hb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Wl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Gl.className="L1L2";ae.registerClass(Gl);function PB(e){return Hb(e),new Gl({l1:e!=null?e.l1:null,l2:0})}function zB(e){return Hb(e),new Gl({l2:e!=null?e.l2:null,l1:0})}var Vx={l1l2:"L1L2"};function it(e){return wb(e)}function Wx(e,t={}){return Ll(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Vx?Vx[e]:e,config:{}};return Wx(n)}else return e instanceof TS?e:Wx(e)}var qb=class extends Ge{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Vn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};qb.className="ReLU";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Xg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jb.className="LeakyReLU";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),sb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:it(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Kb.className="PReLU";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return fp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Xb.className="ELU";ae.registerClass(Xb);var Yb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Yb.className="ThresholdedReLU";ae.registerClass(Yb);var Qb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Qb.className="Softmax";ae.registerClass(Qb);function Xi(e,t,n){if(typeof e=="number")return fa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!tL(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(a==="channelsFirst"&&(e=qe(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Zk(e,t,s,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ws(o,n)),o})}function Ux(e,t,n,s=[1,1],r="valid",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Zb(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=ha.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),a==="channelsFirst"&&(u=qe(u,[0,3,1,2])),u})}function LB(e,t,n,s=[1,1,1],r="valid",a,i){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=$S(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=eI(o,t,s,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ws(o,n)),a==="channelsFirst"&&(o=qe(o,[0,4,1,2,3])),o})}var Jb=class extends Ge{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Jb.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xi(t.kernelSize,e,"kernelSize"),this.strides=Xi(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Xi(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`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 U(`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 U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!kb(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Hl=class extends Jb{constructor(e,t){super(e,t),this.kernel=null,Hl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=UI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=MB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=LB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},_S=class extends Hl{constructor(e){super(2,e),_S.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!kb(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},zp=_S;zp.className="Conv2D";ae.registerClass(zp);var AS=class extends Hl{constructor(e){super(3,e),AS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Mp=AS;Mp.className="Conv3D";ae.registerClass(Mp);var ey=class extends zp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,1]));let g=Jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ey.className="Conv2DTranspose";ae.registerClass(ey);var ty=class extends Mp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat==="channelsFirst"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,4,1]));let k=tR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ty.className="Conv3DTranspose";ae.registerClass(ty);var ES=class extends Hl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=qe(e,[0,2,3,1])),n=c3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};ES.className="SeparableConv";var ny=class extends ES{constructor(e){super(2,e)}};ny.className="SeparableConv2D";ae.registerClass(ny);var RS=class extends Hl{constructor(e){super(1,e),RS.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!kb(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},sy=RS;sy.className="Conv1D";ae.registerClass(sy);var ry=class extends Ge{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Gc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ry.className="Cropping2D";ae.registerClass(ry);var ay=class extends Ge{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,ZM(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation==="nearest"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ay.className="UpSampling2D";ae.registerClass(ay);function BB(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=bs()),Ct(r);let i=Zb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=hp(i,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}var iy=class extends Jb{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=BB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=gs(t,this.kernelSize[0],this.padding,this.strides[0]),a=gs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};iy.className="DepthwiseConv2D";ae.registerClass(iy);function DS(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function FS(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe("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."),r!=null&&(r=ce(ce(r,"bool"),"float32"),r.rank===u-1&&(r=Pn(r,-1)),r=qe(r,l)),s&&(t=Zn(t,0),r!=null&&(r=Zn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],T=ge(Qn(k),k),N=ie(V(v[0],k),V(d[0],T)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,T)));return{output:N,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Jn(c,1)),[p,g,d]})}var OS=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Vp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ym(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");ym(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=DS(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},u=FS((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Vl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?gm(t,[1,n]):t):this.cell.stateSize>1?[gm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===OS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=ms(s,n);return new e(Object.assign(t,{cell:r}))}},Ar=OS;Ar.className="RNN";ae.registerClass(Ar);var ql=class extends Ge{},Lp=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Lp.className="SimpleRNNCell";ae.registerClass(Lp);var oy=class extends Ar{constructor(e){e.cell=new Lp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};oy.className="SimpleRNN";ae.registerClass(oy);var Bp=class extends ql{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Bp.className="GRUCell";ae.registerClass(Bp);var uy=class extends Ar{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Bp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};uy.className="GRU";ae.registerClass(uy);var jl=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new Np().apply([a]),c=r.apply([a*2]);return $x($x(u,l),c)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};jl.className="LSTMCell";ae.registerClass(jl);var ly=class extends Ar{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new jl(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ly.className="LSTM";ae.registerClass(ly);var Vp=class extends ql{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){ym(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ea(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(ms(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return vm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Pb(t)}};Vp.className="StackedRNNCells";ae.registerClass(Vp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):XI(t(),n),o=()=>Ul(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var PS=class extends Ar{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],c=gs(u,s[0],r,a[0],i[0]),p=gs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};PS.className="ConvRNN2D";var Wp=class extends jl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Xi(n,2,"kernelSize"),this.kernelSize.forEach(o=>Bt(o,"kernelSize")),this.strides=Xi(s||1,2,"strides"),this.strides.forEach(o=>Bt(o,"strides")),this.padding=r||"valid",Un(this.padding),this.dataFormat=a||"channelsLast",Ct(this.dataFormat),this.dilationRate=Xi(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Bt(o,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return $b([d,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,T]=Bn(this.kernel.read(),i,y),[N,E,A,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,N,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,T,P,this.padding);let[R,F,$,z]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ca(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ca(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Wp.className="ConvLSTM2DCell";ae.registerClass(Wp);var cy=class extends PS{constructor(e){let t=new Wp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};cy.className="ConvLSTM2D";ae.registerClass(cy);var Up=class extends Ge{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Ul(()=>XI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Up.className="Dropout";ae.registerClass(Up);var dy=class extends Up{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};dy.className="SpatialDropout1D";ae.registerClass(dy);var py=class extends Ge{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=UI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};py.className="Dense";ae.registerClass(py);var hy=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return rL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};hy.className="Flatten";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};fy.className="Activation";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),nL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};my.className="RepeatVector";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};gy.className="Reshape";ae.registerClass(gy);var by=class extends Ge{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};by.className="Permute";ae.registerClass(by);var yy=class extends Ge{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return pm(Xu(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=pm(Xu(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};yy.className="Masking";ae.registerClass(yy);var vy=class extends Ge{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Xu(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Sp(n,"int32"));let s=KI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};vy.className="Embedding";ae.registerClass(vy);var bi=class extends Ge{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Vl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},xy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};xy.className="Add";ae.registerClass(xy);var wy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};wy.className="Multiply";ae.registerClass(wy);var ky=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};ky.className="Average";ae.registerClass(ky);var Iy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$r(t,e[n]);return t})}};Iy.className="Maximum";ae.registerClass(Iy);var Sy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=bp(t,e[n]);return t})}};Sy.className="Minimum";ae.registerClass(Sy);var Cy=class extends bi{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>$b(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Qn(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Pn(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return qk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="Concatenate";ae.registerClass(Cy);function Cu(e,t){for(;e<0;)e+=t;return e}function VB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=Pn(o,1)),o})}var Ny=class extends bi{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Cu(r,e[a].shape.length)):s=[Cu(this.axes,t.shape.length),Cu(this.axes,n.shape.length)],this.normalize&&(t=Nd(t,s[0]),n=Nd(n,s[1])),VB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Cu(this.axes,e.length),Cu(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="Dot";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Ul(()=>ie(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Ty.className="GaussianNoise";ae.registerClass(Ty);var $y=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Ul(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};$y.className="GaussianDropout";ae.registerClass($y);var _y=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Ul(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=Ko(Ml(n),this.rate);u=Sp(u,"float32");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};_y.className="AlphaDropout";ae.registerClass(_y);function Ju(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=$E(e,t,n,s,r,a);else if(e.rank===3)i=AE(e,t,n,s,r,a);else if(e.rank===4)i=RE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function WB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance;return[Ju(e,i,o,n,t,r),i,o]})}function UB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Ju(e,l,c,d,p,r),i,o]})}function GB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?WB(e,t,n,s,r):UB(e,t,n,s,r)}var Ay=class extends Ge{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.movingMeanInitializer=ht(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=fa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Ju(s,b,y,v,x,this.epsilon)}else return Ju(s,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 p();let[d,h,f]=GB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),T=V(ge(k,y),x);b.write(ge(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="BatchNormalization";ae.registerClass(Ay);var Ey=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=tb(n,this.axis,!0),u=fa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=ps(i,d),o=ps(o,d),c=ps(c,h),p=ps(p,h),Ju(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="LayerNormalization";ae.registerClass(Ey);function HB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=bs()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],fi(e,s)})}var Ry=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>HB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ry.className="ZeroPadding2D";ae.registerClass(Ry);function Gp(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=Zb(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=eb(e,t,n,o):i=Gg(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}function zS(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=$S(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=dI(e,t,n,o):i=Yk(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,4,1,2,3])),i})}var MS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=gs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Vl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Dy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"max")}};Dy.className="MaxPooling1D";ae.registerClass(Dy);var Fy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"avg")}};Fy.className="AveragePooling1D";ae.registerClass(Fy);var LS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"max")}};Oy.className="MaxPooling2D";ae.registerClass(Oy);var Py=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"avg")}};Py.className="AveragePooling2D";ae.registerClass(Py);var BS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),s=gs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,"max")}};zy.className="MaxPooling3D";ae.registerClass(zy);var My=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,"avg")}};My.className="AveragePooling3D";ae.registerClass(My);var VS=class extends Ge{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Ly=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return St(n,1)})}};Ly.className="GlobalAveragePooling1D";ae.registerClass(Ly);var By=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};By.className="GlobalMaxPooling1D";ae.registerClass(By);var WS=class extends Ge{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Vy=class extends WS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};Vy.className="GlobalAveragePooling2D";ae.registerClass(Vy);var Wy=class extends WS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};Wy.className="GlobalMaxPooling2D";ae.registerClass(Wy);var US=class extends Ge{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=ms(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Uy=class extends US{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Oe(e),FS((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Uy.className="TimeDistributed";ae.registerClass(Uy);function qB(e){mi(QM,"BidirectionalMergeMode",e)}var jB="concat",Gy=class extends US{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=ms(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=ms(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?jB:e.mergeMode,qB(this.mergeMode),e.weights)throw new Fe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=DS(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(s!=null)throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");let o=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==o)throw new U("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Zn(r,1));let 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j(()=>i4(a,i,o));case"dynamic":return o4(a,i,o);case"evaluation":return j(()=>u4(a,i,o));case"image":return j(()=>p4(a,i,o));case"graph":return j(()=>l4(a,i,o));case"logical":return j(()=>h4(a,i,o));case"matrices":return j(()=>f4(a,i,o));case"normalization":return j(()=>m4(a,i,o));case"reduction":return j(()=>g4(a,i,o));case"slice_join":return j(()=>b4(a,i,o));case"sparse":return j(()=>y4(a,i,o));case"spectral":return j(()=>v4(a,i,o));case"string":return j(()=>x4(a,i,o));case"transformation":return j(()=>w4(a,i,o));case"hash_table":return d4(a,i,o,s);case"custom":let u=XS(a.op);if(u&&u.customExecutor)return u.customExecutor(new YW(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Zx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>An(d)[0]),c=[];s!=null&&(c=s.map(d=>An(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((b0(d)||N4(d)||T4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function k4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>An(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var I4=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],S4=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],C4=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function b0(e){return I4.indexOf(e.op)>=0}function N4(e){return S4.indexOf(e.op)>=0}function T4(e){return C4.indexOf(e.op)>=0}var zm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new zm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return 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l}processChildNodes(e,t,n,s,r,a){e.children.forEach(i=>{let[o]=Ts(i.name,n);r[o]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=An(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((o,u)=>a[u]===-1||a[u]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=An(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=An(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},$4=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},_4="?tfjs-format=file",A4="model.json",E4=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new $4}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=En.browserHTTPRequest(e,this.loadOptions);else{let t=En.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(En.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=En.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new zm(qx.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=qx.Instance.transformGraph(e.modelInitializer);this.initializer=new zm(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=En.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof et)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Rpe(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},A0=class extends Ut{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!X().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new A0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),fs(n,t)}},E0=class extends Ut{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Ki([a,r,o,i],[1,4])}else this.cropBox=Ki([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!X().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new E0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Nk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=Pn(ce(e,"float32"),0),n;n=qn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},R0=class{},D0=class extends Ut{split(e){return new aU(this,e)}},aU=class extends D0{constructor(e,t){super();this.upstream=e,this.impl=new iU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},iU=class extends Qy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},oU=class extends Ut{decodeUTF8(){return new uU(this)}},uU=class extends D0{constructor(e){super();this.upstream=e,this.impl=new lU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},lU=class extends Qy{constructor(e){super();if(this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Ww();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},F0=class extends oU{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(X().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=i=>{let o=r.result;if(o instanceof 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R0{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return O0(this.url)?new P0(this.url,this.fileOptions).iterator():cU(this.url,this.fileOptions)}};function pU(e,t={}){return new _0(new z0(e),t)}function hU(e){let t=Yy(e);return _n(async()=>t)}function fU(e){return _n(async()=>{let t=await e();return Yy(()=>t.next())})}async function mU(e,t){return E0.create(e,t)}async function gU(e){return A0.create(e)}var bU="0.0.0";function be(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var yU=xs.whereImpl,M0=class extends tl{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ud(this,Ss())}nextDataId(){return M0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().get("IS_NODE")&&S.warn(`
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s=I("size",e,t,n),r=I("dtype",e,t,n),a=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),u=I("identicalElementShapes",e,t,n),l=I("name",e,t,n),c=new JW(l,r,s,a,u,i,o);return n.addTensorArray(c),[c.idTensor,we(1)]}case"TensorArrayWriteV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case"TensorArrayReadV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(s.id).read(r)]}case"TensorArrayGatherV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("dtype",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case"TensorArrayScatterV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("tensor",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case"TensorArrayConcatV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id),a=I("dtype",e,t,n);return[r.concat(a)]}case"TensorArraySplitV3":{let s=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),a=I("lengths",e,t,n),i=n.getTensorArray(s.id);return i.split(a,r),[i.idTensor]}case"TensorArraySizeV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return[we(r.size(),"int32")]}case"TensorArrayCloseV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),i=n.getTensorList(s.id);return i.setItem(r,a),[i.idTensor]}case"TensorListGetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(s.id).getItem(r,a,i)]}case"TensorListScatterV2":case"TensorListScatter":{let s=I("indices",e,t,n),r=I("tensor",e,t,n),a=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=n4(r,s,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let 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s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,i=mr(r[0]).shape,o=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(mr(u).shape,i))throw new Error("the input tensors shape does not match");return l?u:G(u,a)});return[Jn(o,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return Fs(r,s)}case"Tile":{let s=I("reps",e,t,n);return[ps(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return Bn(a,r,s)}case"ScatterNd":{let s=I("indices",e,t,n),r=I("values",e,t,n),a=I("shape",e,t,n);return[X3(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[J3(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[NI(s,a,r,a.dtype===i.dtype?i:ce(i,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},y4=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Wc.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,i]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Wc.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Wc.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Wc.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},v4=(e,t,n)=>{switch(e.op){case"FFT":return[lb(I("x",e,t,n))];case"IFFT":return[Id(I("x",e,t,n))];case"RFFT":return[cb(I("x",e,t,n))];case"IRFFT":return[xI(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},x4=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Mf.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=Mf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Mf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},w4=(e,t,n)=>{switch(e.op){case"Cast":return[ce(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[Pn(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[mr(I("x",e,t,n),s)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[xD(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[fi(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[nb(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Hg(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[cR(I("x",e,t,n),s,r)]}case"BroadcastTo":return[nd(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[OE(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Yx(e,t,n,s){let r=((a,i,o)=>{switch(a.category){case"arithmetic":return j(()=>QW(a,i,o));case"basic_math":return j(()=>ZW(a,i,o));case"control":return r4(a,i,o);case"convolution":return j(()=>a4(a,i,o));case"creation":return j(()=>i4(a,i,o));case"dynamic":return o4(a,i,o);case"evaluation":return j(()=>u4(a,i,o));case"image":return j(()=>p4(a,i,o));case"graph":return j(()=>l4(a,i,o));case"logical":return j(()=>h4(a,i,o));case"matrices":return j(()=>f4(a,i,o));case"normalization":return j(()=>m4(a,i,o));case"reduction":return j(()=>g4(a,i,o));case"slice_join":return j(()=>b4(a,i,o));case"sparse":return j(()=>y4(a,i,o));case"spectral":return j(()=>v4(a,i,o));case"string":return j(()=>x4(a,i,o));case"transformation":return j(()=>w4(a,i,o));case"hash_table":return d4(a,i,o,s);case"custom":let u=XS(a.op);if(u&&u.customExecutor)return u.customExecutor(new YW(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Zx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>An(d)[0]),c=[];s!=null&&(c=s.map(d=>An(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((b0(d)||N4(d)||T4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function k4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>An(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var I4=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],S4=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],C4=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function b0(e){return I4.indexOf(e.op)>=0}function N4(e){return S4.indexOf(e.op)>=0}function T4(e){return C4.indexOf(e.op)>=0}var zm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new zm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Zx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`)}return k4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[An(c)[0]]),r=t.map(c=>An(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return j(()=>{let c=new Qx(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=An(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=Yx(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. 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You can use model.execute() instead.");let b=o.filter(y=>!b0(y)&&!un(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${y}`)}return h}processStack(e,t,n,s,r,a,i,o,u){let l=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p="";if(c.node.op==="Enter"&&I("isConstant",c.node,s,n)&&([p]=Ts(c.node.name,n)),s[c.node.name]==null){let d=Yx(c.node,s,n,this._resourceManager);p||([p]=Ts(c.node.name,n));let h=n.currentContext;w.isPromise(d)?l.push(d.then(f=>(s[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u),f))):(s[p]=d,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u))}else this.processChildNodes(c.node,t,n,s,r,u)}return l}processChildNodes(e,t,n,s,r,a){e.children.forEach(i=>{let[o]=Ts(i.name,n);r[o]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=An(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((o,u)=>a[u]===-1||a[u]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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Ut{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Ut?{value:a.next().then(o=>(t++,o.done&&n++,o.value)),recurse:!1}:{value:null,recurse:!0}}let r=await x0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},T0=class extends Ut{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new w0(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},eU=class extends T0{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=F4.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Jo=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),_n(async()=>(await n.iterator()).columnMajorBatch(e,t,sU),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,_n(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,_n(async()=>(await t.iterator()).filter(s=>j(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return _n(async()=>(await t.iterator()).map(n=>j(()=>e(n))),this.size)}mapAsync(e){let t=this;return _n(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return _n(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,_n(async()=>{let s=Yy(async()=>({value:await t.iterator(),done:!1}));return V4(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,_n(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=D4.alea(t||w.now().toString());return _n(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,_n(async()=>(await t.iterator()).take(e),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()}};Jo.MAX_BUFFER_SIZE=1e4;function _n(e,t=null){return new class extends Jo{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function tU(e){return _n(async()=>S0(e),e.length)}function nU(e){if(!to(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return _n(async()=>{let n=await x0(e,s=>{if(s instanceof Jo)return{value:s.iterator(),recurse:!1};if(to(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return W4(n,1)},t)}function sU(e){if(e===null)return null;let t=e[0];return z4(t)?{value:rU(e),recurse:!1}:{value:null,recurse:!0}}function rU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?Jn(e):fs(e)}var $0=class extends Jo{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Kc='"',Tu=Symbol("out"),ew=Symbol("field"),Xc=Symbol("quote"),qf=Symbol("quoteafterquote"),tw=Symbol("quoteinquote"),_0=class extends Jo{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new $0(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype==="bool"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else switch(i.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(o);break;default:u=l}}i&&i.isLabel?s[a]=u:n[a]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Tu;for(let i=0;i<r;i++)switch(a){case Tu:switch(e.charAt(i)){case Kc:s=i+1,a=Xc;break;case this.delimiter:if(s=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Tu;break;default:a=ew,s=i;break}break;case ew:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i)),a=Tu,s=i+1;break;default:}break;case Xc:switch(e.charAt(i)){case Kc:a=qf;break;default:}break;case qf:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i-1)),a=Tu,s=i+1;break;case Kc:a=Xc;break;default:a=tw;break}break;case tw:switch(e.charAt(i)){case Kc:a=Xc;break;default:}break;default:}if(a===qf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},A0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!X().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new A0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),fs(n,t)}},E0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Ki([a,r,o,i],[1,4])}else this.cropBox=Ki([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!X().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new E0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Nk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=Pn(ce(e,"float32"),0),n;n=qn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},R0=class{},D0=class extends Ut{split(e){return new aU(this,e)}},aU=class extends D0{constructor(e,t){super(),this.upstream=e,this.impl=new iU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},iU=class extends Qy{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},oU=class extends Ut{decodeUTF8(){return new uU(this)}},uU=class extends D0{constructor(e){super(),this.upstream=e,this.impl=new lU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},lU=class extends Qy{constructor(e){if(super(),this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Ww();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},F0=class extends oU{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(X().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=i=>{let o=r.result;if(o instanceof 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R0{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return O0(this.url)?new P0(this.url,this.fileOptions).iterator():cU(this.url,this.fileOptions)}};function pU(e,t={}){return new _0(new z0(e),t)}function hU(e){let t=Yy(e);return _n(async()=>t)}function fU(e){return _n(async()=>{let t=await e();return Yy(()=>t.next())})}async function mU(e,t){return E0.create(e,t)}async function gU(e){return A0.create(e)}var bU="0.0.0";function be(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var yU=xs.whereImpl,M0=class extends nl{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ud(this,Ss())}nextDataId(){return M0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().get("IS_NODE")&&S.warn(`
 ============================
 Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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n.makeTensorInfo(r.shape,r.dtype,m)}var lH={kernelName:Ma,backendName:"cpu",kernelFunc:uH};function cH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;be([r],"batchToSpaceND");let o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=mt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ga({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var dH={kernelName:io,backendName:"cpu",kernelFunc:cH};function pH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=ev(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var 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Vt(d.outShape,r.dtype),k=w.computeStrides(r.shape),T=w.computeStrides(a.shape),N=k[0],E=v?k[1]:k[2],A=v?k[2]:1,P=v?1:k[1],R=x.strides[0],F=v?x.strides[1]:x.strides[2],$=v?x.strides[2]:1,z=v?1:x.strides[1],W=n.data.get(r.dataId).values,q=n.data.get(a.dataId).values,K=x.values;for(let Y=0;Y<d.batchSize;++Y){let Z=Y*N,te=Y*R;for(let ee=0;ee<d.outHeight;++ee){let se=te+ee*F,ne=ee*d.strideHeight-y;for(let oe=0;oe<h;++oe){let re=ne+oe*m;if(re<0||re>=d.inHeight)continue;let le=oe*T[0],me=Z+re*E;for(let ke=0;ke<d.outWidth;++ke){let Se=se+ke*$,Ee=ke*d.strideWidth-b;for(let Pe=0;Pe<f;++Pe){let Xe=Ee+Pe*g;if(Xe<0||Xe>=d.inWidth)continue;let Je=le+Pe*T[1],Ye=me+Xe*A,tt=Je;for(let Ce=0;Ce<d.inChannels;++Ce){let ut=W[Ye+Ce*P];for(let at=0;at<d.outChannels;++at)K[Se+at*z]+=ut*q[tt+at];tt+=d.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,K)}var kH={kernelName:$a,backendName:"cpu",kernelFunc:RC};function IH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"conv2dBackpropFilter");let p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,b=d.dataFormat==="channelsLast",y=new Vt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,k=n.data.get(r.dataId).values,T=n.data.get(a.dataId).values,N=new Vt(r.shape,r.dtype,k),E=new Vt(a.shape,a.dtype,T);for(let A=0;A<m;++A){let P=Math.max(0,Math.ceil((x-A)/h)),R=Math.min(d.outHeight,(d.inHeight+x-A)/h);for(let F=0;F<g;++F){let $=Math.max(0,Math.ceil((v-F)/f)),z=Math.min(d.outWidth,(d.inWidth+v-F)/f);for(let W=0;W<d.inChannels;++W)for(let q=0;q<d.outChannels;++q){let K=0;for(let Y=0;Y<d.batchSize;++Y)for(let Z=P;Z<R;++Z){let te=A+Z*h-x;for(let ee=$;ee<z;++ee){let se=F+ee*f-v;b?K+=N.get(Y,te,se,W)*E.get(Y,Z,ee,q):K+=N.get(Y,W,te,se)*E.get(Y,q,Z,ee)}}y.set(K,A,F,W,q)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var SH={kernelName:cg,backendName:"cpu",kernelFunc:IH};function CH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;be([r,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=S.convertConv2DDataFormat(l),f=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:T,filterHeight:N,filterWidth:E,inChannels:A,inHeight:P,inWidth:R,outChannels:F,outHeight:$,outWidth:z,strideHeight:W,strideWidth:q}=f;h=f.dataFormat;let K=N-1-f.padInfo.top,Y=E-1-f.padInfo.left,Z=h==="channelsLast",te=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,ne=Z?1:m.strides[1],oe=d[0],re=Z?d[1]:d[2],le=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<T;++ke)for(let Se=0;Se<A;++Se)for(let Ee=0;Ee<P;++Ee){let Pe=Ee-K,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min($,(N+Pe)/W);for(let Ye=0;Ye<R;++Ye){let tt=Ye-Y,Ce=Math.max(0,Math.ceil(tt/q)),ut=Math.min(z,(E+tt)/q),at=0;for(let Nt=Xe;Nt<Je;++Nt){let Cn=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let en=Et*q-tt,Nn=oe*ke+re*Nt+le*Et,Tn=v*(N-1-Cn)+x*(E-1-en)+k*Se;for(let Yt=0;Yt<F;++Yt){let Dn=b[Nn+me*Yt],tn=y[Tn+Yt];at+=Dn*tn}}}let Jt=te*ke+ee*Ee+se*Ye+ne*Se;g[Jt]=at}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var NH={kernelName:_a,backendName:"cpu",kernelFunc:CH};function TH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s;be([r,a],"conv3d");let l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,T=n.data.get(a.dataId).values,N=x.values,E=w.computeStrides(r.shape),A=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let R=P*E[0],F=P*x.strides[0];for(let $=0;$<l.outDepth;++$){let z=F+$*x.strides[1],W=$*l.strideDepth-b;for(let q=0;q<c;++q){let K=W+q*h;if(K<0||K>=l.inDepth)continue;let Y=q*A[0],Z=R+K*E[1];for(let te=0;te<l.outHeight;++te){let ee=z+te*x.strides[2],se=te*l.strideHeight-v;for(let ne=0;ne<p;++ne){let oe=se+ne*f;if(oe<0||oe>=l.inHeight)continue;let re=Y+ne*A[1],le=Z+oe*E[2];for(let me=0;me<l.outWidth;++me){let ke=ee+me*l.outChannels,Se=me*l.strideWidth-y;for(let Ee=0;Ee<d;++Ee){let Pe=Se+Ee*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=re+Ee*A[2],Je=le+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)N[ke+ut]+=Ce*T[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var $H={kernelName:Xd,backendName:"cpu",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s;be([r,a],"conv3dBackpropFilterV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=S.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,"float32"),v=y.values,[x,k,T,N]=y.strides,E=n.data.get(a.dataId).values,[A,P,R,F]=c,$=n.data.get(r.dataId).values,[z,W,q,K]=l,Y=p.padInfo.front,Z=p.padInfo.left,te=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((Y-ee)/d)),ne=Math.min(p.outDepth,(p.inDepth+Y-ee)/d),oe=ee*x;for(let re=0;re<g;++re){let le=Math.max(0,Math.ceil((te-re)/h)),me=Math.min(p.outHeight,(p.inHeight+te-re)/h),ke=re*k+oe;for(let Se=0;Se<b;++Se){let Ee=Math.max(0,Math.ceil((Z-Se)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Se)/f),Xe=Se*T+ke;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*N+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let at=ut*z,Jt=ut*A;for(let Nt=se;Nt<ne;++Nt){let Et=(ee+Nt*d-Y)*W+at,en=Nt*P+Jt;for(let Nn=le;Nn<me;++Nn){let Yt=(re+Nn*h-te)*q+Et,Dn=Nn*R+en;for(let tn=Ee;tn<Pe;++tn){let Ls=(Se+tn*f-Z)*K+Yt,ki=tn*F+Dn;Ce+=$[Ls+Je]*E[ki+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var AH={kernelName:dg,backendName:"cpu",kernelFunc:_H};function EH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s;be([r],"conv3dBackpropInputV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=S.computeConv3DInfo(u,a.shape,o,1,i),d=new Vt(p.inShape,"float32"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,T]=l,N=n.data.get(a.dataId).values,[E,A,P,R]=c,{batchSize:F,filterDepth:$,filterHeight:z,filterWidth:W,inChannels:q,inDepth:K,inHeight:Y,inWidth:Z,outChannels:te,outDepth:ee,outHeight:se,outWidth:ne,strideDepth:oe,strideHeight:re,strideWidth:le}=p,me=$-1-p.padInfo.front,ke=z-1-p.padInfo.top,Se=W-1-p.padInfo.left;for(let Ee=0;Ee<F;++Ee)for(let Pe=0;Pe<q;++Pe)for(let Xe=0;Xe<K;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/oe)),tt=Math.min(ee,($+Je)/oe);for(let Ce=0;Ce<Y;++Ce){let ut=Ce-ke,at=Math.max(0,Math.ceil(ut/re)),Jt=Math.min(se,(z+ut)/re);for(let Nt=0;Nt<Z;++Nt){let Cn=Nt-Se,Et=Math.max(0,Math.ceil(Cn/le)),en=Math.min(ne,(W+Cn)/le),Nn=0;for(let Tn=Ye;Tn<tt;++Tn){let Yt=Tn*oe-Je;for(let Dn=at;Dn<Jt;++Dn){let tn=Dn*re-ut;for(let Ms=Et;Ms<en;++Ms){let Ls=Ms*le-Cn,ki=v*Ee+x*Tn+k*Dn+T*Ms,Js=E*($-1-Yt)+A*(z-1-tn)+P*(W-1-Ls)+R*Pe;for(let Bs=0;Bs<te;++Bs){let du=y[ki+Bs],Ii=N[Js+Bs];Nn+=du*Ii}}}}h[f*Ee+m*Xe+g*Ce+b*Nt+Pe]=Nn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var RH={kernelName:pg,backendName:"cpu",kernelFunc:EH},DH=st(Aa,e=>Math.cos(e)),FH={kernelName:Aa,backendName:"cpu",kernelFunc:DH},OH=st(Ea,e=>Math.cosh(e)),PH={kernelName:Ea,backendName:"cpu",kernelFunc:OH};function zH(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=o,b=De([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),T=w.computeStrides(b.shape);for(let N=0;N<f;N++){let E=N*4,A=y[E],P=y[E+1],R=y[E+2],F=y[E+3],$=v[N];if($>=c)continue;let z=m>1?(R-A)*(p-1)/(m-1):0,W=g>1?(F-P)*(d-1)/(g-1):0;for(let q=0;q<m;q++){let K=m>1?A*(p-1)+q*z:.5*(A+R)*(p-1);if(K<0||K>p-1){for(let Y=0;Y<g;Y++)for(let Z=0;Z<h;Z++){let te=Z+Y*T[2]+q*T[1]+N*T[0];b.values[te]=l}continue}if(u==="bilinear"){let Y=Math.floor(K),Z=Math.ceil(K),te=K-Y;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+F)*(d-1);if(se<0||se>d-1){for(let le=0;le<h;le++){let me=le+ee*T[2]+q*T[1]+N*T[0];b.values[me]=l}continue}let ne=Math.floor(se),oe=Math.ceil(se),re=se-ne;for(let le=0;le<h;le++){let me=le+ne*k[2]+Y*k[1]+$*k[0],ke=x[me];me=le+oe*k[2]+Y*k[1]+$*k[0];let Se=x[me];me=le+ne*k[2]+Z*k[1]+$*k[0];let Ee=x[me];me=le+oe*k[2]+Z*k[1]+$*k[0];let Pe=x[me],Xe=ke+(Se-ke)*re,Je=Ee+(Pe-Ee)*re;me=le+ee*T[2]+q*T[1]+N*T[0],b.values[me]=Xe+(Je-Xe)*te}}}else for(let Y=0;Y<g;++Y){let Z=g>1?P*(d-1)+Y*W:.5*(P+F)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let ne=se+Y*T[2]+q*T[1]+N*T[0];b.values[ne]=l}continue}let te=Math.round(Z),ee=Math.round(K);for(let se=0;se<h;se++){let ne=se+te*k[2]+ee*k[1]+$*k[0],oe=se+Y*T[2]+q*T[1]+N*T[0];b.values[oe]=x[ne]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var MH={kernelName:lo,backendName:"cpu",kernelFunc:zH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,"cumprod");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.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=cn(l.dtype,"int32"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?1:h[v];else{let x=m(b,y-1);d[v]=i?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var BH={kernelName:pl,backendName:"cpu",kernelFunc:LH};function VH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,"cumsum");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.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=cn(l.dtype,"int32"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var WH={kernelName:uo,backendName:"cpu",kernelFunc:VH};function UH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=ev(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=W0(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var GH={kernelName:hg,backendName:"cpu",kernelFunc:UH};function HH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],u=r.shape[1],l=r.shape[2],c=r.shape[3],p=u*a,d=l*a,h=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(o*p*d*h),g=0;for(let b=0;b<o;++b)for(let y=0;y<p;++y){let v=Math.floor(y/a),x=y%a;for(let k=0;k<d;++k){let T=Math.floor(k/a),N=k%a,E=(x*a+N)*h;for(let A=0;A<h;++A){let R=A+E+c*(T+l*(v+u*b));m[g++]=f[R]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,m)}var qH={kernelName:co,backendName:"cpu",kernelFunc:HH};function DC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s;be([r,a],"depthwiseConv2DNative");let c=w.computeStrides(r.shape),p=w.computeStrides(a.shape),d=u;d==null&&(d=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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KH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"depthwiseConv2dNativeBackpropFilter");let p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,"float32"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),T=n.data.get(a.dataId).values,N=new Vt(a.shape,a.dtype,T);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*N.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var 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c=t.slice();return c[c.length-1]=s,[De(c,n,u),De(c,"int32",l)]}function IC(e,t,n,s){let r=w.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let i={},o=new Int32Array(n[r]),u=new Vt(a,s,e),l=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let b=0;b<a[0];b++)for(let y=0;y<a[2];y++)g.push(u.get(b,f,y));m=g.join(",")}if(i[m]!==void 0)o[f]=i[m];else{let g=Object.keys(i).length;i[m]=g,o[f]=g,l.push(f)}}let p=a.slice();p[1]=Object.keys(i).length;let d=new Vt(p,s);l.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let b=0;b<a[2];b++)d.set(u.get(g,f,b),g,m,b)});let h=n.slice();return h[r]=p[1],{outputValues:d.values,outputShape:h,indices:o}}var Ope="0.0.0";pp("cpu",()=>new L0,1);var SC=st(Fa,e=>e>=0?e:Math.exp(e)-1),yG={kernelName:Fa,backendName:"cpu",kernelFunc:SC};function CC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;be([r],"leakyRelu");let 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CG={kernelName:Ca,backendName:"cpu",kernelFunc:_C};function NG(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=_C({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),i&&(h=Kl({inputs:{a:d,b:i},backend:n}),m.push(d),d=h),c&&(f=ov(n,d,c,o,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var TG={kernelName:ra,backendName:"cpu",kernelFunc:NG},$G=st(sl,e=>Math.acos(e)),_G={kernelName:sl,backendName:"cpu",kernelFunc:$G},AG=st(rl,e=>Math.acosh(e)),EG={kernelName:rl,backendName:"cpu",kernelFunc:AG};function RG(e){let{inputs:t,backend:n}=e,s=t;be(t,"addN");let r=s.map(o=>n.data.get(o.dataId).values),a=De(s[0].shape,s[0].dtype),i=a.values;for(let o=0;o<s.length;o++){let u=r[o];for(let l=0;l<i.length;l++)i[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var DG={kernelName:ka,backendName:"cpu",kernelFunc:RG};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var lH={kernelName:Ma,backendName:"cpu",kernelFunc:uH};function cH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;be([r],"batchToSpaceND");let o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=mt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ga({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var dH={kernelName:oo,backendName:"cpu",kernelFunc:cH};function pH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=ev(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var 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IH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"conv2dBackpropFilter");let p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,b=d.dataFormat==="channelsLast",y=new Vt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,k=n.data.get(r.dataId).values,T=n.data.get(a.dataId).values,N=new Vt(r.shape,r.dtype,k),E=new Vt(a.shape,a.dtype,T);for(let A=0;A<m;++A){let P=Math.max(0,Math.ceil((x-A)/h)),R=Math.min(d.outHeight,(d.inHeight+x-A)/h);for(let F=0;F<g;++F){let $=Math.max(0,Math.ceil((v-F)/f)),z=Math.min(d.outWidth,(d.inWidth+v-F)/f);for(let W=0;W<d.inChannels;++W)for(let q=0;q<d.outChannels;++q){let K=0;for(let Y=0;Y<d.batchSize;++Y)for(let Z=P;Z<R;++Z){let te=A+Z*h-x;for(let ee=$;ee<z;++ee){let se=F+ee*f-v;b?K+=N.get(Y,te,se,W)*E.get(Y,Z,ee,q):K+=N.get(Y,W,te,se)*E.get(Y,q,Z,ee)}}y.set(K,A,F,W,q)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var SH={kernelName:cg,backendName:"cpu",kernelFunc:IH};function CH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;be([r,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=S.convertConv2DDataFormat(l),f=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:T,filterHeight:N,filterWidth:E,inChannels:A,inHeight:P,inWidth:R,outChannels:F,outHeight:$,outWidth:z,strideHeight:W,strideWidth:q}=f;h=f.dataFormat;let K=N-1-f.padInfo.top,Y=E-1-f.padInfo.left,Z=h==="channelsLast",te=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,ne=Z?1:m.strides[1],oe=d[0],re=Z?d[1]:d[2],le=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<T;++ke)for(let Se=0;Se<A;++Se)for(let Ee=0;Ee<P;++Ee){let 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l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=S.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,"float32"),v=y.values,[x,k,T,N]=y.strides,E=n.data.get(a.dataId).values,[A,P,R,F]=c,$=n.data.get(r.dataId).values,[z,W,q,K]=l,Y=p.padInfo.front,Z=p.padInfo.left,te=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((Y-ee)/d)),ne=Math.min(p.outDepth,(p.inDepth+Y-ee)/d),oe=ee*x;for(let re=0;re<g;++re){let le=Math.max(0,Math.ceil((te-re)/h)),me=Math.min(p.outHeight,(p.inHeight+te-re)/h),ke=re*k+oe;for(let Se=0;Se<b;++Se){let Ee=Math.max(0,Math.ceil((Z-Se)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Se)/f),Xe=Se*T+ke;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*N+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let at=ut*z,Jt=ut*A;for(let Nt=se;Nt<ne;++Nt){let Et=(ee+Nt*d-Y)*W+at,en=Nt*P+Jt;for(let Nn=le;Nn<me;++Nn){let Yt=(re+Nn*h-te)*q+Et,Dn=Nn*R+en;for(let tn=Ee;tn<Pe;++tn){let Ls=(Se+tn*f-Z)*K+Yt,ki=tn*F+Dn;Ce+=$[Ls+Je]*E[ki+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var AH={kernelName:dg,backendName:"cpu",kernelFunc:_H};function EH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s;be([r],"conv3dBackpropInputV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=S.computeConv3DInfo(u,a.shape,o,1,i),d=new Vt(p.inShape,"float32"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,T]=l,N=n.data.get(a.dataId).values,[E,A,P,R]=c,{batchSize:F,filterDepth:$,filterHeight:z,filterWidth:W,inChannels:q,inDepth:K,inHeight:Y,inWidth:Z,outChannels:te,outDepth:ee,outHeight:se,outWidth:ne,strideDepth:oe,strideHeight:re,strideWidth:le}=p,me=$-1-p.padInfo.front,ke=z-1-p.padInfo.top,Se=W-1-p.padInfo.left;for(let Ee=0;Ee<F;++Ee)for(let Pe=0;Pe<q;++Pe)for(let Xe=0;Xe<K;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/oe)),tt=Math.min(ee,($+Je)/oe);for(let Ce=0;Ce<Y;++Ce){let ut=Ce-ke,at=Math.max(0,Math.ceil(ut/re)),Jt=Math.min(se,(z+ut)/re);for(let Nt=0;Nt<Z;++Nt){let Cn=Nt-Se,Et=Math.max(0,Math.ceil(Cn/le)),en=Math.min(ne,(W+Cn)/le),Nn=0;for(let Tn=Ye;Tn<tt;++Tn){let Yt=Tn*oe-Je;for(let Dn=at;Dn<Jt;++Dn){let tn=Dn*re-ut;for(let Ms=Et;Ms<en;++Ms){let Ls=Ms*le-Cn,ki=v*Ee+x*Tn+k*Dn+T*Ms,Js=E*($-1-Yt)+A*(z-1-tn)+P*(W-1-Ls)+R*Pe;for(let Bs=0;Bs<te;++Bs){let pu=y[ki+Bs],Ii=N[Js+Bs];Nn+=pu*Ii}}}}h[f*Ee+m*Xe+g*Ce+b*Nt+Pe]=Nn}}}return n.makeTensorInfo(d.shape,d.dtype,d.values)}var RH={kernelName:pg,backendName:"cpu",kernelFunc:EH},DH=st(Aa,e=>Math.cos(e)),FH={kernelName:Aa,backendName:"cpu",kernelFunc:DH},OH=st(Ea,e=>Math.cosh(e)),PH={kernelName:Ea,backendName:"cpu",kernelFunc:OH};function zH(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=o,b=De([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),T=w.computeStrides(b.shape);for(let N=0;N<f;N++){let E=N*4,A=y[E],P=y[E+1],R=y[E+2],F=y[E+3],$=v[N];if($>=c)continue;let z=m>1?(R-A)*(p-1)/(m-1):0,W=g>1?(F-P)*(d-1)/(g-1):0;for(let q=0;q<m;q++){let K=m>1?A*(p-1)+q*z:.5*(A+R)*(p-1);if(K<0||K>p-1){for(let Y=0;Y<g;Y++)for(let Z=0;Z<h;Z++){let te=Z+Y*T[2]+q*T[1]+N*T[0];b.values[te]=l}continue}if(u==="bilinear"){let Y=Math.floor(K),Z=Math.ceil(K),te=K-Y;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+F)*(d-1);if(se<0||se>d-1){for(let le=0;le<h;le++){let me=le+ee*T[2]+q*T[1]+N*T[0];b.values[me]=l}continue}let ne=Math.floor(se),oe=Math.ceil(se),re=se-ne;for(let le=0;le<h;le++){let me=le+ne*k[2]+Y*k[1]+$*k[0],ke=x[me];me=le+oe*k[2]+Y*k[1]+$*k[0];let Se=x[me];me=le+ne*k[2]+Z*k[1]+$*k[0];let Ee=x[me];me=le+oe*k[2]+Z*k[1]+$*k[0];let Pe=x[me],Xe=ke+(Se-ke)*re,Je=Ee+(Pe-Ee)*re;me=le+ee*T[2]+q*T[1]+N*T[0],b.values[me]=Xe+(Je-Xe)*te}}}else for(let Y=0;Y<g;++Y){let Z=g>1?P*(d-1)+Y*W:.5*(P+F)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let ne=se+Y*T[2]+q*T[1]+N*T[0];b.values[ne]=l}continue}let te=Math.round(Z),ee=Math.round(K);for(let se=0;se<h;se++){let ne=se+te*k[2]+ee*k[1]+$*k[0],oe=se+Y*T[2]+q*T[1]+N*T[0];b.values[oe]=x[ne]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var MH={kernelName:co,backendName:"cpu",kernelFunc:zH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,"cumprod");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.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=cn(l.dtype,"int32"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?1:h[v];else{let x=m(b,y-1);d[v]=i?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var BH={kernelName:hl,backendName:"cpu",kernelFunc:LH};function VH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,"cumsum");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.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=cn(l.dtype,"int32"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var WH={kernelName:lo,backendName:"cpu",kernelFunc:VH};function UH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=ev(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=W0(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var GH={kernelName:hg,backendName:"cpu",kernelFunc:UH};function HH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);let o=r.shape[0],u=r.shape[1],l=r.shape[2],c=r.shape[3],p=u*a,d=l*a,h=c/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(o*p*d*h),g=0;for(let b=0;b<o;++b)for(let y=0;y<p;++y){let v=Math.floor(y/a),x=y%a;for(let k=0;k<d;++k){let T=Math.floor(k/a),N=k%a,E=(x*a+N)*h;for(let A=0;A<h;++A){let R=A+E+c*(T+l*(v+u*b));m[g++]=f[R]}}}return n.makeTensorInfo([o,p,d,h],r.dtype,m)}var qH={kernelName:po,backendName:"cpu",kernelFunc:HH};function DC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s;be([r,a],"depthwiseConv2DNative");let c=w.computeStrides(r.shape),p=w.computeStrides(a.shape),d=u;d==null&&(d=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=S.computeConv2DInfo(r.shape,a.shape,i,d,o,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,T=new Vt(h.outShape,r.dtype),N=n.data.get(r.dataId).values,E=n.data.get(a.dataId).values,A=T.values;for(let P=0;P<h.batchSize;++P){let R=P*c[0],F=P*T.strides[0];for(let $=0;$<h.outHeight;++$){let z=F+$*T.strides[1],W=$*h.strideHeight-x;for(let q=0;q<f;++q){let K=W+q*g;if(K<0||K>=h.inHeight)continue;let Y=q*p[0],Z=R+K*c[1];for(let te=0;te<h.outWidth;++te){let ee=z+te*T.strides[2],se=te*h.strideWidth-v;for(let ne=0;ne<m;++ne){let oe=se+ne*b;if(oe<0||oe>=h.inWidth)continue;let re=Y+ne*p[1],le=Z+oe*h.inChannels,me=ee,ke=re;for(let Se=0;Se<h.inChannels;++Se){let Ee=N[le+Se];for(let Pe=0;Pe<k;++Pe)A[me+Pe]+=Ee*E[ke+Pe];me+=k,ke+=k}}}}}}return n.makeTensorInfo(T.shape,T.dtype,T.values)}var jH={kernelName:Ra,backendName:"cpu",kernelFunc:DC};function KH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"depthwiseConv2dNativeBackpropFilter");let p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,"float32"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),T=n.data.get(a.dataId).values,N=new Vt(a.shape,a.dtype,T);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*N.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var XH={kernelName:fg,backendName:"cpu",kernelFunc:KH};function YH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,T]=p,N=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let ke=me-se,Se=Math.max(0,Math.ceil(ke/te)),Ee=Math.min(Y,(F+ke)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-ke;for(let at=Je;at<Ye;++at){let Jt=at*ee-Xe,Nt=x*re+k*Ce+T*at,Cn=E*(F-1-ut)+A*($-1-Jt)+P*le;for(let Et=0;Et<oe;++Et){let en=le*oe+Et,Nn=v[Nt+en],Tn=N[Cn+Et];tt+=Nn*Tn}}}m[g*re+b*me+y*Pe+le]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var QH={kernelName:mg,backendName:"cpu",kernelFunc:YH};function ZH(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,i=De([r,r],s.dtype),o=i.values;for(let l=0;l<a.length;l++)o[l*r+l]=a[l];let u=[...s.shape,...s.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var 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e!==2?!1:vs(e).fenceSync!=null}function nu(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ne=X();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>Wm(2)?2:Wm(1)?1:0);Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ne.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ne.get("WEBGL_VERSION")===2);Ne.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ne.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ne.registerFlag("WEBGL_PACK",()=>Ne.getBool("HAS_WEBGL"));Ne.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_CLIP",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_REDUCE",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_CONV_IM2COL",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>a1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>i1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ne.getNumber("WEBGL_VERSION");return e===0?0:o1(e)});Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!dp.isMobile());Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>u1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ne.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>l1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_FENCE_API_ENABLED",()=>c1(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ne.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>dp.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new 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       bool isnan_custom(float val) {
         uint floatToUint = floatBitsToUint(val);
         return (floatToUint & 0x7fffffffu) > 0x7f800000u;
@@ -156,7 +156,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 `,{getBroadcastDims:p1}=S;function V5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=mv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
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-`)}function nu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return cK(e,t);case 1:return pK(e,t);case 2:return fK(e,t);case 3:return gK(e,t);case 4:return yK(e,t);case 5:return vK(e);case 6:return xK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function h1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return lK(e);case 1:return dK(e,t);case 2:return hK(e,t);case 3:return mK(e,t);default:return bK(e,t)}}function W5(e,t,n=!1,s){let r="";n?r+=h1(e,s):r+=nu(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=wK(e,t):r+=kK(e,t)),r}function U5(e,t,n){switch(e.length){case 0:return f1();case 1:return J5(e,t,n);case 2:return oK(e,t,n);case 3:return tK(e,t,n);default:return sK(e,t,n)}}function G5(e,t,n){switch(e.length){case 0:return f1();case 1:return eK(e,t,n);case 2:return uK(e,t,n);case 3:return nK(e,t,n);case 4:return rK(e,t,n);case 5:return aK(e,t);case 6:return iK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function H5(e){return`
+`)}function su(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return cK(e,t);case 1:return pK(e,t);case 2:return fK(e,t);case 3:return gK(e,t);case 4:return yK(e,t);case 5:return vK(e);case 6:return xK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function h1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return lK(e);case 1:return dK(e,t);case 2:return hK(e,t);case 3:return mK(e,t);default:return bK(e,t)}}function W5(e,t,n=!1,s){let r="";n?r+=h1(e,s):r+=su(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=wK(e,t):r+=kK(e,t)),r}function U5(e,t,n){switch(e.length){case 0:return f1();case 1:return J5(e,t,n);case 2:return oK(e,t,n);case 3:return tK(e,t,n);default:return sK(e,t,n)}}function G5(e,t,n){switch(e.length){case 0:return f1();case 1:return eK(e,t,n);case 2:return uK(e,t,n);case 3:return nK(e,t,n);case 4:return rK(e,t,n);case 5:return aK(e,t);case 6:return iK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function H5(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
@@ -574,7 +574,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     }
   `}function pK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
       float ${s}(int index) {
-        ${su(e)}
+        ${ru(e)}
       }
     `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`
       float ${s}(int index) {
@@ -644,15 +644,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
       return sampleTexture(${s}, uv);
     }
-  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=ru(e,u),h=["row","col"];return`
-      ${nu(d,t)}
+  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=au(e,u),h=["row","col"];return`
+      ${su(d,t)}
       float ${r}(int row, int col) {
-        return ${r}(${au(h,o)});
+        return ${r}(${iu(h,o)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
-        ${su(e)}
+        ${ru(e)}
       }
     `;let l=a[0],c=a[1],p=vi(s);return c===1?t?`
       float ${r}(int row, int col) {
@@ -692,10 +692,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${l}, ${c}, index);
     return sampleTexture(${s}, uv);
   }
-`}function mK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=ru(e,d),m=["b","row","col"];return`
+`}function mK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=au(e,d),m=["b","row","col"];return`
         ${h1(f,t)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${au(m,h)});
+          return ${r}(${iu(m,h)});
         }
       `}let o=fn();if(t)return`
     vec4 ${r}(int b, int row, int col) {
@@ -712,16 +712,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${u}, ${l}, ${p}, ${c}, b, row, col);
       return ${o.texture2D}(${s}, uv);
     }
-  `}function gK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=ru(e,l),g=["row","col","depth"];return`
-        ${nu(m,t)}
+  `}function gK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=au(e,l),g=["row","col","depth"];return`
+        ${su(m,t)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${au(g,u)});
+          return ${r}(${iu(g,u)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${a}, ${i}, 1)));
-        ${su(e)}
+        ${ru(e)}
       }
     `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
       float ${r}(int row, int col, int depth) {
@@ -790,16 +790,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
       return ${r.texture2D}(${n}, uv);
     }
-  `}function yK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=ru(e,u),v=["row","col","depth","depth2"];return`
-      ${nu(y,t)}
+  `}function yK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=au(e,u),v=["row","col","depth","depth2"];return`
+      ${su(y,t)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${au(v,l)});
+        return ${r}(${iu(v,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${o}, ${i}, ${a}, 1)));
-        ${su(e)}
+        ${ru(e)}
       }
     `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`
       float ${r}(int row, int col, int depth, int depth2) {
@@ -860,10 +860,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});
       return sampleTexture(${s}, uv);
     }
-  `}function vK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=ru(e,u),g=["row","col","depth","depth2","depth3"];return`
-      ${nu(m)}
+  `}function vK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=au(e,u),g=["row","col","depth","depth2","depth3"];return`
+      ${su(m)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${au(g,l)});
+        return ${s}(${iu(g,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -871,7 +871,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${a}, ${r})) +
           depth3;
-        ${su(e)}
+        ${ru(e)}
       }
     `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -901,11 +901,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function xK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=ru(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
-      ${nu(g)}
+  `}function xK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=au(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
+      ${su(g)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${au(b,a)});
+        return ${s}(${iu(b,a)});
       }
     `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth,
@@ -916,7 +916,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${su(e)}
+        ${ru(e)}
       }
     `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`
       float ${s}(int row, int col, int depth,
@@ -951,7 +951,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${h}, ${f}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function su(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function ru(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
@@ -982,7 +982,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${d}
       return get${s}(${f});
     }
-  `}function rt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function mv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function ru(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function au(e,t){return t.map(n=>e[n]).join(", ")}function IK(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=V5(r,i,t),u=GC(e.gl,o),l=e.createProgram(u);return X().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...m1(e,t,l)}}function m1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),X().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,"outShape",d),l=e.getUniformLocation(n,"outShapeStrides",d),u=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function rw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function SK(e,t,n,s,r){t.program.enableShapeUniforms||(rw(t.inShapeInfos,n),rw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=mv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function CK(e,t,n){let s="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=mv(e.packedInputs,i.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=S.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?"uniform":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,a}function Sn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var NK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+  `}function rt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function mv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function au(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function iu(e,t){return t.map(n=>e[n]).join(", ")}function IK(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=V5(r,i,t),u=GC(e.gl,o),l=e.createProgram(u);return X().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...m1(e,t,l)}}function m1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),X().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,"outShape",d),l=e.getUniformLocation(n,"outShapeStrides",d),u=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function rw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function SK(e,t,n,s,r){t.program.enableShapeUniforms||(rw(t.inShapeInfos,n),rw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=mv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function CK(e,t,n){let s="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=mv(e.packedInputs,i.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=S.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?"uniform":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,a}function Sn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var NK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
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@@ -1120,7 +1120,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
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       resultUV = uv;
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fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,o,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function $1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function _1(e,t,n,s){let[r,a]=Yl(t,n),i=4,o=new Uint8Array(T5(t*n,i));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function A1(e,t,n,s,r,a,i,o){let u=e,l=new Float32Array($5(a,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 E1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Kf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=X().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,S5(t,e)):this.gl=vs(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),X().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Fu(this.gl,r),Ln(this.gl,a))this.textureHalfFloatExtension=Fu(this.gl,a);else if(X().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),Ln(this.gl,s))this.colorBufferHalfFloatExtension=Fu(this.gl,s);else if(X().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",Ln(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ln(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=b1(this.gl),this.indexBuffer=y1(this.gl),this.framebuffer=QC(this.gl),this.textureConfig=dv(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),v1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),x1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),w1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),N1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),C1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),I1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),k1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Vm(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>_1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return A1(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return $1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=T1(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(X().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>E1(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=g1(t));let n=HC(t);return fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),qC(t,n),this.debug&&rd(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=S1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&rd(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?JC(this.gl,e,t):e1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),t1(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=tu(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&rd(this.gl,this.program),Ou(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Fu(this.gl,X().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(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=DK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ad(this.gl,e,this.framebuffer),this.debug&&Ou(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ad(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ou(this.gl)):Vm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;ad(s,e,this.framebuffer),this.debug&&Ou(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}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 DK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:FK,bincountImpl:R1,bincountReduceImpl:OK,ceilImpl:PK,concatImpl:zK,equalImpl:MK,expImpl:LK,expm1Impl:BK,floorImpl:VK,gatherNdImpl:WK,gatherV2Impl:UK,greaterImpl:GK,greaterEqualImpl:HK,lessImpl:qK,lessEqualImpl:jK,linSpaceImpl:KK,logImpl:XK,maxImpl:YK,maximumImpl:QK,minimumImpl:ZK,multiplyImpl:JK,negImpl:eX,notEqualImpl:tX,prodImpl:nX,rangeImpl:sX,rsqrtImpl:rX,sigmoidImpl:aX,simpleAbsImpl:D1,sliceImpl:iX,sparseFillEmptyRowsImpl:oX,sparseReshapeImpl:uX,sparseSegmentReductionImpl:F1,sqrtImpl:lX,stridedSliceImpl:cX,stringNGramsImpl:dX,stringSplitImpl:pX,stringToHashBucketFastImpl:hX,subImpl:fX,tileImpl:mX,topKImpl:gX,transposeImpl:wv,uniqueImpl:bX}=Zy;function O1(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function ln(e,t){return t===1?[e]:O1(e,t)}function yX(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var vX=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -1180,7 +1180,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${t?B5(["r","c","d"],"inputShape"):yi(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var wX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=iw(t,n),r=ow(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=aw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=iw(n,s),a=ow(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=aw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function kX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function aw(e,t,n,s,r){let a=IX(t,s),i;if(r){let[u,l]=eu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=kX(n,a);return i*o}function IX(e,t){switch(e){case 3:return vv(t);case 4:return xv(t);case 1:return gv(t);case 0:return bv(t);case 2:return yv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SX(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function iw(e,t){if(e===1)return 3;if(e===0||e==null)return SX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function ow(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+  `}var wX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=iw(t,n),r=ow(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=aw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=iw(n,s),a=ow(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=aw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function kX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function aw(e,t,n,s,r){let a=IX(t,s),i;if(r){let[u,l]=tu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=kX(n,a);return i*o}function IX(e,t){switch(e){case 3:return vv(t);case 4:return xv(t);case 1:return gv(t);case 0:return bv(t);case 2:return yv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SX(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function iw(e,t){if(e===1)return 3;if(e===0||e==null)return SX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function ow(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1242,10 +1242,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},PX=xs.whereImpl,zX=1e-7,MX=1e-4,Zc={};function LX(e){return e in Zc||(Zc[e]={}),Zc[e]}var BX=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),VX=600;function WX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*VX/1024/1024}var z1=class extends tl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Kf)t=e;else{let n=vs(X().getNumber("WEBGL_VERSION"),e);t=new Kf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber("WEBGL_VERSION"));t=new Kf(n),this.binaryCache=LX(X().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new wX(this.gpgpu),this.numMBBeforeWarning=WX(),this.texData=new Ud(this,Ss())}nextDataId(){return z1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Zr(i,Mi):p=new Hs(i,Mi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=S.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Zr(s,Mi):h=new Hs(s,Mi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=S.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;o?d=new Zr(r,Mi):d=new Hs(r,Mi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!VC(n))throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool("WEBGL_PACK")&&s===!0,i=a?id(t):t,o=a?new _K(i):new $K(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=BX){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return PX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Zr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=D1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,uw,e.dtype);let t=new Hs(e.shape,uw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new OX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ba(e.shape),...ya(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ba(t),...ya(t)],a=new P1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=id(r),o;s?o=new TK(i):o=new NK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.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(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!Ju(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=CK(e,l,c),d=this.getAndSaveBinary(p,()=>IK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),X().get("ENGINE_COMPILE_ONLY")||SK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zX:MX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=r1(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=eu(c[0],c[1])),o?d=new EK(p,m):d=new AK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),T=this.texData.get(k.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=UX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await OI(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(pv(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=m1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},M1=z1;M1.nextDataId=0;function UX(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ope="0.0.0";function GX(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}dp.isBrowser()&&pp("webgl",()=>new M1,2);var Ppe={forceHalfFloat:GX},L1=`
+    `}},PX=xs.whereImpl,zX=1e-7,MX=1e-4,Zc={};function LX(e){return e in Zc||(Zc[e]={}),Zc[e]}var BX=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),VX=600;function WX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*VX/1024/1024}var z1=class extends nl{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Kf)t=e;else{let n=vs(X().getNumber("WEBGL_VERSION"),e);t=new Kf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber("WEBGL_VERSION"));t=new Kf(n),this.binaryCache=LX(X().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new wX(this.gpgpu),this.numMBBeforeWarning=WX(),this.texData=new Ud(this,Ss())}nextDataId(){return z1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Zr(i,Mi):p=new Hs(i,Mi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=S.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Zr(s,Mi):h=new Hs(s,Mi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=S.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;o?d=new Zr(r,Mi):d=new Hs(r,Mi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!VC(n))throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool("WEBGL_PACK")&&s===!0,i=a?id(t):t,o=a?new _K(i):new $K(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=BX){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return PX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Zr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=D1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,uw,e.dtype);let t=new Hs(e.shape,uw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new OX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ba(e.shape),...ya(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ba(t),...ya(t)],a=new P1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=id(r),o;s?o=new TK(i):o=new NK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.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(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!el(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=CK(e,l,c),d=this.getAndSaveBinary(p,()=>IK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),X().get("ENGINE_COMPILE_ONLY")||SK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zX:MX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=r1(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=tu(c[0],c[1])),o?d=new EK(p,m):d=new AK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),T=this.texData.get(k.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=UX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await OI(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(pv(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=m1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},M1=z1;M1.nextDataId=0;function UX(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ppe="0.0.0";function GX(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}dp.isBrowser()&&pp("webgl",()=>new M1,2);var zpe={forceHalfFloat:GX},L1=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,so=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+`,ro=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1307,10 +1307,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     `}};function kn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var HX={kernelName:Ba,backendName:"webgl",kernelFunc:kn};function Rr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(a.dataId),o=kn({inputs:{x:s},backend:n}),u=kn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var qX={kernelName:jd,backendName:"webgl",kernelFunc:Rr},B1="return (a < 0.) ? b * a : a;",V1=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function jX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(V1,r.shape,i.shape):new so(B1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),u}var KX={kernelName:Va,backendName:"webgl",kernelFunc:jX},W1="return (a < 0.) ? b * a : a;",U1=`
+`;function jX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(V1,r.shape,i.shape):new ro(B1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),u}var KX={kernelName:Va,backendName:"webgl",kernelFunc:jX},W1="return (a < 0.) ? b * a : a;",U1=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function XX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(U1,s.shape,r.shape):new so(W1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var YX={kernelName:Ja,backendName:"webgl",kernelFunc:XX},iu="if (isnan(x)) return x;",QX=`
+`;function XX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(U1,s.shape,r.shape):new ro(W1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var YX={kernelName:Ja,backendName:"webgl",kernelFunc:XX},ou="if (isnan(x)) return x;",QX=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,ZX=`
@@ -1318,7 +1318,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Zr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new so(e,u.shape,l.shape);return c.runWebGLProgram(E,[T,N],cn(x.dtype,k.dtype))}),y=Rr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(f):f,b=u.dtype==="string"?S.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new so(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Kp(e,t=!1){if(e==="linear")return t?AX:CX;if(e==="relu")return t?RX:TX;if(e==="elu")return t?EX:NX;if(e==="relu6")return t?DX:$X;if(e==="prelu")return t?U1:W1;if(e==="leakyrelu")return t?V1:B1;if(e==="sigmoid")return t?FX:_X;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var G1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Zr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new ro(e,u.shape,l.shape);return c.runWebGLProgram(E,[T,N],cn(x.dtype,k.dtype))}),y=Rr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(f):f,b=u.dtype==="string"?S.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new ro(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Kp(e,t=!1){if(e==="linear")return t?AX:CX;if(e==="relu")return t?RX:TX;if(e==="elu")return t?EX:NX;if(e==="relu6")return t?DX:$X;if(e==="prelu")return t?U1:W1;if(e==="leakyrelu")return t?V1:B1;if(e==="sigmoid")return t?FX:_X;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var G1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:u?m=`vec4 activation(vec4 a) {
@@ -1370,7 +1370,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},dw="return a * b;";function kv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=S.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new cw(lw.REAL,s.shape,r.shape),c=new cw(lw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Rr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=JK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Zl(dw,s.shape,r.shape):i=new so(dw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var JX={kernelName:Ya,backendName:"webgl",kernelFunc:kv};function e8(e,t,n){let s=[ba(e.shape),...ya(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ba(t),...ya(t)],i=new P1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!Ju(r.shape,u)&&!(c.texture!==null&&Ju(c.shape,u))?e8(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var t8={kernelName:Ao,backendName:"webgl",kernelFunc:he},pw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
+    `}},dw="return a * b;";function kv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=S.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new cw(lw.REAL,s.shape,r.shape),c=new cw(lw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Rr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=JK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Zl(dw,s.shape,r.shape):i=new ro(dw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var JX={kernelName:Ya,backendName:"webgl",kernelFunc:kv};function e8(e,t,n){let s=[ba(e.shape),...ya(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ba(t),...ya(t)],i=new P1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!el(r.shape,u)&&!(c.texture!==null&&el(c.shape,u))?e8(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var t8={kernelName:Eo,backendName:"webgl",kernelFunc:he},pw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
         if (inIdx < 0 || inIdx >= ${r}) {
           return 0.0;
         }
@@ -1537,14 +1537,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function Xp(e,t,n){let s=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new i8(e.shape,t):new r8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function o8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=S.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Xp(e,u,s),o=S.getInnerMostAxes(o.length,a)),S.assertAxesAreInnerMostDims("sum",o,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=S.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=cp(e.dtype),v=xi(b,y,"sum",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Yp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return o8(r,a,i,n)}var u8={kernelName:oi,backendName:"webgl",kernelFunc:Yp};function qt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=wv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Xp(r,a,i);return l}var l8={kernelName:pi,backendName:"webgl",kernelFunc:qt},H1=1e3;function zd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=n?N.shape[1]:N.shape[2],F=a!=null,$=i!=null,z=u==="leakyrelu",W=u!=null?Kp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>H1&&q===!1){let Z=N,te=E;n&&(Z=qt({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=qt({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=kv({inputs:{a:ne,b:re},backend:r});K=Yp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=cn(e.dtype,t.dtype),te=new G1(k,T,[P,h,f],n,s,F,W,$,z),ee=[N,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function c8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return zd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var d8={kernelName:ra,backendName:"webgl",kernelFunc:c8},hw="return abs(x);";function p8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),i=D1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zr(s.shape,hw):r=new Hs(s.shape,hw),n.runWebGLProgram(r,[s],s.dtype)}var h8={kernelName:ao,backendName:"webgl",kernelFunc:p8},f8=ss+`
+    `}};function Xp(e,t,n){let s=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new i8(e.shape,t):new r8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function o8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=S.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Xp(e,u,s),o=S.getInnerMostAxes(o.length,a)),S.assertAxesAreInnerMostDims("sum",o,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=S.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=cp(e.dtype),v=xi(b,y,"sum",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Yp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return o8(r,a,i,n)}var u8={kernelName:oi,backendName:"webgl",kernelFunc:Yp};function qt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=wv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Xp(r,a,i);return l}var l8={kernelName:pi,backendName:"webgl",kernelFunc:qt},H1=1e3;function zd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=n?N.shape[1]:N.shape[2],F=a!=null,$=i!=null,z=u==="leakyrelu",W=u!=null?Kp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>H1&&q===!1){let Z=N,te=E;n&&(Z=qt({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=qt({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=kv({inputs:{a:ne,b:re},backend:r});K=Yp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=cn(e.dtype,t.dtype),te=new G1(k,T,[P,h,f],n,s,F,W,$,z),ee=[N,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function c8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return zd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var d8={kernelName:ra,backendName:"webgl",kernelFunc:c8},hw="return abs(x);";function p8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),i=D1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zr(s.shape,hw):r=new Hs(s.shape,hw),n.runWebGLProgram(r,[s],s.dtype)}var h8={kernelName:io,backendName:"webgl",kernelFunc:p8},f8=ss+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,m8=Ke({opSnippet:f8}),g8={kernelName:nl,backendName:"webgl",kernelFunc:m8},b8=ss+`
+`,m8=Ke({opSnippet:f8}),g8={kernelName:sl,backendName:"webgl",kernelFunc:m8},b8=ss+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:sl,backendName:"webgl",kernelFunc:y8},fw="return a + b;",x8=jt({opSnippet:fw,packedOpSnippet:fw,supportsComplex:!0,cpuKernelImpl:FK}),w8={kernelName:Ir,backendName:"webgl",kernelFunc:x8},k8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:rl,backendName:"webgl",kernelFunc:y8},fw="return a + b;",x8=jt({opSnippet:fw,packedOpSnippet:fw,supportsComplex:!0,cpuKernelImpl:FK}),w8={kernelName:Ir,backendName:"webgl",kernelFunc:x8},k8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1560,7 +1560,7 @@ return log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:sl,back
         vec4 result = ${s};
         setOutput(result);
       }
-    `}};function ld(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return kn({inputs:{x:s[0]},backend:n});if(s.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=ld({inputs:s.slice(0,u),backend:n}),c=ld({inputs:s.slice(u),backend:n});return ld({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),o=X().getBool("WEBGL_PACK")?new I8(s[0].shape,a):new k8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var S8={kernelName:ka,backendName:"webgl",kernelFunc:ld};function C8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims("all",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"all",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var N8={kernelName:rl,backendName:"webgl",kernelFunc:C8};function T8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims("any",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"any",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var $8={kernelName:al,backendName:"webgl",kernelFunc:T8},_8=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function ld(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return kn({inputs:{x:s[0]},backend:n});if(s.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=ld({inputs:s.slice(0,u),backend:n}),c=ld({inputs:s.slice(u),backend:n});return ld({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),o=X().getBool("WEBGL_PACK")?new I8(s[0].shape,a):new k8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var S8={kernelName:ka,backendName:"webgl",kernelFunc:ld};function C8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims("all",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"all",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var N8={kernelName:al,backendName:"webgl",kernelFunc:C8};function T8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims("any",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"any",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var $8={kernelName:il,backendName:"webgl",kernelFunc:T8},_8=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1642,23 +1642,23 @@ return log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:sl,back
         }
         setOutput(bestIndex);
       }
-    `}};function q1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=S.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new _8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=q1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function j1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=S.computeOptimalWindowSize(a),o=new A8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let c=j1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function K1(e,t,n,s){let r=[n];if(S.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=q1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return j1(e,t,s)}function E8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=K1(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var R8={kernelName:Ia,backendName:"webgl",kernelFunc:E8};function D8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=K1(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var F8={kernelName:il,backendName:"webgl",kernelFunc:D8},O8=ss+`
+    `}};function q1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=S.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new _8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=q1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function j1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=S.computeOptimalWindowSize(a),o=new A8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let c=j1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function K1(e,t,n,s){let r=[n];if(S.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=q1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return j1(e,t,s)}function E8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=K1(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var R8={kernelName:Ia,backendName:"webgl",kernelFunc:E8};function D8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=K1(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var F8={kernelName:ol,backendName:"webgl",kernelFunc:D8},O8=ss+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,P8=Ke({opSnippet:O8}),z8={kernelName:ol,backendName:"webgl",kernelFunc:P8},M8=ss+"return log(x + sqrt(x * x + 1.0));",L8=Ke({opSnippet:M8}),B8={kernelName:ul,backendName:"webgl",kernelFunc:L8},V8=ss+`
+`,P8=Ke({opSnippet:O8}),z8={kernelName:ul,backendName:"webgl",kernelFunc:P8},M8=ss+"return log(x + sqrt(x * x + 1.0));",L8=Ke({opSnippet:M8}),B8={kernelName:ll,backendName:"webgl",kernelFunc:L8},V8=ss+`
   return atan(x);
-`,W8=Ke({opSnippet:V8}),U8={kernelName:ll,backendName:"webgl",kernelFunc:W8},G8=QX+`
+`,W8=Ke({opSnippet:V8}),U8={kernelName:cl,backendName:"webgl",kernelFunc:W8},G8=QX+`
   return atan(a, b);
 `,H8=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+ZX+`
   return result;
-`,q8=jt({opSnippet:G8,packedOpSnippet:H8}),j8={kernelName:dl,backendName:"webgl",kernelFunc:q8},K8=ss+`
+`,q8=jt({opSnippet:G8,packedOpSnippet:H8}),j8={kernelName:pl,backendName:"webgl",kernelFunc:q8},K8=ss+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelName:cl,backendName:"webgl",kernelFunc:X8},el=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelName:dl,backendName:"webgl",kernelFunc:X8},tl=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${h});
 
@@ -1962,7 +1962,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
           setOutput(${k});
         }
       }
-    `}};function Q8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tu(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.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(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new el(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var Z8={kernelName:Sa,backendName:"webgl",kernelFunc:Q8};function J8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Iv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var eY={kernelName:qd,backendName:"webgl",kernelFunc:J8},tY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
+    `}};function Q8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.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(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new tl(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var Z8={kernelName:Sa,backendName:"webgl",kernelFunc:Q8};function J8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Iv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var eY={kernelName:qd,backendName:"webgl",kernelFunc:J8},tY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${l}, ${c});
       const float avgMultiplier = float(${p});
 
@@ -2060,7 +2060,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function sY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new nY(d);return n.runWebGLProgram(h,[r],i.dtype)}var rY={kernelName:og,backendName:"webgl",kernelFunc:sY};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;tu([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=new tY(c);return n.runWebGLProgram(p,[r],i.dtype)}var iY={kernelName:ig,backendName:"webgl",kernelFunc:aY};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return zd({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var uY={kernelName:Ca,backendName:"webgl",kernelFunc:oY},lY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="0.0";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function sY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new nY(d);return n.runWebGLProgram(h,[r],i.dtype)}var rY={kernelName:og,backendName:"webgl",kernelFunc:sY};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;nu([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=new tY(c);return n.runWebGLProgram(p,[r],i.dtype)}var iY={kernelName:ig,backendName:"webgl",kernelFunc:aY};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return zd({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var uY={kernelName:Ca,backendName:"webgl",kernelFunc:oY},lY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="0.0";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ${o}
         setOutput(result);
       }
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+    `}};function gY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=wt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function uu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=iX(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=wt.isSliceContinous(r.shape,o,u);if(l||!c){let p=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new mY(u):new hY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),gY(r,o,u,n)}var bY={kernelName:Po,backendName:"webgl",kernelFunc:uu},yY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=uu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},vY={kernelName:oo,backendName:"webgl",kernelFunc:yY};function xY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=R1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var wY={kernelName:ug,backendName:"webgl",kernelFunc:xY};function kY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var IY={kernelName:lg,backendName:"webgl",kernelFunc:kY},SY="return float(a != b);",X1=jt({opSnippet:SY,cpuKernelImpl:tX,dtype:"bool"}),CY={kernelName:So,backendName:"webgl",kernelFunc:X1};function Jl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.real},backend:n})}var NY={kernelName:np,backendName:"webgl",kernelFunc:Jl},TY="return float(int(x));";function $Y(e,t){let n=new Hs(e.shape,TY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Hm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return kn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Hm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Rr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=Jl({inputs:{input:r},backend:n}),o=Hm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=kn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return $Y(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=X1({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var _Y={kernelName:Na,backendName:"webgl",kernelFunc:Hm},mw="return ceil(x);",AY=Ke({opSnippet:mw,packedOpSnippet:mw,cpuKernelImpl:PK}),EY={kernelName:Ta,backendName:"webgl",kernelFunc:AY},RY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
 
       void main() {
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@@ -2203,7 +2203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(result);
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-    `}};function Jc(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function Qp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var VY={kernelName:Zd,backendName:"webgl",kernelFunc:Qp};function Gi(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>Jl({inputs:{input:m},backend:n})),p=e.map(m=>Qp({inputs:{input:m},backend:n})),d=Gi(c,t,n),h=Gi(p,t,n),f=Rr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=S.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=zK(p,d,s,h),m=S.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),p=Gi(e.slice(0,c),t,n),d=Gi(e.slice(c),t,n),h=Gi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new BY(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=WY(e,t,n),o=new LY(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function WY(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function Y1(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return kn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Gi(o,a,n)}var UY={kernelName:oo,backendName:"webgl",kernelFunc:Y1},Q1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(s?v=`float activation(float a) {
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       }
-    `}};function Z1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>H1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(Ju(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let N=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=zd({a:k,b:N,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=T,A.shape=n.outShape,g=kn({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=zd({a:k,b:T,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:N},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(N)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function J1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let N=new HY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(N,[k],"float32",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o==="leakyrelu",z=o?Kp(o,!0):null,W=new G1(P.shape,T.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,T];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,"float32"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function qY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=Z1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=J1({x:r,filter:a,convInfo:d,backend:n});else{let m=new Q1(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var jY={kernelName:$a,backendName:"webgl",kernelFunc:qY},KY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function Z1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>H1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(el(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let N=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=zd({a:k,b:N,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=T,A.shape=n.outShape,g=kn({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=zd({a:k,b:T,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:N},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(N)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function J1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let N=new HY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(N,[k],"float32",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o==="leakyrelu",z=o?Kp(o,!0):null,W=new G1(P.shape,T.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,T];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,"float32"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function qY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=Z1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=J1({x:r,filter:a,convInfo:d,backend:n});else{let m=new Q1(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var jY={kernelName:$a,backendName:"webgl",kernelFunc:qY},KY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2666,7 +2666,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function ZY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new KY(d);return n.runWebGLProgram(h,[r,a],"float32")}var JY={kernelName:cg,backendName:"webgl",kernelFunc:ZY};function e9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new XY(d);return n.runWebGLProgram(h,[r,a],"float32")}var t9={kernelName:_a,backendName:"webgl",kernelFunc:e9};function n9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new GY(l);return n.runWebGLProgram(c,[r,a],"float32")}var s9={kernelName:Xd,backendName:"webgl",kernelFunc:n9};function r9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=S.computeConv3DInfo(r.shape,u,i,1,o),c=new YY(l);return n.runWebGLProgram(c,[r,a],"float32")}var a9={kernelName:dg,backendName:"webgl",kernelFunc:r9};function i9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=S.computeConv3DInfo(u,a.shape,o,1,i),c=new QY(l);return n.runWebGLProgram(c,[r,a],"float32")}var o9={kernelName:pg,backendName:"webgl",kernelFunc:i9},u9=iu+`
+    `}};function ZY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new KY(d);return n.runWebGLProgram(h,[r,a],"float32")}var JY={kernelName:cg,backendName:"webgl",kernelFunc:ZY};function e9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new XY(d);return n.runWebGLProgram(h,[r,a],"float32")}var t9={kernelName:_a,backendName:"webgl",kernelFunc:e9};function n9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new GY(l);return n.runWebGLProgram(c,[r,a],"float32")}var s9={kernelName:Xd,backendName:"webgl",kernelFunc:n9};function r9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=S.computeConv3DInfo(r.shape,u,i,1,o),c=new YY(l);return n.runWebGLProgram(c,[r,a],"float32")}var a9={kernelName:dg,backendName:"webgl",kernelFunc:r9};function i9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=S.computeConv3DInfo(u,a.shape,o,1,i),c=new QY(l);return n.runWebGLProgram(c,[r,a],"float32")}var o9={kernelName:pg,backendName:"webgl",kernelFunc:i9},u9=ou+`
   return cos(x);
 `,l9=Ke({opSnippet:u9}),c9={kernelName:Aa,backendName:"webgl",kernelFunc:l9},d9=`
   float e2x = exp(-x);
@@ -2732,7 +2732,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
           setOutput(newValue);
         }
       }
-    `}},m9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new f9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],"float32")},g9={kernelName:lo,backendName:"webgl",kernelFunc:m9},bw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"1.0":`getX(${yw(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},m9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new f9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],"float32")},g9={kernelName:co,backendName:"webgl",kernelFunc:m9},bw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"1.0":`getX(${yw(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${rt(s)} coords = getOutputCoords();
         int end = ${vw(s,"coords")};
@@ -2745,7 +2745,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(val);
       }
-    `}};function yw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function vw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function b9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new bw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new bw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var y9={kernelName:pl,backendName:"webgl",kernelFunc:b9},xw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${ww(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}};function yw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function vw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function b9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new bw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new bw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var y9={kernelName:hl,backendName:"webgl",kernelFunc:b9},xw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${ww(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${rt(s)} coords = getOutputCoords();
         int end = ${kw(s,"coords")};
@@ -2758,7 +2758,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(val);
       }
-    `}};function ww(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function kw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function v9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new xw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new xw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var x9={kernelName:uo,backendName:"webgl",kernelFunc:v9};function w9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=R1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=OK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var k9={kernelName:hg,backendName:"webgl",kernelFunc:w9},I9=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}};function ww(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function kw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function v9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new xw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new xw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var x9={kernelName:lo,backendName:"webgl",kernelFunc:v9};function w9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=R1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=OK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var k9={kernelName:hg,backendName:"webgl",kernelFunc:w9},I9=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2777,7 +2777,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
       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 S9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=new I9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var C9={kernelName:co,backendName:"webgl",kernelFunc:S9},e2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
+  `}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 S9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=new I9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var C9={kernelName:po,backendName:"webgl",kernelFunc:S9},e2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?u=`float activation(float a) {
@@ -3153,9 +3153,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
 `,G9=Ke({opSnippet:W9,packedOpSnippet:U9}),H9={kernelName:Fa,backendName:"webgl",kernelFunc:G9},q9="return (b >= 1.0) ? a : a * (b + 1.0);",j9=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,K9=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(j9,s.shape,r.shape):new so(q9,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},X9={kernelName:bg,backendName:"webgl",kernelFunc:K9},Y9=`
+`,K9=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(j9,s.shape,r.shape):new ro(q9,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},X9={kernelName:bg,backendName:"webgl",kernelFunc:K9},Y9=`
   return vec4(equal(a, b));
-`,Q9="return float(a == b);",Z9=jt({opSnippet:Q9,packedOpSnippet:Y9,dtype:"bool",cpuKernelImpl:MK}),J9={kernelName:po,backendName:"webgl",kernelFunc:Z9},eQ=`
+`,Q9="return float(a == b);",Z9=jt({opSnippet:Q9,packedOpSnippet:Y9,dtype:"bool",cpuKernelImpl:MK}),J9={kernelName:ho,backendName:"webgl",kernelFunc:Z9},eQ=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -3170,7 +3170,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   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));
-`,tQ=Ke({opSnippet:eQ}),nQ={kernelName:hl,backendName:"webgl",kernelFunc:tQ},sQ=iu+`
+`,tQ=Ke({opSnippet:eQ}),nQ={kernelName:fl,backendName:"webgl",kernelFunc:tQ},sQ=ou+`
   return exp(x);
 `,rQ=`
   vec4 result = exp(x);
@@ -3181,7 +3181,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,n2=Ke({opSnippet:sQ,packedOpSnippet:rQ,cpuKernelImpl:LK,dtype:"float32"}),aQ={kernelName:Oa,backendName:"webgl",kernelFunc:n2};function qm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var iQ={kernelName:ho,backendName:"webgl",kernelFunc:qm},Iw="return exp(x) - 1.0;",oQ=Ke({opSnippet:Iw,packedOpSnippet:Iw,cpuKernelImpl:BK}),uQ={kernelName:fo,backendName:"webgl",kernelFunc:oQ},Sw=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,n2=Ke({opSnippet:sQ,packedOpSnippet:rQ,cpuKernelImpl:LK,dtype:"float32"}),aQ={kernelName:Oa,backendName:"webgl",kernelFunc:n2};function qm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var iQ={kernelName:fo,backendName:"webgl",kernelFunc:qm},Iw="return exp(x) - 1.0;",oQ=Ke({opSnippet:Iw,packedOpSnippet:Iw,cpuKernelImpl:BK}),uQ={kernelName:mo,backendName:"webgl",kernelFunc:oQ},Sw=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${r};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -3219,7 +3219,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}};function ec(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new dQ(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var pQ={kernelName:fl,backendName:"webgl",kernelFunc:ec},hQ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function ec(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new dQ(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var pQ={kernelName:ml,backendName:"webgl",kernelFunc:ec},hQ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3233,7 +3233,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},fQ={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new hQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Cw="return floor(x);",mQ=Ke({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:VK}),gQ={kernelName:Pa,backendName:"webgl",kernelFunc:mQ},bQ=`
+    `}},fQ={kernelName:go,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new hQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Cw="return floor(x);",mQ=Ke({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:VK}),gQ={kernelName:Pa,backendName:"webgl",kernelFunc:mQ},bQ=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3331,22 +3331,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function AQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=WK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new _Q(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var EQ={kernelName:bo,backendName:"webgl",kernelFunc:AQ},RQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=DQ(e,2);this.userCode=`
+      `}};function AQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=WK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new _Q(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var EQ={kernelName:yo,backendName:"webgl",kernelFunc:AQ},RQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=DQ(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         int index = int(getIndices(resRC.x, resRC.z));
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${s}));
       }
-    `}};function DQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function r2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=UK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new RQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var FQ={kernelName:go,backendName:"webgl",kernelFunc:r2},OQ="return float(a > b);",PQ=`
+    `}};function DQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function r2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=UK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new RQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var FQ={kernelName:bo,backendName:"webgl",kernelFunc:r2},OQ="return float(a > b);",PQ=`
   return vec4(greaterThan(a, b));
-`,zQ=jt({opSnippet:OQ,packedOpSnippet:PQ,cpuKernelImpl:GK,dtype:"bool"}),MQ={kernelName:yo,backendName:"webgl",kernelFunc:zQ},LQ="return float(a >= b);",BQ=`
+`,zQ=jt({opSnippet:OQ,packedOpSnippet:PQ,cpuKernelImpl:GK,dtype:"bool"}),MQ={kernelName:vo,backendName:"webgl",kernelFunc:zQ},LQ="return float(a >= b);",BQ=`
   return vec4(greaterThanEqual(a, b));
-`,VQ=jt({opSnippet:LQ,packedOpSnippet:BQ,dtype:"bool",cpuKernelImpl:HK}),WQ={kernelName:La,backendName:"webgl",kernelFunc:VQ};function UQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return s2(s,!0,n)}var GQ={kernelName:vg,backendName:"webgl",kernelFunc:UQ},HQ="return float(!isnan(x) && !isinf(x));",qQ=Ke({opSnippet:HQ,dtype:"bool"}),jQ={kernelName:ml,backendName:"webgl",kernelFunc:qQ},KQ="return float(isinf(x));",XQ=Ke({opSnippet:KQ,dtype:"bool"}),YQ={kernelName:gl,backendName:"webgl",kernelFunc:XQ},QQ="return float(isnan(x));",ZQ=Ke({opSnippet:QQ,dtype:"bool"}),JQ={kernelName:bl,backendName:"webgl",kernelFunc:ZQ},eZ="return float(a < b);",tZ=`
+`,VQ=jt({opSnippet:LQ,packedOpSnippet:BQ,dtype:"bool",cpuKernelImpl:HK}),WQ={kernelName:La,backendName:"webgl",kernelFunc:VQ};function UQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return s2(s,!0,n)}var GQ={kernelName:vg,backendName:"webgl",kernelFunc:UQ},HQ="return float(!isnan(x) && !isinf(x));",qQ=Ke({opSnippet:HQ,dtype:"bool"}),jQ={kernelName:gl,backendName:"webgl",kernelFunc:qQ},KQ="return float(isinf(x));",XQ=Ke({opSnippet:KQ,dtype:"bool"}),YQ={kernelName:bl,backendName:"webgl",kernelFunc:XQ},QQ="return float(isnan(x));",ZQ=Ke({opSnippet:QQ,dtype:"bool"}),JQ={kernelName:yl,backendName:"webgl",kernelFunc:ZQ},eZ="return float(a < b);",tZ=`
   return vec4(lessThan(a, b));
-`,nZ=jt({opSnippet:eZ,packedOpSnippet:tZ,cpuKernelImpl:qK,dtype:"bool"}),sZ={kernelName:vo,backendName:"webgl",kernelFunc:nZ},rZ="return float(a <= b);",aZ=`
+`,nZ=jt({opSnippet:eZ,packedOpSnippet:tZ,cpuKernelImpl:qK,dtype:"bool"}),sZ={kernelName:xo,backendName:"webgl",kernelFunc:nZ},rZ="return float(a <= b);",aZ=`
   return vec4(lessThanEqual(a, b));
-`,iZ=jt({opSnippet:rZ,packedOpSnippet:aZ,cpuKernelImpl:jK,dtype:"bool"}),oZ={kernelName:xo,backendName:"webgl",kernelFunc:iZ};function uZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=KK(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}var lZ={kernelName:xg,backendName:"webgl",kernelFunc:uZ},cZ=iu+`
+`,iZ=jt({opSnippet:rZ,packedOpSnippet:aZ,cpuKernelImpl:jK,dtype:"bool"}),oZ={kernelName:wo,backendName:"webgl",kernelFunc:iZ};function uZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=KK(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}var lZ={kernelName:xg,backendName:"webgl",kernelFunc:uZ},cZ=ou+`
   return x < 0.0 ? 0./0. : log(x);
 `,dZ=`
   vec4 result = log(x);
@@ -3356,13 +3356,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   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;
-`,pZ=Ke({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:XK}),hZ={kernelName:Wa,backendName:"webgl",kernelFunc:pZ},fZ=iu+`
+`,pZ=Ke({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:XK}),hZ={kernelName:Wa,backendName:"webgl",kernelFunc:pZ},fZ=ou+`
   return log(1.0 + x);
-`,mZ=Ke({opSnippet:fZ}),gZ={kernelName:yl,backendName:"webgl",kernelFunc:mZ},bZ="return float(a >= 1.0 && b >= 1.0);",yZ=`
+`,mZ=Ke({opSnippet:fZ}),gZ={kernelName:vl,backendName:"webgl",kernelFunc:mZ},bZ="return float(a >= 1.0 && b >= 1.0);",yZ=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,vZ=jt({opSnippet:bZ,packedOpSnippet:yZ,dtype:"bool"}),xZ={kernelName:wo,backendName:"webgl",kernelFunc:vZ},wZ="return float(!(x >= 1.0));",kZ=Ke({opSnippet:wZ}),IZ={kernelName:vl,backendName:"webgl",kernelFunc:kZ},SZ="return float(a >= 1.0 || b >= 1.0);",CZ=`
+`,vZ=jt({opSnippet:bZ,packedOpSnippet:yZ,dtype:"bool"}),xZ={kernelName:ko,backendName:"webgl",kernelFunc:vZ},wZ="return float(!(x >= 1.0));",kZ=Ke({opSnippet:wZ}),IZ={kernelName:xl,backendName:"webgl",kernelFunc:kZ},SZ="return float(a >= 1.0 || b >= 1.0);",CZ=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
@@ -3510,7 +3510,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+jp+`
   return result;
-`,LZ=jt({opSnippet:zZ,packedOpSnippet:MZ,cpuKernelImpl:QK}),BZ={kernelName:Ga,backendName:"webgl",kernelFunc:LZ};function VZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tu(r,"maxPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.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(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new el(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var WZ={kernelName:Ha,backendName:"webgl",kernelFunc:VZ};function UZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Iv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var GZ={kernelName:tp,backendName:"webgl",kernelFunc:UZ},HZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
+`,LZ=jt({opSnippet:zZ,packedOpSnippet:MZ,cpuKernelImpl:QK}),BZ={kernelName:Ga,backendName:"webgl",kernelFunc:LZ};function VZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,"maxPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.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(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new tl(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var WZ={kernelName:Ha,backendName:"webgl",kernelFunc:VZ};function UZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Iv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var GZ={kernelName:tp,backendName:"webgl",kernelFunc:UZ},HZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3620,7 +3620,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function jZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new Iv(d,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new qZ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var KZ={kernelName:Ig,backendName:"webgl",kernelFunc:jZ};function XZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;tu([a,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=S.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new el(d,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new HZ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var YZ={kernelName:kg,backendName:"webgl",kernelFunc:XZ};function QZ(e,t,n,s){let r=new el(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new el(n,"max",!0,!0,t);let i=s.runWebGLProgram(r,[e],"float32");return[a,i]}var ZZ={kernelName:Sg,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(S.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=S.computePool2DInfo(s.shape,r,a,l,i),[p,d]=QZ(s,o,c,u);return[p,d]}};function JZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=xi(o,"float32","mean",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}var e7={kernelName:qa,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=i.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=i.texData.get(f.dataId).values,k=new Array(o);for(let E=0;E<k.length;E++)k[E]=s.shape[c[E]];let T=wv(x,s.shape,s.dtype,c,k);f=i.makeTensorInfo(k,s.dtype);let N=i.texData.get(f.dataId);N.values=T}else f=Xp(s,c,i);h.push(f),l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims("sum",l,o);let[m,g]=S.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=S.expandShapeToKeepDim(m,u));let y=JZ(f,g,b,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return y}};function t7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,r.shape.length)),S.assertAxesAreInnerMostDims("min",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"min",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var n7={kernelName:ja,backendName:"webgl",kernelFunc:t7},s7=L1+`
+    `}};function jZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new Iv(d,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new qZ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var KZ={kernelName:Ig,backendName:"webgl",kernelFunc:jZ};function XZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;nu([a,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=S.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new tl(d,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new HZ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var YZ={kernelName:kg,backendName:"webgl",kernelFunc:XZ};function QZ(e,t,n,s){let r=new tl(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new tl(n,"max",!0,!0,t);let i=s.runWebGLProgram(r,[e],"float32");return[a,i]}var ZZ={kernelName:Sg,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(S.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=S.computePool2DInfo(s.shape,r,a,l,i),[p,d]=QZ(s,o,c,u);return[p,d]}};function JZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=xi(o,"float32","mean",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}var e7={kernelName:qa,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=i.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=i.texData.get(f.dataId).values,k=new Array(o);for(let E=0;E<k.length;E++)k[E]=s.shape[c[E]];let T=wv(x,s.shape,s.dtype,c,k);f=i.makeTensorInfo(k,s.dtype);let N=i.texData.get(f.dataId);N.values=T}else f=Xp(s,c,i);h.push(f),l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims("sum",l,o);let[m,g]=S.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=S.expandShapeToKeepDim(m,u));let y=JZ(f,g,b,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return y}};function t7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,r.shape.length)),S.assertAxesAreInnerMostDims("min",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"min",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var n7={kernelName:ja,backendName:"webgl",kernelFunc:t7},s7=L1+`
   return min(a, b);
 `,r7=`
   vec4 result = vec4(min(a, b));
@@ -3718,7 +3718,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
   `+jp+`
   return result;
-`,h7=jt({opSnippet:d7,packedOpSnippet:p7}),f7={kernelName:xl,backendName:"webgl",kernelFunc:h7},m7=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
+`,h7=jt({opSnippet:d7,packedOpSnippet:p7}),f7={kernelName:wl,backendName:"webgl",kernelFunc:h7},m7=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -3772,14 +3772,14 @@ return a / b;`,b7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`;function C7(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[i,o]=eX(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zr(s.shape,S7):r=new Hs(s.shape,I7),n.runWebGLProgram(r,[s],s.dtype)}var N7={kernelName:ko,backendName:"webgl",kernelFunc:C7},T7=xs.nonMaxSuppressionV3Impl;function $7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=T7(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var _7={kernelName:So,backendName:"webgl",kernelFunc:$7},A7=xs.nonMaxSuppressionV4Impl;function E7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=A7(c,p,i,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var R7={kernelName:wl,backendName:"webgl",kernelFunc:E7},D7=xs.nonMaxSuppressionV5Impl;function F7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=D7(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var O7={kernelName:Co,backendName:"webgl",kernelFunc:F7},P7=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`;function C7(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[i,o]=eX(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zr(s.shape,S7):r=new Hs(s.shape,I7),n.runWebGLProgram(r,[s],s.dtype)}var N7={kernelName:Io,backendName:"webgl",kernelFunc:C7},T7=xs.nonMaxSuppressionV3Impl;function $7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=T7(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var _7={kernelName:Co,backendName:"webgl",kernelFunc:$7},A7=xs.nonMaxSuppressionV4Impl;function E7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=A7(c,p,i,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var R7={kernelName:kl,backendName:"webgl",kernelFunc:E7},D7=xs.nonMaxSuppressionV5Impl;function F7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=D7(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var O7={kernelName:No,backendName:"webgl",kernelFunc:F7},P7=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${s}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},z7=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),l=new P7(u,a,i,o),c=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=he({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},M7={kernelName:To,backendName:"webgl",kernelFunc:z7};function Md(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=Md({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=Rr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var L7={kernelName:Go,backendName:"webgl",kernelFunc:Md};function l2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=l2({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=Rr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var B7={kernelName:No,backendName:"webgl",kernelFunc:l2};function V7(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=Y1({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var W7={kernelName:$o,backendName:"webgl",kernelFunc:V7},U7=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=rt(s),a=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
+    `}},z7=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),l=new P7(u,a,i,o),c=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=he({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},M7={kernelName:$o,backendName:"webgl",kernelFunc:z7};function Md(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=Md({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=Rr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var L7={kernelName:Ho,backendName:"webgl",kernelFunc:Md};function l2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=l2({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=Rr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var B7={kernelName:To,backendName:"webgl",kernelFunc:l2};function V7(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=Y1({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var W7={kernelName:_o,backendName:"webgl",kernelFunc:V7},U7=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=rt(s),a=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
         int start = ${a};
         int end = ${i};
 
@@ -3853,7 +3853,7 @@ return a / b;`,b7=`
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
   `+jp+`
   return result;
-`,K7=jt({opSnippet:q7,packedOpSnippet:j7}),X7={kernelName:Za,backendName:"webgl",kernelFunc:K7};function Y7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=S.getAxesPermutation(c,o),d=r;p!=null&&(d=qt({inputs:{x:r},backend:n,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,o),u.push(d)),S.assertAxesAreInnerMostDims("prod",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=nX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=S.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=cp(r.dtype),v=xi(b,y,"prod",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=S.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Q7={kernelName:_o,backendName:"webgl",kernelFunc:Y7},d2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=sX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Z7={kernelName:kl,backendName:"webgl",kernelFunc:d2},J7="return 1.0 / x;",eJ=Ke({opSnippet:J7}),tJ={kernelName:Il,backendName:"webgl",kernelFunc:eJ},nJ=ss+`
+`,K7=jt({opSnippet:q7,packedOpSnippet:j7}),X7={kernelName:Za,backendName:"webgl",kernelFunc:K7};function Y7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=S.getAxesPermutation(c,o),d=r;p!=null&&(d=qt({inputs:{x:r},backend:n,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,o),u.push(d)),S.assertAxesAreInnerMostDims("prod",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=nX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=S.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=cp(r.dtype),v=xi(b,y,"prod",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=S.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Q7={kernelName:Ao,backendName:"webgl",kernelFunc:Y7},d2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=sX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Z7={kernelName:Il,backendName:"webgl",kernelFunc:d2},J7="return 1.0 / x;",eJ=Ke({opSnippet:J7}),tJ={kernelName:Sl,backendName:"webgl",kernelFunc:eJ},nJ=ss+`
   return (x < 0.0) ? 0.0 : x;
 `,sJ=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
@@ -4131,7 +4131,7 @@ return a / b;`,b7=`
 
         setOutput(newValue);
       }
-    `}};function vJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new yJ(r.shape,u,l,a,i):new bJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var xJ={kernelName:Sl,backendName:"webgl",kernelFunc:vJ},wJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function vJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new yJ(r.shape,u,l,a,i):new bJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var xJ={kernelName:Cl,backendName:"webgl",kernelFunc:vJ},wJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4239,7 +4239,7 @@ return a / b;`,b7=`
           }
           setOutput(result);
         }
-    `;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function NJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return kn({inputs:{x:r},backend:n});let u=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new CJ(r.shape,o):new SJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var TJ={kernelName:Eo,backendName:"webgl",kernelFunc:NJ},$J=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
+    `;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function NJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return kn({inputs:{x:r},backend:n});let u=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new CJ(r.shape,o):new SJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var TJ={kernelName:Ro,backendName:"webgl",kernelFunc:NJ},$J=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -4258,7 +4258,7 @@ return a / b;`,b7=`
           }
           setOutput(outputValue);
         }
-    `}},_J={kernelName:Ho,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new $J(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},AJ=`
+    `}},_J={kernelName:qo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new $J(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},AJ=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -4273,7 +4273,7 @@ return a / b;`,b7=`
       return base + 1.0;
     }
   }
-`,EJ=Ke({opSnippet:AJ}),RJ={kernelName:Ro,backendName:"webgl",kernelFunc:EJ},DJ="return inversesqrt(x);",FJ=Ke({opSnippet:DJ,cpuKernelImpl:rX}),OJ={kernelName:si,backendName:"webgl",kernelFunc:FJ},p2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let o=rt(r.length),u=rt(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
+`,EJ=Ke({opSnippet:AJ}),RJ={kernelName:Do,backendName:"webgl",kernelFunc:EJ},DJ="return inversesqrt(x);",FJ=Ke({opSnippet:DJ,cpuKernelImpl:rX}),OJ={kernelName:si,backendName:"webgl",kernelFunc:FJ},p2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let o=rt(r.length),u=rt(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${r});
 
         void main() {
@@ -4293,7 +4293,7 @@ return a / b;`,b7=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function PJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new p2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var zJ={kernelName:Do,backendName:"webgl",kernelFunc:PJ},MJ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=rt(n);this.userCode=`
+      `}};function PJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new p2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var zJ={kernelName:Fo,backendName:"webgl",kernelFunc:PJ},MJ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=rt(n);this.userCode=`
       void main() {
         ${a} resRC = getOutputCoords();
         float cVal = getC(${s});
@@ -4303,13 +4303,13 @@ return a / b;`,b7=`
           setOutput(getB(${r}));
         }
       }
-    `}};function LJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new MJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var BJ={kernelName:Fo,backendName:"webgl",kernelFunc:LJ},VJ=`
+    `}};function LJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new MJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var BJ={kernelName:Oo,backendName:"webgl",kernelFunc:LJ},VJ=`
   // 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);
-`,WJ=Ke({opSnippet:VJ}),UJ={kernelName:Cl,backendName:"webgl",kernelFunc:WJ},GJ=iu+`
+`,WJ=Ke({opSnippet:VJ}),UJ={kernelName:Nl,backendName:"webgl",kernelFunc:WJ},GJ=ou+`
   return 1.0 / (1.0 + exp(-1.0 * x));
 `,HJ=`
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
@@ -4324,12 +4324,12 @@ return a / b;`,b7=`
 `,qJ=Ke({opSnippet:GJ,packedOpSnippet:HJ,cpuKernelImpl:aX}),jJ={kernelName:ai,backendName:"webgl",kernelFunc:qJ},KJ=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,XJ=Ke({opSnippet:KJ}),YJ={kernelName:Nl,backendName:"webgl",kernelFunc:XJ},QJ=iu+`
+`,XJ=Ke({opSnippet:KJ}),YJ={kernelName:Tl,backendName:"webgl",kernelFunc:XJ},QJ=ou+`
   return sin(x);
 `,ZJ=Ke({opSnippet:QJ}),JJ={kernelName:ri,backendName:"webgl",kernelFunc:ZJ},eee=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,tee=Ke({opSnippet:eee}),nee={kernelName:Po,backendName:"webgl",kernelFunc:tee},see=`
+`,tee=Ke({opSnippet:eee}),nee={kernelName:zo,backendName:"webgl",kernelFunc:tee},see=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -4349,15 +4349,15 @@ return a / b;`,b7=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,ree=Ke({opSnippet:see}),aee={kernelName:Tl,backendName:"webgl",kernelFunc:ree},iee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=c2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=he({inputs:{x:c},backend:n,attrs:{shape:p}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},oee={kernelName:zo,backendName:"webgl",kernelFunc:iee};function uee(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
+`,ree=Ke({opSnippet:see}),aee={kernelName:$l,backendName:"webgl",kernelFunc:ree},iee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=c2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=he({inputs:{x:c},backend:n,attrs:{shape:p}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},oee={kernelName:Mo,backendName:"webgl",kernelFunc:iee};function uee(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
          ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
          ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
          ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
-        ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[p,d,h,f,m]=oX(o,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var lee={kernelName:sp,backendName:"webgl",kernelFunc:uee};function cee(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=uX(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var dee={kernelName:$l,backendName:"webgl",kernelFunc:cee};function pee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+        ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[p,d,h,f,m]=oX(o,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var lee={kernelName:sp,backendName:"webgl",kernelFunc:uee};function cee(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=uX(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var dee={kernelName:_l,backendName:"webgl",kernelFunc:cee};function pee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
               ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
               ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=F1(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var hee={kernelName:rp,backendName:"webgl",kernelFunc:pee};function fee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=F1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var mee={kernelName:ap,backendName:"webgl",kernelFunc:fee};function gee(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=new p2(l,u,r.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var bee={kernelName:ip,backendName:"webgl",kernelFunc:gee};function yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=ou({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var vee={kernelName:Mo,backendName:"webgl",kernelFunc:yee},Tw="return sqrt(x);",xee=Ke({opSnippet:Tw,packedOpSnippet:Tw,cpuKernelImpl:lX}),wee={kernelName:ii,backendName:"webgl",kernelFunc:xee},kee="return x * x;",Iee=Ke({opSnippet:kee}),See={kernelName:_l,backendName:"webgl",kernelFunc:Iee},$w="return (a - b) * (a - b);",Cee=jt({opSnippet:$w,packedOpSnippet:$w}),Nee={kernelName:li,backendName:"webgl",kernelFunc:Cee};function Tee({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ss+`
+             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=F1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var mee={kernelName:ap,backendName:"webgl",kernelFunc:fee};function gee(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=new p2(l,u,r.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var bee={kernelName:ip,backendName:"webgl",kernelFunc:gee};function yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=uu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var vee={kernelName:Lo,backendName:"webgl",kernelFunc:yee},Tw="return sqrt(x);",xee=Ke({opSnippet:Tw,packedOpSnippet:Tw,cpuKernelImpl:lX}),wee={kernelName:ii,backendName:"webgl",kernelFunc:xee},kee="return x * x;",Iee=Ke({opSnippet:kee}),See={kernelName:Al,backendName:"webgl",kernelFunc:Iee},$w="return (a - b) * (a - b);",Cee=jt({opSnippet:$w,packedOpSnippet:$w}),Nee={kernelName:li,backendName:"webgl",kernelFunc:Cee};function Tee({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ss+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
   `,a=new Hs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var $ee={kernelName:hi,backendName:"webgl",kernelFunc:Tee},_ee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=rt(n.length),a=rt(n.length),i="";if(s===1)i="coords * strides + begin";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
       ${r} begin = ${r}(${e});
@@ -4367,7 +4367,7 @@ return a / b;`,b7=`
         ${a} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function Aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let N=wt.computeOutShape(y,v,x),E=ou({inputs:{x:r},backend:n,attrs:{begin:y,size:N}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),A=De(r.shape,r.dtype,E),P=cX(h,A,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new _ee(y,x,h);k=n.runWebGLProgram(E,[r],r.dtype)}let T=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),T}var Eee={kernelName:Lo,backendName:"webgl",kernelFunc:Aee};function Ree(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=dX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Dee={kernelName:op,backendName:"webgl",kernelFunc:Ree};function Fee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=pX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var Oee={kernelName:$g,backendName:"webgl",kernelFunc:Fee};function Pee(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(a.dataId),o=hX(i,r);return n.makeTensorInfo(a.shape,"int32",o)}var zee={kernelName:_g,backendName:"webgl",kernelFunc:Pee},Mee="return tan(x);",Lee=Ke({opSnippet:Mee}),Bee={kernelName:Bo,backendName:"webgl",kernelFunc:Lee},Vee=`
+    `}};function Aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let N=wt.computeOutShape(y,v,x),E=uu({inputs:{x:r},backend:n,attrs:{begin:y,size:N}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),A=De(r.shape,r.dtype,E),P=cX(h,A,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new _ee(y,x,h);k=n.runWebGLProgram(E,[r],r.dtype)}let T=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),T}var Eee={kernelName:Bo,backendName:"webgl",kernelFunc:Aee};function Ree(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=dX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Dee={kernelName:op,backendName:"webgl",kernelFunc:Ree};function Fee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=pX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var Oee={kernelName:$g,backendName:"webgl",kernelFunc:Fee};function Pee(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(a.dataId),o=hX(i,r);return n.makeTensorInfo(a.shape,"int32",o)}var zee={kernelName:_g,backendName:"webgl",kernelFunc:Pee},Mee="return tan(x);",Lee=Ke({opSnippet:Mee}),Bee={kernelName:Vo,backendName:"webgl",kernelFunc:Lee},Vee=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
 `,Wee=Ke({opSnippet:Vee}),Uee={kernelName:di,backendName:"webgl",kernelFunc:Wee},Gee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=Hee(e);this.userCode=`
@@ -4449,7 +4449,7 @@ return a / b;`,b7=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function Ur(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function _w(e){let t=1;for(;t<e;)t*=2;return t}function Xee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=gX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Ur(n,h);let b=_w(a),y=_w(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new jee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,"int32",q),Ur(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Kee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,"int32",z),Ur(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let T=v;v=ou({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Ur(n,T);let N=r2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Ur(n,g);let E=l.slice(0,-1);E.push(a),T=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Ur(n,T);let A=N;return N=he({inputs:{x:N},attrs:{shape:E},backend:n}),Ur(n,A),[N,v]}var Yee={kernelName:Vo,backendName:"webgl",kernelFunc:Xee},Qee=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+     `}};function Ur(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function _w(e){let t=1;for(;t<e;)t*=2;return t}function Xee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=gX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Ur(n,h);let b=_w(a),y=_w(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new jee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,"int32",q),Ur(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Kee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,"int32",z),Ur(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let T=v;v=uu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Ur(n,T);let N=r2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Ur(n,g);let E=l.slice(0,-1);E.push(a),T=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Ur(n,T);let A=N;return N=he({inputs:{x:N},attrs:{shape:E},backend:n}),Ur(n,A),[N,v]}var Yee={kernelName:Wo,backendName:"webgl",kernelFunc:Xee},Qee=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4561,7 +4561,7 @@ return a / b;`,b7=`
               }
               setOutput(outputValue);
             }
-        `}};function Zee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Qee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var Jee={kernelName:Wo,backendName:"webgl",kernelFunc:Zee};function ete(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;tu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=bX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],"int32",l)]}var tte={kernelName:Ag,backendName:"webgl",kernelFunc:ete};function nte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=ou({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var ste={kernelName:Uo,backendName:"webgl",kernelFunc:nte},rte=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
+        `}};function Zee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Qee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var Jee={kernelName:Uo,backendName:"webgl",kernelFunc:Zee};function ete(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;nu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=bX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],"int32",l)]}var tte={kernelName:Ag,backendName:"webgl",kernelFunc:ete};function nte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=uu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var ste={kernelName:Go,backendName:"webgl",kernelFunc:nte},rte=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
         sumValue += dot(values, segFilter);
     `,d="";r%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${r}) {
@@ -4667,7 +4667,7 @@ return a / b;`,b7=`
         }
         setOutput(${u});
       }
-    `}};function ate(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=S.getAxesPermutation([l],o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,o)[0]);let d=S.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=cp(r.dtype),g=(x,k,T,N,E)=>{let A=x.shape[0],P=x.shape[1],R=S.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new rte(F,k),z=n.compileAndRun($,[x,T],N);if(u.push(z),z.shape[1]===E)return z;let W=d2({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),q=h2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,N,E)},b=g(f,"unsortedSegmentSum",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=S.getUndoAxesPermutation(c);v=qt({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var ite={kernelName:up,backendName:"webgl",kernelFunc:ate},ote=[d8,h8,g8,v8,w8,S8,N8,$8,R8,F8,z8,B8,U8,j8,Y8,Z8,eY,rY,iY,uY,pY,vY,wY,IY,_Y,EY,OY,qX,MY,UY,jY,JY,t9,s9,a9,o9,c9,h9,g9,y9,x9,k9,C9,T9,E9,D9,P9,L9,V9,H9,X9,J9,nQ,aQ,iQ,uQ,cQ,pQ,fQ,gQ,xQ,IQ,NQ,$Q,EQ,FQ,MQ,WQ,HX,GQ,VY,jQ,YQ,JQ,KX,sZ,oZ,lZ,hZ,gZ,xZ,IZ,TZ,EZ,FZ,PZ,BZ,WZ,GZ,KZ,YZ,ZZ,e7,n7,i7,c7,f7,k7,JX,N7,_7,R7,O7,CY,M7,B7,W7,H7,X7,YX,Q7,Z7,NY,y7,tJ,aJ,lJ,t8,hJ,gJ,xJ,IJ,TJ,_J,RJ,OJ,zJ,BJ,UJ,jJ,YJ,JJ,nee,bY,x7,aee,oee,lee,dee,hee,mee,bee,vee,wee,See,Nee,$ee,Eee,Dee,Oee,zee,v7,u8,Bee,Uee,qee,Yee,Jee,l8,tte,ste,ite,L7];for(let e of ote)Al(e);var zs=X();zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zs.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zs.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zs.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);zs.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zs.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zs.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var ute="return a + b;",lte="return areal * breal - aimag * bimag;",cte="return areal * bimag + aimag * breal;",dte="return a / b;",pte="return a * b;",hte="return (a - b) * (a - b);",fte="return a - b;",mte="return f32(a == b);",gte="return vec4<f32>(a == b);",bte="return f32(a > b);",yte="return vec4<f32>(a > b);",vte="return f32(a >= b);",xte="return vec4<f32>(a >= b);",wte="return f32(a < b);",kte="return vec4<f32>(a < b);",Ite="return f32(a <= b);",Ste="return vec4<f32>(a <= b);",Cte="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Nte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
+    `}};function ate(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=S.getAxesPermutation([l],o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,o)[0]);let d=S.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=cp(r.dtype),g=(x,k,T,N,E)=>{let A=x.shape[0],P=x.shape[1],R=S.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new rte(F,k),z=n.compileAndRun($,[x,T],N);if(u.push(z),z.shape[1]===E)return z;let W=d2({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),q=h2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,N,E)},b=g(f,"unsortedSegmentSum",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=S.getUndoAxesPermutation(c);v=qt({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var ite={kernelName:up,backendName:"webgl",kernelFunc:ate},ote=[d8,h8,g8,v8,w8,S8,N8,$8,R8,F8,z8,B8,U8,j8,Y8,Z8,eY,rY,iY,uY,pY,vY,wY,IY,_Y,EY,OY,qX,MY,UY,jY,JY,t9,s9,a9,o9,c9,h9,g9,y9,x9,k9,C9,T9,E9,D9,P9,L9,V9,H9,X9,J9,nQ,aQ,iQ,uQ,cQ,pQ,fQ,gQ,xQ,IQ,NQ,$Q,EQ,FQ,MQ,WQ,HX,GQ,VY,jQ,YQ,JQ,KX,sZ,oZ,lZ,hZ,gZ,xZ,IZ,TZ,EZ,FZ,PZ,BZ,WZ,GZ,KZ,YZ,ZZ,e7,n7,i7,c7,f7,k7,JX,N7,_7,R7,O7,CY,M7,B7,W7,H7,X7,YX,Q7,Z7,NY,y7,tJ,aJ,lJ,t8,hJ,gJ,xJ,IJ,TJ,_J,RJ,OJ,zJ,BJ,UJ,jJ,YJ,JJ,nee,bY,x7,aee,oee,lee,dee,hee,mee,bee,vee,wee,See,Nee,$ee,Eee,Dee,Oee,zee,v7,u8,Bee,Uee,qee,Yee,Jee,l8,tte,ste,ite,L7];for(let e of ote)El(e);var zs=X();zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zs.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zs.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zs.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);zs.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zs.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zs.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var ute="return a + b;",lte="return areal * breal - aimag * bimag;",cte="return areal * bimag + aimag * breal;",dte="return a / b;",pte="return a * b;",hte="return (a - b) * (a - b);",fte="return a - b;",mte="return f32(a == b);",gte="return vec4<f32>(a == b);",bte="return f32(a > b);",yte="return vec4<f32>(a > b);",vte="return f32(a >= b);",xte="return vec4<f32>(a >= b);",wte="return f32(a < b);",kte="return vec4<f32>(a < b);",Ite="return f32(a <= b);",Ste="return vec4<f32>(a <= b);",Cte="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Nte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
   vec4<f32>(b >= vec4<f32>(1.0)));`,Tte=`
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
@@ -4773,7 +4773,10 @@ return a / b;`,b7=`
   }
   return resFloat;
 `,Ute="return exp(a);",Gte="return floor(a);",Hte="return a;",qte=`if (a < 0.0) { return 1.0/0.0; }
-  return log(a);`,jte="return f32(!(a >= 1.0));",Kte="return -a;",Xte="if (a < 0.0) { return uniforms.alpha * a; } return a;",Yte="if(a < 0.0) { return 0.0; } return a;",Qte="return clamp(a, 0.0, 6.0);",Zte="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Jte=`
+  return log(a);`,jte="return f32(!(a >= 1.0));",Kte="return -a;",Xte="if (a < 0.0) { return uniforms.alpha * a; } return a;",Yte=`
+  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
+  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
+`,Qte="if(a < 0.0) { return 0.0; } return a;",Zte="return clamp(a, 0.0, 6.0);",Jte="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",ene=`
   var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
   let isNaN = isnanVec4(a);
 
@@ -4790,13 +4793,13 @@ return a / b;`,b7=`
     resFloat.a = a.a;
   }
   return resFloat;
-`,ene="return 1.0/sqrt(a);",tne="return 1.0 / (1.0 + exp(-1.0 * a));",nne="return sin(a);",sne=`
+`,tne="return 1.0/sqrt(a);",nne="return 1.0 / (1.0 + exp(-1.0 * a));",sne="return sin(a);",rne=`
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
-`,rne="return sqrt(a);",ane="return a * a;",ine=`
+`,ane="return sqrt(a);",ine="return a * a;",one=`
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`,one="return f32(i32((a)));";function Hr(e,t){switch(e){case 0:return Pte;case 2:return Mte;case 3:return Lte;case 1:return zte;case 4:return t?Wte:Vte;case 5:return Ute;case 6:return Bte;case 7:return Gte;case 8:return Hte;case 9:return qte;case 10:return jte;case 11:return Kte;case 14:return Xte;case 12:return t?Jte:Yte;case 13:return t?Zte:Qte;case 15:return ene;case 18:return tne;case 16:return nne;case 17:return sne;case 19:return rne;case 20:return ane;case 21:return ine;case 22:return one;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e==="linear")return Hr(8);if(e==="relu")return Hr(12,t);if(e==="elu")return Hr(4,t);if(e==="relu6")return Hr(13,t);if(e==="prelu")return tc(14,t);if(e==="sigmoid")return Hr(18);if(e==="leakyrelu")return Hr(14);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function une(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Sv(){return`
+`,une="return f32(i32((a)));";function Hr(e,t){switch(e){case 0:return Pte;case 2:return Mte;case 3:return Lte;case 1:return zte;case 4:return t?Wte:Vte;case 5:return Ute;case 6:return Bte;case 7:return Gte;case 8:return Hte;case 9:return qte;case 10:return jte;case 11:return Kte;case 14:return t?Yte:Xte;case 12:return t?ene:Qte;case 13:return t?Jte:Zte;case 15:return tne;case 18:return nne;case 16:return sne;case 17:return rne;case 19:return ane;case 20:return ine;case 21:return one;case 22:return une;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e==="linear")return Hr(8);if(e==="relu")return Hr(12,t);if(e==="elu")return Hr(4,t);if(e==="relu6")return Hr(13,t);if(e==="prelu")return tc(14,t);if(e==="sigmoid")return Hr(18,t);if(e==="leakyrelu")return Hr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function lne(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Sv(){return`
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
 `}function Dr(){return`
   ${Sv()}
@@ -4809,7 +4812,7 @@ return a / b;`,b7=`
 `}function Ue(){return`
     ${Dr()}
       let index = getGlobalIndex();
-`}function lne(e,t,n,s=!1){let r=[];if(r.push(`
+`}function cne(e,t,n,s=!1){let r=[];if(r.push(`
     let workGroupSizeX = ${n.workGroupSize[0]}u;
     let workGroupSizeY = ${n.workGroupSize[1]}u;
     let workGroupSizeZ = ${n.workGroupSize[2]}u;
@@ -4855,8 +4858,8 @@ return a / b;`,b7=`
     @group(0) @binding(${1+d}) var<storage, read> ${p}: array<${cd(e[d].dtype,n.isVec4)}>;
     `)}),a!==""&&r.push(`
     @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;
-    `);let[o,u]=mne(t.shape,n.dispatchLayout),l=[Ew,r.join(`
-`),Rw(t.shape),o,cne(t.shape.length)];if(n.atomic||l.push(dne(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>pne(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
+    `);let[o,u]=gne(t.shape,n.dispatchLayout),l=[Ew,r.join(`
+`),Rw(t.shape),o,dne(t.shape.length)];if(n.atomic||l.push(pne(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>hne(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
 `);l.push(p)}return l.push(n.getUserCode()),l.join(`
 `)}var Ew=`
   // Checks whether coordinates lie within the bounds of the shape.
@@ -4905,7 +4908,7 @@ return a / b;`,b7=`
   fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
-`;function cne(e){let t="";switch(e){case 0:case 1:t+=`
+`;function dne(e){let t="";switch(e){case 0:case 1:t+=`
         fn getOutputIndexFromCoords(coords : i32) -> i32 {
           return coords;
         }
@@ -4922,7 +4925,7 @@ return a / b;`,b7=`
           return dot(coords, vec4<i32>(
             uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
         }
-        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function dne(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function pne(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${r}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -4950,7 +4953,7 @@ return a / b;`,b7=`
         let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
         setOutputAtIndexI32(flatIndex, value);
       }
-    `}return a}function pne(e,t,n,s){let r=hne(e,n);return e.shape.length<=t.length&&(r+=fne(e,t,n,s)),r}function hne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),i=["d0","d1","d2","d3"].slice(0,s),o=i.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
+    `}return a}function hne(e,t,n,s){let r=fne(e,n);return e.shape.length<=t.length&&(r+=mne(e,t,n,s)),r}function fne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),i=["d0","d1","d2","d3"].slice(0,s),o=i.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
         fn ${a}() -> vec4<f32> {
           return vec4<f32>(${n}[0]);
         }
@@ -4968,7 +4971,7 @@ return a / b;`,b7=`
       return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(",")}),
         ${u})]);
     }
-   `}function fne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i="get"+a+"ByOutput",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`
+   `}function mne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i="get"+a+"ByOutput",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`
         fn ${i}Index(globalIndex : i32) -> vec4<f32> {
           return vec4<f32>(${r}[globalIndex]);
         }
@@ -5025,11 +5028,11 @@ return a / b;`,b7=`
       ${d}
       return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
     }
-  `}function mne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{
+  `}function gne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{
       let globalIndex = getGlobalIndex();
       return getCoordsFromIndex(globalIndex);
     }
-    `,a];let i="",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=une(h,"uniforms.outShape");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {
+    `,a];let i="",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=lne(h,"uniforms.outShape");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {
     ${i}
   `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(",")}); }`,[p,u]}function Rw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
       let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
@@ -5106,7 +5109,7 @@ return a / b;`,b7=`
                  globalCol,
                  acc[innerRow], globalId);
     }
-  }`}var gne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  }`}var bne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
         }
         return vec4<f32>(0.0)`,t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
@@ -5228,7 +5231,7 @@ return a / b;`,b7=`
         }
       }
     }
-  `}function bne(e){return`
+  `}function yne(e){return`
     let TileSize = ${e[0]*4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
 
@@ -5313,8 +5316,8 @@ return a / b;`,b7=`
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${this.outputShape[1]>1?$v([this.workPerThread,this.workPerThread,1],this.workGroupSize):bne(this.workGroupSize)}
-    `}};function yne(){return`
+      ${this.outputShape[1]>1?$v([this.workPerThread,this.workPerThread,1],this.workGroupSize):yne(this.workGroupSize)}
+    `}};function vne(){return`
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
     ${Dr()}
       let coords = getOutputCoords();
@@ -5345,7 +5348,7 @@ return a / b;`,b7=`
         mm_write(batch, row, col, sum);
       }
     }
-  `}var vne=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+  `}var xne=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${i}
             }`:n=`
@@ -5372,8 +5375,8 @@ return a / b;`,b7=`
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${yne()}
-    `}};function xne(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
+      ${vne()}
+    `}};function wne(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
   var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
   var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
   var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
@@ -5460,7 +5463,7 @@ return a / b;`,b7=`
       mm_write(writeCol, globalCol, acc, globalId);
     }
   }
-  `}var wne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push("bias");let o=a!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var kne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push("bias");let o=a!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
           return A[batch * batchASize + row * uniforms.dimInner + col];
         }
         return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
@@ -5494,8 +5497,8 @@ return a / b;`,b7=`
           setOutputAtCoords(batch, row, col, value);
         }
       }
-      ${xne(this.workGroupSize)}
-    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var kne={kernelName:Ao,backendName:"webgpu",kernelFunc:Me};function _v({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new vne([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new wne(k,T,[P,h,f],a,u,i):R?F=new gne(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,i):F=new v2(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,u,i);let $=[N,E];a&&$.push(a),i&&$.push(i);let z=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}];u==="leakyrelu"&&(z.push({type:"float32",data:[o]}),F.uniforms+=" alpha : f32,");let W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function Ine(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return _v({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var Sne={kernelName:ra,backendName:"webgpu",kernelFunc:Ine},Dw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+      ${wne(this.workGroupSize)}
+    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Ine={kernelName:Eo,backendName:"webgpu",kernelFunc:Me};function _v({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new xne([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new kne(k,T,[P,h,f],a,u,i):R?F=new bne(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,i):F=new v2(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,u,i);let $=[N,E];a&&$.push(a),i&&$.push(i);let z=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}];u==="leakyrelu"&&(z.push({type:"float32",data:[o]}),F.uniforms+=" alpha : f32,");let W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function Sne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return _v({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var Cne={kernelName:ra,backendName:"webgpu",kernelFunc:Sne},Dw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
         ${tc(this.op,!1)}
@@ -5510,7 +5513,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
         }
       }
-    `}},Cne=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
+    `}},Nne=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
          let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
          let b = getBByOutputCoords(coords);`;return`
         fn binaryOperation(a : f32, b : f32) -> f32 {
@@ -5537,7 +5540,7 @@ return a / b;`,b7=`
             }
           }
         }
-        `}},Nne=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
+        `}},Tne=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
       fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
         ${tc(this.op,this.isVec4)}
       }
@@ -5559,7 +5562,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-      `}};function Fw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new Nne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Cne(e,t,n,a):new x2(e,t,n)}function es(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Tne={kernelName:Ba,backendName:"webgpu",kernelFunc:es};function uu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.tensorMap.get(a.dataId),o=es({inputs:{x:s},backend:n}),u=es({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var $ne={kernelName:jd,backendName:"webgpu",kernelFunc:uu},nc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+      `}};function Fw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new Tne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Nne(e,t,n,a):new x2(e,t,n)}function es(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var $ne={kernelName:Ba,backendName:"webgpu",kernelFunc:es};function lu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.tensorMap.get(a.dataId),o=es({inputs:{x:s},backend:n}),u=es({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var _ne={kernelName:jd,backendName:"webgpu",kernelFunc:lu},nc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
         ${Hr(this.op,!1)}
       }
@@ -5569,7 +5572,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype==="complex64"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Dw(17,i.shape,o.shape),b=new Dw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=uu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype==="string"?S.fromUint8ToStringArray(p):p,f=i.dtype==="string"?S.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:_ne,ceilImpl:Ane,concatImpl:Ene,equalImpl:Rne,expImpl:Dne,expm1Impl:Fne,floorImpl:One,gatherNdImpl:Pne,gatherV2Impl:zne,greaterEqualImpl:Mne,greaterImpl:Lne,lessEqualImpl:Bne,lessImpl:Vne,logImpl:Wne,maxImpl:Une,maximumImpl:Gne,minimumImpl:Hne,multiplyImpl:qne,negImpl:jne,notEqualImpl:Kne,prodImpl:Xne,rangeImpl:Yne,rsqrtImpl:Qne,simpleAbsImpl:Zne,sliceImpl:Jne,stridedSliceImpl:ese,stringNGramsImpl:tse,subImpl:nse,tileImpl:sse,topKImpl:rse,transposeImpl:ase,uniqueImpl:zpe}=Zy,ise=Kt({opType:0,cpuKernelImpl:Zne}),ose={kernelName:ao,backendName:"webgpu",kernelFunc:ise},use=mn({opSnippet:1,cpuKernelImpl:_ne,supportsComplex:!0}),lse={kernelName:Ir,backendName:"webgpu",kernelFunc:use},cse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
+      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype==="complex64"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Dw(17,i.shape,o.shape),b=new Dw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=lu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype==="string"?S.fromUint8ToStringArray(p):p,f=i.dtype==="string"?S.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:Ane,ceilImpl:Ene,concatImpl:Rne,equalImpl:Dne,expImpl:Fne,expm1Impl:One,floorImpl:Pne,gatherNdImpl:zne,gatherV2Impl:Mne,greaterEqualImpl:Lne,greaterImpl:Bne,lessEqualImpl:Vne,lessImpl:Wne,logImpl:Une,maxImpl:Gne,maximumImpl:Hne,minimumImpl:qne,multiplyImpl:jne,negImpl:Kne,notEqualImpl:Xne,prodImpl:Yne,rangeImpl:Qne,rsqrtImpl:Zne,simpleAbsImpl:Jne,sliceImpl:ese,stridedSliceImpl:tse,stringNGramsImpl:nse,subImpl:sse,tileImpl:rse,topKImpl:ase,transposeImpl:ise,uniqueImpl:Mpe}=Zy,ose=Kt({opType:0,cpuKernelImpl:Jne}),use={kernelName:io,backendName:"webgpu",kernelFunc:ose},lse=mn({opSnippet:1,cpuKernelImpl:Ane,supportsComplex:!0}),cse={kernelName:Ir,backendName:"webgpu",kernelFunc:lse},dse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
       ${Ue()}
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5581,7 +5584,7 @@ return a / b;`,b7=`
           }
         }
       }
-    `}};function dse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return es({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>cn(o,u)),a=s.map(o=>o.shape),i=new cse(a);return n.runWebGPUProgram(i,s,r)}var pse={kernelName:ka,backendName:"webgpu",kernelFunc:dse},w2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let s=[t];S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=S.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
+    `}};function pse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return es({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>cn(o,u)),a=s.map(o=>o.shape),i=new dse(a);return n.runWebGPUProgram(i,s,r)}var hse={kernelName:ka,backendName:"webgpu",kernelFunc:pse},w2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let s=[t];S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=S.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
       var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
       var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
     `,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${r}]`;return`
@@ -5661,7 +5664,7 @@ return a / b;`,b7=`
           setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
         }
       }
-    `}},hse=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
+    `}},fse=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
       ${Sv()}
@@ -5683,7 +5686,7 @@ return a / b;`,b7=`
             [localId.y]);
         }
       }
-    `}},fse=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=mse(this.newDim);return`
+    `}},mse=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=gse(this.newDim);return`
       ${Ue()}
 
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
@@ -5695,7 +5698,7 @@ return a / b;`,b7=`
           }
         }
       }
-    `}};function mse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function wi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=ase(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new hse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new fse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var gse={kernelName:pi,backendName:"webgpu",kernelFunc:wi};function bse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],"max"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var yse={kernelName:Ia,backendName:"webgpu",kernelFunc:bse};function vse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],"min"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var xse={kernelName:il,backendName:"webgpu",kernelFunc:vse},k2=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+    `}};function gse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function wi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=ise(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new fse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new mse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var bse={kernelName:pi,backendName:"webgpu",kernelFunc:wi};function yse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],"max"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var vse={kernelName:Ia,backendName:"webgpu",kernelFunc:yse};function xse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],"min"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var wse={kernelName:ol,backendName:"webgpu",kernelFunc:xse},k2=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
       ${Ue()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -5743,7 +5746,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function wse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new I2(c):(p=new k2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var kse={kernelName:Sa,backendName:"webgpu",kernelFunc:wse};function Ise(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return _v({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Sse={kernelName:Ca,backendName:"webgpu",kernelFunc:Ise},Cse=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=Nse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Km[a]} = uniforms.start[${a}] + coords.${Km[a]};`),`
+    `}};function kse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new I2(c):(p=new k2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var Ise={kernelName:Sa,backendName:"webgpu",kernelFunc:kse};function Sse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return _v({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Cse={kernelName:Ca,backendName:"webgpu",kernelFunc:Sse},Nse=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=Tse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Km[a]} = uniforms.start[${a}] + coords.${Km[a]};`),`
       ${Ue()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -5753,7 +5756,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, getSource(${t}));
         }
       }
-    `}},Km=["x","y","z","w","u","v"];function Nse(e){if(e===1)return"sourceLoc";if(e<=6)return Km.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function lu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=Jne(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new Cse(o,u),c=[{type:"int32",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var Tse={kernelName:Oo,backendName:"webgpu",kernelFunc:lu},$se=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=lu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},_se={kernelName:io,backendName:"webgpu",kernelFunc:$se},S2=mn({opSnippet:10,dtype:"bool",cpuKernelImpl:Kne}),Ase={kernelName:Io,backendName:"webgpu",kernelFunc:S2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.real},backend:n})}var Ese={kernelName:np,backendName:"webgpu",kernelFunc:sc};function Rse(e,t){let n=new nc(e.shape,22),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Xm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return es({inputs:{x:r},backend:n});let i=$t(r.shape),o=Xm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=uu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype==="complex64"){let i=sc({inputs:{input:r},backend:n}),o=Xm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=es({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return Rse(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=S2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Dse={kernelName:Na,backendName:"webgpu",kernelFunc:Xm},Fse=Kt({opType:1,cpuKernelImpl:Ane}),Ose={kernelName:Ta,backendName:"webgpu",kernelFunc:Fse},Pse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+    `}},Km=["x","y","z","w","u","v"];function Tse(e){if(e===1)return"sourceLoc";if(e<=6)return Km.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function cu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=ese(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new Nse(o,u),c=[{type:"int32",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var $se={kernelName:Po,backendName:"webgpu",kernelFunc:cu},_se=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=cu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},Ase={kernelName:oo,backendName:"webgpu",kernelFunc:_se},S2=mn({opSnippet:10,dtype:"bool",cpuKernelImpl:Xne}),Ese={kernelName:So,backendName:"webgpu",kernelFunc:S2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.real},backend:n})}var Rse={kernelName:np,backendName:"webgpu",kernelFunc:sc};function Dse(e,t){let n=new nc(e.shape,22),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Xm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return es({inputs:{x:r},backend:n});let i=$t(r.shape),o=Xm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=lu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype==="complex64"){let i=sc({inputs:{input:r},backend:n}),o=Xm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=es({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return Dse(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=S2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Fse={kernelName:Na,backendName:"webgpu",kernelFunc:Xm},Ose=Kt({opType:1,cpuKernelImpl:Ene}),Pse={kernelName:Ta,backendName:"webgpu",kernelFunc:Ose},zse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
       ${Ue()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5769,7 +5772,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, clampedValue);
         }
       }
-    `}},zse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
+    `}},Mse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
       ${Ue()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5780,7 +5783,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
         }
       }
-    `}};function Mse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:"float32",data:[a]},{type:"float32",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new Pse(r.shape):o=new zse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Lse={kernelName:Sr,backendName:"webgpu",kernelFunc:Mse},Bse=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
+    `}};function Lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:"float32",data:[a]},{type:"float32",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new zse(r.shape):o=new Mse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Bse={kernelName:Sr,backendName:"webgpu",kernelFunc:Lse},Vse=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
       ${Ue()}
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5794,7 +5797,7 @@ return a / b;`,b7=`
           }
         }
       }
-    `}};function Zp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Vse={kernelName:Zd,backendName:"webgpu",kernelFunc:Zp};function Ym(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>Zp({inputs:{input:y},backend:n})),m=Ym(h,t,n),g=Ym(f,t,n),b=uu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=S.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=Ene(f,m,s,g),y=S.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Wse(e,t,n),o=a.map(h=>h.shape),u=new Bse(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Wse(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return es({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Ym(o,a,n)}var Use={kernelName:oo,backendName:"webgpu",kernelFunc:C2},Gse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+    `}};function Zp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Wse={kernelName:Zd,backendName:"webgpu",kernelFunc:Zp};function Ym(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>Zp({inputs:{input:y},backend:n})),m=Ym(h,t,n),g=Ym(f,t,n),b=lu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=S.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=Rne(f,m,s,g),y=S.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Use(e,t,n),o=a.map(h=>h.shape),u=new Vse(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Use(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return es({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Ym(o,a,n)}var Gse={kernelName:uo,backendName:"webgpu",kernelFunc:C2},Hse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
     let divBy4Remainder${e} = flatIndex${e} % 4;
     let divBy4Index${e} = flatIndex${e} / 4;
@@ -5893,7 +5896,7 @@ return a / b;`,b7=`
           }
         }
         ${e}
-      `}},Hse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=$v(this.elementsPerThread,this.workGroupSize),t=`
+      `}},qse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=$v(this.elementsPerThread,this.workGroupSize),t=`
     let outRow = row / uniforms.outShape[2];
     let outCol = row % uniforms.outShape[2];
 
@@ -5948,7 +5951,7 @@ return a / b;`,b7=`
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
     ${e}
-  `}},qse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
+  `}},jse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${r}
              }`:e=`
@@ -6003,7 +6006,7 @@ return a / b;`,b7=`
 
         writeResult(batch, coords[1], coords[2], outChannel, acc);
       }
-    `}},jse=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
+    `}},Kse=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
       inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
     ${Ue()}
 
@@ -6033,7 +6036,7 @@ return a / b;`,b7=`
         }
       }
     }
-  `}};function Kse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat==="channelsLast",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=_v({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Xse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y==="channelsLast",x=u*l*c,k=m*f,T=[k,x],N=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new jse(T,v),$=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[p,d]},{type:"int32",data:[g,b]},{type:"int32",data:[f]},{type:"int32",data:[c*u]},{type:"int32",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,T[0],T[1]]}});A.push(z),A.push(W);let q=[1,T[0],T[1]],K=new v2(q,[1,k,n.outChannels],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),N,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:"int32",data:[Y]},{type:"int32",data:[te]},{type:"int32",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a),o==="leakyrelu"&&($.push({type:"float32",data:[i]}),K.uniforms+=" alpha : f32,");let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function N2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Kse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return Xse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type==="VALID")&&n.outChannels%4===0&&n.outChannels>=32,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new qse(n,u,o,l);else{h?c=new Gse(n,u,o,l):c=new Hse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),o==="leakyrelu"&&(m.push({type:"float32",data:[i]}),c.uniforms+=" alpha : f32,"),s.runWebGPUProgram(c,g,e.dtype,m)}function Yse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return N2({x:r,filter:a,convInfo:d,backend:s})}var Qse={kernelName:$a,backendName:"webgpu",kernelFunc:Yse},Zse=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
+  `}};function Xse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat==="channelsLast",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=_v({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Yse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y==="channelsLast",x=u*l*c,k=m*f,T=[k,x],N=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new Kse(T,v),$=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[p,d]},{type:"int32",data:[g,b]},{type:"int32",data:[f]},{type:"int32",data:[c*u]},{type:"int32",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,T[0],T[1]]}});A.push(z),A.push(W);let q=[1,T[0],T[1]],K=new v2(q,[1,k,n.outChannels],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),N,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:"int32",data:[Y]},{type:"int32",data:[te]},{type:"int32",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a),o==="leakyrelu"&&($.push({type:"float32",data:[i]}),K.uniforms+=" alpha : f32,");let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function N2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Xse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return Yse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type==="VALID")&&n.outChannels%4===0,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new jse(n,u,o,l);else{h?c=new Hse(n,u,o,l):c=new qse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),o==="leakyrelu"&&(m.push({type:"float32",data:[i]}),c.uniforms+=" alpha : f32,"),s.runWebGPUProgram(c,g,e.dtype,m)}function Qse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return N2({x:r,filter:a,convInfo:d,backend:s})}var Zse={kernelName:$a,backendName:"webgpu",kernelFunc:Qse},Jse=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
     fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
       var batch = i32(globalId.z);
       if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
@@ -6087,7 +6090,7 @@ return a / b;`,b7=`
     }
 
     ${$v(this.elementsPerThread,this.workGroupSize)}
-  `}},Jse=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
+  `}},ere=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
     ${Ue()} {
       if(index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6136,7 +6139,7 @@ return a / b;`,b7=`
         setOutputAtIndex(index, dotProd);
       }
     }
-  `}};function ere(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Jse(d);else{f=new Zse(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var tre={kernelName:_a,backendName:"webgpu",kernelFunc:ere},nre=Kt({opType:2}),sre={kernelName:Aa,backendName:"webgpu",kernelFunc:nre},rre=Kt({opType:3}),are={kernelName:Ea,backendName:"webgpu",kernelFunc:rre},ire=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
+  `}};function tre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new ere(d);else{f=new Jse(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var nre={kernelName:_a,backendName:"webgpu",kernelFunc:tre},sre=Kt({opType:2}),rre={kernelName:Aa,backendName:"webgpu",kernelFunc:sre},are=Kt({opType:3}),ire={kernelName:Ea,backendName:"webgpu",kernelFunc:are},ore=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
       ${Ue()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6192,7 +6195,7 @@ return a / b;`,b7=`
         }
       }
     }
-    `}},ore=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new ire(r.shape[3],a.shape,o,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],"float32",p)},ure={kernelName:lo,backendName:"webgpu",kernelFunc:ore},lre=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+    `}},ure=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new ore(r.shape[3],a.shape,o,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],"float32",p)},lre={kernelName:co,backendName:"webgpu",kernelFunc:ure},cre=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6212,7 +6215,7 @@ return a / b;`,b7=`
           let rlt = ${this.getInputSamplingString()};
           setOutputAtIndex(index, rlt);
         }
-      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function cre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=[{type:"int32",data:[a]}],g=new lre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var dre={kernelName:co,backendName:"webgpu",kernelFunc:cre},T2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function dre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=[{type:"int32",data:[a]}],g=new cre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var pre={kernelName:po,backendName:"webgpu",kernelFunc:dre},T2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
         }`:e=`
@@ -6366,7 +6369,7 @@ return a / b;`,b7=`
         ${t}
         writeResult(batch, coords[1], coords[2], d2, dotProd);
       }
-    `}};function pre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new T2(p):(h=new $2(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var hre={kernelName:Ra,backendName:"webgpu",kernelFunc:pre},_2=mn({opSnippet:0,cpuKernelImpl:qne,supportsComplex:!0}),fre={kernelName:Ya,backendName:"webgpu",kernelFunc:_2},mre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
+    `}};function hre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new T2(p):(h=new $2(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var fre={kernelName:Ra,backendName:"webgpu",kernelFunc:hre},_2=mn({opSnippet:0,cpuKernelImpl:jne,supportsComplex:!0}),mre={kernelName:Ya,backendName:"webgpu",kernelFunc:_2},gre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
          if (isnan(candidate)) {
           bestValue = uniforms.NAN;
          } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
@@ -6414,13 +6417,13 @@ return a / b;`,b7=`
           ${n}
         }
        }
-     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=S.getAxesPermutation(u,a),c=e;l!=null&&(c=wi({inputs:{x:e},attrs:{perm:l},backend:r}),u=S.getInnerMostAxes(u.length,a),i.push(c)),S.assertAxesAreInnerMostDims(s,u,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=S.expandShapeToKeepDim(p,o));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=Une(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=Xne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":cp(e.dtype),x=[{type:"int32",data:[m]}],k=new mre(y,s),T=r.runWebGPUProgram(k,[c],v,x);i.push(T),f=Me({inputs:{x:T},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Av(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"sum",n)}var gre={kernelName:oi,backendName:"webgpu",kernelFunc:Av};function bre(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=wi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=_2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Av({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var yre={kernelName:Qd,backendName:"webgpu",kernelFunc:bre},vre=Kt({opType:4}),xre={kernelName:Fa,backendName:"webgpu",kernelFunc:vre},wre=mn({opSnippet:4,dtype:"bool",cpuKernelImpl:Rne}),kre={kernelName:po,backendName:"webgpu",kernelFunc:wre},A2=Kt({opType:5,cpuKernelImpl:Dne,dtype:"float32"}),Ire={kernelName:Oa,backendName:"webgpu",kernelFunc:A2};function Qm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var Sre={kernelName:ho,backendName:"webgpu",kernelFunc:Qm},Cre=Kt({opType:6,cpuKernelImpl:Fne}),Nre={kernelName:fo,backendName:"webgpu",kernelFunc:Cre},Tre=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=S.getAxesPermutation(u,a),c=e;l!=null&&(c=wi({inputs:{x:e},attrs:{perm:l},backend:r}),u=S.getInnerMostAxes(u.length,a),i.push(c)),S.assertAxesAreInnerMostDims(s,u,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=S.expandShapeToKeepDim(p,o));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=Gne(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=Yne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":cp(e.dtype),x=[{type:"int32",data:[m]}],k=new gre(y,s),T=r.runWebGPUProgram(k,[c],v,x);i.push(T),f=Me({inputs:{x:T},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Av(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"sum",n)}var bre={kernelName:oi,backendName:"webgpu",kernelFunc:Av};function yre(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=wi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=_2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Av({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var vre={kernelName:Qd,backendName:"webgpu",kernelFunc:yre},xre=Kt({opType:4}),wre={kernelName:Fa,backendName:"webgpu",kernelFunc:xre},kre=mn({opSnippet:4,dtype:"bool",cpuKernelImpl:Dne}),Ire={kernelName:ho,backendName:"webgpu",kernelFunc:kre},A2=Kt({opType:5,cpuKernelImpl:Fne,dtype:"float32"}),Sre={kernelName:Oa,backendName:"webgpu",kernelFunc:A2};function Qm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var Cre={kernelName:fo,backendName:"webgpu",kernelFunc:Qm},Nre=Kt({opType:6,cpuKernelImpl:One}),Tre={kernelName:mo,backendName:"webgpu",kernelFunc:Nre},$re=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
     ${Ue()}
       if (index < uniforms.size) {
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function cu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new Tre(s),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var $re={kernelName:fl,backendName:"webgpu",kernelFunc:cu},_re=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+  `}};function du(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new $re(s),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var _re={kernelName:ml,backendName:"webgpu",kernelFunc:du},Are=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6429,7 +6432,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, outputValue);
         }
       }
-    `}},Are={kernelName:mo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new _re(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Ere=Kt({opType:7,cpuKernelImpl:One}),Rre={kernelName:Pa,backendName:"webgpu",kernelFunc:Ere},Dre=mn({opSnippet:12,dtype:"int32"}),Fre={kernelName:za,backendName:"webgpu",kernelFunc:Dre},Ore=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},E2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=lne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function R2(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function Ow(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,"int32"),c=n.getFromPixelsProgram(a?"import":"copyExternal");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?"import":"copyExternal"],h=R2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>E2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var Pre={kernelName:fd,backendName:"webgpu",kernelFunc:zre},Bi;function zre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool("WEBGPU_USE_IMPORT")&&i)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Bi==null&&(Bi=document.createElement("canvas").getContext("2d")),Bi.canvas.width=c,Bi.canvas.height=p,Bi.drawImage(r,0,0,c,p),r=Bi.canvas),l||u||i||o)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Mre=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+    `}},Ere={kernelName:go,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Are(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Rre=Kt({opType:7,cpuKernelImpl:Pne}),Dre={kernelName:Pa,backendName:"webgpu",kernelFunc:Rre},Fre=mn({opSnippet:12,dtype:"int32"}),Ore={kernelName:za,backendName:"webgpu",kernelFunc:Fre},Pre=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},E2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=cne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function R2(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function Ow(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,"int32"),c=n.getFromPixelsProgram(a?"import":"copyExternal");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?"import":"copyExternal"],h=R2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>E2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var zre={kernelName:fd,backendName:"webgpu",kernelFunc:Mre},Bi;function Mre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool("WEBGPU_USE_IMPORT")&&i)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Bi==null&&(Bi=document.createElement("canvas").getContext("2d")),Bi.canvas.width=c,Bi.canvas.height=p,Bi.drawImage(r,0,0,c,p),r=Bi.canvas),l||u||i||o)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Lre=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
       ${Ue()}
         if (index < uniforms.size)
         {
@@ -6442,7 +6445,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
         }
       }
-  `}},Lre={kernelName:Ma,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Mre(s.shape,i.shape,o.shape,p,d),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Bre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return N2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Vre={kernelName:aa,backendName:"webgpu",kernelFunc:Bre};function Wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=S.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=i!=null,y=o!=null;b&&g.push(i),y&&g.push(o);let v=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new T2(m,b,d,y):(x=new $2(m,b,d,y),v.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),d==="leakyrelu"&&(v.push({type:"float32",data:[h]}),x.uniforms+=" alpha : f32,"),n.runWebGPUProgram(x,g,"float32",v)}var Ure={kernelName:ia,backendName:"webgpu",kernelFunc:Wre},Gre=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+  `}},Bre={kernelName:Ma,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Lre(s.shape,i.shape,o.shape,p,d),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Vre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return N2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Wre={kernelName:aa,backendName:"webgpu",kernelFunc:Vre};function Ure(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=S.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=i!=null,y=o!=null;b&&g.push(i),y&&g.push(o);let v=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new T2(m,b,d,y):(x=new $2(m,b,d,y),v.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),d==="leakyrelu"&&(v.push({type:"float32",data:[h]}),x.uniforms+=" alpha : f32,"),n.runWebGPUProgram(x,g,"float32",v)}var Gre={kernelName:ia,backendName:"webgpu",kernelFunc:Ure},Hre=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
         ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6456,7 +6459,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, getA(flattenIndex, coords[1]));
         }
       }
-      `}};function Hre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Pne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Gre(i,[l,c]),m=[{type:"int32",data:[i]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var qre={kernelName:bo,backendName:"webgpu",kernelFunc:Hre},jre=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Kre(this.aShape);return`
+      `}};function qre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=zne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Hre(i,[l,c]),m=[{type:"int32",data:[i]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var jre={kernelName:yo,backendName:"webgpu",kernelFunc:qre},Kre=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Xre(this.aShape);return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6465,7 +6468,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, inBounds * getA(${e}));
         }
       }
-    `}};function Kre(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function D2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),T=n.tensorMap.get(d.dataId).values,N=De(d.shape,d.dtype,T),E=zne(N,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new jre(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Xre={kernelName:go,backendName:"webgpu",kernelFunc:D2},Yre=mn({opSnippet:5,cpuKernelImpl:Lne,dtype:"bool"}),Qre={kernelName:yo,backendName:"webgpu",kernelFunc:Yre},Zre=mn({opSnippet:6,dtype:"bool",cpuKernelImpl:Mne}),Jre={kernelName:La,backendName:"webgpu",kernelFunc:Zre};function eae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:"float32",data:[a]}],o=new nc(r.shape,14);return o.uniforms="alpha : f32,",n.runWebGPUProgram(o,[r],"float32",i)}var tae={kernelName:Va,backendName:"webgpu",kernelFunc:eae},nae=mn({opSnippet:7,dtype:"bool",cpuKernelImpl:Vne}),sae={kernelName:vo,backendName:"webgpu",kernelFunc:nae},rae=mn({opSnippet:8,dtype:"bool",cpuKernelImpl:Bne}),aae={kernelName:xo,backendName:"webgpu",kernelFunc:rae},iae=Kt({opType:9,cpuKernelImpl:Wne}),oae={kernelName:Wa,backendName:"webgpu",kernelFunc:iae},uae=mn({opSnippet:9,dtype:"bool"}),lae={kernelName:wo,backendName:"webgpu",kernelFunc:uae},cae=Kt({opType:10}),dae={kernelName:vl,backendName:"webgpu",kernelFunc:cae};function F2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,"max",n)}var pae={kernelName:Ua,backendName:"webgpu",kernelFunc:F2},hae=mn({opSnippet:15,cpuKernelImpl:Gne}),fae={kernelName:Ga,backendName:"webgpu",kernelFunc:hae};function mae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});p=new I2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new k2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var gae={kernelName:Ha,backendName:"webgpu",kernelFunc:mae};function bae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,"mean",n)}var yae={kernelName:qa,backendName:"webgpu",kernelFunc:bae};function vae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"min",n)}var xae={kernelName:ja,backendName:"webgpu",kernelFunc:vae},wae=mn({opSnippet:16,cpuKernelImpl:Hne}),kae={kernelName:Ka,backendName:"webgpu",kernelFunc:wae},Iae=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Wt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}};function Xre(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function D2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),T=n.tensorMap.get(d.dataId).values,N=De(d.shape,d.dtype,T),E=Mne(N,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new Kre(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Yre={kernelName:bo,backendName:"webgpu",kernelFunc:D2},Qre=mn({opSnippet:5,cpuKernelImpl:Bne,dtype:"bool"}),Zre={kernelName:vo,backendName:"webgpu",kernelFunc:Qre},Jre=mn({opSnippet:6,dtype:"bool",cpuKernelImpl:Lne}),eae={kernelName:La,backendName:"webgpu",kernelFunc:Jre};function tae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:"float32",data:[a]}],o=new nc(r.shape,14);return o.uniforms="alpha : f32,",n.runWebGPUProgram(o,[r],"float32",i)}var nae={kernelName:Va,backendName:"webgpu",kernelFunc:tae},sae=mn({opSnippet:7,dtype:"bool",cpuKernelImpl:Wne}),rae={kernelName:xo,backendName:"webgpu",kernelFunc:sae},aae=mn({opSnippet:8,dtype:"bool",cpuKernelImpl:Vne}),iae={kernelName:wo,backendName:"webgpu",kernelFunc:aae},oae=Kt({opType:9,cpuKernelImpl:Une}),uae={kernelName:Wa,backendName:"webgpu",kernelFunc:oae},lae=mn({opSnippet:9,dtype:"bool"}),cae={kernelName:ko,backendName:"webgpu",kernelFunc:lae},dae=Kt({opType:10}),pae={kernelName:xl,backendName:"webgpu",kernelFunc:dae};function F2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,"max",n)}var hae={kernelName:Ua,backendName:"webgpu",kernelFunc:F2},fae=mn({opSnippet:15,cpuKernelImpl:Hne}),mae={kernelName:Ga,backendName:"webgpu",kernelFunc:fae};function gae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});p=new I2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new k2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var bae={kernelName:Ha,backendName:"webgpu",kernelFunc:gae};function yae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,"mean",n)}var vae={kernelName:qa,backendName:"webgpu",kernelFunc:yae};function xae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"min",n)}var wae={kernelName:ja,backendName:"webgpu",kernelFunc:xae},kae=mn({opSnippet:16,cpuKernelImpl:qne}),Iae={kernelName:Ka,backendName:"webgpu",kernelFunc:kae},Sae=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Wt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${Ue()}
         if (index < uniforms.size) {
           let start = ${i}(${t});
@@ -6482,7 +6485,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, getX(${o}));
         }
       }
-    `}},Sae={kernelName:Xa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new Iae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function Cae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=jne(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var Nae={kernelName:ko,backendName:"webgpu",kernelFunc:Cae};function Tae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var $ae={kernelName:So,backendName:"webgpu",kernelFunc:Tae};function _ae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Aae={kernelName:Co,backendName:"webgpu",kernelFunc:_ae};function Ld(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=Ld({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Eae={kernelName:Go,backendName:"webgpu",kernelFunc:Ld};function O2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=O2({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Rae={kernelName:No,backendName:"webgpu",kernelFunc:O2};function Dae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=C2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Fae={kernelName:$o,backendName:"webgpu",kernelFunc:Dae},Oae=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}},Cae={kernelName:Xa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new Sae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function Nae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=Kne(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var Tae={kernelName:Io,backendName:"webgpu",kernelFunc:Nae};function $ae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var _ae={kernelName:Co,backendName:"webgpu",kernelFunc:$ae};function Aae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Eae={kernelName:No,backendName:"webgpu",kernelFunc:Aae};function Ld(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=Ld({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Rae={kernelName:Ho,backendName:"webgpu",kernelFunc:Ld};function O2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=O2({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Dae={kernelName:To,backendName:"webgpu",kernelFunc:O2};function Fae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=C2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Oae={kernelName:_o,backendName:"webgpu",kernelFunc:Fae},Pae=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${Ue()}
         if (index < uniforms.size) {
           let start = ${r};
@@ -6497,7 +6500,7 @@ return a / b;`,b7=`
           }
         }
       }
-    `}},P2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return es({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return cu({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];a.map(l=>o.push({type:"int32",data:[l[0],l[1]]}));let u=new Oae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},Pae={kernelName:Qa,backendName:"webgpu",kernelFunc:P2},zae=mn({opSnippet:13}),Mae={kernelName:Za,backendName:"webgpu",kernelFunc:zae};function Lae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new x2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Bae={kernelName:Ja,backendName:"webgpu",kernelFunc:Lae};function Vae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"prod",n)}var Wae={kernelName:_o,backendName:"webgpu",kernelFunc:Vae},Uae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Yne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Gae={kernelName:kl,backendName:"webgpu",kernelFunc:Uae},z2=mn({opSnippet:3}),Hae={kernelName:Da,backendName:"webgpu",kernelFunc:z2},qae=Kt({opType:12}),jae={kernelName:ei,backendName:"webgpu",kernelFunc:qae},Kae=Kt({opType:13}),Xae={kernelName:ni,backendName:"webgpu",kernelFunc:Kae},Yae=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+    `}},P2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return es({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return du({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];a.map(l=>o.push({type:"int32",data:[l[0],l[1]]}));let u=new Pae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},zae={kernelName:Qa,backendName:"webgpu",kernelFunc:P2},Mae=mn({opSnippet:13}),Lae={kernelName:Za,backendName:"webgpu",kernelFunc:Mae};function Bae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new x2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Vae={kernelName:Ja,backendName:"webgpu",kernelFunc:Bae};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"prod",n)}var Uae={kernelName:Ao,backendName:"webgpu",kernelFunc:Wae},Gae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Qne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Hae={kernelName:Il,backendName:"webgpu",kernelFunc:Gae},z2=mn({opSnippet:3}),qae={kernelName:Da,backendName:"webgpu",kernelFunc:z2},jae=Kt({opType:12}),Kae={kernelName:ei,backendName:"webgpu",kernelFunc:jae},Xae=Kt({opType:13}),Yae={kernelName:ni,backendName:"webgpu",kernelFunc:Xae},Qae=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6540,7 +6543,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Qae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[o?.5:0]}],f=new Yae(r.shape,u,l);return n.runWebGPUProgram(f,[r],"float32",h)}var Zae={kernelName:ti,backendName:"webgpu",kernelFunc:Qae},Jae=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
+    `}};function Zae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[o?.5:0]}],f=new Qae(r.shape,u,l);return n.runWebGPUProgram(f,[r],"float32",h)}var Jae={kernelName:ti,backendName:"webgpu",kernelFunc:Zae},eie=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6571,7 +6574,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function eie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new Jae(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var tie={kernelName:Sl,backendName:"webgpu",kernelFunc:eie},nie=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
+    `}};function tie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new eie(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var nie={kernelName:Cl,backendName:"webgpu",kernelFunc:tie},sie=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
         ${Ue()}
 
@@ -6593,7 +6596,7 @@ return a / b;`,b7=`
             setOutputAtIndex(index, outputValue);
           }
         }
-      `}},sie={kernelName:Ho,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new nie(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},rie=Kt({opType:15,cpuKernelImpl:Qne}),aie={kernelName:si,backendName:"webgpu",kernelFunc:rie},iie=class{constructor(e,t,n,s,r,a,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
+      `}},rie={kernelName:qo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new sie(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},aie=Kt({opType:15,cpuKernelImpl:Zne}),iie={kernelName:si,backendName:"webgpu",kernelFunc:aie},oie=class{constructor(e,t,n,s,r,a,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
       fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
         return index;
       }
@@ -6630,7 +6633,7 @@ return a / b;`,b7=`
 
          ${o}
         }
-      }`}};function oie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=cu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new iie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var uie={kernelName:Do,backendName:"webgpu",kernelFunc:oie},lie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`
+      }`}};function uie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=du({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new oie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var lie={kernelName:Fo,backendName:"webgpu",kernelFunc:uie},cie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6642,7 +6645,7 @@ return a / b;`,b7=`
           }
         }
       }
-    `}};function cie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new lie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var die={kernelName:Fo,backendName:"webgpu",kernelFunc:cie},pie=Kt({opType:18}),hie={kernelName:ai,backendName:"webgpu",kernelFunc:pie},fie=Kt({opType:16}),mie={kernelName:ri,backendName:"webgpu",kernelFunc:fie},gie=Kt({opType:17}),bie={kernelName:Po,backendName:"webgpu",kernelFunc:gie},M2=mn({opSnippet:2,cpuKernelImpl:nse,supportsComplex:!0}),yie={kernelName:ci,backendName:"webgpu",kernelFunc:M2};function vie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=F2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=M2({inputs:{a:r,b:l},backend:n}),p=A2({inputs:{x:c},backend:n}),d=Av({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=z2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var xie={kernelName:ui,backendName:"webgpu",kernelFunc:vie},wie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=P2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},kie={kernelName:zo,backendName:"webgpu",kernelFunc:wie},Iie=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
+    `}};function die(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new cie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var pie={kernelName:Oo,backendName:"webgpu",kernelFunc:die},hie=Kt({opType:18}),fie={kernelName:ai,backendName:"webgpu",kernelFunc:hie},mie=Kt({opType:16}),gie={kernelName:ri,backendName:"webgpu",kernelFunc:mie},bie=Kt({opType:17}),yie={kernelName:zo,backendName:"webgpu",kernelFunc:bie},M2=mn({opSnippet:2,cpuKernelImpl:sse,supportsComplex:!0}),vie={kernelName:ci,backendName:"webgpu",kernelFunc:M2};function xie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=F2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=M2({inputs:{a:r,b:l},backend:n}),p=A2({inputs:{x:c},backend:n}),d=Av({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=z2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var wie={kernelName:ui,backendName:"webgpu",kernelFunc:xie},kie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=P2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},Iie={kernelName:Mo,backendName:"webgpu",kernelFunc:kie},Sie=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
       ${Ue()}
 
         let globalIndex = index * ${this.workPerThread};
@@ -6672,21 +6675,21 @@ return a / b;`,b7=`
             }
           }
         }
-      }`}};function Sie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:c}],f=new Iie(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var Cie={kernelName:ip,backendName:"webgpu",kernelFunc:Sie};function Nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=lu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var Tie={kernelName:Mo,backendName:"webgpu",kernelFunc:Nie},$ie=Kt({opType:19}),_ie={kernelName:ii,backendName:"webgpu",kernelFunc:$ie},Aie={kernelName:_l,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},Eie=mn({opSnippet:11}),Rie={kernelName:li,backendName:"webgpu",kernelFunc:Eie},Die=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+      }`}};function Cie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:c}],f=new Sie(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var Nie={kernelName:ip,backendName:"webgpu",kernelFunc:Cie};function Tie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=cu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var $ie={kernelName:Lo,backendName:"webgpu",kernelFunc:Tie},_ie=Kt({opType:19}),Aie={kernelName:ii,backendName:"webgpu",kernelFunc:_ie},Eie={kernelName:Al,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},Rie=mn({opSnippet:11}),Die={kernelName:li,backendName:"webgpu",kernelFunc:Rie},Fie=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
        ${Ue()}
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            setOutputAtIndex(index, getX(${t}));
          }
        }
-     `}};function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),N=lu({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=Me({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeData(N.dataId)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),E=De(r.shape,r.dtype,N),A=ese(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let N=new Die(h),E=[{type:"int32",data:y},{type:"int32",data:x}],A=n.runWebGPUProgram(N,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Oie={kernelName:Lo,backendName:"webgpu",kernelFunc:Fie};function Pie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=tse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var zie={kernelName:op,backendName:"webgpu",kernelFunc:Pie},Mie=Kt({opType:21}),Lie={kernelName:di,backendName:"webgpu",kernelFunc:Mie},Bie=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Vie(this.rank,"uniforms.");return`
+     `}};function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),N=cu({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=Me({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeData(N.dataId)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),E=De(r.shape,r.dtype,N),A=tse(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let N=new Fie(h),E=[{type:"int32",data:y},{type:"int32",data:x}],A=n.runWebGPUProgram(N,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Pie={kernelName:Bo,backendName:"webgpu",kernelFunc:Oie};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=nse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Mie={kernelName:op,backendName:"webgpu",kernelFunc:zie},Lie=Kt({opType:21}),Bie={kernelName:di,backendName:"webgpu",kernelFunc:Lie},Vie=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Wie(this.rank,"uniforms.");return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function Vie(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Wie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=sse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Bie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var Uie={kernelName:Cr,backendName:"webgpu",kernelFunc:Wie},Gie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
+    `}};function Wie(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Uie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=rse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Vie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var Gie={kernelName:Cr,backendName:"webgpu",kernelFunc:Uie},Hie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
         ${Ue()}
           if (index < uniforms.size) {
@@ -6754,7 +6757,7 @@ return a / b;`,b7=`
             }
           }
         }
-      `}},Hie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
+      `}},qie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
         ${Ue()}
           if (index < uniforms.size) {
             let outC = getCoordsFromIndex(index);
@@ -6813,7 +6816,7 @@ return a / b;`,b7=`
             }
           }
         }
-      `}};function Vi(e,t){t!==null&&e.disposeData(t.dataId)}function Pw(e){let t=1;for(;t<e;)t*=2;return t}function qie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[T,N]=rse(k,o,r.dtype,a,i);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(N.shape,N.dtype,N.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,"int32",[])];if(u===1)return[r,cu({attrs:{shape:o,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Pw(a),h=Pw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,T,N)=>{let E=m(),A=new Gie(N),R=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[T]}],F=f;f=n.runWebGPUProgram(A,E,"int32",R),Vi(n,F)};for(let k=1;k<d;k*=2){let T=k*2;for(let N=k;N>=1;N/=2)g(T,N,[c,h])}for(let k=h;k>d;k/=2){let T=m(),N=new Hie([c,k/2]),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram(N,T,"int32",A),Vi(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=lu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Vi(n,b);let y=D2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Vi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Vi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Vi(n,x),[y,f]}var jie={kernelName:Vo,backendName:"webgpu",kernelFunc:qie},Kie=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function Vi(e,t){t!==null&&e.disposeData(t.dataId)}function Pw(e){let t=1;for(;t<e;)t*=2;return t}function jie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[T,N]=ase(k,o,r.dtype,a,i);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(N.shape,N.dtype,N.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,"int32",[])];if(u===1)return[r,du({attrs:{shape:o,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Pw(a),h=Pw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,T,N)=>{let E=m(),A=new Hie(N),R=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[T]}],F=f;f=n.runWebGPUProgram(A,E,"int32",R),Vi(n,F)};for(let k=1;k<d;k*=2){let T=k*2;for(let N=k;N>=1;N/=2)g(T,N,[c,h])}for(let k=h;k>d;k/=2){let T=m(),N=new qie([c,k/2]),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram(N,T,"int32",A),Vi(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=cu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Vi(n,b);let y=D2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Vi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Vi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Vi(n,x),[y,f]}var Kie={kernelName:Wo,backendName:"webgpu",kernelFunc:jie},Xie=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
           fn mapCoord(outCoord : f32, len : f32) -> f32{
             var inCoord = outCoord;
             if(uniforms.fillModeId == 2) {
@@ -6929,7 +6932,7 @@ return a / b;`,b7=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function Xie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Kie(g),y=i==="nearest"?1:2,v;switch(o){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var Yie={kernelName:Wo,backendName:"webgpu",kernelFunc:Xie};function Qie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=lu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Zie={kernelName:Uo,backendName:"webgpu",kernelFunc:Qie},Jie=[Sne,ose,lse,pse,yse,xse,kse,Sse,_se,Dse,Ose,Lse,$ne,Use,Qse,tre,sre,are,ure,dre,hre,yre,xre,kre,Ire,Sre,Nre,$re,Are,Pre,Rre,Fre,Lre,Vre,Ure,qre,Xre,Qre,Jre,Tne,Vse,tae,sae,aae,oae,lae,dae,pae,fae,gae,yae,xae,kae,Sae,fre,Nae,$ae,Aae,Ase,Rae,Fae,Pae,Mae,Bae,Wae,Gae,Ese,Hae,jae,Xae,kne,Zae,tie,sie,aie,uie,die,hie,mie,bie,Tse,Oie,zie,xie,kie,Cie,Tie,_ie,Aie,Rie,yie,gre,Lie,Uie,jie,Yie,gse,Zie,Eae];for(let e of Jie)Al(e);var eoe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=zw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=zw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function zw(e,t){return`${e}_${t}`}var L2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+        `}};function Yie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Xie(g),y=i==="nearest"?1:2,v;switch(o){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var Qie={kernelName:Uo,backendName:"webgpu",kernelFunc:Yie};function Zie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=cu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Jie={kernelName:Go,backendName:"webgpu",kernelFunc:Zie},eoe=[Cne,use,cse,hse,vse,wse,Ise,Cse,Ase,Fse,Pse,Bse,_ne,Gse,Zse,nre,rre,ire,lre,pre,fre,vre,wre,Ire,Sre,Cre,Tre,_re,Ere,zre,Dre,Ore,Bre,Wre,Gre,jre,Yre,Zre,eae,$ne,Wse,nae,rae,iae,uae,cae,pae,hae,mae,bae,vae,wae,Iae,Cae,mre,Tae,_ae,Eae,Ese,Dae,Oae,zae,Lae,Vae,Uae,Hae,Rse,qae,Kae,Yae,Ine,Jae,nie,rie,iie,lie,pie,fie,gie,yie,$se,Pie,Mie,wie,Iie,Nie,$ie,Aie,Eie,Die,vie,bre,Bie,Gie,Kie,Qie,bse,Jie,Rae];for(let e of eoe)El(e);var toe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=zw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=zw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function zw(e,t){return`${e}_${t}`}var L2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
       @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
 
       ${Ue()}
@@ -6943,10 +6946,10 @@ return a / b;`,b7=`
           }
         }
       }
-  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},toe=class extends L2{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},noe=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Mw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},B2=class extends tl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Tv())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new eoe(this.device),this.tensorMap=new Ud(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return B2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*jm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*jm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new L2),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new toe),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=S.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=b2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type==="int32"?new Int32Array(s,o,a.data.length).set(a.data):a.type==="uint32"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let N=this.tensorMap.get(r.dataId);return N.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Mw(this.device,e);let a=[{type:"float32",data:[NaN]}],i=t.concat(r).map(N=>N.shape),o="int32";i.map(N=>{a.push({type:o,data:N})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let N=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((N,E)=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[E]}}),p=c.map(N=>N.dtype).concat(r.dtype),d=c.map(N=>S.getBroadcastDims(N.shape,r.shape)),h=c.map(N=>w.arraysEqual(N.shape,r.shape)).join("_"),f=d.map(N=>N.join("_")).join(";"),m=R2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>E2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Ore(this.device,g,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let T={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(T),X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Mw(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=noe){return X().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Ev=B2;Ev.nextDataId=0;var soe={};Ae(soe,{WebGPUBackend:()=>Ev,webgpu_util:()=>m2});Tv()&&pp("webgpu",async()=>{X().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:X().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new Ev(a,r)},3);var It=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(It||{}),Jp=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(Jp||{}),V2;function roe(e){V2=e.wasm.cwrap(ra,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function aoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Jp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=qo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return V2(d,T,r.shape.length,h,N,a.shape.length,u,l,g,f,m,p||0,k),x}var ioe={kernelName:ra,backendName:"wasm",setupFunc:roe,kernelFunc:aoe};function Xt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,It[o.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var ooe=Xt(ao);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=S.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,It[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var uoe=!0,loe=gn(Ir,uoe),W2;function coe(e){W2=e.wasm.cwrap(ka,null,["array","number","number","number"])}function doe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return W2(a,r.length,It[s.dtype],i),s}var poe={kernelName:ka,backendName:"wasm",setupFunc:coe,kernelFunc:doe};function eh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var hoe={kernelName:Ba,backendName:"wasm",kernelFunc:eh},U2;function foe(e){U2=e.wasm.cwrap(pi,null,["number","array","number","number","number","array","number"])}function wr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=goe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=moe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=eh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return U2(c,h,u.shape.length,It[u.dtype],p,d,a.length),l}function moe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function goe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var boe={kernelName:pi,backendName:"wasm",kernelFunc:wr,setupFunc:foe};function Fr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=S.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=S.getInnerMostAxes(i.length,r),u=wr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var G2;function yoe(e){G2=e.wasm.cwrap(rl,null,["number, number, number"])}function voe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims("all",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;G2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var xoe={kernelName:rl,backendName:"wasm",setupFunc:yoe,kernelFunc:voe},H2;function woe(e){H2=e.wasm.cwrap(al,null,["number, number, number"])}function koe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims("any",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;H2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ioe={kernelName:al,backendName:"wasm",setupFunc:woe,kernelFunc:koe},q2;function Soe(e){q2=e.wasm.cwrap(Ia,null,["number","number","number","number","number"])}function Coe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Fr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return q2(o,It[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var Noe={kernelName:Ia,backendName:"wasm",kernelFunc:Coe,setupFunc:Soe},j2;function Toe(e){j2=e.wasm.cwrap(Sa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $oe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${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}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,"float32"),k=s.dataIdMap.get(x.dataId).id;return j2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var _oe={kernelName:Sa,backendName:"wasm",setupFunc:Toe,kernelFunc:$oe};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Aoe={kernelName:Ao,backendName:"wasm",kernelFunc:yn},K2;function Eoe(e){K2=e.wasm.cwrap(Ca,null,["number","array","number","number","array","number","number","number","number"])}function Roe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=qo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],T=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),N=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(T.dataId).id,A=n.dataIdMap.get(N.dataId).id,P=i?T.shape[2]:T.shape[1],R=o?N.shape[1]:N.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],T.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(T.shape).buffer),q=new Uint8Array(new Int32Array(N.shape).buffer);return K2(E,W,T.shape.length,A,q,N.shape.length,i,o,z),n.disposeData(T.dataId),n.disposeData(N.dataId),$.shape=v,$}var Doe={kernelName:Ca,backendName:"wasm",setupFunc:Eoe,kernelFunc:Roe};function va(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype==="string"){let f=Fd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Foe(u,c[0],d,a,i);else if(h===3)Ooe(u,c[0],c[1],d,a,i);else if(h===4)Poe(u,c[0],c[1],c[2],d,a,i);else{let f=Fd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function Foe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Ooe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function Poe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var zoe={kernelName:Oo,backendName:"wasm",kernelFunc:va};function Moe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=va({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Loe={kernelName:io,backendName:"wasm",kernelFunc:Moe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Boe={kernelName:Na,backendName:"wasm",kernelFunc:ac},Voe=Xt(Ta),X2;function Woe(e){X2=e.wasm.cwrap(Sr,null,["number","number","number","number"])}function 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Yoe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=S.convertConv2DDataFormat(u),h=S.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:T,strideHeight:N,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,"float32"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return Z2(me,ke,f,m,g,y,v,b,k,T,x,N,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var Qoe={kernelName:_a,backendName:"wasm",setupFunc:Xoe,kernelFunc:Yoe},Zoe=Xt(Aa),Joe=Xt(Ea),J2=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(J2||{}),eN;function eue(e){eN=e.wasm.cwrap(lo,null,["number","number","number","number","array","number","number","number","number","number"])}function tue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=ac({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return eN(g,b,y,c,k,p,d,J2[r],a,x),m!=null&&t.disposeData(m.dataId),v}var nue={kernelName:lo,backendName:"wasm",setupFunc:eue,kernelFunc:tue},tN;function sue(e){tN=e.wasm.cwrap(pl,null,["number","number","number","number","number","number"])}function 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cue(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return sN(b,a,i==="NHWC"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var due={kernelName:co,backendName:"wasm",setupFunc:lue,kernelFunc:cue},rN;function pue(e){rN=e.wasm.cwrap(Ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=S.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,T=h.strideHeight,N=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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x=S.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!=="float32"&&t.disposeData(y.dataId),v}var ple={kernelName:qa,backendName:"wasm",setupFunc:cle,kernelFunc:dle},gN;function hle(e){gN=e.wasm.cwrap(ja,null,["number","number","number","number"])}function fle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;S.assertAxesAreInnerMostDims("min",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;gN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var mle={kernelName:ja,backendName:"wasm",setupFunc:hle,kernelFunc:fle},gle=!1,ble=gn(Ka,gle),bN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(bN||{}),yN;function yle(e){yN=e.wasm.cwrap(Xa,null,["number","array","number","number","array","array","number","number"])}function vle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return yN(i,l,t.shape.length,It[t.dtype],d,h,bN[r],u),o}var xle={kernelName:Xa,backendName:"wasm",kernelFunc:vle,setupFunc:yle},wle=!0,kle=gn(Ya,wle),Ile=Xt(ko);function Rv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var vN;function Sle(e){vN=e.wasm.cwrap(So,"number",["number","number","number","number","number"])}function Cle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=vN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Rv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var Nle={kernelName:So,backendName:"wasm",setupFunc:Sle,kernelFunc:Cle},xN;function Tle(e){xN=e.wasm.cwrap(wl,"number",["number","number","number","number","number","bool"])}function $le(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=xN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(m);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var _le={kernelName:wl,backendName:"wasm",setupFunc:Tle,kernelFunc:$le},wN;function Ale(e){wN=e.wasm.cwrap(Co,"number",["number","number","number","number","number","number"])}function Ele(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=wN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(g);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[b,y]}var Rle={kernelName:Co,backendName:"wasm",setupFunc:Ale,kernelFunc:Ele},Dle=!1,Fle=gn(Io,Dle,"bool"),kN;function Ole(e){kN=e.wasm.cwrap(To,null,["number","number","number","number","number"])}function Ple(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],"int32"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return kN(p,a,i,o,l),u}var zle={kernelName:To,backendName:"wasm",setupFunc:Ole,kernelFunc:Ple};function Mle(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Lle={kernelName:No,backendName:"wasm",kernelFunc:Mle};function Ble(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Zm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Zm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=Y2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Vle={kernelName:$o,backendName:"wasm",kernelFunc:Ble},IN;function Wle(e){IN=e.wasm.cwrap(Qa,null,["number","array","number","number","array","array","number","number"])}function Ule(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return aN({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return IN(i,c,t.shape.length,It[t.dtype],h,f,r,l),o}var SN={kernelName:Qa,backendName:"wasm",kernelFunc:Ule,setupFunc:Wle},Gle=!1,Hle=gn(Za,Gle),CN;function qle(e){CN=e.wasm.cwrap(Ja,null,["number","number","number"])}function jle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!=="float32"&&(l=ac({backend:n,inputs:{x:s},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,"float32"),p=n.dataIdMap.get(c.dataId).id;return CN(o,i,p),u.dtype!=="float32"&&n.disposeData(l.dataId),c}var Kle={kernelName:Ja,backendName:"wasm",setupFunc:qle,kernelFunc:jle},NN;function Xle(e){NN=e.wasm.cwrap(_o,null,["number","number","number","number"])}function Yle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v,f=S.getInnerMostAxes(f.length,l.shape.length))}S.assertAxesAreInnerMostDims("prod",f,l.shape.length);let[m,g]=S.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;NN(u,b,It[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Qle={kernelName:_o,backendName:"wasm",setupFunc:Xle,kernelFunc:Yle},Zle=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=rv(s,r,a,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},Jle={kernelName:kl,backendName:"wasm",kernelFunc:Zle},ece=!0,tce=gn(Da,ece),nce=Xt(ei),sce=Xt(ni),TN;function rce(e){TN=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number"])}function ace(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,[c,p,d,h]=r.shape,f=[c,u,l,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=ac({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let b=m.id,y=t.makeOutput(f,"float32");if(w.sizeFromShape(r.shape)===0)return y;let v=t.dataIdMap.get(y.dataId).id;return TN(b,c,p,d,h,u,l,a?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),y}var ice={kernelName:ti,backendName:"wasm",setupFunc:rce,kernelFunc:ace},$N;function oce(e){$N=e.wasm.cwrap(Eo,null,["number","array","number","array","number","number"])}function uce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=w.parseAxisParam(a,r.shape);if(r.shape.length===0)return eh({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);$N(u,c,i.length,p,r.shape.length,l);let d=yn({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var lce={kernelName:Eo,backendName:"wasm",kernelFunc:uce,setupFunc:oce},_N;function cce(e){_N=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number","number","number","array","number","number"])}function dce(e){let{inputs:t,backend:n,attrs:s}=e,{image:r}=t,{radians:a,fillValue:i,center:o}=s,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(u.dataId).id,[p,d,h,f]=r.shape,[m,g]=S.getImageCenter(o,d,h),b=i===0,y=255,v=typeof i=="number"?[i,i,i,b?0:y]:[...i,y],x=new Uint8Array(new Int32Array(v).buffer);return _N(l,p,d,h,f,a,m,g,x,v.length,c),u}var pce={kernelName:Ho,backendName:"wasm",kernelFunc:dce,setupFunc:cce},hce=Xt(Ro),fce=Xt(si),AN;function mce(e){AN=e.wasm.cwrap(Do,null,["number","number","number","number","number","number","array","number","number"])}function gce(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:i}=s,o=t.makeOutput(i,a.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=Ak.calculateShapes(a,r,i),f=t.dataIdMap.get(r.dataId).id,g=t.dataIdMap.get(a.dataId).id,b=new Uint8Array(new Int32Array(p).buffer),y=t.dataIdMap.get(o.dataId).id;return AN(f,g,It[a.dtype],u,l,c,b,d,y),o}var bce={kernelName:Do,backendName:"wasm",setupFunc:mce,kernelFunc:gce},EN;function yce(e){EN=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function vce(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id,p=s.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:w.sizeFromShape(r.shape.slice(1));return EN(i,o,u,h,c),l}var xce={kernelName:Fo,backendName:"wasm",kernelFunc:vce,setupFunc:yce},RN;function wce(e){RN=e.wasm.cwrap(ai,null,["number","number"])}function kce(e){let{backend:t,inputs:{x:n}}=e,s=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(r.dataId).id;return w.sizeFromShape(r.shape)===0||RN(s,a),r}var Ice={kernelName:"Sigmoid",backendName:"wasm",setupFunc:wce,kernelFunc:kce},Sce=Xt(ri),DN;function Cce(e){DN=e.wasm.cwrap(ui,null,["number","number","number","number"])}function Nce(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(a.dataId).id,o=n.shape[s],u=w.sizeFromShape(n.shape)/o;return w.sizeFromShape(a.shape)===0||DN(r,i,o,u),a}var Tce={kernelName:ui,backendName:"wasm",setupFunc:Cce,kernelFunc:Nce};function $ce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s,o=w.sizeFromShape(a),u=[[0,0]];u.push(...i);for(let T=1+a.length;T<r.shape.length;++T)u.push([0,0]);let l=SN.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),c=S.getReshaped(l.shape,a,o,!1),p=S.getPermuted(c.length,a.length,!1),d=S.getReshapedPermuted(l.shape,a,o,!1),m=yn({inputs:{x:l},backend:n,attrs:{shape:c}}),y=wr({inputs:{x:m},backend:n,attrs:{perm:p}}),k=yn({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeData(l.dataId),n.disposeData(m.dataId),n.disposeData(y.dataId),k}var _ce={kernelName:zo,backendName:"wasm",kernelFunc:$ce},FN;function Ace(e){FN=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function Ece(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=n,o=s.shape[0],u=s.shape[1],l=t.readSync(a.dataId)[0],c=[o+l,u],p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(c,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(c.slice(0,1),r.dtype),b=t.dataIdMap.get(g.dataId).id,y=t.makeOutput([l],"bool"),v=t.dataIdMap.get(y.dataId).id,x=t.makeOutput([o],s.dtype),k=t.dataIdMap.get(x.dataId).id,T=t.makeOutput([4],"int32"),N=t.dataIdMap.get(T.dataId).id,E=FN(p,d,It[r.dtype],o,l,u,h,m,b,v,k,N),A=t.readSync(T.dataId),P;switch(A[0]){case 1:{P=S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(A[1]);break}case 2:{P=S.getSparseFillEmptyRowsNegativeIndexErrorMessage(A[1],A[2]);break}case 3:P=S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(A[1],A[2],A[3]);break;default:P=""}if(t.disposeData(T.dataId),P)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(y.dataId),t.disposeData(x.dataId),new Error(P);let R=f,F=g;return E!==c[0]&&(R=va({inputs:{x:f},attrs:{begin:0,size:[E,u]},backend:t}),F=va({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[R,F,y,x]}var Rce={kernelName:sp,backendName:"wasm",setupFunc:Ace,kernelFunc:Ece},ON;function Dce(e){ON=e.wasm.cwrap($l,null,["number","number","number","number","number","number","number"])}function Fce(e){let{backend:t,inputs:n}=e,{inputIndices:s,inputShape:r,newShape:a}=n;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},noe=class extends L2{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},soe=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Mw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},B2=class extends nl{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Tv())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new toe(this.device),this.tensorMap=new Ud(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return B2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*jm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*jm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new L2),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new noe),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=S.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=b2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type==="int32"?new Int32Array(s,o,a.data.length).set(a.data):a.type==="uint32"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let N=this.tensorMap.get(r.dataId);return N.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Mw(this.device,e);let a=[{type:"float32",data:[NaN]}],i=t.concat(r).map(N=>N.shape),o="int32";i.map(N=>{a.push({type:o,data:N})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let N=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((N,E)=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[E]}}),p=c.map(N=>N.dtype).concat(r.dtype),d=c.map(N=>S.getBroadcastDims(N.shape,r.shape)),h=c.map(N=>w.arraysEqual(N.shape,r.shape)).join("_"),f=d.map(N=>N.join("_")).join(";"),m=R2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>E2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Pre(this.device,g,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let T={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(T),X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Mw(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=soe){return X().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Ev=B2;Ev.nextDataId=0;var roe={};Ae(roe,{WebGPUBackend:()=>Ev,webgpu_util:()=>m2});Tv()&&pp("webgpu",async()=>{X().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:X().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new Ev(a,r)},3);var It=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(It||{}),Jp=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(Jp||{}),V2;function aoe(e){V2=e.wasm.cwrap(ra,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function ioe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Jp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return V2(d,T,r.shape.length,h,N,a.shape.length,u,l,g,f,m,p||0,k),x}var ooe={kernelName:ra,backendName:"wasm",setupFunc:aoe,kernelFunc:ioe};function Xt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,It[o.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var uoe=Xt(io);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=S.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,It[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var loe=!0,coe=gn(Ir,loe),W2;function doe(e){W2=e.wasm.cwrap(ka,null,["array","number","number","number"])}function poe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return W2(a,r.length,It[s.dtype],i),s}var hoe={kernelName:ka,backendName:"wasm",setupFunc:doe,kernelFunc:poe};function eh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var foe={kernelName:Ba,backendName:"wasm",kernelFunc:eh},U2;function moe(e){U2=e.wasm.cwrap(pi,null,["number","array","number","number","number","array","number"])}function wr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=boe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=goe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=eh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return U2(c,h,u.shape.length,It[u.dtype],p,d,a.length),l}function goe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function boe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var yoe={kernelName:pi,backendName:"wasm",kernelFunc:wr,setupFunc:moe};function Fr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=S.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=S.getInnerMostAxes(i.length,r),u=wr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var G2;function voe(e){G2=e.wasm.cwrap(al,null,["number, number, number"])}function xoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims("all",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;G2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var woe={kernelName:al,backendName:"wasm",setupFunc:voe,kernelFunc:xoe},H2;function koe(e){H2=e.wasm.cwrap(il,null,["number, number, number"])}function Ioe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims("any",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;H2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Soe={kernelName:il,backendName:"wasm",setupFunc:koe,kernelFunc:Ioe},q2;function Coe(e){q2=e.wasm.cwrap(Ia,null,["number","number","number","number","number"])}function Noe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Fr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return q2(o,It[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var Toe={kernelName:Ia,backendName:"wasm",kernelFunc:Noe,setupFunc:Coe},j2;function $oe(e){j2=e.wasm.cwrap(Sa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _oe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${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}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,"float32"),k=s.dataIdMap.get(x.dataId).id;return j2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var Aoe={kernelName:Sa,backendName:"wasm",setupFunc:$oe,kernelFunc:_oe};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Eoe={kernelName:Eo,backendName:"wasm",kernelFunc:yn},K2;function Roe(e){K2=e.wasm.cwrap(Ca,null,["number","array","number","number","array","number","number","number","number"])}function Doe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],T=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),N=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(T.dataId).id,A=n.dataIdMap.get(N.dataId).id,P=i?T.shape[2]:T.shape[1],R=o?N.shape[1]:N.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],T.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(T.shape).buffer),q=new Uint8Array(new Int32Array(N.shape).buffer);return K2(E,W,T.shape.length,A,q,N.shape.length,i,o,z),n.disposeData(T.dataId),n.disposeData(N.dataId),$.shape=v,$}var Foe={kernelName:Ca,backendName:"wasm",setupFunc:Roe,kernelFunc:Doe};function va(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype==="string"){let f=Fd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Ooe(u,c[0],d,a,i);else if(h===3)Poe(u,c[0],c[1],d,a,i);else if(h===4)zoe(u,c[0],c[1],c[2],d,a,i);else{let f=Fd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function Ooe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Poe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function zoe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Moe={kernelName:Po,backendName:"wasm",kernelFunc:va};function Loe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=va({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Boe={kernelName:oo,backendName:"wasm",kernelFunc:Loe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Voe={kernelName:Na,backendName:"wasm",kernelFunc:ac},Woe=Xt(Ta),X2;function Uoe(e){X2=e.wasm.cwrap(Sr,null,["number","number","number","number"])}function 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y.stringBytes=S.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var qoe={kernelName:uo,backendName:"wasm",kernelFunc:Y2},Q2;function joe(e){Q2=e.wasm.cwrap($a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Koe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=S.convertConv2DDataFormat(d),f=S.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,T=f.dilationWidth,N=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Qoe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=S.convertConv2DDataFormat(u),h=S.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:T,strideHeight:N,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,"float32"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return Z2(me,ke,f,m,g,y,v,b,k,T,x,N,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var Zoe={kernelName:_a,backendName:"wasm",setupFunc:Yoe,kernelFunc:Qoe},Joe=Xt(Aa),eue=Xt(Ea),J2=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(J2||{}),eN;function tue(e){eN=e.wasm.cwrap(co,null,["number","number","number","number","array","number","number","number","number","number"])}function nue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=ac({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return eN(g,b,y,c,k,p,d,J2[r],a,x),m!=null&&t.disposeData(m.dataId),v}var sue={kernelName:co,backendName:"wasm",setupFunc:tue,kernelFunc:nue},tN;function rue(e){tN=e.wasm.cwrap(hl,null,["number","number","number","number","number","number"])}function aue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumprod",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;tN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var iue={kernelName:hl,backendName:"wasm",setupFunc:rue,kernelFunc:aue},nN;function oue(e){nN=e.wasm.cwrap(lo,null,["number","number","number","number","number","number"])}function uue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;nN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var lue={kernelName:lo,backendName:"wasm",setupFunc:oue,kernelFunc:uue},sN;function cue(e){sN=e.wasm.cwrap(po,null,["number","number","number","array","number","array","array","number","number"])}function due(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return sN(b,a,i==="NHWC"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var pue={kernelName:po,backendName:"wasm",setupFunc:cue,kernelFunc:due},rN;function hue(e){rN=e.wasm.cwrap(Ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=S.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,T=h.strideHeight,N=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(h.outShape,"float32"),F=s.dataIdMap.get(R.dataId).id;return rN(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,T,N,E,A,F),R}var mue={kernelName:Ra,backendName:"wasm",setupFunc:hue,kernelFunc:fue},gue=Xt(Fa),bue=!1,yue=gn(ho,bue,"bool"),vue=Xt(Oa,"float32");function Zm(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:o}})}var xue={kernelName:fo,backendName:"wasm",kernelFunc:Zm};function aN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var wue={kernelName:ml,backendName:"wasm",kernelFunc:aN},iN;function kue(e){iN=e.wasm.cwrap(go,null,["number","number","number","number","number","number"])}function Iue(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return iN(a,o,u,l,c,i),r}var Sue={kernelName:go,backendName:"wasm",kernelFunc:Iue,setupFunc:kue},Cue=Xt(Pa),Nue=!1,Tue=gn(za,Nue),oN;function $ue(e){oN=e.wasm.cwrap(Ma,null,["number","number","number","number","number","number","number"])}function _ue(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return oN(c,p,d,h,f,r,g),m}var Aue={kernelName:Ma,backendName:"wasm",setupFunc:$ue,kernelFunc:_ue},uN;function Eue(e){uN=e.wasm.cwrap(aa,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 Rue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type==="SAME"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. 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x=S.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!=="float32"&&t.disposeData(y.dataId),v}var hle={kernelName:qa,backendName:"wasm",setupFunc:dle,kernelFunc:ple},gN;function fle(e){gN=e.wasm.cwrap(ja,null,["number","number","number","number"])}function mle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;S.assertAxesAreInnerMostDims("min",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;gN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var gle={kernelName:ja,backendName:"wasm",setupFunc:fle,kernelFunc:mle},ble=!1,yle=gn(Ka,ble),bN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(bN||{}),yN;function vle(e){yN=e.wasm.cwrap(Xa,null,["number","array","number","number","array","array","number","number"])}function xle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return yN(i,l,t.shape.length,It[t.dtype],d,h,bN[r],u),o}var wle={kernelName:Xa,backendName:"wasm",kernelFunc:xle,setupFunc:vle},kle=!0,Ile=gn(Ya,kle),Sle=Xt(Io);function Rv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var vN;function Cle(e){vN=e.wasm.cwrap(Co,"number",["number","number","number","number","number"])}function Nle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=vN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Rv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var Tle={kernelName:Co,backendName:"wasm",setupFunc:Cle,kernelFunc:Nle},xN;function $le(e){xN=e.wasm.cwrap(kl,"number",["number","number","number","number","number","bool"])}function _le(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=xN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(m);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var Ale={kernelName:kl,backendName:"wasm",setupFunc:$le,kernelFunc:_le},wN;function Ele(e){wN=e.wasm.cwrap(No,"number",["number","number","number","number","number","number"])}function Rle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=wN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(g);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[b,y]}var Dle={kernelName:No,backendName:"wasm",setupFunc:Ele,kernelFunc:Rle},Fle=!1,Ole=gn(So,Fle,"bool"),kN;function Ple(e){kN=e.wasm.cwrap($o,null,["number","number","number","number","number"])}function zle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],"int32"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return kN(p,a,i,o,l),u}var Mle={kernelName:$o,backendName:"wasm",setupFunc:Ple,kernelFunc:zle};function Lle(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Ble={kernelName:To,backendName:"wasm",kernelFunc:Lle};function Vle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Zm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Zm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=Y2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Wle={kernelName:_o,backendName:"wasm",kernelFunc:Vle},IN;function Ule(e){IN=e.wasm.cwrap(Qa,null,["number","array","number","number","array","array","number","number"])}function Gle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return aN({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return IN(i,c,t.shape.length,It[t.dtype],h,f,r,l),o}var SN={kernelName:Qa,backendName:"wasm",kernelFunc:Gle,setupFunc:Ule},Hle=!1,qle=gn(Za,Hle),CN;function jle(e){CN=e.wasm.cwrap(Ja,null,["number","number","number"])}function 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einsum,fp as elu,Dde as enableDebugMode,Rde as enableProdMode,sF as enclosingPowerOfTwo,Ss as engine,X as env,Kn as equal,CR as erf,Xn as exp,Pn as expandDims,_R as expm1,rI as eye,lb as fft,Ol as fill,Wde as findBackend,Ude as findBackendFactory,mp as floor,Hk as floorDiv,GX as forceHalfFloat,ha as fused,ju as gather,J3 as gatherND,$k as gather_util,Bde as getBackend,nx as getGradient,Zf as getKernel,Jf as getKernelsForBackend,Vpe as getThreadsCount,RK as gpgpu_util,Yde as grad,Qde as grads,Wn as greater,jo as greaterEqual,Id as ifft,Kg as imag,qn as image,wpe as inTopKAsync,qM as initializers,OB as input,En as io,xI as irfft,Kde as isFinite,Xde as isInf,BR as isNaN,Ht as keep,xs as kernel_impls,$L as layers,Xg as leakyRelu,aI as less,Ko as lessEqual,VO as linalg,GR as linspace,Rpe as loadGraphModel,Tpe as loadLayersModel,qR as localResponseNormalization,Yn as log,Yg as log1p,epe as logSigmoid,iI as logSoftmax,iD as logSumExp,Ds as logicalAnd,Jg as logicalNot,cI as logicalOr,tpe as logicalXor,Spe as losses,We as matMul,iA as math,As as max,eb as maxPool,dI as maxPool3d,fD as maxPoolWithArgmax,$r as maximum,St as mean,dm as memory,npe as meshgrid,rW as metrics,fm as min,bp as minimum,xD as mirrorPad,kD as mod,Cpe as model,xW as models,tb as moments,xpe as movingAverage,V as mul,spe as multiRNNCell,TD as multinomial,kt as neg,OI as nextFrame,CI as norm,Ku as notEqual,vd as oneHot,Mn as ones,Qn as onesLike,L as op,rpe as outerProduct,fi as pad,ape as pad1d,ipe as pad2d,ope as pad3d,upe as pad4d,lpe as pool,pa as pow,sb as prelu,U_ as print,pI as prod,Pde as profile,cpe as rand,dpe as randomGamma,KD as randomNormal,zl as randomUniform,Xu as range,Lde as ready,wd as real,ZD as reciprocal,pp as registerBackend,$pe as registerCallbackConstructor,D$ as registerGradient,Al as registerKernel,Ape as registerOp,wW as regularizers,Xs as relu,hI as relu6,Vde as removeBackend,G as reshape,Zn as reverse,ppe as reverse1d,hpe as reverse2d,fpe as reverse3d,mpe as reverse4d,cb as 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v=Array.from(t.readSync(r.dataId)),x=Array.from(t.readSync(h.dataId));y=S.getSparseReshapeInputOutputMismatchErrorMessage(v,x);break}default:y=""}if(t.disposeData(m.dataId),y)throw t.disposeData(p.dataId),t.disposeData(h.dataId),new Error(y);return[p,h]}var Pce={kernelName:_l,backendName:"wasm",setupFunc:Fce,kernelFunc:Oce},PN;function zN(e){PN=e.wasm.cwrap("SparseSegmentReduction",null,["number","number","number","number","number","number","number","number","number"])}function MN(e,t){let{backend:n,inputs:s}=e,{data:r,indices:a,segmentIds:i}=s,o=a.shape[0],u=n.readSync(i.dataId,o-1,o)[0],c=o>0?u+1:0;if(c<0)throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let p=r.shape.slice();p[0]=c;let d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=n.dataIdMap.get(i.dataId).id,m=n.makeOutput(p,r.dtype),g=n.dataIdMap.get(m.dataId).id,b=n.makeOutput([4],"int32"),y=n.dataIdMap.get(b.dataId).id;PN(d,It[r.dtype],r.shape[0],h,f,g,y,t,0);let 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Wce={kernelName:Lo,backendName:"wasm",kernelFunc:Vce},Uce=Xt(ii),Gce=Xt(Al),Hce=!0,qce=gn(li,Hce),LN;function jce(e){LN=e.wasm.cwrap(hi,null,["number","number","number","number"])}function Kce(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=t.makeOutput(a.shape,a.dtype),u=t.dataIdMap.get(o.dataId).id;return LN(i,r,It[a.dtype],u),o}var Xce={kernelName:hi,backendName:"wasm",setupFunc:jce,kernelFunc:Kce},BN;function Yce(e){BN=e.wasm.cwrap(Bo,null,["number","array","number","array","array","array","array","array","number","number"])}function Qce(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=yn({inputs:{x:r},backend:t,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, 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as CallbackList,Na as Cast,Ta as Ceil,Sr as ClipByValue,jd as Complex,Kd as ComplexAbs,uo as Concat,$a as Conv2D,cg as Conv2DBackpropFilter,_a as Conv2DBackpropInput,Xd as Conv3D,dg as Conv3DBackpropFilterV2,pg as Conv3DBackpropInputV2,Aa as Cos,Ea as Cosh,co as CropAndResize,hl as Cumprod,lo as Cumsum,BL as CustomCallback,Ud as DataStorage,hg as DenseBincount,po as DepthToSpace,Ra as DepthwiseConv2dNative,fg as DepthwiseConv2dNativeBackpropFilter,mg as DepthwiseConv2dNativeBackpropInput,gg as Diag,Yd as Dilation2D,Qf as Dilation2DBackpropFilter,Yf as Dilation2DBackpropInput,Jw as ENV,NW as EarlyStopping,Qd as Einsum,Fa as Elu,bg as EluGrad,S$ as Environment,ho as Equal,fl as Erf,Oa as Exp,fo as ExpandDims,mo as Expm1,yg as FFT,ml as Fill,go as FlipLeftRight,Pa as Floor,za as FloorDiv,fd as FromPixels,Ma as FusedBatchNorm,aa as FusedConv2D,ia as FusedDepthwiseConv2D,Kf as GPGPUContext,yo as GatherNd,bo as GatherV2,E4 as GraphModel,vo as Greater,La as GreaterEqual,LL as History,vg as IFFT,Ba as Identity,Zd as Imag,Dt as InputSpec,gl as IsFinite,bl as IsInf,yl as IsNan,nl as KernelBackend,ep as LRN,wg as LRNGrad,AL as LayerVariable,pr as LayersModel,Va as LeakyRelu,xo as Less,wo as LessEqual,xg as LinSpace,Wa as Log,vl as Log1p,E$ as LogSoftmax,ko as LogicalAnd,xl as LogicalNot,Jd as LogicalOr,L0 as MathBackendCPU,M1 as MathBackendWebGL,Ua as Max,Ha as MaxPool,tp as MaxPool3D,Ig as MaxPool3DGrad,kg as MaxPoolGrad,Sg as MaxPoolWithArgmax,Ga as Maximum,qa as Mean,ja as Min,Ka as Minimum,Xa as MirrorPad,wl as Mod,yb as MomentumOptimizer,Cg as Multinomial,Ya as Multiply,Io as Neg,Co as NonMaxSuppressionV3,kl as NonMaxSuppressionV4,No as NonMaxSuppressionV5,So as NotEqual,l_ as OP_SCOPE_SUFFIX,$o as OneHot,To as OnesLike,_r as Optimizer,Wr as OptimizerConstructors,_o as Pack,Qa as PadV2,$de as Pool,Za as Pow,Ja as Prelu,Ao as Prod,vb as RMSPropOptimizer,Ar as RNN,Il as Range,e_ as Rank,np as Real,Da as RealDiv,Sl as Reciprocal,oO as Reduction,ei as Relu,ni as Relu6,Eo as Reshape,ti as ResizeBilinear,Tg as ResizeBilinearGrad,Cl as ResizeNearestNeighbor,Ng as ResizeNearestNeighborGrad,Ro as Reverse,qo as RotateWithOffset,Do as Round,si as Rsqrt,Ip as SGDOptimizer,Fo as ScatterNd,Oo as Select,Nl as Selu,Ub as Sequential,ai as Sigmoid,Tl as Sign,ri as Sin,zo as Sinh,Po as Slice,ui as Softmax,$l as Softplus,Mo as SpaceToBatchND,sp as SparseFillEmptyRows,_l as SparseReshape,rp as SparseSegmentMean,ap as SparseSegmentSum,ip as SparseToDense,Lo as SplitV,ii as Sqrt,Al as Square,li as SquaredDifference,hi as Step,Bo as StridedSlice,op as StringNGrams,$g as StringSplit,_g as StringToHashBucketFast,ci as Sub,oi as Sum,$s as SymbolicTensor,Vo as Tan,di as Tanh,et as Tensor,Vt as TensorBuffer,Cr as Tile,Wo as TopK,Uo as Transform,pi as Transpose,Ag as Unique,Go as Unpack,up as UnsortedSegmentSum,gd as Variable,Ho as ZerosLike,ra as _FusedMatMul,Mt as abs,GA as acos,qA as acosh,ie as add,KA as addN,qk as all,pm as any,Hu as argMax,JA as argMin,tE as asin,sE as asinh,aE as atan,oE as atan2,lE as atanh,Gg as avgPool,Yk as avgPool3d,zA as backend,S as backend_util,qde as basicLSTMCell,ju as batchNorm,$E as batchNorm2d,AE as batchNorm3d,RE as batchNorm4d,Hg as batchToSpaceND,Qk as bincount,xpe as booleanMaskAsync,OE as broadcastArgs,nd as broadcastTo,jo as broadcast_util,Nk as browser,De as buffer,Ape as callbacks,ce as cast,ME as ceil,Vn as clipByValue,lr as clone,oa as complex,Ft as concat,VE as concat1d,UE as concat2d,HE as concat3d,jE as concat4d,MM as constraints,Zk as conv1d,ca as conv2d,Jk as conv2dTranspose,eI as conv3d,tR as conv3dTranspose,Ede as copyRegisteredKernels,jg as cos,nI as cosh,TI as cosineWindow,aR as cumprod,sI as cumsum,js as customGrad,R4 as data,uR as denseBincount,Gk as deprecationWarn,cR as depthToSpace,hp as depthwiseConv2d,Rpe as deregisterOp,dp as device_util,jde as diag,fR as dilation2d,Ode as disableDeprecationWarnings,Re as dispose,Pde as disposeVariables,xe as div,vR as divNoNan,Kde as dot,nF as dropout,kR as einsum,fp as elu,Fde as enableDebugMode,Dde as enableProdMode,sF as enclosingPowerOfTwo,Ss as engine,X as env,Kn as equal,CR as erf,Xn as exp,Pn as expandDims,_R as expm1,rI as eye,lb as fft,Pl as fill,Ude as findBackend,Gde as findBackendFactory,mp as floor,Hk as floorDiv,GX as forceHalfFloat,ha as fused,Ku as gather,J3 as gatherND,$k as gather_util,Vde as getBackend,nx as getGradient,Zf as getKernel,Jf as getKernelsForBackend,Wpe as getThreadsCount,RK as gpgpu_util,Qde as grad,Zde as grads,Wn as greater,Ko as greaterEqual,Id as ifft,Kg as imag,qn as image,kpe as inTopKAsync,qM as initializers,OB as input,En as io,xI as irfft,Xde as isFinite,Yde as isInf,BR as isNaN,Ht as keep,xs as kernel_impls,$L as layers,Xg as leakyRelu,aI as less,Xo as lessEqual,VO as linalg,GR as linspace,Dpe as loadGraphModel,$pe as loadLayersModel,qR as localResponseNormalization,Yn as log,Yg as log1p,tpe as logSigmoid,iI as logSoftmax,iD as logSumExp,Ds as logicalAnd,Jg as logicalNot,cI as logicalOr,npe as logicalXor,Cpe as losses,We as matMul,iA as math,As as max,eb as maxPool,dI as maxPool3d,fD as maxPoolWithArgmax,$r as maximum,St as mean,dm as memory,spe as meshgrid,rW as metrics,fm as min,bp as minimum,xD as mirrorPad,kD as mod,Npe as model,xW as models,tb as moments,wpe as movingAverage,V as mul,rpe as multiRNNCell,TD as multinomial,kt as neg,OI as nextFrame,CI as norm,Xu as notEqual,vd as oneHot,Mn as ones,Qn as onesLike,L as op,ape as outerProduct,fi as pad,ipe as pad1d,ope as pad2d,upe as pad3d,lpe as pad4d,cpe as pool,pa as pow,sb as prelu,U_ as print,pI as prod,zde as profile,dpe as rand,ppe as randomGamma,KD as randomNormal,Ml as randomUniform,Yu as range,Bde as ready,wd as real,ZD as reciprocal,pp as registerBackend,_pe as registerCallbackConstructor,D$ as registerGradient,El as registerKernel,Epe as registerOp,wW as regularizers,Xs as relu,hI as relu6,Wde as removeBackend,G as reshape,Zn as reverse,hpe as reverse1d,fpe as reverse2d,mpe as reverse3d,gpe as reverse4d,cb as rfft,fI as round,mI as rsqrt,we as scalar,X3 as scatterND,Ak as scatter_util,gI as selu,c3 as separableConv2d,Tpe as sequential,ae as serialization,Lde as setBackend,Hde as setPlatform,Vpe as setThreadsCount,Lpe as setWasmPath,Bpe as setWasmPaths,S5 as setWebGLContext,p3 as setdiff1dAsync,Zy as shared,qs as sigmoid,f3 as sign,Spe as signal,bI as sin,yI as sinh,He as slice,ib as slice1d,vI as slice2d,ob as slice3d,kd as slice4d,wt as slice_util,ub as softmax,zl as softplus,nb as spaceToBatchND,Wc as sparse,NI as sparseToDense,Ipe as spectral,Bn as split,dn as sqrt,ct as square,wI as squaredDifference,mr as squeeze,Jn as stack,yp as step,D3 as stridedSlice,Mf as string,ge as sub,ve as sum,cp as sumOutType,O3 as tan,qu as tanh,fs as tensor,Zt as tensor1d,Ki as tensor2d,pA as tensor3d,bpe as tensor4d,ype as tensor5d,vpe as tensor6d,_s as tensor_util,_A as test_util,j as tidy,ps as tile,Mde as time,z3 as topk,Pi as train,qe as transpose,db as truncatedNormal,hx as unique,Ade as unregisterGradient,_de as unregisterKernel,V3 as unsortedSegmentSum,Fs as unstack,cn as upcastType,w as util,Jde as valueAndGrad,epe as valueAndGrads,U3 as variable,XR as variableGrads,Gpe as version,Fpe as version_converter,Rde as version_core,Ope as version_cpu,uS as version_layers,Upe as version_wasm,Ppe as version_webgl,zpe as webgl,I5 as webgl_util,roe as webgpu,vn as where,II as whereAsync,$t as zeros,je as zerosLike};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/package.json b/package.json
index 799c1094..59b1197d 100644
--- a/package.json
+++ b/package.json
@@ -67,14 +67,14 @@
     "@tensorflow/tfjs-node-gpu": "^3.15.0",
     "@types/node": "^17.0.23",
     "@types/offscreencanvas": "^2019.6.4",
-    "@typescript-eslint/eslint-plugin": "^5.17.0",
-    "@typescript-eslint/parser": "^5.17.0",
+    "@typescript-eslint/eslint-plugin": "^5.18.0",
+    "@typescript-eslint/parser": "^5.18.0",
     "@vladmandic/build": "^0.7.2",
     "@vladmandic/pilogger": "^0.4.4",
     "@vladmandic/tfjs": "github:vladmandic/tfjs",
     "canvas": "^2.9.1",
     "dayjs": "^1.11.0",
-    "esbuild": "^0.14.29",
+    "esbuild": "^0.14.31",
     "eslint": "8.12.0",
     "eslint-config-airbnb-base": "^15.0.0",
     "eslint-plugin-html": "^6.2.0",
diff --git a/test/build.log b/test/build.log
index 075b145e..48ebbc48 100644
--- a/test/build.log
+++ b/test/build.log
@@ -1,24 +1,24 @@
-2022-04-01 12:36:43 INFO:  Application: {"name":"@vladmandic/human","version":"2.6.5"} 
-2022-04-01 12:36:43 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2022-04-01 12:36:43 INFO:  Toolchain: {"build":"0.7.2","esbuild":"0.14.29","typescript":"4.6.3","typedoc":"0.22.13","eslint":"8.12.0"} 
-2022-04-01 12:36:43 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2022-04-01 12:36:43 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":595} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":64,"inputBytes":564501,"outputBytes":293301} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":599} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":64,"inputBytes":564505,"outputBytes":293305} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":651} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":64,"inputBytes":564557,"outputBytes":293355} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":394} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1068,"outputBytes":615} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":64,"inputBytes":564521,"outputBytes":292260} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1344131} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":64,"inputBytes":1908037,"outputBytes":1635407} 
-2022-04-01 12:36:43 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":64,"inputBytes":1908037,"outputBytes":2113578} 
-2022-04-01 12:36:48 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":112} 
-2022-04-01 12:36:49 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":72,"generated":true} 
-2022-04-01 12:36:49 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5862,"outputBytes":2915} 
-2022-04-01 12:36:49 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
-2022-04-01 12:36:56 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":96,"errors":0,"warnings":0} 
-2022-04-01 12:36:56 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2022-04-01 12:36:56 INFO:  Done... 
+2022-04-05 07:49:00 INFO:  Application: {"name":"@vladmandic/human","version":"2.6.5"} 
+2022-04-05 07:49:00 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2022-04-05 07:49:00 INFO:  Toolchain: {"build":"0.7.2","esbuild":"0.14.31","typescript":"4.6.3","typedoc":"0.22.13","eslint":"8.12.0"} 
+2022-04-05 07:49:00 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
+2022-04-05 07:49:00 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":595} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":64,"inputBytes":564501,"outputBytes":293301} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":599} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":64,"inputBytes":564505,"outputBytes":293305} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":651} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":64,"inputBytes":564557,"outputBytes":293355} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":394} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1068,"outputBytes":615} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":64,"inputBytes":564521,"outputBytes":292260} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1344714} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":64,"inputBytes":1908620,"outputBytes":1635990} 
+2022-04-05 07:49:00 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":64,"inputBytes":1908620,"outputBytes":2113774} 
+2022-04-05 07:49:06 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":112} 
+2022-04-05 07:49:07 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":72,"generated":true} 
+2022-04-05 07:49:07 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5862,"outputBytes":2915} 
+2022-04-05 07:49:07 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
+2022-04-05 07:49:15 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":96,"errors":0,"warnings":0} 
+2022-04-05 07:49:15 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2022-04-05 07:49:15 INFO:  Done... 
diff --git a/test/test.log b/test/test.log
index a784eb5b..7859dc77 100644
--- a/test/test.log
+++ b/test/test.log
@@ -1,684 +1,684 @@
-2022-04-01 09:12:39 INFO:  @vladmandic/human version 2.6.5 
-2022-04-01 09:12:39 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.4.0 
-2022-04-01 09:12:39 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
-2022-04-01 09:12:39 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
-2022-04-01 09:12:39 INFO:  
-2022-04-01 09:12:39 INFO:  test-node.js start 
-2022-04-01 09:12:39 INFO:  test-node.js test: configuration validation 
-2022-04-01 09:12:39 STATE: test-node.js passed: configuration default validation [] 
-2022-04-01 09:12:39 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-04-01 09:12:39 INFO:  test-node.js test: model load 
-2022-04-01 09:12:39 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
-2022-04-01 09:12:39 INFO:  test-node.js test: warmup 
-2022-04-01 09:12:39 STATE: test-node.js passed: create human 
-2022-04-01 09:12:39 INFO:  test-node.js human version: 2.6.5 
-2022-04-01 09:12:39 INFO:  test-node.js platform: linux x64 agent: NodeJS v17.4.0 
-2022-04-01 09:12:39 INFO:  test-node.js tfjs version: 3.15.0 
-2022-04-01 09:12:39 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
-2022-04-01 09:12:39 STATE: test-node.js passed: set backend: tensorflow 
-2022-04-01 09:12:39 STATE: test-node.js tensors 1919 
-2022-04-01 09:12:39 STATE: test-node.js passed: load models 
-2022-04-01 09:12:39 STATE: test-node.js  result: defined models: 22 loaded models: 12 
-2022-04-01 09:12:39 STATE: test-node.js passed: warmup: none default 
-2022-04-01 09:12:39 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-04-01 09:12:39 DATA:  test-node.js  result: performance: load: null total: null 
-2022-04-01 09:12:39 STATE: test-node.js passed: warmup none result match 
-2022-04-01 09:12:39 STATE: test-node.js event: image 
-2022-04-01 09:12:40 STATE: test-node.js event: detect 
-2022-04-01 09:12:40 STATE: test-node.js event: warmup 
-2022-04-01 09:12:40 STATE: test-node.js passed: warmup: face default 
-2022-04-01 09:12:40 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-04-01 09:12:40 DATA:  test-node.js  result: performance: load: null total: 385 
-2022-04-01 09:12:40 STATE: test-node.js passed: warmup face result match 
-2022-04-01 09:12:40 STATE: test-node.js event: image 
-2022-04-01 09:12:40 STATE: test-node.js event: detect 
-2022-04-01 09:12:40 STATE: test-node.js event: warmup 
-2022-04-01 09:12:40 STATE: test-node.js passed: warmup: body default 
-2022-04-01 09:12:40 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:40 DATA:  test-node.js  result: performance: load: null total: 273 
-2022-04-01 09:12:40 STATE: test-node.js passed: warmup body result match 
-2022-04-01 09:12:40 STATE: test-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.89},"emotion":[{"score":0.35,"emotion":"fear"},{"score":0.28,"emotion":"angry"},{"score":0.15,"emotion":"sad"},{"score":0.1,"emotion":"surprise"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-04-01 09:12:40 INFO:  test-node.js test: details verification 
-2022-04-01 09:12:40 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:40 STATE: test-node.js event: image 
-2022-04-01 09:12:40 STATE: test-node.js event: detect 
-2022-04-01 09:12:40 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:40 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:40 DATA:  test-node.js  result: performance: load: null total: 249 
-2022-04-01 09:12:40 STATE: test-node.js passed: details face length 1 
-2022-04-01 09:12:40 STATE: test-node.js passed: details face score 1 0.93 1 
-2022-04-01 09:12:40 STATE: test-node.js passed: details face age/gender 29.6 female 0.91 73.26 
-2022-04-01 09:12:40 STATE: test-node.js passed: details face arrays 4 478 1024 
-2022-04-01 09:12:40 STATE: test-node.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
-2022-04-01 09:12:40 STATE: test-node.js passed: details face anti-spoofing 0.78 
-2022-04-01 09:12:40 STATE: test-node.js passed: details face liveness 0.83 
-2022-04-01 09:12:40 STATE: test-node.js passed: details body length 1 
-2022-04-01 09:12:40 STATE: test-node.js passed: details body 0.92 17 6 
-2022-04-01 09:12:40 STATE: test-node.js passed: details hand length 1 
-2022-04-01 09:12:40 STATE: test-node.js passed: details hand 0.51 0.73 point 
-2022-04-01 09:12:40 STATE: test-node.js passed: details hand arrays 21 5 7 
-2022-04-01 09:12:40 STATE: test-node.js passed: details gesture length 6 
-2022-04-01 09:12:40 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-04-01 09:12:40 STATE: test-node.js passed: details object length 1 
-2022-04-01 09:12:40 STATE: test-node.js passed: details object 0.72 person 
-2022-04-01 09:12:40 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-04-01 09:12:40 STATE: test-node.js event: image 
-2022-04-01 09:12:41 STATE: test-node.js event: detect 
-2022-04-01 09:12:41 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-04-01 09:12:41 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-04-01 09:12:41 STATE: test-node.js event: image 
-2022-04-01 09:12:41 STATE: test-node.js event: detect 
-2022-04-01 09:12:41 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-04-01 09:12:41 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:41 STATE: test-node.js event: image 
-2022-04-01 09:12:42 STATE: test-node.js event: detect 
-2022-04-01 09:12:42 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-04-01 09:12:42 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:42 STATE: test-node.js event: image 
-2022-04-01 09:12:42 STATE: test-node.js event: detect 
-2022-04-01 09:12:42 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-04-01 09:12:42 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-04-01 09:12:42 STATE: test-node.js event: image 
-2022-04-01 09:12:42 STATE: test-node.js event: detect 
-2022-04-01 09:12:42 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-04-01 09:12:42 INFO:  test-node.js test default 
-2022-04-01 09:12:43 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:43 STATE: test-node.js event: image 
-2022-04-01 09:12:43 STATE: test-node.js event: detect 
-2022-04-01 09:12:43 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:43 DATA:  test-node.js  result: performance: load: null total: 225 
-2022-04-01 09:12:43 STATE: test-node.js passed: default result face match 1 female 0.91 
-2022-04-01 09:12:43 INFO:  test-node.js test sync 
-2022-04-01 09:12:43 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:43 STATE: test-node.js event: image 
-2022-04-01 09:12:43 STATE: test-node.js event: detect 
-2022-04-01 09:12:43 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:43 DATA:  test-node.js  result: performance: load: null total: 226 
-2022-04-01 09:12:43 STATE: test-node.js passed: default sync 1 female 0.91 
-2022-04-01 09:12:43 INFO:  test-node.js test: image process 
-2022-04-01 09:12:43 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:12:43 STATE: test-node.js passed: image input null [1,256,256,3] 
-2022-04-01 09:12:43 INFO:  test-node.js test: image null 
-2022-04-01 09:12:43 STATE: test-node.js passed: invalid input could not convert input to tensor 
-2022-04-01 09:12:43 INFO:  test-node.js test face similarity 
-2022-04-01 09:12:43 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:12:43 STATE: test-node.js event: image 
-2022-04-01 09:12:43 STATE: test-node.js event: detect 
-2022-04-01 09:12:43 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:12:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-04-01 09:12:43 DATA:  test-node.js  result: performance: load: null total: 219 
-2022-04-01 09:12:44 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:44 STATE: test-node.js event: image 
-2022-04-01 09:12:44 STATE: test-node.js event: detect 
-2022-04-01 09:12:44 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:44 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:44 DATA:  test-node.js  result: performance: load: null total: 222 
-2022-04-01 09:12:44 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-01 09:12:44 STATE: test-node.js event: image 
-2022-04-01 09:12:44 STATE: test-node.js event: detect 
-2022-04-01 09:12:44 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:12:44 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-04-01 09:12:44 DATA:  test-node.js  result: performance: load: null total: 207 
-2022-04-01 09:12:44 STATE: test-node.js passed: face descriptor 
-2022-04-01 09:12:44 STATE: test-node.js passed: face similarity {"similarity":[1,0.5632803981762933,0.5194114531612539],"descriptors":[1024,1024,1024]} 
-2022-04-01 09:12:44 INFO:  test-node.js test face matching 
-2022-04-01 09:12:44 STATE: test-node.js passed: face database 40 
-2022-04-01 09:12:44 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.8797924743573865}} {"second":{"index":4,"similarity":0.548081908581344}} {"third":{"index":4,"similarity":0.4999696807961951}} 
-2022-04-01 09:12:44 INFO:  test-node.js test object 
-2022-04-01 09:12:44 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:44 STATE: test-node.js event: image 
-2022-04-01 09:12:45 STATE: test-node.js event: detect 
-2022-04-01 09:12:45 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:45 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:45 DATA:  test-node.js  result: performance: load: null total: 227 
-2022-04-01 09:12:45 STATE: test-node.js passed: object result match 
-2022-04-01 09:12:45 INFO:  test-node.js test sensitive 
-2022-04-01 09:12:45 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:45 STATE: test-node.js event: image 
-2022-04-01 09:12:45 STATE: test-node.js event: detect 
-2022-04-01 09:12:45 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:45 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:45 DATA:  test-node.js  result: performance: load: null total: 246 
-2022-04-01 09:12:45 STATE: test-node.js passed: sensitive result match 
-2022-04-01 09:12:45 STATE: test-node.js passed: sensitive face result match 
-2022-04-01 09:12:45 STATE: test-node.js passed: sensitive face emotion result [{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}] 
-2022-04-01 09:12:45 STATE: test-node.js passed: sensitive body result match 
-2022-04-01 09:12:45 STATE: test-node.js passed: sensitive hand result match 
-2022-04-01 09:12:45 INFO:  test-node.js test detectors 
-2022-04-01 09:12:45 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:45 STATE: test-node.js event: image 
-2022-04-01 09:12:45 STATE: test-node.js event: detect 
-2022-04-01 09:12:45 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:45 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:45 DATA:  test-node.js  result: performance: load: null total: 168 
-2022-04-01 09:12:45 STATE: test-node.js passed: detector result face match 
-2022-04-01 09:12:45 STATE: test-node.js passed: detector result hand match 
-2022-04-01 09:12:45 INFO:  test-node.js test: multi-instance 
-2022-04-01 09:12:45 STATE: test-node.js event: image 
-2022-04-01 09:12:46 STATE: test-node.js event: detect 
-2022-04-01 09:12:46 STATE: test-node.js passed: detect: random default 
-2022-04-01 09:12:46 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-04-01 09:12:46 DATA:  test-node.js  result: performance: load: null total: 152 
-2022-04-01 09:12:46 INFO:  test-node.js test: first instance 
-2022-04-01 09:12:46 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-01 09:12:46 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:12:46 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:12:46 DATA:  test-node.js  result: performance: load: null total: 160 
-2022-04-01 09:12:46 INFO:  test-node.js test: second instance 
-2022-04-01 09:12:46 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-01 09:12:46 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:12:46 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:12:46 DATA:  test-node.js  result: performance: load: null total: 168 
-2022-04-01 09:12:46 INFO:  test-node.js test: concurrent 
-2022-04-01 09:12:46 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:12:46 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:12:46 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:46 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:47 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-01 09:12:47 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-01 09:12:47 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:12:47 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:47 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-01 09:12:47 STATE: test-node.js event: image 
-2022-04-01 09:12:47 STATE: test-node.js event: image 
-2022-04-01 09:12:47 STATE: test-node.js event: image 
-2022-04-01 09:12:48 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:12:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:12:48 DATA:  test-node.js  result: performance: load: null total: 1527 
-2022-04-01 09:12:48 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:12:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:12:48 DATA:  test-node.js  result: performance: load: null total: 1527 
-2022-04-01 09:12:48 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:12:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-01 09:12:48 DATA:  test-node.js  result: performance: load: null total: 1659 
-2022-04-01 09:12:48 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:12:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-01 09:12:48 DATA:  test-node.js  result: performance: load: null total: 1659 
-2022-04-01 09:12:48 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:48 DATA:  test-node.js  result: performance: load: null total: 1659 
-2022-04-01 09:12:48 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:48 DATA:  test-node.js  result: performance: load: null total: 1659 
-2022-04-01 09:12:48 STATE: test-node.js event: detect 
-2022-04-01 09:12:48 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:12:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:12:48 DATA:  test-node.js  result: performance: load: null total: 1385 
-2022-04-01 09:12:48 STATE: test-node.js event: detect 
-2022-04-01 09:12:48 STATE: test-node.js event: detect 
-2022-04-01 09:12:48 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:12:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-01 09:12:48 DATA:  test-node.js  result: performance: load: null total: 1386 
-2022-04-01 09:12:48 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:48 DATA:  test-node.js  result: performance: load: null total: 1386 
-2022-04-01 09:12:48 INFO:  test-node.js test: monkey-patch 
-2022-04-01 09:12:48 STATE: test-node.js event: image 
-2022-04-01 09:12:48 STATE: test-node.js event: detect 
-2022-04-01 09:12:48 STATE: test-node.js passed: monkey patch 
-2022-04-01 09:12:48 STATE: test-node.js passed: segmentation [65536] 
-2022-04-01 09:12:48 STATE: test-node.js passeed: equal usage 
-2022-04-01 09:12:48 INFO:  test-node.js test: input compare 
-2022-04-01 09:12:48 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:12:49 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-01 09:12:49 STATE: test-node.js passed: image compare 0 23.275441687091504 
-2022-04-01 09:12:49 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
-2022-04-01 09:12:49 INFO:  test-node.js tensors 1925 
-2022-04-01 09:12:49 INFO:  test-node.js test complete: 9737 ms 
-2022-04-01 09:12:49 INFO:  
-2022-04-01 09:12:49 INFO:  test-node-gpu.js start 
-2022-04-01 09:12:50 INFO:  test-node-gpu.js test: configuration validation 
-2022-04-01 09:12:50 STATE: test-node-gpu.js passed: configuration default validation [] 
-2022-04-01 09:12:50 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-04-01 09:12:50 INFO:  test-node-gpu.js test: model load 
-2022-04-01 09:12:50 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
-2022-04-01 09:12:50 INFO:  test-node-gpu.js test: warmup 
-2022-04-01 09:12:50 STATE: test-node-gpu.js passed: create human 
-2022-04-01 09:12:50 INFO:  test-node-gpu.js human version: 2.6.5 
-2022-04-01 09:12:50 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v17.4.0 
-2022-04-01 09:12:50 INFO:  test-node-gpu.js tfjs version: 3.15.0 
-2022-04-01 09:12:50 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
-2022-04-01 09:12:50 STATE: test-node-gpu.js passed: set backend: tensorflow 
-2022-04-01 09:12:50 STATE: test-node-gpu.js tensors 1919 
-2022-04-01 09:12:50 STATE: test-node-gpu.js passed: load models 
-2022-04-01 09:12:50 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
-2022-04-01 09:12:50 STATE: test-node-gpu.js passed: warmup: none default 
-2022-04-01 09:12:50 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-04-01 09:12:50 DATA:  test-node-gpu.js  result: performance: load: null total: null 
-2022-04-01 09:12:50 STATE: test-node-gpu.js passed: warmup none result match 
-2022-04-01 09:12:51 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:55 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:55 STATE: test-node-gpu.js event: warmup 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: warmup: face default 
-2022-04-01 09:12:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-04-01 09:12:55 DATA:  test-node-gpu.js  result: performance: load: null total: 4014 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: warmup face result match 
-2022-04-01 09:12:55 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:55 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:55 STATE: test-node-gpu.js event: warmup 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: warmup: body default 
-2022-04-01 09:12:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:55 DATA:  test-node-gpu.js  result: performance: load: null total: 177 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: warmup body result match 
-2022-04-01 09:12:55 STATE: test-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.89},"emotion":[{"score":0.35,"emotion":"fear"},{"score":0.28,"emotion":"angry"},{"score":0.15,"emotion":"sad"},{"score":0.1,"emotion":"surprise"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-04-01 09:12:55 INFO:  test-node-gpu.js test: details verification 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:12:55 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:55 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:55 DATA:  test-node-gpu.js  result: performance: load: null total: 152 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details face length 1 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details face age/gender 29.6 female 0.91 73.26 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details face anti-spoofing 0.78 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details face liveness 0.83 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details body length 1 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details body 0.92 17 6 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details hand length 1 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details gesture length 6 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details object length 1 
-2022-04-01 09:12:55 STATE: test-node-gpu.js passed: details object 0.72 person 
-2022-04-01 09:12:56 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-04-01 09:12:56 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:56 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:56 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-04-01 09:12:56 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-04-01 09:12:56 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:56 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:56 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-04-01 09:12:56 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:12:56 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:57 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:57 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-04-01 09:12:57 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:12:57 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:57 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:57 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-04-01 09:12:57 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-04-01 09:12:57 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:57 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:57 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-04-01 09:12:57 INFO:  test-node-gpu.js test default 
-2022-04-01 09:12:57 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:12:57 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:58 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:58 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:58 DATA:  test-node-gpu.js  result: performance: load: null total: 162 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: default result face match 1 female 0.91 
-2022-04-01 09:12:58 INFO:  test-node-gpu.js test sync 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:12:58 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:58 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:58 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:58 DATA:  test-node-gpu.js  result: performance: load: null total: 159 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: default sync 1 female 0.91 
-2022-04-01 09:12:58 INFO:  test-node-gpu.js test: image process 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
-2022-04-01 09:12:58 INFO:  test-node-gpu.js test: image null 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
-2022-04-01 09:12:58 INFO:  test-node-gpu.js test face similarity 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:12:58 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:58 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:12:58 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-04-01 09:12:58 DATA:  test-node-gpu.js  result: performance: load: null total: 153 
-2022-04-01 09:12:58 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:12:58 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:59 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:59 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:59 DATA:  test-node-gpu.js  result: performance: load: null total: 161 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-01 09:12:59 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:59 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:12:59 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-04-01 09:12:59 DATA:  test-node-gpu.js  result: performance: load: null total: 141 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: face descriptor 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.5630857211196999,0.5193140083083794],"descriptors":[1024,1024,1024]} 
-2022-04-01 09:12:59 INFO:  test-node-gpu.js test face matching 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: face database 40 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.8799993617830532}} {"second":{"index":4,"similarity":0.5479640431307572}} {"third":{"index":4,"similarity":0.49994916272803597}} 
-2022-04-01 09:12:59 INFO:  test-node-gpu.js test object 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:12:59 STATE: test-node-gpu.js event: image 
-2022-04-01 09:12:59 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:12:59 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:12:59 DATA:  test-node-gpu.js  result: performance: load: null total: 206 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: object result match 
-2022-04-01 09:12:59 INFO:  test-node-gpu.js test sensitive 
-2022-04-01 09:12:59 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:12:59 STATE: test-node-gpu.js event: image 
-2022-04-01 09:13:00 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: performance: load: null total: 147 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: sensitive result match 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: sensitive face result match 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: sensitive face emotion result [{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}] 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: sensitive body result match 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: sensitive hand result match 
-2022-04-01 09:13:00 INFO:  test-node-gpu.js test detectors 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:13:00 STATE: test-node-gpu.js event: image 
-2022-04-01 09:13:00 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: performance: load: null total: 82 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: detector result face match 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: detector result hand match 
-2022-04-01 09:13:00 INFO:  test-node-gpu.js test: multi-instance 
-2022-04-01 09:13:00 STATE: test-node-gpu.js event: image 
-2022-04-01 09:13:00 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: detect: random default 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: performance: load: null total: 75 
-2022-04-01 09:13:00 INFO:  test-node-gpu.js test: first instance 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: performance: load: null total: 76 
-2022-04-01 09:13:00 INFO:  test-node-gpu.js test: second instance 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:00 DATA:  test-node-gpu.js  result: performance: load: null total: 73 
-2022-04-01 09:13:00 INFO:  test-node-gpu.js test: concurrent 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:13:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:13:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:13:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-01 09:13:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-01 09:13:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:13:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:13:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-01 09:13:01 STATE: test-node-gpu.js event: image 
-2022-04-01 09:13:01 STATE: test-node-gpu.js event: image 
-2022-04-01 09:13:01 STATE: test-node-gpu.js event: image 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: performance: load: null total: 958 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: performance: load: null total: 958 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: performance: load: null total: 1014 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: performance: load: null total: 1014 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: performance: load: null total: 1014 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: performance: load: null total: 1014 
-2022-04-01 09:13:02 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: performance: load: null total: 736 
-2022-04-01 09:13:02 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:13:02 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: performance: load: null total: 736 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:02 DATA:  test-node-gpu.js  result: performance: load: null total: 736 
-2022-04-01 09:13:02 INFO:  test-node-gpu.js test: monkey-patch 
-2022-04-01 09:13:02 STATE: test-node-gpu.js event: image 
-2022-04-01 09:13:02 STATE: test-node-gpu.js event: detect 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: monkey patch 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: segmentation [65536] 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passeed: equal usage 
-2022-04-01 09:13:02 INFO:  test-node-gpu.js test: input compare 
-2022-04-01 09:13:02 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-01 09:13:03 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-01 09:13:03 STATE: test-node-gpu.js passed: image compare 0 23.275441687091504 
-2022-04-01 09:13:03 INFO:  test-node-gpu.js events: {"image":21,"detect":21,"warmup":2} 
-2022-04-01 09:13:03 INFO:  test-node-gpu.js tensors 1925 
-2022-04-01 09:13:03 INFO:  test-node-gpu.js test complete: 12312 ms 
-2022-04-01 09:13:04 INFO:  
-2022-04-01 09:13:04 INFO:  test-node-wasm.js start 
-2022-04-01 09:13:04 STATE: test-node-wasm.js passed: model server: https://vladmandic.github.io/human/models/ 
-2022-04-01 09:13:04 INFO:  test-node-wasm.js test: configuration validation 
-2022-04-01 09:13:04 STATE: test-node-wasm.js passed: configuration default validation [] 
-2022-04-01 09:13:04 STATE: test-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-04-01 09:13:04 INFO:  test-node-wasm.js test: model load 
-2022-04-01 09:13:05 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
-2022-04-01 09:13:05 INFO:  test-node-wasm.js test: warmup 
-2022-04-01 09:13:05 STATE: test-node-wasm.js passed: create human 
-2022-04-01 09:13:05 INFO:  test-node-wasm.js human version: 2.6.5 
-2022-04-01 09:13:05 INFO:  test-node-wasm.js platform: linux x64 agent: NodeJS v17.4.0 
-2022-04-01 09:13:05 INFO:  test-node-wasm.js tfjs version: 3.15.0 
-2022-04-01 09:13:05 STATE: test-node-wasm.js passed: set backend: wasm 
-2022-04-01 09:13:05 STATE: test-node-wasm.js tensors 1919 
-2022-04-01 09:13:05 STATE: test-node-wasm.js passed: load models 
-2022-04-01 09:13:05 STATE: test-node-wasm.js  result: defined models: 22 loaded models: 12 
-2022-04-01 09:13:05 STATE: test-node-wasm.js passed: warmup: none default 
-2022-04-01 09:13:05 DATA:  test-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-04-01 09:13:05 DATA:  test-node-wasm.js  result: performance: load: null total: null 
-2022-04-01 09:13:05 STATE: test-node-wasm.js passed: warmup none result match 
-2022-04-01 09:13:05 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:06 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:06 STATE: test-node-wasm.js event: warmup 
-2022-04-01 09:13:06 STATE: test-node-wasm.js passed: warmup: face default 
-2022-04-01 09:13:06 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-04-01 09:13:06 DATA:  test-node-wasm.js  result: performance: load: null total: 562 
-2022-04-01 09:13:06 STATE: test-node-wasm.js passed: warmup face result match 
-2022-04-01 09:13:06 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:06 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:06 STATE: test-node-wasm.js event: warmup 
-2022-04-01 09:13:06 STATE: test-node-wasm.js passed: warmup: body default 
-2022-04-01 09:13:06 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:06 DATA:  test-node-wasm.js  result: performance: load: null total: 431 
-2022-04-01 09:13:06 STATE: test-node-wasm.js passed: warmup body result match 
-2022-04-01 09:13:06 STATE: test-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.91},"emotion":[{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-04-01 09:13:06 INFO:  test-node-wasm.js test: details verification 
-2022-04-01 09:13:06 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:06 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:07 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:07 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:07 DATA:  test-node-wasm.js  result: performance: load: null total: 392 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details face length 1 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details face score 1 0.93 1 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details face age/gender 29.6 female 0.91 73.26 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details face anti-spoofing 0.78 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details face liveness 0.83 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details body length 1 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details body 0.92 17 6 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details hand length 1 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details hand 0.51 0.73 point 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details hand arrays 21 5 7 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details gesture length 6 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details object length 1 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: details object 0.72 person 
-2022-04-01 09:13:07 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
-2022-04-01 09:13:07 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:08 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:08 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-04-01 09:13:08 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
-2022-04-01 09:13:08 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:08 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:08 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-04-01 09:13:08 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:08 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:09 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:09 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-04-01 09:13:09 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:09 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:09 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:09 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-04-01 09:13:10 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-04-01 09:13:10 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:10 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:10 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-04-01 09:13:10 INFO:  test-node-wasm.js test default 
-2022-04-01 09:13:10 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:10 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:11 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:11 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:11 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:11 DATA:  test-node-wasm.js  result: performance: load: null total: 382 
-2022-04-01 09:13:11 STATE: test-node-wasm.js passed: default result face match 1 female 0.93 
-2022-04-01 09:13:11 INFO:  test-node-wasm.js test sync 
-2022-04-01 09:13:11 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:11 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:11 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:11 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:11 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:11 DATA:  test-node-wasm.js  result: performance: load: null total: 401 
-2022-04-01 09:13:11 STATE: test-node-wasm.js passed: default sync 1 female 0.93 
-2022-04-01 09:13:11 INFO:  test-node-wasm.js test: image process 
-2022-04-01 09:13:11 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-01 09:13:11 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
-2022-04-01 09:13:11 INFO:  test-node-wasm.js test: image null 
-2022-04-01 09:13:11 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
-2022-04-01 09:13:11 INFO:  test-node-wasm.js test face similarity 
-2022-04-01 09:13:11 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-01 09:13:11 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:12 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:13:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-04-01 09:13:12 DATA:  test-node-wasm.js  result: performance: load: null total: 372 
-2022-04-01 09:13:12 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:12 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:12 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:12 DATA:  test-node-wasm.js  result: performance: load: null total: 374 
-2022-04-01 09:13:13 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-01 09:13:13 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:13 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:13 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:13 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-04-01 09:13:13 DATA:  test-node-wasm.js  result: performance: load: null total: 348 
-2022-04-01 09:13:13 STATE: test-node-wasm.js passed: face descriptor 
-2022-04-01 09:13:13 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5888036339038205,0.4689331535670693],"descriptors":[1024,1024,1024]} 
-2022-04-01 09:13:13 INFO:  test-node-wasm.js test face matching 
-2022-04-01 09:13:13 STATE: test-node-wasm.js passed: face database 40 
-2022-04-01 09:13:13 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.8797924799049534}} {"second":{"index":4,"similarity":0.573642308237338}} {"third":{"index":4,"similarity":0.45582039900188964}} 
-2022-04-01 09:13:13 INFO:  test-node-wasm.js test object 
-2022-04-01 09:13:13 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:13 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:14 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:14 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:14 DATA:  test-node-wasm.js  result: performance: load: null total: 386 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: object result match 
-2022-04-01 09:13:14 INFO:  test-node-wasm.js test sensitive 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:14 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:14 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:14 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:14 DATA:  test-node-wasm.js  result: performance: load: null total: 428 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: sensitive result match 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: sensitive face result match 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.54,"emotion":"angry"},{"score":0.2,"emotion":"fear"},{"score":0.16,"emotion":"surprise"}] 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: sensitive body result match 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: sensitive hand result match 
-2022-04-01 09:13:14 INFO:  test-node-wasm.js test detectors 
-2022-04-01 09:13:14 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:14 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:15 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:15 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:15 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:15 DATA:  test-node-wasm.js  result: performance: load: null total: 284 
-2022-04-01 09:13:15 STATE: test-node-wasm.js passed: detector result face match 
-2022-04-01 09:13:15 STATE: test-node-wasm.js passed: detector result hand match 
-2022-04-01 09:13:15 INFO:  test-node-wasm.js test: multi-instance 
-2022-04-01 09:13:15 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:15 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:15 STATE: test-node-wasm.js passed: detect: random default 
-2022-04-01 09:13:15 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.06,"keypoints":15} 
-2022-04-01 09:13:15 DATA:  test-node-wasm.js  result: performance: load: null total: 240 
-2022-04-01 09:13:15 INFO:  test-node-wasm.js test: first instance 
-2022-04-01 09:13:15 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-01 09:13:15 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:15 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:15 DATA:  test-node-wasm.js  result: performance: load: null total: 265 
-2022-04-01 09:13:15 INFO:  test-node-wasm.js test: second instance 
-2022-04-01 09:13:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-01 09:13:16 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:16 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:16 DATA:  test-node-wasm.js  result: performance: load: null total: 270 
-2022-04-01 09:13:16 INFO:  test-node-wasm.js test: concurrent 
-2022-04-01 09:13:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-01 09:13:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-01 09:13:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-01 09:13:17 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-01 09:13:17 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-01 09:13:17 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:17 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-01 09:13:17 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:17 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:17 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: performance: load: null total: 2440 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: performance: load: null total: 2440 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: performance: load: null total: 2714 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: performance: load: null total: 2714 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: performance: load: null total: 2714 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: performance: load: null total: 2714 
-2022-04-01 09:13:19 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: performance: load: null total: 2375 
-2022-04-01 09:13:19 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:19 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: performance: load: null total: 2375 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-01 09:13:19 DATA:  test-node-wasm.js  result: performance: load: null total: 2375 
-2022-04-01 09:13:19 INFO:  test-node-wasm.js test: monkey-patch 
-2022-04-01 09:13:19 STATE: test-node-wasm.js event: image 
-2022-04-01 09:13:19 STATE: test-node-wasm.js event: detect 
-2022-04-01 09:13:19 STATE: test-node-wasm.js passed: monkey patch 
-2022-04-01 09:13:20 STATE: test-node-wasm.js passed: segmentation [65536] 
-2022-04-01 09:13:20 STATE: test-node-wasm.js passeed: equal usage 
-2022-04-01 09:13:20 INFO:  test-node-wasm.js test: input compare 
-2022-04-01 09:13:20 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-01 09:13:20 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-01 09:13:20 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
-2022-04-01 09:13:20 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
-2022-04-01 09:13:20 INFO:  test-node-wasm.js tensors 1927 
-2022-04-01 09:13:20 INFO:  test-node-wasm.js test complete: 15701 ms 
-2022-04-01 09:13:20 INFO:  all tests complete 
-2022-04-01 09:13:20 INFO:  failed: {"count":0,"messages":[]} 
-2022-04-01 09:13:20 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
-2022-04-01 09:13:20 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
-2022-04-01 09:13:20 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0} 
+2022-04-05 07:49:34 INFO:  @vladmandic/human version 2.6.5 
+2022-04-05 07:49:34 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.4.0 
+2022-04-05 07:49:34 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
+2022-04-05 07:49:34 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
+2022-04-05 07:49:34 INFO:  
+2022-04-05 07:49:34 INFO:  test-node.js start 
+2022-04-05 07:49:34 INFO:  test-node.js test: configuration validation 
+2022-04-05 07:49:34 STATE: test-node.js passed: configuration default validation [] 
+2022-04-05 07:49:34 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-04-05 07:49:34 INFO:  test-node.js test: model load 
+2022-04-05 07:49:34 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
+2022-04-05 07:49:34 INFO:  test-node.js test: warmup 
+2022-04-05 07:49:34 STATE: test-node.js passed: create human 
+2022-04-05 07:49:34 INFO:  test-node.js human version: 2.6.5 
+2022-04-05 07:49:34 INFO:  test-node.js platform: linux x64 agent: NodeJS v17.4.0 
+2022-04-05 07:49:34 INFO:  test-node.js tfjs version: 3.15.0 
+2022-04-05 07:49:34 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
+2022-04-05 07:49:34 STATE: test-node.js passed: set backend: tensorflow 
+2022-04-05 07:49:34 STATE: test-node.js tensors 1919 
+2022-04-05 07:49:34 STATE: test-node.js passed: load models 
+2022-04-05 07:49:34 STATE: test-node.js  result: defined models: 22 loaded models: 12 
+2022-04-05 07:49:34 STATE: test-node.js passed: warmup: none default 
+2022-04-05 07:49:34 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-04-05 07:49:34 DATA:  test-node.js  result: performance: load: null total: null 
+2022-04-05 07:49:34 STATE: test-node.js passed: warmup none result match 
+2022-04-05 07:49:34 STATE: test-node.js event: image 
+2022-04-05 07:49:34 STATE: test-node.js event: detect 
+2022-04-05 07:49:34 STATE: test-node.js event: warmup 
+2022-04-05 07:49:34 STATE: test-node.js passed: warmup: face default 
+2022-04-05 07:49:34 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-04-05 07:49:34 DATA:  test-node.js  result: performance: load: null total: 333 
+2022-04-05 07:49:34 STATE: test-node.js passed: warmup face result match 
+2022-04-05 07:49:34 STATE: test-node.js event: image 
+2022-04-05 07:49:35 STATE: test-node.js event: detect 
+2022-04-05 07:49:35 STATE: test-node.js event: warmup 
+2022-04-05 07:49:35 STATE: test-node.js passed: warmup: body default 
+2022-04-05 07:49:35 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:35 DATA:  test-node.js  result: performance: load: null total: 250 
+2022-04-05 07:49:35 STATE: test-node.js passed: warmup body result match 
+2022-04-05 07:49:35 STATE: test-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.89},"emotion":[{"score":0.35,"emotion":"fear"},{"score":0.28,"emotion":"angry"},{"score":0.15,"emotion":"sad"},{"score":0.1,"emotion":"surprise"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-04-05 07:49:35 INFO:  test-node.js test: details verification 
+2022-04-05 07:49:35 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:35 STATE: test-node.js event: image 
+2022-04-05 07:49:35 STATE: test-node.js event: detect 
+2022-04-05 07:49:35 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:35 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:35 DATA:  test-node.js  result: performance: load: null total: 233 
+2022-04-05 07:49:35 STATE: test-node.js passed: details face length 1 
+2022-04-05 07:49:35 STATE: test-node.js passed: details face score 1 0.93 1 
+2022-04-05 07:49:35 STATE: test-node.js passed: details face age/gender 29.6 female 0.91 73.26 
+2022-04-05 07:49:35 STATE: test-node.js passed: details face arrays 4 478 1024 
+2022-04-05 07:49:35 STATE: test-node.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
+2022-04-05 07:49:35 STATE: test-node.js passed: details face anti-spoofing 0.78 
+2022-04-05 07:49:35 STATE: test-node.js passed: details face liveness 0.83 
+2022-04-05 07:49:35 STATE: test-node.js passed: details body length 1 
+2022-04-05 07:49:35 STATE: test-node.js passed: details body 0.92 17 6 
+2022-04-05 07:49:35 STATE: test-node.js passed: details hand length 1 
+2022-04-05 07:49:35 STATE: test-node.js passed: details hand 0.51 0.73 point 
+2022-04-05 07:49:35 STATE: test-node.js passed: details hand arrays 21 5 7 
+2022-04-05 07:49:35 STATE: test-node.js passed: details gesture length 6 
+2022-04-05 07:49:35 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-04-05 07:49:35 STATE: test-node.js passed: details object length 1 
+2022-04-05 07:49:35 STATE: test-node.js passed: details object 0.72 person 
+2022-04-05 07:49:35 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-04-05 07:49:35 STATE: test-node.js event: image 
+2022-04-05 07:49:36 STATE: test-node.js event: detect 
+2022-04-05 07:49:36 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-04-05 07:49:36 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-04-05 07:49:36 STATE: test-node.js event: image 
+2022-04-05 07:49:36 STATE: test-node.js event: detect 
+2022-04-05 07:49:36 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-04-05 07:49:36 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:36 STATE: test-node.js event: image 
+2022-04-05 07:49:36 STATE: test-node.js event: detect 
+2022-04-05 07:49:36 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-04-05 07:49:37 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:37 STATE: test-node.js event: image 
+2022-04-05 07:49:37 STATE: test-node.js event: detect 
+2022-04-05 07:49:37 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-04-05 07:49:37 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-04-05 07:49:37 STATE: test-node.js event: image 
+2022-04-05 07:49:37 STATE: test-node.js event: detect 
+2022-04-05 07:49:37 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-04-05 07:49:37 INFO:  test-node.js test default 
+2022-04-05 07:49:37 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:37 STATE: test-node.js event: image 
+2022-04-05 07:49:38 STATE: test-node.js event: detect 
+2022-04-05 07:49:38 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:38 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:38 DATA:  test-node.js  result: performance: load: null total: 207 
+2022-04-05 07:49:38 STATE: test-node.js passed: default result face match 1 female 0.91 
+2022-04-05 07:49:38 INFO:  test-node.js test sync 
+2022-04-05 07:49:38 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:38 STATE: test-node.js event: image 
+2022-04-05 07:49:38 STATE: test-node.js event: detect 
+2022-04-05 07:49:38 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:38 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:38 DATA:  test-node.js  result: performance: load: null total: 206 
+2022-04-05 07:49:38 STATE: test-node.js passed: default sync 1 female 0.91 
+2022-04-05 07:49:38 INFO:  test-node.js test: image process 
+2022-04-05 07:49:38 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:38 STATE: test-node.js passed: image input null [1,256,256,3] 
+2022-04-05 07:49:38 INFO:  test-node.js test: image null 
+2022-04-05 07:49:38 STATE: test-node.js passed: invalid input could not convert input to tensor 
+2022-04-05 07:49:38 INFO:  test-node.js test face similarity 
+2022-04-05 07:49:38 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:38 STATE: test-node.js event: image 
+2022-04-05 07:49:38 STATE: test-node.js event: detect 
+2022-04-05 07:49:38 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:49:38 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-04-05 07:49:38 DATA:  test-node.js  result: performance: load: null total: 196 
+2022-04-05 07:49:38 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:38 STATE: test-node.js event: image 
+2022-04-05 07:49:38 STATE: test-node.js event: detect 
+2022-04-05 07:49:38 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:38 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:38 DATA:  test-node.js  result: performance: load: null total: 205 
+2022-04-05 07:49:39 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-05 07:49:39 STATE: test-node.js event: image 
+2022-04-05 07:49:39 STATE: test-node.js event: detect 
+2022-04-05 07:49:39 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:39 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-04-05 07:49:39 DATA:  test-node.js  result: performance: load: null total: 187 
+2022-04-05 07:49:39 STATE: test-node.js passed: face descriptor 
+2022-04-05 07:49:39 STATE: test-node.js passed: face similarity {"similarity":[1,0.5632803981762933,0.5194114531612539],"descriptors":[1024,1024,1024]} 
+2022-04-05 07:49:39 INFO:  test-node.js test face matching 
+2022-04-05 07:49:39 STATE: test-node.js passed: face database 40 
+2022-04-05 07:49:39 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.8797924743573865}} {"second":{"index":4,"similarity":0.548081908581344}} {"third":{"index":4,"similarity":0.4999696807961951}} 
+2022-04-05 07:49:39 INFO:  test-node.js test object 
+2022-04-05 07:49:39 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:39 STATE: test-node.js event: image 
+2022-04-05 07:49:39 STATE: test-node.js event: detect 
+2022-04-05 07:49:39 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:39 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:39 DATA:  test-node.js  result: performance: load: null total: 202 
+2022-04-05 07:49:39 STATE: test-node.js passed: object result match 
+2022-04-05 07:49:39 INFO:  test-node.js test sensitive 
+2022-04-05 07:49:39 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:39 STATE: test-node.js event: image 
+2022-04-05 07:49:40 STATE: test-node.js event: detect 
+2022-04-05 07:49:40 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:40 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:40 DATA:  test-node.js  result: performance: load: null total: 220 
+2022-04-05 07:49:40 STATE: test-node.js passed: sensitive result match 
+2022-04-05 07:49:40 STATE: test-node.js passed: sensitive face result match 
+2022-04-05 07:49:40 STATE: test-node.js passed: sensitive face emotion result [{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}] 
+2022-04-05 07:49:40 STATE: test-node.js passed: sensitive body result match 
+2022-04-05 07:49:40 STATE: test-node.js passed: sensitive hand result match 
+2022-04-05 07:49:40 INFO:  test-node.js test detectors 
+2022-04-05 07:49:40 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:40 STATE: test-node.js event: image 
+2022-04-05 07:49:40 STATE: test-node.js event: detect 
+2022-04-05 07:49:40 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:40 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:40 DATA:  test-node.js  result: performance: load: null total: 148 
+2022-04-05 07:49:40 STATE: test-node.js passed: detector result face match 
+2022-04-05 07:49:40 STATE: test-node.js passed: detector result hand match 
+2022-04-05 07:49:40 INFO:  test-node.js test: multi-instance 
+2022-04-05 07:49:40 STATE: test-node.js event: image 
+2022-04-05 07:49:40 STATE: test-node.js event: detect 
+2022-04-05 07:49:40 STATE: test-node.js passed: detect: random default 
+2022-04-05 07:49:40 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.08,"keypoints":15} 
+2022-04-05 07:49:40 DATA:  test-node.js  result: performance: load: null total: 135 
+2022-04-05 07:49:40 INFO:  test-node.js test: first instance 
+2022-04-05 07:49:40 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-05 07:49:40 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:40 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:40 DATA:  test-node.js  result: performance: load: null total: 136 
+2022-04-05 07:49:40 INFO:  test-node.js test: second instance 
+2022-04-05 07:49:40 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-05 07:49:40 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:40 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:40 DATA:  test-node.js  result: performance: load: null total: 137 
+2022-04-05 07:49:40 INFO:  test-node.js test: concurrent 
+2022-04-05 07:49:40 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:40 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:41 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:41 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:41 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-05 07:49:41 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-05 07:49:41 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:41 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:41 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-05 07:49:41 STATE: test-node.js event: image 
+2022-04-05 07:49:41 STATE: test-node.js event: image 
+2022-04-05 07:49:41 STATE: test-node.js event: image 
+2022-04-05 07:49:42 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:42 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:42 DATA:  test-node.js  result: performance: load: null total: 1429 
+2022-04-05 07:49:42 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:42 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:42 DATA:  test-node.js  result: performance: load: null total: 1429 
+2022-04-05 07:49:43 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:49:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-05 07:49:43 DATA:  test-node.js  result: performance: load: null total: 1550 
+2022-04-05 07:49:43 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:49:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-05 07:49:43 DATA:  test-node.js  result: performance: load: null total: 1550 
+2022-04-05 07:49:43 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:43 DATA:  test-node.js  result: performance: load: null total: 1550 
+2022-04-05 07:49:43 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:43 DATA:  test-node.js  result: performance: load: null total: 1550 
+2022-04-05 07:49:43 STATE: test-node.js event: detect 
+2022-04-05 07:49:43 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:43 DATA:  test-node.js  result: performance: load: null total: 1292 
+2022-04-05 07:49:43 STATE: test-node.js event: detect 
+2022-04-05 07:49:43 STATE: test-node.js event: detect 
+2022-04-05 07:49:43 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:49:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-05 07:49:43 DATA:  test-node.js  result: performance: load: null total: 1292 
+2022-04-05 07:49:43 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:43 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:43 DATA:  test-node.js  result: performance: load: null total: 1292 
+2022-04-05 07:49:43 INFO:  test-node.js test: monkey-patch 
+2022-04-05 07:49:43 STATE: test-node.js event: image 
+2022-04-05 07:49:43 STATE: test-node.js event: detect 
+2022-04-05 07:49:43 STATE: test-node.js passed: monkey patch 
+2022-04-05 07:49:43 STATE: test-node.js passed: segmentation [65536] 
+2022-04-05 07:49:43 STATE: test-node.js passeed: equal usage 
+2022-04-05 07:49:43 INFO:  test-node.js test: input compare 
+2022-04-05 07:49:43 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:43 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-05 07:49:43 STATE: test-node.js passed: image compare 0 23.275441687091504 
+2022-04-05 07:49:43 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
+2022-04-05 07:49:43 INFO:  test-node.js tensors 1925 
+2022-04-05 07:49:43 INFO:  test-node.js test complete: 9010 ms 
+2022-04-05 07:49:43 INFO:  
+2022-04-05 07:49:43 INFO:  test-node-gpu.js start 
+2022-04-05 07:49:44 INFO:  test-node-gpu.js test: configuration validation 
+2022-04-05 07:49:44 STATE: test-node-gpu.js passed: configuration default validation [] 
+2022-04-05 07:49:44 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-04-05 07:49:44 INFO:  test-node-gpu.js test: model load 
+2022-04-05 07:49:44 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
+2022-04-05 07:49:44 INFO:  test-node-gpu.js test: warmup 
+2022-04-05 07:49:44 STATE: test-node-gpu.js passed: create human 
+2022-04-05 07:49:44 INFO:  test-node-gpu.js human version: 2.6.5 
+2022-04-05 07:49:44 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v17.4.0 
+2022-04-05 07:49:44 INFO:  test-node-gpu.js tfjs version: 3.15.0 
+2022-04-05 07:49:44 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
+2022-04-05 07:49:44 STATE: test-node-gpu.js passed: set backend: tensorflow 
+2022-04-05 07:49:44 STATE: test-node-gpu.js tensors 1919 
+2022-04-05 07:49:44 STATE: test-node-gpu.js passed: load models 
+2022-04-05 07:49:44 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
+2022-04-05 07:49:44 STATE: test-node-gpu.js passed: warmup: none default 
+2022-04-05 07:49:44 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-04-05 07:49:44 DATA:  test-node-gpu.js  result: performance: load: null total: null 
+2022-04-05 07:49:44 STATE: test-node-gpu.js passed: warmup none result match 
+2022-04-05 07:49:44 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:48 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:48 STATE: test-node-gpu.js event: warmup 
+2022-04-05 07:49:48 STATE: test-node-gpu.js passed: warmup: face default 
+2022-04-05 07:49:48 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-04-05 07:49:48 DATA:  test-node-gpu.js  result: performance: load: null total: 3871 
+2022-04-05 07:49:48 STATE: test-node-gpu.js passed: warmup face result match 
+2022-04-05 07:49:48 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:48 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:48 STATE: test-node-gpu.js event: warmup 
+2022-04-05 07:49:48 STATE: test-node-gpu.js passed: warmup: body default 
+2022-04-05 07:49:48 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:48 DATA:  test-node-gpu.js  result: performance: load: null total: 177 
+2022-04-05 07:49:48 STATE: test-node-gpu.js passed: warmup body result match 
+2022-04-05 07:49:48 STATE: test-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.89},"emotion":[{"score":0.35,"emotion":"fear"},{"score":0.28,"emotion":"angry"},{"score":0.15,"emotion":"sad"},{"score":0.1,"emotion":"surprise"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-04-05 07:49:48 INFO:  test-node-gpu.js test: details verification 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:49 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:49 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:49 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:49 DATA:  test-node-gpu.js  result: performance: load: null total: 217 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details face length 1 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details face age/gender 29.6 female 0.91 73.26 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details face anti-spoofing 0.78 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details face liveness 0.83 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details body length 1 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details body 0.92 17 6 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details hand length 1 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details gesture length 6 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details object length 1 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: details object 0.72 person 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-04-05 07:49:49 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:49 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:49 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-04-05 07:49:50 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-04-05 07:49:50 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:50 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:50 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-04-05 07:49:50 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:50 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:50 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:50 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-04-05 07:49:50 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:50 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:50 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:50 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-04-05 07:49:51 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-04-05 07:49:51 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:51 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:51 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-04-05 07:49:51 INFO:  test-node-gpu.js test default 
+2022-04-05 07:49:51 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:51 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:51 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:51 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:51 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:51 DATA:  test-node-gpu.js  result: performance: load: null total: 223 
+2022-04-05 07:49:51 STATE: test-node-gpu.js passed: default result face match 1 female 0.91 
+2022-04-05 07:49:51 INFO:  test-node-gpu.js test sync 
+2022-04-05 07:49:51 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:51 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:52 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:52 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:52 DATA:  test-node-gpu.js  result: performance: load: null total: 207 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: default sync 1 female 0.91 
+2022-04-05 07:49:52 INFO:  test-node-gpu.js test: image process 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
+2022-04-05 07:49:52 INFO:  test-node-gpu.js test: image null 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
+2022-04-05 07:49:52 INFO:  test-node-gpu.js test face similarity 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:52 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:52 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:49:52 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-04-05 07:49:52 DATA:  test-node-gpu.js  result: performance: load: null total: 166 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:52 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:52 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:52 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:52 DATA:  test-node-gpu.js  result: performance: load: null total: 137 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-04-05 07:49:52 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:52 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:52 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-04-05 07:49:52 DATA:  test-node-gpu.js  result: performance: load: null total: 125 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: face descriptor 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.5630857211196999,0.5193140083083794],"descriptors":[1024,1024,1024]} 
+2022-04-05 07:49:52 INFO:  test-node-gpu.js test face matching 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: face database 40 
+2022-04-05 07:49:52 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.8799993617830532}} {"second":{"index":4,"similarity":0.5479640431307572}} {"third":{"index":4,"similarity":0.49994916272803597}} 
+2022-04-05 07:49:52 INFO:  test-node-gpu.js test object 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:53 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:53 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:53 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:53 DATA:  test-node-gpu.js  result: performance: load: null total: 132 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: object result match 
+2022-04-05 07:49:53 INFO:  test-node-gpu.js test sensitive 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:53 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:53 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:53 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:53 DATA:  test-node-gpu.js  result: performance: load: null total: 145 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: sensitive result match 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: sensitive face result match 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: sensitive face emotion result [{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}] 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: sensitive body result match 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: sensitive hand result match 
+2022-04-05 07:49:53 INFO:  test-node-gpu.js test detectors 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:53 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:53 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:53 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:53 DATA:  test-node-gpu.js  result: performance: load: null total: 73 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: detector result face match 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: detector result hand match 
+2022-04-05 07:49:53 INFO:  test-node-gpu.js test: multi-instance 
+2022-04-05 07:49:53 STATE: test-node-gpu.js event: image 
+2022-04-05 07:49:53 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: detect: random default 
+2022-04-05 07:49:53 DATA:  test-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
+2022-04-05 07:49:53 DATA:  test-node-gpu.js  result: performance: load: null total: 74 
+2022-04-05 07:49:53 INFO:  test-node-gpu.js test: first instance 
+2022-04-05 07:49:53 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:54 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:54 DATA:  test-node-gpu.js  result: performance: load: null total: 79 
+2022-04-05 07:49:54 INFO:  test-node-gpu.js test: second instance 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:54 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:54 DATA:  test-node-gpu.js  result: performance: load: null total: 83 
+2022-04-05 07:49:54 INFO:  test-node-gpu.js test: concurrent 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-04-05 07:49:54 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
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+2022-04-05 07:49:55 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
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+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: performance: load: null total: 868 
+2022-04-05 07:49:55 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: performance: load: null total: 868 
+2022-04-05 07:49:55 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: performance: load: null total: 916 
+2022-04-05 07:49:55 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: performance: load: null total: 916 
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+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: performance: load: null total: 916 
+2022-04-05 07:49:55 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: performance: load: null total: 916 
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+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: performance: load: null total: 643 
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+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: performance: load: null total: 643 
+2022-04-05 07:49:55 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:49:55 DATA:  test-node-gpu.js  result: performance: load: null total: 643 
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+2022-04-05 07:49:55 STATE: test-node-gpu.js event: detect 
+2022-04-05 07:49:55 STATE: test-node-gpu.js passed: monkey patch 
+2022-04-05 07:49:56 STATE: test-node-gpu.js passed: segmentation [65536] 
+2022-04-05 07:49:56 STATE: test-node-gpu.js passeed: equal usage 
+2022-04-05 07:49:56 INFO:  test-node-gpu.js test: input compare 
+2022-04-05 07:49:56 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-05 07:49:56 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-05 07:49:56 STATE: test-node-gpu.js passed: image compare 0 23.275441687091504 
+2022-04-05 07:49:56 INFO:  test-node-gpu.js events: {"image":21,"detect":21,"warmup":2} 
+2022-04-05 07:49:56 INFO:  test-node-gpu.js tensors 1925 
+2022-04-05 07:49:56 INFO:  test-node-gpu.js test complete: 11941 ms 
+2022-04-05 07:49:57 INFO:  
+2022-04-05 07:49:57 INFO:  test-node-wasm.js start 
+2022-04-05 07:49:57 STATE: test-node-wasm.js passed: model server: https://vladmandic.github.io/human/models/ 
+2022-04-05 07:49:57 INFO:  test-node-wasm.js test: configuration validation 
+2022-04-05 07:49:57 STATE: test-node-wasm.js passed: configuration default validation [] 
+2022-04-05 07:49:57 STATE: test-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-04-05 07:49:57 INFO:  test-node-wasm.js test: model load 
+2022-04-05 07:49:59 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
+2022-04-05 07:49:59 INFO:  test-node-wasm.js test: warmup 
+2022-04-05 07:49:59 STATE: test-node-wasm.js passed: create human 
+2022-04-05 07:49:59 INFO:  test-node-wasm.js human version: 2.6.5 
+2022-04-05 07:49:59 INFO:  test-node-wasm.js platform: linux x64 agent: NodeJS v17.4.0 
+2022-04-05 07:49:59 INFO:  test-node-wasm.js tfjs version: 3.15.0 
+2022-04-05 07:49:59 STATE: test-node-wasm.js passed: set backend: wasm 
+2022-04-05 07:49:59 STATE: test-node-wasm.js tensors 1919 
+2022-04-05 07:49:59 STATE: test-node-wasm.js passed: load models 
+2022-04-05 07:49:59 STATE: test-node-wasm.js  result: defined models: 22 loaded models: 12 
+2022-04-05 07:49:59 STATE: test-node-wasm.js passed: warmup: none default 
+2022-04-05 07:49:59 DATA:  test-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-04-05 07:49:59 DATA:  test-node-wasm.js  result: performance: load: null total: null 
+2022-04-05 07:49:59 STATE: test-node-wasm.js passed: warmup none result match 
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+2022-04-05 07:50:00 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:00 STATE: test-node-wasm.js event: warmup 
+2022-04-05 07:50:00 STATE: test-node-wasm.js passed: warmup: face default 
+2022-04-05 07:50:00 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-04-05 07:50:00 DATA:  test-node-wasm.js  result: performance: load: null total: 510 
+2022-04-05 07:50:00 STATE: test-node-wasm.js passed: warmup face result match 
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+2022-04-05 07:50:00 STATE: test-node-wasm.js event: warmup 
+2022-04-05 07:50:00 STATE: test-node-wasm.js passed: warmup: body default 
+2022-04-05 07:50:00 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:00 DATA:  test-node-wasm.js  result: performance: load: null total: 374 
+2022-04-05 07:50:00 STATE: test-node-wasm.js passed: warmup body result match 
+2022-04-05 07:50:00 STATE: test-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.91},"emotion":[{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-04-05 07:50:00 INFO:  test-node-wasm.js test: details verification 
+2022-04-05 07:50:00 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:00 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:01 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:01 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:01 DATA:  test-node-wasm.js  result: performance: load: null total: 391 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details face length 1 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details face score 1 0.93 1 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details face age/gender 29.6 female 0.91 73.26 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details face anti-spoofing 0.78 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details face liveness 0.83 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details body length 1 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details body 0.92 17 6 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details hand length 1 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details hand 0.51 0.73 point 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details hand arrays 21 5 7 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details gesture length 6 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details object length 1 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: details object 0.72 person 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
+2022-04-05 07:50:01 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:01 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-04-05 07:50:01 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
+2022-04-05 07:50:02 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:02 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:02 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-04-05 07:50:02 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:02 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:03 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:03 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-04-05 07:50:03 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:03 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:03 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:03 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-04-05 07:50:03 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-04-05 07:50:03 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:04 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:04 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-04-05 07:50:04 INFO:  test-node-wasm.js test default 
+2022-04-05 07:50:04 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:04 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:04 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:04 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:04 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:04 DATA:  test-node-wasm.js  result: performance: load: null total: 391 
+2022-04-05 07:50:04 STATE: test-node-wasm.js passed: default result face match 1 female 0.93 
+2022-04-05 07:50:04 INFO:  test-node-wasm.js test sync 
+2022-04-05 07:50:05 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:05 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:05 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:05 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:05 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:05 DATA:  test-node-wasm.js  result: performance: load: null total: 372 
+2022-04-05 07:50:05 STATE: test-node-wasm.js passed: default sync 1 female 0.93 
+2022-04-05 07:50:05 INFO:  test-node-wasm.js test: image process 
+2022-04-05 07:50:05 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-05 07:50:05 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
+2022-04-05 07:50:05 INFO:  test-node-wasm.js test: image null 
+2022-04-05 07:50:05 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
+2022-04-05 07:50:05 INFO:  test-node-wasm.js test face similarity 
+2022-04-05 07:50:05 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-05 07:50:05 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:05 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:05 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:50:05 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-04-05 07:50:05 DATA:  test-node-wasm.js  result: performance: load: null total: 349 
+2022-04-05 07:50:06 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:06 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:06 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:06 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:06 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:06 DATA:  test-node-wasm.js  result: performance: load: null total: 341 
+2022-04-05 07:50:06 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-05 07:50:06 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:06 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:06 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:50:06 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-04-05 07:50:06 DATA:  test-node-wasm.js  result: performance: load: null total: 315 
+2022-04-05 07:50:06 STATE: test-node-wasm.js passed: face descriptor 
+2022-04-05 07:50:06 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5888036339038205,0.4689331535670693],"descriptors":[1024,1024,1024]} 
+2022-04-05 07:50:06 INFO:  test-node-wasm.js test face matching 
+2022-04-05 07:50:06 STATE: test-node-wasm.js passed: face database 40 
+2022-04-05 07:50:06 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.8797924799049534}} {"second":{"index":4,"similarity":0.573642308237338}} {"third":{"index":4,"similarity":0.45582039900188964}} 
+2022-04-05 07:50:06 INFO:  test-node-wasm.js test object 
+2022-04-05 07:50:07 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:07 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:07 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:07 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:07 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:07 DATA:  test-node-wasm.js  result: performance: load: null total: 378 
+2022-04-05 07:50:07 STATE: test-node-wasm.js passed: object result match 
+2022-04-05 07:50:07 INFO:  test-node-wasm.js test sensitive 
+2022-04-05 07:50:07 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:07 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:08 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:08 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:08 DATA:  test-node-wasm.js  result: performance: load: null total: 408 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: sensitive result match 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: sensitive face result match 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.54,"emotion":"angry"},{"score":0.2,"emotion":"fear"},{"score":0.16,"emotion":"surprise"}] 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: sensitive body result match 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: sensitive hand result match 
+2022-04-05 07:50:08 INFO:  test-node-wasm.js test detectors 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:08 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:08 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:08 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:08 DATA:  test-node-wasm.js  result: performance: load: null total: 236 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: detector result face match 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: detector result hand match 
+2022-04-05 07:50:08 INFO:  test-node-wasm.js test: multi-instance 
+2022-04-05 07:50:08 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:08 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:08 STATE: test-node-wasm.js passed: detect: random default 
+2022-04-05 07:50:08 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.05,"keypoints":15} 
+2022-04-05 07:50:08 DATA:  test-node-wasm.js  result: performance: load: null total: 219 
+2022-04-05 07:50:08 INFO:  test-node-wasm.js test: first instance 
+2022-04-05 07:50:09 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-05 07:50:09 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:50:09 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:50:09 DATA:  test-node-wasm.js  result: performance: load: null total: 233 
+2022-04-05 07:50:09 INFO:  test-node-wasm.js test: second instance 
+2022-04-05 07:50:09 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-05 07:50:09 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:50:09 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:50:09 DATA:  test-node-wasm.js  result: performance: load: null total: 232 
+2022-04-05 07:50:09 INFO:  test-node-wasm.js test: concurrent 
+2022-04-05 07:50:09 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-05 07:50:09 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-05 07:50:09 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:10 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:10 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-05 07:50:10 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-05 07:50:10 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-05 07:50:10 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:10 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-05 07:50:10 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:10 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:10 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: performance: load: null total: 2218 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: performance: load: null total: 2218 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: performance: load: null total: 2452 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: performance: load: null total: 2452 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: performance: load: null total: 2452 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: performance: load: null total: 2452 
+2022-04-05 07:50:12 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: performance: load: null total: 2122 
+2022-04-05 07:50:12 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:12 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: performance: load: null total: 2122 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-05 07:50:12 DATA:  test-node-wasm.js  result: performance: load: null total: 2122 
+2022-04-05 07:50:12 INFO:  test-node-wasm.js test: monkey-patch 
+2022-04-05 07:50:12 STATE: test-node-wasm.js event: image 
+2022-04-05 07:50:12 STATE: test-node-wasm.js event: detect 
+2022-04-05 07:50:12 STATE: test-node-wasm.js passed: monkey patch 
+2022-04-05 07:50:13 STATE: test-node-wasm.js passed: segmentation [65536] 
+2022-04-05 07:50:13 STATE: test-node-wasm.js passeed: equal usage 
+2022-04-05 07:50:13 INFO:  test-node-wasm.js test: input compare 
+2022-04-05 07:50:13 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-05 07:50:13 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-05 07:50:13 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
+2022-04-05 07:50:13 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
+2022-04-05 07:50:13 INFO:  test-node-wasm.js tensors 1927 
+2022-04-05 07:50:13 INFO:  test-node-wasm.js test complete: 15317 ms 
+2022-04-05 07:50:13 INFO:  all tests complete 
+2022-04-05 07:50:13 INFO:  failed: {"count":0,"messages":[]} 
+2022-04-05 07:50:13 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
+2022-04-05 07:50:13 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
+2022-04-05 07:50:13 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0}