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Error(Q(153));if(t.child!==null){for(e=t.child,n=Wr(e,e.pendingProps),t.child=n,n.return=t;e.sibling!==null;)e=e.sibling,n=n.sibling=Wr(e,e.pendingProps),n.return=t;n.sibling=null}return t.child}function Y3(e,t,n){switch(t.tag){case 3:$_(t),sa();break;case 5:Qw(t);break;case 1:Kt(t.type)&&Al(t);break;case 4:Op(t,t.stateNode.containerInfo);break;case 10:var r=t.type._context,i=t.memoizedProps.value;Ye(zl,r._currentValue),r._currentValue=i;break;case 13:if(r=t.memoizedState,r!==null)return r.dehydrated!==null?(Ye(nt,nt.current&1),t.flags|=128,null):n&t.child.childLanes?b_(e,t,n):(Ye(nt,nt.current&1),e=gr(e,t,n),e!==null?e.sibling:null);Ye(nt,nt.current&1);break;case 19:if(r=(n&t.childLanes)!==0,e.flags&128){if(r)return x_(e,t,n);t.flags|=128}if(i=t.memoizedState,i!==null&&(i.rendering=null,i.tail=null,i.lastEffect=null),Ye(nt,nt.current),r)break;return null;case 22:case 23:return t.lanes=0,__(e,t,n)}return gr(e,t,n)}var S_,Ic,E_,k_;S_=function(e,t){for(var n=t.child;n!==null;){if(n.tag===5||n.tag===6)e.appendChild(n.stateNode);else if(n.tag!==4&&n.child!==null){n.child.return=n,n=n.child;continue}if(n===t)break;for(;n.sibling===null;){if(n.return===null||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}};Ic=function(){};E_=function(e,t,n,r){var i=e.memoizedProps;if(i!==r){e=t.stateNode,ui(Xn.current);var a=null;switch(n){case"input":i=Xd(e,i),r=Xd(e,r),a=[];break;case"select":i=it({},i,{value:void 0}),r=it({},r,{value:void 0}),a=[];break;case"textarea":i=ec(e,i),r=ec(e,r),a=[];break;default:typeof i.onClick!="function"&&typeof r.onClick=="function"&&(e.onclick=Tl)}nc(n,r);var s;n=null;for(d in i)if(!r.hasOwnProperty(d)&&i.hasOwnProperty(d)&&i[d]!=null)if(d==="style"){var o=i[d];for(s in o)o.hasOwnProperty(s)&&(n||(n={}),n[s]="")}else 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Wa(e,t){if(!et)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;t!==null;)t.alternate!==null&&(n=t),t=t.sibling;n===null?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;n!==null;)n.alternate!==null&&(r=n),n=n.sibling;r===null?t||e.tail===null?e.tail=null:e.tail.sibling=null:r.sibling=null}}function At(e){var t=e.alternate!==null&&e.alternate.child===e.child,n=0,r=0;if(t)for(var i=e.child;i!==null;)n|=i.lanes|i.childLanes,r|=i.subtreeFlags&14680064,r|=i.flags&14680064,i.return=e,i=i.sibling;else for(i=e.child;i!==null;)n|=i.lanes|i.childLanes,r|=i.subtreeFlags,r|=i.flags,i.return=e,i=i.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Q3(e,t,n){var r=t.pendingProps;switch(Sp(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return At(t),null;case 1:return Kt(t.type)&&Il(),At(t),null;case 3:return 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t.flags&128?(t.lanes=n,t):(r=r!==null,r!==(e!==null&&e.memoizedState!==null)&&r&&(t.child.flags|=8192,t.mode&1&&(e===null||nt.current&1?wt===0&&(wt=3):Gp())),t.updateQueue!==null&&(t.flags|=4),At(t),null);case 4:return la(),Ic(e,t),e===null&&Ts(t.stateNode.containerInfo),At(t),null;case 10:return Tp(t.type._context),At(t),null;case 17:return Kt(t.type)&&Il(),At(t),null;case 19:if(Ze(nt),a=t.memoizedState,a===null)return 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  fn main(${a}) {
    ${s}
  `}appendVariableUniforms(e){e.rank!==0&&(e.shape.startsWith("uniforms.")&&this.uniforms.push({name:e.shape.replace("uniforms.",""),type:"u32",length:e.rank}),e.strides.startsWith("uniforms.")&&this.uniforms.push({name:e.strides.replace("uniforms.",""),type:"u32",length:e.rank}))}declareVariable(e,t){if(e.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(e),this.appendVariableUniforms(e);let n=e.usage==="input"?"read":"read_write",r=e.type.storage;return`@group(0) @binding(${t}) var<storage, ${n}> ${e.name}: array<${r}>;`}declareVariables(...e){return e.map(t=>this.declareVariable(t,this.variableIndex++)).join(`
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      struct Uniforms { ${e.join(", ")} };
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`)}},Av=e=>new Bm(e),Ns=(e,t)=>{let n=e.length,r=[];for(let i=0;i<n;i++){let a=n-1-i,s=e[a]||1;(t[t.length-1-i]||1)>1&&s===1&&r.unshift(a)}return r},xt=e=>!0}),Dm,_d,Nm,Lm,Gr,Ov,Mv,qs=ee(()=>{ze(),St(),Oe(),Dm=e=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.")},_d=(e,t)=>t&&t.length!==e?[...new Array(e).keys()].reverse():t,Nm=(e,t)=>ne.sortBasedOnPerm(e,_d(e.length,t)),Lm=(e,t,n,r)=>{let i=[];i.push(`fn perm(i: ${r.type.indices}) -> ${n.type.indices} {
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  ${f.registerUniform("output_size","u32").declareVariables(c,d)}

  ${Lm(i,r,c,d)}

  ${f.mainStart()}
    ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

    let indices = ${d.offsetToIndices("global_idx")};
    let aIndices = perm(indices);

    ${d.setByOffset("global_idx",c.getByIndices("aIndices"))}
  }`;return{name:"Transpose",shaderCache:{hint:`${t}`,inputDependencies:a?["rank"]:["dims"]},getRunData:f=>{let h=ne.size(s);return{outputs:[{dims:s,dataType:f[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:a?[{type:"uint32",data:h},...le(f[0].dims),...le(s)]:[{type:"uint32",data:h}]}},getShaderSource:u}},Ov=(e,t)=>{Dm(e.inputs),e.compute(Gr(e.inputs[0],t.perm))},Mv=e=>tt({perm:e.perm})}),Fm,Um,Wm,Vm,jm,Gm,Hm,qm,Km,Ym,bn,zv,Rv,Pv,Bv,Dv,Nv,Lv,Fv,Uv,Wv,sE=ee(()=>{ze(),Oe(),gf(),qs(),Fm={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},Um={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},Wm={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},Vm={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},jm=(e,t)=>{let n=[];for(let r=t-e;r<t;++r)n.push(r);return n},Gm=(e,t)=>{let n=[],r=e.length;for(let a=0;a<r;a++)t.indexOf(a)===-1&&n.push(e[a]);let i=t.map(a=>e[a]);return[n,i]},Hm=(e,t)=>{let n=e.length+t.length,r=[],i=0;for(let a=0;a<n;a++)t.indexOf(a)===-1?r.push(e[i++]):r.push(1);return r},qm=(e,t)=>{for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0},Km=(e,t)=>{let n=[];if(!qm(e,t)){for(let r=0;r<t;++r)e.indexOf(r)===-1&&n.push(r);e.forEach(r=>n.push(r))}return n},Ym=(e,t,n,r,i,a,s)=>{let o=n[0].dims,l=ne.size(a),d=ne.size(s),c=te("_A",n[0].dataType,o),u=ke("output",i,a),f=32,h=`
          var<workgroup> aBestValues : array<${u.type.storage}, ${f}>;
       `;return{name:e,shaderCache:t,getShaderSource:g=>`
        ${g.registerUniform("reduceSize","u32").declareVariables(c,u)}
        ${h}
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
          return ((a - 1u) / b + 1u);
         }
         ${g.mainStart(f)}

          let outputIndex = global_idx / ${f};
          let offset = outputIndex * uniforms.reduceSize;

          var bestValue = ${u.type.storage}(${Wm[r]});
          let Length = uniforms.reduceSize;
          for (var k = local_idx; k < Length; k = k + ${f}) {
           let candidate = ${u.type.storage}(${c.getByOffset("offset + k")});
           bestValue = ${Fm[r]};
          }
          aBestValues[local_idx] = bestValue;
          workgroupBarrier();

         var reduceSize = min(Length, ${f}u);
         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
             currentSize = reduceSize / 2u) {
           let interval = DIV_CEIL(reduceSize, 2u);
           if (local_idx < currentSize) {
            let candidate = aBestValues[local_idx + interval];
            bestValue = ${Um[r]};
            aBestValues[local_idx] = bestValue;
           }
           reduceSize = interval;
           workgroupBarrier();
         }

         if (local_idx == 0u) {
          ${u.setByOffset("outputIndex",`${r==="mean"?`bestValue / ${u.type.storage}(uniforms.reduceSize)`:`${Vm[r]}`}`)};
         }
        }`,getRunData:()=>({outputs:[{dims:a,dataType:i}],dispatchGroup:{x:l},programUniforms:[{type:"uint32",data:d}]})}},bn=(e,t,n,r)=>{let i=e.inputs.length===1?n:Vc(e.inputs,n),a=i.axes;a.length===0&&!i.noopWithEmptyAxes&&(a=e.inputs[0].dims.map((h,g)=>g));let s=ne.normalizeAxes(a,e.inputs[0].dims.length),o=s,l=e.inputs[0],d=Km(o,e.inputs[0].dims.length);d.length>0&&(l=e.compute(Gr(e.inputs[0],d),{inputs:[0],outputs:[-1]})[0],o=jm(o.length,l.dims.length));let[c,u]=Gm(l.dims,o),f=c;i.keepDims&&(f=Hm(c,s)),e.compute(Ym(t,{hint:i.cacheKey,inputDependencies:["type"]},[l],r,e.inputs[0].dataType,f,u),{inputs:[l]})},zv=(e,t)=>{bn(e,"ReduceMeanShared",t,"mean")},Rv=(e,t)=>{bn(e,"ReduceL1Shared",t,"l1")},Pv=(e,t)=>{bn(e,"ReduceL2Shared",t,"l2")},Bv=(e,t)=>{bn(e,"ReduceLogSumExpShared",t,"logSumExp")},Dv=(e,t)=>{bn(e,"ReduceMaxShared",t,"max")},Nv=(e,t)=>{bn(e,"ReduceMinShared",t,"min")},Lv=(e,t)=>{bn(e,"ReduceProdShared",t,"prod")},Fv=(e,t)=>{bn(e,"ReduceSumShared",t,"sum")},Uv=(e,t)=>{bn(e,"ReduceSumSquareShared",t,"sumSquare")},Wv=(e,t)=>{bn(e,"ReduceLogSumShared",t,"logSum")}}),xn,Qm,Xl,Vc,Sn,Xm,Zm,Jm,eg,tg,ng,rg,ig,ag,sg,En,Vv,jv,Gv,Hv,qv,Kv,Yv,Qv,Xv,Zv,gf=ee(()=>{ze(),St(),Oe(),sE(),xn=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Qm=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],Xl=(e,t,n,r,i,a,s=!1,o=!1)=>{let l=[],d=n[0].dims,c=d.length,u=ne.normalizeAxes(i,c),f=!o&&u.length===0;d.forEach(($,E)=>{f||u.indexOf(E)>=0?s&&l.push(1):l.push($)});let h=l.length,g=ne.size(l);return{name:e,shaderCache:t,getShaderSource:$=>{let E=[],y=te("_A",n[0].dataType,c),_=ke("output",a,h),v=r(y,_,u),b=v[2];for(let C=0,I=0;C<c;C++)f||u.indexOf(C)>=0?(s&&I++,b=`for(var j${C}: u32 = 0; j${C} < ${d[C]}; j${C}++) {
                  ${v[2].includes("last_index")?`let last_index = j${C};`:""}
                  ${y.indicesSet("input_indices",C,`j${C}`)}
                  ${b}
                }`):(E.push(`${y.indicesSet("input_indices",C,_.indicesGet("output_indices",I))};`),I++);return`

        ${$.registerUniform("output_size","u32").declareVariables(y,_)}

        ${$.mainStart()}
          ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
          var input_indices: ${y.type.indices};
          let output_indices = ${_.offsetToIndices("global_idx")};

          ${E.join(`
`)}
          ${v[0]}       // init ops for reduce max/min
          ${v[1]}
          ${b}
          ${v[3]}
          ${v.length===4?_.setByOffset("global_idx","value"):v.slice(4).join(`
`)}
        }`},getRunData:()=>({outputs:[{dims:l,dataType:a}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:"uint32",data:g},...le(d),...le(l)]})}},Vc=(e,t)=>{let n=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(r=>n.push(Number(r))),tt({axes:n,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},Sn=(e,t,n,r)=>{let i=e.inputs,a=i.length===1?n:Vc(i,n);e.compute(Xl(t,{hint:a.cacheKey,inputDependencies:["rank"]},[i[0]],a.noopWithEmptyAxes&&a.axes.length===0?Qm:r,a.axes,i[0].dataType,a.keepDims,a.noopWithEmptyAxes),{inputs:[0]})},Xm=(e,t)=>{xn(e.inputs),Sn(e,"ReduceLogSum",t,(n,r)=>[`var value = ${r.type.storage}(0);`,"",`value += ${n.getByIndices("input_indices")};`,"value = log(value);"])},Zm=(e,t)=>{xn(e.inputs),Sn(e,"ReduceL1",t,(n,r)=>[`var value = ${r.type.storage}(0);`,"",`value += abs(${n.getByIndices("input_indices")});`,""])},Jm=(e,t)=>{xn(e.inputs),Sn(e,"ReduceL2",t,(n,r)=>[`var t = ${r.type.value}(0); var value = ${r.type.value}(0);`,"",`t = ${n.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},eg=(e,t)=>{xn(e.inputs),Sn(e,"ReduceLogSumExp",t,(n,r)=>[`var value = ${r.type.storage}(0);`,"",`value += exp(${n.getByIndices("input_indices")});`,"value = log(value);"])},tg=(e,t)=>{xn(e.inputs),Sn(e,"ReduceMax",t,(n,r,i)=>{let a=[];for(let s=0;s<n.rank;s++)(i.indexOf(s)>=0||i.length===0)&&a.push(n.indicesSet("input_indices",s,0));return[`${a.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};`,`value = max(value, ${n.getByIndices("input_indices")});`,""]})},ng=(e,t)=>{xn(e.inputs),Sn(e,"ReduceMean",t,(n,r,i)=>{let a=1;for(let s=0;s<n.rank;s++)(i.indexOf(s)>=0||i.length===0)&&(a*=e.inputs[0].dims[s]);return["var sum = f32(0);","",`sum += f32(${n.getByIndices("input_indices")});`,`let value = ${r.type.value}(sum / ${a});`]})},rg=(e,t)=>{xn(e.inputs),Sn(e,"ReduceMin",t,(n,r,i)=>{let a=[];for(let s=0;s<n.rank;s++)(i.indexOf(s)>=0||i.length===0)&&a.push(`input_indices[${s}] = 0;`);return[`${a.join(`
`)}`,`var value = ${n.getByIndices("input_indices")};`,`value = min(value, ${n.getByIndices("input_indices")});`,""]})},ig=(e,t)=>{xn(e.inputs),Sn(e,"ReduceProd",t,(n,r)=>[`var value = ${r.type.storage}(1);`,"",`value *= ${n.getByIndices("input_indices")};`,""])},ag=(e,t)=>{xn(e.inputs),Sn(e,"ReduceSum",t,(n,r)=>[`var value = ${r.type.storage}(0);`,"",`value += ${n.getByIndices("input_indices")};`,""])},sg=(e,t)=>{xn(e.inputs),Sn(e,"ReduceSumSquare",t,(n,r)=>[`var t = ${r.type.value}(0); var value = ${r.type.value}(0);`,"",`t = ${n.getByIndices("input_indices")}; value += t * t;`,""])},En=(e,t,n)=>{if(t.length===0)return n;let r=1,i=1;for(let a=0;a<t.length;a++)t.indexOf(a)===-1?r*=e[a]:i*=e[a];return i<32&&r>1024},Vv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?ng(e,t):zv(e,t)},jv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Zm(e,t):Rv(e,t)},Gv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Jm(e,t):Pv(e,t)},Hv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?eg(e,t):Bv(e,t)},qv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?tg(e,t):Dv(e,t)},Kv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?rg(e,t):Nv(e,t)},Yv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?ig(e,t):Lv(e,t)},Qv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?ag(e,t):Fv(e,t)},Xv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?sg(e,t):Uv(e,t)},Zv=(e,t)=>{En(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Xm(e,t):Wv(e,t)}}),vd,Jv,e1,jc,oE=ee(()=>{lt(),St(),gf(),vd=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Jv=(e,t)=>{vd(e.inputs);let n=(r,i,a)=>{let s=[];for(let o=0;o<r.rank;o++)(a.indexOf(o)>=0||a.length===0)&&s.push(`input_indices[${o}] = 0;`);return[`${s.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${r.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {
         value = ${r.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",i.setByOffset("global_idx","best_index")]};e.compute(Xl("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],n,[t.axis],7,t.keepDims),{inputs:[0]})},e1=(e,t)=>{vd(e.inputs);let n=(r,i,a)=>{let s=[];for(let o=0;o<r.rank;o++)(a.indexOf(o)>=0||a.length===0)&&s.push(`input_indices[${o}] = 0;`);return[`${s.join(`
`)}`,`var value = ${r.getByIndices("input_indices")};
var best_index : i32 = 0;`,`if (${r.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {
         value = ${r.getByIndices("input_indices")};
         best_index = i32(last_index);
       }`,"",i.setByOffset("global_idx","best_index")]};e.compute(Xl("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],n,[t.axis],7,t.keepDims),{inputs:[0]})},jc=e=>tt(e)}),og,lg,ug,dg,Zl,cg,t1,n1=ee(()=>{lt(),pf(),Oe(),og=(e,t)=>{let n=e[0],r=e[1],i=e[2],a=e[3],s=e[4],o=e[5];if(s&&o)throw new Error("Attention cannot have both past and relative_position_bias");if(n.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let l=n.dims[0],d=n.dims[1],c=n.dims[2];if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(r.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(r.dims[0]!==c)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(i.dims[0]!==r.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let u=i.dims[0]/3,f=u,h=f;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let v of t.qkvHiddenSizes)if(v%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");u=t.qkvHiddenSizes[0],f=t.qkvHiddenSizes[1],h=t.qkvHiddenSizes[2]}let g=d;if(u!==f)throw new Error("qkv_hidden_sizes first element should be same as the second");if(i.dims[0]!==u+f+h)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let $=0;if(s){if(f!==h)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(s.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(s.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(s.dims[1]!==l)throw new Error('Input "past" second dimension must be batch_size');if(s.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(s.dims[4]!==f/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||($=s.dims[3])}let E=g+$,y=-1,_=0;if(a)throw new Error("Mask not supported");if(s)throw new Error("past is not supported");if(o)throw new Error("relativePositionBias is not supported");return{batchSize:l,sequenceLength:d,pastSequenceLength:$,kvSequenceLength:g,totalSequenceLength:E,maxSequenceLength:y,inputHiddenSize:c,hiddenSize:u,vHiddenSize:h,headSize:Math.floor(u/t.numHeads),vHeadSize:Math.floor(h/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:_,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},lg=(e,t,n,r)=>{let i=rn(r),a=64,s=r/i;s<a?a=1:s/8<64&&(a=Math.ceil(s/8));let o=Math.ceil(r/i/a),l=[{type:Kn(t.dataType),data:1/r},{type:"uint32",data:s},{type:"uint32",data:o}],d=wn(t.dataType,i),c=u=>{let f=ke("x",t.dataType,t.dims,i),h="thread_max_vector";i===2?h="max(thread_max_vector.x, thread_max_vector.y)":i===4&&(h="max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))");let g=ar(t.dataType),$=[{name:"d_inv",type:g},{name:"d_comp",type:"u32"},{name:"elements_per_wg",type:"u32"}];return`
  var<workgroup> wgMax: array<f32, ${a}>;
  var<workgroup> wgSum: array<f32, ${a}>;
  ${u.registerUniforms($).declareVariables(f)}
  ${u.mainStart([a,1,1])}
    let localOffset = local_idx * uniforms.elements_per_wg;
    let offset: u32 = workgroup_id.x * uniforms.d_comp + localOffset;

    var thread_max_vector = ${tn("f32",i,"-3.402823e+38f")};
    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
      thread_max_vector = max(${ur(g,i,"x[offset + i]")}, thread_max_vector);
    }
    wgMax[local_idx] = ${h};
    workgroupBarrier();

    var maxValue = -3.402823e+38f;
    for (var i = 0u; i < ${a}; i++) {
      maxValue = max(wgMax[i], maxValue);
    }

    var sumVector = ${tn("f32",i,"0")};
    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
      sumVector += exp(${ur(g,i,"x[offset + i]")} - maxValue);
    }
    wgSum[local_idx] = ${Zn("sumVector",i)};
    workgroupBarrier();

    var sum: f32 = 0;
    for (var i = 0u; i < ${a}; i++) {
      sum += wgSum[i];
    }

    if (sum == 0) {
      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
        x[offset + i] = ${tn("f32",i,"uniforms.d_inv")};
      }
    } else {
      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {
        let f32input = ${ur(g,i,"x[offset + i]")};
        x[offset + i] = ${f.type.value}(exp(f32input - maxValue) / sum);
      }
    }
  }`};e.compute({name:"AttentionProbsSoftmax",shaderCache:{hint:`${a};${d};${i}`},getShaderSource:c,getRunData:()=>({outputs:[],dispatchGroup:{x:n},programUniforms:l})},{inputs:[t],outputs:[]})},ug=(e,t,n,r,i,a)=>{let s=[i.batchSize,i.numHeads,i.sequenceLength,i.kvSequenceLength+i.pastSequenceLength],o=a.scale===0?1/Math.sqrt(i.headSize):a.scale,l=rn(i.headSize),d=i.headSize/l,c=12,u={x:Math.ceil(i.totalSequenceLength/c),y:Math.ceil(i.sequenceLength/c),z:i.batchSize*i.numHeads},f=Kn(t.dataType),h=[{type:"uint32",data:i.sequenceLength},{type:"uint32",data:d},{type:"uint32",data:i.totalSequenceLength},{type:"uint32",data:i.kvSequenceLength},{type:f,data:o}],g=[t,n],$=y=>{let _=te("q",t.dataType,t.dims,l),v=te("key",n.dataType,n.dims,l),b=ke("output",t.dataType,s),C=wn(t.dataType),I=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"alpha",type:C}];return`
  const beta: ${C} = 1.0;
  const TILE_SIZE = ${c}u;

  var<workgroup> tileQ: array<${_.type.storage}, ${c*c}>;
  var<workgroup> tileK: array<${_.type.storage}, ${c*c}>;
  ${y.registerUniforms(I).declareVariables(_,v,b)}
  ${y.mainStart([c,c,1])}
    // x holds the N and y holds the M
    let headIdx = workgroup_id.z;
    let m = workgroup_id.y * TILE_SIZE;
    let n = workgroup_id.x * TILE_SIZE;
    let lm = m + local_id.y;
    let ln = n + local_id.x;

    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;
    let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx + n * uniforms.K;

    var value = ${tn(C,l)};
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m + local_id.y < uniforms.M && w + local_id.x < uniforms.K) {
        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {
        tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];
      }
      workgroupBarrier();

      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k];
      }

      workgroupBarrier();
    }

    let headOffset = headIdx * uniforms.M * uniforms.N;
    if (lm < uniforms.M && ln < uniforms.N) {
      let outputIdx = headOffset + lm * uniforms.N + ln;
      output[outputIdx] = ${Zn("value",l)} * uniforms.alpha;
    }
  }`},E=e.compute({name:"AttentionProbs",shaderCache:{hint:`${l}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:h}),getShaderSource:$},{inputs:g,outputs:[-1]})[0];return lg(e,E,i.batchSize*i.numHeads*i.sequenceLength,i.totalSequenceLength),E},dg=(e,t,n,r)=>{let i=[r.batchSize,r.sequenceLength,r.vHiddenSize],a=12,s={x:Math.ceil(r.vHeadSize/a),y:Math.ceil(r.sequenceLength/a),z:r.batchSize*r.numHeads},o=[{type:"uint32",data:r.sequenceLength},{type:"uint32",data:r.totalSequenceLength},{type:"uint32",data:r.vHeadSize},{type:"uint32",data:r.numHeads},{type:"uint32",data:r.vHiddenSize}],l=d=>{let c=te("probs",t.dataType,t.dims),u=te("v",n.dataType,n.dims),f=ke("output",t.dataType,i),h=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`
  const TILE_SIZE = ${a}u;
  var<workgroup> tileQ: array<${c.type.value}, ${a*a}>;
  var<workgroup> tileK: array<${c.type.value}, ${a*a}>;
  ${d.registerUniforms(h).declareVariables(c,u,f)}
  ${d.mainStart([a,a,1])}
   let headIdx = workgroup_id.z;
   let m = workgroup_id.y * TILE_SIZE + local_id.y;
   let n = workgroup_id.x * TILE_SIZE + local_id.x;

   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;
   let offsetB = headIdx * (uniforms.N * uniforms.K) + n;

   var value = ${c.type.storage}(0);
   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
     if (m < uniforms.M && w + local_id.x < uniforms.K) {
       tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];
     }
     if (n < uniforms.N && w + local_id.y < uniforms.K) {
       tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];
     }
     workgroupBarrier();
     for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];
     }
     workgroupBarrier();
   }

   // we need to transpose output from BNSH_v to BSND_v
   let batchIdx = workgroup_id.z / uniforms.num_heads;
   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;
   let headOffset = (batchIdx * uniforms.M * uniforms.num_heads + currentBatchHeadNumber) * uniforms.N;
   if (m < uniforms.M && n < uniforms.N) {
     let outputIdx = batchIdx * uniforms.M *uniforms.v_hidden_size + m * uniforms.v_hidden_size
       + currentBatchHeadNumber * uniforms.N + n;
     output[outputIdx] = value;
   }
  }`};return e.compute({name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:i,dataType:t.dataType,gpuDataType:0}],dispatchGroup:s,programUniforms:o}),getShaderSource:l},{inputs:[t,n],outputs:[0]})[0]},Zl=(e,t,n,r,i,a,s,o,l,d,c)=>{let u=ug(e,t,n,l,d,c);dg(e,u,r,d)},cg=(e,t)=>{let n=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],r=t.sequenceLength,i=t.inputHiddenSize,a=t.headSize,s=12,o={x:Math.ceil(t.headSize/s),y:Math.ceil(t.sequenceLength/s),z:t.batchSize*t.numHeads},l=[e.inputs[0],e.inputs[1],e.inputs[2]],d=[{type:"uint32",data:r},{type:"uint32",data:i},{type:"uint32",data:a},{type:"uint32",data:t.numHeads},{type:"uint32",data:t.headSize},{type:"uint32",data:t.hiddenSize},{type:"uint32",data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],c=u=>{let f=ke("output_q",l[0].dataType,n),h=ke("output_k",l[0].dataType,n),g=ke("output_v",l[0].dataType,n),$=te("input",l[0].dataType,l[0].dims),E=te("weight",l[1].dataType,l[1].dims),y=te("bias",l[2].dataType,l[2].dims),_=$.type.storage,v=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`
  const TILE_SIZE = ${s}u;
  var<workgroup> tileInput: array<${_}, ${s*s}>;
  var<workgroup> tileWeightQ: array<${_}, ${s*s}>;
  var<workgroup> tileWeightK: array<${_}, ${s*s}>;
  var<workgroup> tileWeightV: array<${_}, ${s*s}>;
  ${u.registerUniforms(v).declareVariables($,E,y,f,h,g)}
  ${u.mainStart([s,s,1])}
    let batchIndex = workgroup_id.z / uniforms.num_heads;
    let headNumber = workgroup_id.z % uniforms.num_heads;
    let m = workgroup_id.y * TILE_SIZE + local_id.y;
    let n = workgroup_id.x * TILE_SIZE + local_id.x;

    let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K;
    let biasOffsetQ = headNumber * uniforms.head_size;
    let biasOffsetK = uniforms.hidden_size + biasOffsetQ;
    let biasOffsetV = uniforms.hidden_size + biasOffsetK;

    var valueQ = ${_}(0);
    var valueK = ${_}(0);
    var valueV = ${_}(0);
    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {
      if (m < uniforms.M && w + local_id.x < uniforms.K) {
        tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x];
      }
      if (n < uniforms.N && w + local_id.y < uniforms.K) {
        let offset = n + (w + local_id.y) * uniforms.ldb;
        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];
        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];
        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];
      }
      workgroupBarrier();
      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {
        let inputTileOffset = TILE_SIZE * local_id.y + k;
        let weightTileOffset = TILE_SIZE * k + local_id.x;
        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];
        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];
        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];
      }

      workgroupBarrier();
    }

    let headOffset = (m * uniforms.N + n) % uniforms.head_size;
    valueQ += bias[headOffset + biasOffsetQ];
    valueK += bias[headOffset + biasOffsetK];
    valueV += bias[headOffset + biasOffsetV];

    let offset = workgroup_id.z * uniforms.M * uniforms.N;
    if (m < uniforms.M && n < uniforms.N) {
      let outputIdx = offset + m * uniforms.N + n;
      output_q[outputIdx] = valueQ;
      output_k[outputIdx] = valueK;
      output_v[outputIdx] = valueV;
    }
  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:n,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:o,programUniforms:d}),getShaderSource:c},{inputs:l,outputs:[-1,-1,-1]})},t1=(e,t)=>{let n=og(e.inputs,t),[r,i,a]=cg(e,n);return Zl(e,r,i,a,e.inputs[4],void 0,void 0,void 0,e.inputs[5],n,t)}}),pg,fg,hg,r1,lE=ee(()=>{Fn(),ze(),St(),Oe(),pg=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let n=(r,i,a)=>{let s=i.length;if(s!==r.length)throw new Error(`${a}: num dimensions != ${s}`);i.forEach((o,l)=>{if(o!==r[l])throw new Error(`${a}: dim[${l}] do not match`)})};if(e[0].dims.length>1){let r=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);n(e[1].dims,r,"Invalid input scale"),n(e[2].dims,r,"Invalid input B"),n(e[3].dims,r,"Invalid input mean"),n(e[4].dims,r,"Invalid input var")}else n(e[1].dims,[1],"Invalid input scale"),n(e[2].dims,[1],"Invalid input B"),n(e[3].dims,[1],"Invalid input mean"),n(e[4].dims,[1],"Invalid input var")},fg=(e,t)=>{let{epsilon:n,spatial:r,format:i}=t,a=e[0].dims,s=r?rn(a[a.length-1]):1,o=i==="NHWC"&&a.length>1?s:1,l=ne.size(a)/s,d=xt(a.length)&&r,c=d?a.length:a,u=te("x",e[0].dataType,e[0].dims,s),f=te("scale",e[1].dataType,e[1].dims,o),h=te("bias",e[2].dataType,e[2].dims,o),g=te("inputMean",e[3].dataType,e[3].dims,o),$=te("inputVar",e[4].dataType,e[4].dims,o),E=ke("y",e[0].dataType,c,s),y=()=>{let v="";if(r)v=`let cOffset = ${a.length===1?"0u":i==="NHWC"?`outputIndices[${a.length-1}] / ${s}`:"outputIndices[1]"};`;else if(i==="NCHW")v=`
            ${E.indicesSet("outputIndices","0","0")}
            let cOffset = ${E.indicesToOffset("outputIndices")};`;else{v=`var cIndices = ${f.type.indices}(0);
                       cIndices[0] = outputIndices[${a.length-1}];`;for(let b=1;b<f.rank;b++)v+=`cIndices[${b}] = outputIndices[${b}];`;v+=`let cOffset = ${f.indicesToOffset("cIndices")};`}return v},_=v=>`
  const epsilon = ${n};
  ${v.registerUniform("outputSize","u32").declareVariables(u,f,h,g,$,E)}
  ${v.mainStart()}
  ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    var outputIndices = ${E.offsetToIndices(`global_idx * ${s}`)};
    ${y()}
    let scale = ${f.getByOffset("cOffset")};
    let bias = ${h.getByOffset("cOffset")};
    let inputMean = ${g.getByOffset("cOffset")};
    let inputVar = ${$.getByOffset("cOffset")};
    let x = ${u.getByOffset("global_idx")};
    let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias;
    ${E.setByOffset("global_idx","value")}
  }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${r}_${s}`,inputDependencies:d?["rank","type","type","type","type"]:void 0},getShaderSource:_,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d?[{type:"uint32",data:l},...le(a)]:[{type:"uint32",data:l}]})}},hg=e=>tt(e),r1=(e,t)=>{let{inputs:n,outputCount:r}=e,i=hg({...t,outputCount:r});if(je.webgpu.validateInputContent&&pg(n,i),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(fg(n,i))}}),mg,gg,i1,uE=ee(()=>{ze(),Oe(),mg=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},gg=e=>{let t=e[0].dims,n=e[0].dims[2],r=ne.size(t)/4,i=e[0].dataType,a=te("input",i,t,4),s=te("bias",i,[n],4),o=te("residual",i,t,4),l=ke("output",i,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(r/64)}}),getShaderSource:d=>`
  const channels = ${n}u / 4;
  ${d.declareVariables(a,s,o,l)}

  ${d.mainStart()}
    ${d.guardAgainstOutOfBoundsWorkgroupSizes(r)}
    let value = ${a.getByOffset("global_idx")}
      + ${s.getByOffset("global_idx % channels")} + ${o.getByOffset("global_idx")};
    ${l.setByOffset("global_idx","value")}
  }`}},i1=e=>{mg(e.inputs),e.compute(gg(e.inputs))}}),yg,Ue,a1,s1,o1,l1,u1,d1,c1,p1,f1,wg,h1,m1,g1,y1,yl,w1,wl,_1,v1,$1,b1,x1,S1,E1,k1,C1,T1,I1,A1,O1,M1,z1,R1,P1,B1=ee(()=>{lt(),ze(),St(),Oe(),yg=(e,t,n,r,i,a)=>{let s=Math.ceil(t/4),o="";typeof i=="string"?o=`${i}(a)`:o=i("a");let l=te("inputData",n,[s],4),d=ke("outputData",r,[s],4);return`
      ${e.registerUniform("vec_size","u32").declareVariables(l,d)}

  ${a??""}

  ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}

    let a = ${l.getByOffset("global_idx")};
    ${d.setByOffset("global_idx",o)}
  }`},Ue=(e,t,n,r,i,a=e.dataType)=>({name:t,shaderCache:{hint:i,inputDependencies:["type"]},getShaderSource:s=>yg(s,ne.size(e.dims),e.dataType,a,n,r),getRunData:s=>({outputs:[{dims:e.dims,dataType:a}],dispatchGroup:{x:Math.ceil(ne.size(s[0].dims)/64/4)},programUniforms:[{type:"uint32",data:Math.ceil(ne.size(e.dims)/4)}]})}),a1=e=>{e.compute(Ue(e.inputs[0],"Abs","abs"))},s1=e=>{e.compute(Ue(e.inputs[0],"Acos","acos"))},o1=e=>{e.compute(Ue(e.inputs[0],"Acosh","acosh"))},l1=e=>{e.compute(Ue(e.inputs[0],"Asin","asin"))},u1=e=>{e.compute(Ue(e.inputs[0],"Asinh","asinh"))},d1=e=>{e.compute(Ue(e.inputs[0],"Atan","atan"))},c1=e=>{e.compute(Ue(e.inputs[0],"Atanh","atanh"))},p1=e=>tt(e),f1=(e,t)=>{let n;switch(t.to){case 10:n="vec4<f16>";break;case 1:n="vec4<f32>";break;case 12:n="vec4<u32>";break;case 6:n="vec4<i32>";break;case 9:n="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(Ue(e.inputs[0],"Cast",n,void 0,t.cacheKey,t.to))},wg=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:ff,n=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:hf;return tt({min:t,max:n})},h1=(e,t)=>{let n=e.inputs.length===1?t:wg(e.inputs),r=ar(e.inputs[0].dataType);e.compute(Ue(e.inputs[0],"Clip",i=>`clamp(${i}, clip_min_, clip_max_)`,`
    const clip_min_: vec4<${r}> = vec4(${r}(${n.min}));
    const clip_max_: vec4<${r}> = vec4(${r}(${n.max}));
`,n.cacheKey),{inputs:[0]})},m1=e=>{e.compute(Ue(e.inputs[0],"Ceil","ceil"))},g1=e=>{e.compute(Ue(e.inputs[0],"Cos","cos"))},y1=e=>{e.compute(Ue(e.inputs[0],"Cosh","cosh"))},yl=e=>tt(e),w1=(e,t)=>{let n=ar(e.inputs[0].dataType);e.compute(Ue(e.inputs[0],"Elu",r=>`elu_vf32(${r})`,`
  const elu_alpha_ = ${n}(${t.alpha});

  fn elu_f32(a: ${n}) -> ${n} {
  return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0);
  }

  fn elu_vf32(v: vec4<${n}>) -> vec4<${n}> {
  return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w));
  }`,t.cacheKey))},wl=(e,t="f32")=>`
const r0: ${t} = 0.3275911;
const r1: ${t} = 0.254829592;
const r2: ${t} = -0.284496736;
const r3: ${t} = 1.421413741;
const r4: ${t} = -1.453152027;
const r5: ${t} = 1.061405429;

fn erf_vf32(v: ${e}) -> ${e} {
  let absv = abs(v);
  let x = 1.0 / (1.0 + r0 * absv);
  return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv));
}`,_1=e=>{let t=ar(e.inputs[0].dataType);e.compute(Ue(e.inputs[0],"Erf",n=>`erf_vf32(${n})`,wl(`vec4<${t}>`,t)))},v1=e=>{e.compute(Ue(e.inputs[0],"Exp","exp"))},$1=e=>{e.compute(Ue(e.inputs[0],"Floor","floor"))},b1=e=>{let t=ar(e.inputs[0].dataType);e.compute(Ue(e.inputs[0],"Gelu",n=>`0.5 * ${n} * (1.0 + erf_vf32(${n} * 0.7071067811865475))`,wl(`vec4<${t}>`,t)))},x1=(e,t)=>{let n=ar(e.inputs[0].dataType);e.compute(Ue(e.inputs[0],"LeakyRelu",r=>`select(leaky_relu_alpha_ * ${r}, ${r}, ${r} >= vec4<${n}>(0.0))`,`const leaky_relu_alpha_ = ${n}(${t.alpha});`,t.cacheKey))},S1=e=>{e.compute(Ue(e.inputs[0],"Not",t=>`!${t}`))},E1=e=>{e.compute(Ue(e.inputs[0],"Neg",t=>`-${t}`))},k1=e=>{e.compute(Ue(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},C1=e=>{let t=ar(e.inputs[0].dataType);e.compute(Ue(e.inputs[0],"Relu",n=>`select(vec4<${t}>(0.0), ${n}, ${n} > vec4<${t}>(0.0))`))},T1=e=>{e.compute(Ue(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},I1=e=>{e.compute(Ue(e.inputs[0],"Sin","sin"))},A1=e=>{e.compute(Ue(e.inputs[0],"Sinh","sinh"))},O1=e=>{e.compute(Ue(e.inputs[0],"Sqrt","sqrt"))},M1=e=>{e.compute(Ue(e.inputs[0],"Tan","tan"))},z1=e=>{e.compute(Ue(e.inputs[0],"Tanh","tanh"))},R1=(e,t)=>{let n=ar(e.inputs[0].dataType);return e.compute(Ue(e.inputs[0],"ThresholdedRelu",r=>`select(vec4<${n}>(0.0), ${r}, ${r} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${n}>(${t.alpha});`,t.cacheKey)),0},P1=e=>{e.compute(Ue(e.inputs[0],"Log","log"))}}),_g,vg,D1,dE=ee(()=>{ze(),Oe(),B1(),_g=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},vg=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let n=te("input",e[0].dataType,e[0].dims,4),r=te("bias",e[0].dataType,[e[0].dims[2]],4),i=ke("output",e[0].dataType,t,4),a=ne.size(t)/4,s=wn(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)}}),getShaderSource:o=>`
  const M_SQRT2 = sqrt(2.0);
  const halfChannels = ${e[0].dims[2]/4/2}u;

  ${o.declareVariables(n,r,i)}

  ${wl(`vec4<${s}>`,s)}

  ${o.mainStart()}
    ${o.guardAgainstOutOfBoundsWorkgroupSizes(a)}
    let biasIdx = global_idx % halfChannels;
    let batchIndex = global_idx / halfChannels;
    let inputOffset = biasIdx + batchIndex * halfChannels * 2;
    let valueLeft = input[inputOffset] + bias[biasIdx];
    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];
    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);

    ${i.setByOffset("global_idx","valueLeft * geluRight")}
  }`}},D1=e=>{_g(e.inputs),e.compute(vg(e.inputs))}}),$g,bg,kn,N1,L1,F1,U1,W1,V1,j1,G1,H1,q1,cE=ee(()=>{lt(),ze(),Oe(),$g=(e,t,n,r,i,a,s,o,l,d,c,u,f)=>{let h,g;typeof o=="string"?h=g=(I,O)=>`${o}((${I}),(${O}))`:typeof o=="function"?h=g=o:(h=o.scalar,g=o.vector);let $=u?t.length:t,E=u?n.length:n,y=u?r.length:r,_=ke("outputData",c,y,4),v=te("aData",l,$,4),b=te("bData",d,E,4),C;if(i)if(a){let I=ne.size(t)===1,O=ne.size(n)===1,M=t.length>0&&t[t.length-1]%4===0,W=n.length>0&&n[n.length-1]%4===0;I||O?C=_.setByOffset("global_idx",g(I?`${v.type.value}(${v.getByOffset("0")}.x)`:v.getByOffset("global_idx"),O?`${b.type.value}(${b.getByOffset("0")}.x)`:b.getByOffset("global_idx"))):C=`
            let outputIndices = ${_.offsetToIndices("global_idx * 4u")};
            let offsetA = ${v.broadcastedIndicesToOffset("outputIndices",_)};
            let offsetB = ${b.broadcastedIndicesToOffset("outputIndices",_)};
            ${_.setByOffset("global_idx",g(s||M?v.getByOffset("offsetA / 4u"):`${v.type.value}(${v.getByOffset("offsetA / 4u")}[offsetA % 4u])`,s||W?b.getByOffset("offsetB / 4u"):`${b.type.value}(${b.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}
          `}else C=_.setByOffset("global_idx",g(v.getByOffset("global_idx"),b.getByOffset("global_idx")));else{if(!a)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let I=(O,M,W="")=>{let G=`aData[indexA${M}][componentA${M}]`,z=`bData[indexB${M}][componentB${M}]`;return`
            let outputIndices${M} = ${_.offsetToIndices(`global_idx * 4u + ${M}u`)};
            let offsetA${M} = ${v.broadcastedIndicesToOffset(`outputIndices${M}`,_)};
            let offsetB${M} = ${b.broadcastedIndicesToOffset(`outputIndices${M}`,_)};
            let indexA${M} = offsetA${M} / 4u;
            let indexB${M} = offsetB${M} / 4u;
            let componentA${M} = offsetA${M} % 4u;
            let componentB${M} = offsetB${M} % 4u;
            ${O}[${M}] = ${W}(${h(G,z)});
          `};c===9?C=`
            var data = vec4<u32>(0);
            ${I("data",0,"u32")}
            ${I("data",1,"u32")}
            ${I("data",2,"u32")}
            ${I("data",3,"u32")}
            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:C=`
            ${I("outputData[global_idx]",0)}
            ${I("outputData[global_idx]",1)}
            ${I("outputData[global_idx]",2)}
            ${I("outputData[global_idx]",3)}
          `}return`
        ${e.registerUniform("vec_size","u32").declareVariables(v,b,_)}

        ${f??""}

        ${e.mainStart()}
        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
        ${C}
      }`},bg=(e,t,n,r,i,a,s=n.dataType)=>{let o=!ne.areEqual(n.dims,r.dims),l=n.dims,d=ne.size(n.dims),c=!1,u=!1,f=[o];if(o){let g=ca.calcShape(n.dims,r.dims,!1);if(!g)throw new Error("Can't perform binary op on the given tensors");l=g,d=ne.size(l);let $=ne.size(n.dims)===1,E=ne.size(r.dims)===1,y=n.dims.length>0&&n.dims[n.dims.length-1]%4===0,_=r.dims.length>0&&r.dims[r.dims.length-1]%4===0;f.push($),f.push(E),f.push(y),f.push(_);let v=1;for(let b=1;b<l.length;b++){let C=n.dims[n.dims.length-b]??1,I=r.dims[r.dims.length-b]??1;if(C===I)v*=C;else break}v%4===0?(u=!0,c=!0):($||E||y||_)&&(c=!0)}else c=!0;f.push(c);let h=xt(n.dims.length)&&xt(r.dims.length)&&xt(l.length);return{name:e,shaderCache:{hint:t+f.map(g=>g.toString()).join("_"),inputDependencies:h?["rank","rank"]:["dims","dims"]},getShaderSource:g=>$g(g,n.dims,r.dims,l,c,o,u,i,n.dataType,r.dataType,s,h,a),getRunData:()=>({outputs:[{dims:l,dataType:s}],dispatchGroup:{x:Math.ceil(d/64/4)},programUniforms:h?[{type:"uint32",data:Math.ceil(ne.size(l)/4)},...le(n.dims),...le(r.dims),...le(l)]:[{type:"uint32",data:Math.ceil(ne.size(l)/4)}]})}},kn=(e,t,n,r,i,a)=>{e.compute(bg(t,i??"",e.inputs[0],e.inputs[1],n,r,a))},N1=e=>{kn(e,"Add",(t,n)=>`${t}+${n}`)},L1=e=>{kn(e,"Div",(t,n)=>`${t}/${n}`)},F1=e=>{kn(e,"Equal",{scalar:(t,n)=>`u32(${t}==${n})`,vector:(t,n)=>`vec4<u32>(${t}==${n})`},void 0,void 0,9)},U1=e=>{kn(e,"Mul",(t,n)=>`${t}*${n}`)},W1=e=>{let t=te("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;kn(e,"Pow",{scalar:(n,r)=>`pow_custom(${n},${r})`,vector:(n,r)=>`pow_vector_custom(${n},${r})`},`
    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {
      if (b == ${t}(0.0)) {
        return ${t}(1.0);
      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {
        return ${t}(pow(f32(a), f32(b))); // NaN
      }
      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));
    }
    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {
      // TODO: implement vectorized pow
      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));
    }
      `)},V1=e=>{kn(e,"Sub",(t,n)=>`${t}-${n}`)},j1=e=>{kn(e,"Greater",{scalar:(t,n)=>`u32(${t}>${n})`,vector:(t,n)=>`vec4<u32>(${t}>${n})`},void 0,void 0,9)},G1=e=>{kn(e,"Less",{scalar:(t,n)=>`u32(${t}<${n})`,vector:(t,n)=>`vec4<u32>(${t}<${n})`},void 0,void 0,9)},H1=e=>{kn(e,"GreaterOrEqual",{scalar:(t,n)=>`u32(${t}>=${n})`,vector:(t,n)=>`vec4<u32>(${t}>=${n})`},void 0,void 0,9)},q1=e=>{kn(e,"LessOrEqual",{scalar:(t,n)=>`u32(${t}<=${n})`,vector:(t,n)=>`vec4<u32>(${t}<=${n})`},void 0,void 0,9)}}),xg,Sg,Eg,kg,K1,Y1,pE=ee(()=>{ze(),St(),Oe(),xg=e=>{if(!e||e.length<1)throw new Error("too few inputs");let t=e[0].dataType,n=e[0].dims.length;for(let r of e){if(r.dataType!==t)throw new Error("input tensors should be one type");if(r.dims.length!==n)throw new Error("input tensors should have the same shape")}},Sg=(e,t)=>`
  fn calculateInputIndex(index: u32) -> u32 {
    let sizeInConcatAxis = array<u32, ${e}u>(${t});
    for (var i: u32 = 0u; i < ${e}; i += 1u ) {
      if (index < sizeInConcatAxis[i]) {
        return i;
      }
    }
    return ${e}u;
  }`,Eg=(e,t)=>{let n=e.length,r=[];for(let i=0;i<n;++i){let a=t.setByOffset("global_idx",e[i].getByIndices("indices"));n===1?r.push(a):i===0?r.push(`if (inputIndex == ${i}u) { ${a} }`):i===n-1?r.push(`else { ${a} }`):r.push(`else if (inputIndex == ${i}) { ${a} }`)}return r.join(`
`)},kg=(e,t)=>{let n=e[0].dims.slice();if(t>=n.length||t<-1*n.length)throw new Error("axis specified for concat doesn't match input dimensionality");let r=t<0?n.length+t:t,i=n.slice(0);for(let b=1;b<e.length;b++){let C=e[b].dims.slice();for(let I=0;I<n.length;I++)if(I===r)i[r]+=C[I];else if(n[I]!==C[I])throw new Error("non concat dimensions must match")}let a=ne.size(i),s=new Array(e.length),o=new Array(e.length),l=e[0].dataType,d=0,c=[],u=[],f=[],h=[{type:"uint32",data:a}];for(let b=0;b<e.length;++b)d+=e[b].dims[r],s[b]=d,f.push(xt(e[b].dims.length)),u.push(f[b]?e[b].dims.length:e[b].dims),o[b]=te(`input${b}`,l,u[b]),c.push(f[b]?"rank":"dims"),h.push({type:"uint32",data:s[b]});for(let b=0;b<e.length;++b)f[b]&&h.push(...le(e[b].dims));let g=xt(i.length);g&&h.push(...le(i));let $=g?i.length:i,E=ke("output",l,$),y=E.indicesGet("indices",r),_=Array.from(Array(s.length).keys()).map(b=>`uniforms.sizeInConcatAxis${b}`).join(","),v=b=>`

  ${(()=>{b.registerUniform("outputSize","u32");for(let C=0;C<e.length;C++)b.registerUniform(`sizeInConcatAxis${C}`,"u32");return b.declareVariables(...o,E)})()}

  ${Sg(s.length,_)}

  ${b.mainStart()}
    ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

    var indices = ${E.offsetToIndices("global_idx")};

    let inputIndex = calculateInputIndex(${y});
    if (inputIndex != 0u) {
      let sizeInConcatAxis = array<u32, ${s.length}u>(${_});
      ${y} -= sizeInConcatAxis[inputIndex - 1u];
    }

    ${Eg(o,E)}
  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:i,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:h}),getShaderSource:v}},K1=(e,t)=>{xg(e.inputs),e.compute(kg(e.inputs,t.axis))},Y1=e=>tt({axis:e.axis})}),ya,yf,xi=ee(()=>{ze(),ya=(e,t)=>{switch(e.activation){case"Relu":return{activationFunction:"",applyActivation:`value = max(value, ${t}(0.0));`};case"Sigmoid":return{activationFunction:"",applyActivation:`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`};case"Clip":return{activationFunction:`const clip_min_=${t}(${e.clipMin});const clip_max_=${t}(${e.clipMax});`,applyActivation:"value = clamp(value, clip_min_, clip_max_);"};default:return{activationFunction:"",applyActivation:""}}},yf=e=>{let t=(e==null?void 0:e.activation)||"";if(t==="Clip"){let[n,r]=(e==null?void 0:e.activation_params)||[ff,hf];return{activation:t,clipMax:r,clipMin:n,activationCacheKey:`${t}:${n},${r}`}}return{activation:t,activationCacheKey:t}}}),Mt,wf,_f=ee(()=>{Mt=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},wf=e=>`
      ${e?"value = value + getBiasByOutputCoords(coords);":""}
      `}),vf,Q1=ee(()=>{vf=e=>`
fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
}
fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
  return dot(coords, vec4<i32>(
    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));
}
`}),Cg,Tg,Jl,$d,Ig,eu,Ag,$f,xu=ee(()=>{ze(),Oe(),xi(),_f(),Cg=(e,t)=>e?`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          kStart + inputRow,
          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});
        `:`
        mm_Asub[inputRow][inputCol] = mm_readA(batch,
          globalRow + innerRow,
          kStart / innerElementSize + inputCol${t?", batchIndices":""});
        `,Tg=(e,t)=>e?`
        let ACached0 = mm_Asub[k * innerElementSize][localRow];
        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];
        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];
        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}
        for (var i = 0; i < rowPerThread; i = i + 1) {
          acc[i] = BCached0 * ACached0[i] + acc[i];
          acc[i] = BCached1 * ACached1[i] + acc[i];
          acc[i] = BCached2 * ACached2[i] + acc[i];
          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}
        }`:`
        for (var i = 0; i < rowPerThread; i = i + 1) {
          let ACached = mm_Asub[tileRow + i][k];
          acc[i] = BCached0 * ACached.x + acc[i];
          acc[i] = BCached1 * ACached.y + acc[i];
          acc[i] = BCached2 * ACached.z + acc[i];
          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}
        }`,Jl=(e,t,n="f32",r,i=!1,a=32,s=!1,o=32)=>{let l=t[1]*e[1],d=t[0]*e[0],c=i?l:a,u=i?a:l,f=c/t[0],h=a/t[1];if(!((i&&f===4&&e[1]===4||!i&&(f===3||f===4))&&c%t[0]===0&&a%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${i} is true, innerElementSize ${f} and workPerThread[1] ${e[1]} must be 4.
      Otherwise, innerElementSize ${f} must be 3 or 4.
  tileAWidth ${c} must be divisible by workgroupSize[0]${t[0]}. tileInner ${a} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`
var<workgroup> mm_Asub: array<array<vec${f}<${n}>, ${c/f}>, ${u}>;
var<workgroup> mm_Bsub: array<array<vec4<${n}>, ${d/e[0]}>, ${a}>;

const rowPerThread = ${e[1]};
const colPerThread = ${e[0]};
const innerElementSize = ${f};
const tileInner = ${a};

@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
        @builtin(global_invocation_id) globalId : vec3<u32>,
        @builtin(workgroup_id) workgroupId : vec3<u32>) {
  let localRow = i32(localId.y);
  let tileRow = localRow * rowPerThread;
  let tileCol = i32(localId.x);

  let globalRow =i32(globalId.y) * rowPerThread;
  let globalCol = i32(globalId.x);
  let batch = ${s?"0":"i32(globalId.z)"};
  ${r?`let batchIndices = ${r.offsetToIndices("u32(batch)")};`:""}
  let globalRowStart = i32(workgroupId.y) * ${l};

  let numTiles = ${s?`${Math.ceil(o/a)}`:"(uniforms.dimInner - 1) / tileInner + 1"};
  var kStart = ${s?`i32(globalId.z) * ${o}`:"0"};

  var acc: array<vec4<${n}>, rowPerThread>;

  // Loop over shared dimension.
  let tileRowB = localRow * ${h};
  for (var t = 0; t < numTiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let inputRow = tileRow + innerRow;
          let inputCol = tileCol;
          ${Cg(i,r)}
      }

      // Load one tile of B into local memory.
      for (var innerRow = 0; innerRow < ${h}; innerRow = innerRow + 1) {
          let inputRow = tileRowB + innerRow;
          let inputCol = tileCol;
          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${r?", batchIndices":""});
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

      // Compute acc values for a single thread.
      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {
          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];
          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];
          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];
          ${f===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}

          ${Tg(i,f)}
      }

      workgroupBarrier();
  }

  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
  }
}`},$d=(e,t)=>e?`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              kStart + inputRow,
              globalRowStart + inputCol${t?", batchIndices":""});
            `:`
            mm_Asub[inputRow][inputCol] = mm_readA(batch,
              globalRowStart + inputRow,
              kStart + inputCol${t?", batchIndices":""});
            `,Ig=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",eu=(e,t,n="f32",r,i=!1,a=32,s=!1,o=32,l=!1)=>{let d=e[1]*t[1],c=e[0]*t[0],u=i?d:a,f=i?a:d;if(!(f%t[1]===0&&u%t[0]===0&&a%t[1]===0))throw new Error(`tileAHight ${f} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${u} must be divisible by workgroupSize[0]${t[0]}, tileInner ${a} must be divisible by workgroupSize[1]${t[1]}`);let h=f/t[1],g=u/t[0],$=a/t[1],E=l?`
    let localRow = i32(localId.y);
    let localCol = i32(localId.x);
    let globalRowStart = i32(workgroupId.y) * ${d};
    let globalColStart = i32(workgroupId.x) * ${c};

    // Loop over shared dimension.
    for (var t = 0; t < numTiles; t = t + 1) {
      // Load one tile of A into local memory.
      for (var inputRow = localRow; inputRow < ${f}; inputRow = inputRow + ${t[1]}) {
        for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${t[0]}) {
          ${$d(i,r)}
        }
      }
      // Load one tile of B into local memory.
      for (var inputRow = localRow; inputRow < ${a}; inputRow = inputRow + ${t[1]}) {
            for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${t[0]}) {
          mm_Bsub[inputRow][inputCol] = mm_readB(batch,
            kStart + inputRow,
            globalColStart + inputCol${r?", batchIndices":""});
        }
      }
      kStart = kStart + tileInner;
      workgroupBarrier();

      // Compute acc values for a single thread.
      var BCached : array<${n}, colPerThread>;
      for (var k = 0; k < tileInner; k = k + 1) {
        for (var inner = 0; inner < colPerThread; inner = inner + 1) {
          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];
        }
        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
          let ACached = ${i?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}
          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
            acc[innerRow][innerCol] = acc[innerRow][innerCol] +
                ACached * BCached[innerCol];
          }
        }
      }
      workgroupBarrier();
    }
    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      let gRow = globalRowStart + localRow + innerRow * ${t[1]};
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        let gCol = globalColStart + localCol + innerCol * ${t[0]};
        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);
      }
    }
    `:`
let tileRow = i32(localId.y) * rowPerThread;
let tileCol = i32(localId.x) * colPerThread;

let globalRow = i32(globalId.y) * rowPerThread;
let globalCol = i32(globalId.x) * colPerThread;
let globalRowStart = i32(workgroupId.y) * ${d};

let tileRowA = i32(localId.y) * ${h};
let tileColA = i32(localId.x) * ${g};
let tileRowB = i32(localId.y) * ${$};
// Loop over shared dimension.
for (var t = 0; t < numTiles; t = t + 1) {
  // Load one tile of A into local memory.
  for (var innerRow = 0; innerRow < ${h}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < ${g}; innerCol = innerCol + 1) {
      let inputRow = tileRowA + innerRow;
      let inputCol = tileColA + innerCol;
      ${$d(i,r)}
    }
  }

  // Load one tile of B into local memory.
  for (var innerRow = 0; innerRow < ${$}; innerRow = innerRow + 1) {
    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
      let inputRow = tileRowB + innerRow;
      let inputCol = tileCol + innerCol;
      mm_Bsub[inputRow][inputCol] = mm_readB(batch,
        kStart + inputRow,
        globalCol + innerCol${r?", batchIndices":""});
    }
  }
  kStart = kStart + tileInner;
  workgroupBarrier();

  // Compute acc values for a single thread.
  var BCached : array<${n}, colPerThread>;
  for (var k = 0; k < tileInner; k = k + 1) {
    for (var inner = 0; inner < colPerThread; inner = inner + 1) {
      BCached[inner] = mm_Bsub[k][tileCol + inner];
    }

    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      ${Ig(i)}
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
      }
    }
  }

  workgroupBarrier();
}

for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
    mm_write(batch, globalRow + innerRow, globalCol + innerCol,
        acc[innerRow][innerCol]);
  }
}
`;return`
  var<workgroup> mm_Asub : array<array<${n}, ${u}>, ${f}>;
  var<workgroup> mm_Bsub : array<array<${n}, ${c}>, ${a}>;
  const rowPerThread = ${e[1]};
  const colPerThread = ${e[0]};
  const tileInner = ${a};

@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})
fn main(@builtin(local_invocation_id) localId : vec3<u32>,
        @builtin(global_invocation_id) globalId : vec3<u32>,
        @builtin(workgroup_id) workgroupId : vec3<u32>) {
    let batch = ${s?"0":"i32(globalId.z)"};
    ${r?`let batchIndices = ${r.offsetToIndices("u32(batch)")};`:""}
    let numTiles = ${s?`${Math.ceil(o/a)}`:"(uniforms.dimInner - 1) / tileInner + 1"};
    var kStart = ${s?`i32(globalId.z) * ${o}`:"0"};

    var acc : array<array<${n}, colPerThread>, rowPerThread>;

    // Without this initialization strange values show up in acc.
    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {
      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {
        acc[innerRow][innerCol] = 0.0;
      }
    }
    ${E}
  }
`},Ag=(e,t,n,r,i,a=!1)=>{let[s,o,l]=i,[d,c,u,f]=r,h=Ns(s,l),g=Ns(o,l),$=wn(r[0].type.tensor),E=()=>{let _=c.rank,v=d.rank,b=`var aIndices: ${c.type.indices};`;for(let C=_-2-1,I=v-1;C>=0;C--,I--)b+=`
aIndices[${C}] = ${v>1?`batchIndices[${I}]`:"batchIndices"};`;return h.forEach(C=>{b+=`
aIndices[${C}] = 0;`}),b+=`
aIndices[${_-2}] = u32(row);
                   aIndices[${_-1}] = u32(colIn);`,b},y=()=>{let _=u.rank,v=d.rank,b=`var bIndices: ${u.type.indices};`;for(let C=_-2-1,I=v-1;C>=0;C--,I--)b+=`
bIndices[${C}] = ${v>1?`batchIndices[${I}]`:"batchIndices"};`;return g.forEach(C=>{b+=`
bIndices[${C}] = 0;`}),b+=`
bIndices[${_-2}] = u32(row);
                   bIndices[${_-1}] = u32(colIn);`,b};return`
    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${Mt(e,$)} {
      var value = ${Mt(e,$)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dimAOuter && col < uniforms.dimInner)
      {
        ${E()}
        value = ${c.getByIndices("aIndices")};
      }
      return value;
    }

    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${d.type.indices}) -> ${Mt(e,$)} {
      var value = ${Mt(e,$)}(0.0);
      let col = colIn * ${e};
      if(row < uniforms.dimInner && col < uniforms.dimBOuter)
      {
        ${y()}
        value = ${u.getByIndices("bIndices")};
      }
      return value;
    }

    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Mt(e,$)}) {
      let col = colIn * ${e};
      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {
        var value = valueIn;
        let coords = vec3<i32>(batch, row, colIn);
        ${t?`value = value + ${a?"bias[colIn]":`${Mt(e,$)}(bias[row])`};`:""}
        ${n}
        ${f.setByIndices("vec3<u32>(coords)","value")}
      }
    }
    `},$f=(e,t,n,r,i=!1)=>{let a=e[0].dims,s=e[1].dims,o=a.slice(0,-2),l=s.slice(0,-2),d=r?r.slice(0,-2):n.slice(0,-2),c=xt(d.length),u=c?d.length:d,f=mf("batchDims",e[0].dataType,u,1),h=ne.size(d),g=a[a.length-2],$=a[a.length-1],E=s[s.length-1],y=$%4===0&&E%4===0,_=g<=8?[4,1,1]:[4,4,1],v=[8,8,1],b=[Math.ceil(E/v[0]/_[0]),Math.ceil(g/v[1]/_[1]),Math.ceil(h/v[2]/_[2])],C=wn(e[0].dataType),I=y?4:1,O=[...o,g,$/I],M=xt(O.length),W=M?O.length:O,G=[...l,$,E/I],z=xt(G.length),K=z?G.length:G,ae=[h,g,E/I],ue=te("a",e[0].dataType,W,I),R=te("b",e[1].dataType,K,I),H=ke("result",e[0].dataType,ae.length,I),re=[ue,R],L=[{type:"int32",data:g},{type:"int32",data:E},{type:"int32",data:$}];c&&L.push(...le(d)),M&&L.push(...le(O)),z&&L.push(...le(G));let X=[];X.push(M?"rank":"dims"),X.push(z?"rank":"dims");let Z=e.length>2,{activationFunction:V,applyActivation:ie}=ya(t,H.type.value),xe=Ag(I,Z,ie,[f,ue,R,H],[o,l,d],i);if(Z){let He=i?I:1;re.push(te("bias",e[2].dataType,e[2].dims.length,He)),L.push(...le(e[2].dims)),X.push("rank")}L.push(...le(ae));let Re=He=>`
  ${He.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").registerInternalVariables(f).declareVariables(...re,H)}
  ${V}
  ${xe}
  ${y?Jl(_,v,C,f):eu(_,v,C,f)}
                   `;return{name:"MatMul",shaderCache:{hint:t.activationCacheKey+`${_}${y}${i}`,inputDependencies:X},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:b[0],y:b[1],z:b[2]},programUniforms:L}),getShaderSource:Re}}}),Og,X1,fE=ee(()=>{bi(),Oe(),xi(),_f(),Q1(),xu(),Og=(e,t,n,r,i=!1,a,s=4,o=4,l=4,d="f32")=>{let c=G=>{switch(G){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${d}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${G} is not supported.`)}},u=G=>{switch(G){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${G} is not supported.`)}},f=e?`
    let coord = vec4<i32>(batch, xRow, xCol, xCh);
    `:`
    let coord = vec4<i32>(batch, xCh, xRow, xCol);
    `,h=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,g=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",$=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",E=e?"row":"col",y=e?"col":"row",_=`
    let inChannels = i32(uniforms.w_shape[2]);
    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
    let outRow = ${E} / outWidth;
    let outCol = ${E} % outWidth;

    let WRow = ${y} / (filterDims[1] * inChannels);
    let WCol = ${y} / inChannels % filterDims[1];
    let xRow = outRow * stride[0] + dilation[0] * WRow - pad[0];
    let xCol = outCol * stride[1] + dilation[1] * WCol - pad[1];
    let xCh = ${y} % inChannels;
    var resData = ${Mt(s,d)}(0.0);
    // The bounds checking is always needed since we use it to pad zero for
    // the 'same' padding type.
    if (xRow >= 0 && xRow < ${g} && xCol >= 0 && xCol < ${$}) {
      ${f}
      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));
      ${c(s)}
    }
    return resData;`,v=e?t&&r?`
    let col = colIn * ${s};
    ${_}`:`
    let col = colIn * ${s};
    if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      ${_}
    }
    return ${Mt(s,d)}(0.0);`:r&&n?`
    let col = colIn * ${s};
    ${_}`:`
    let col = colIn * ${s};
    if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
      ${_}
    }
    return ${Mt(s,d)}(0.0);`,b=`${u(o)}`,C=Mt(l,d),I=Mt(e?s:o,d),O=Mt(e?o:s,d),{activationFunction:M,applyActivation:W}=ya(a,C);return`
    ${M}
    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${I} {
      ${e?v:b}
    }

    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${O} {
      ${e?b:v}
    }

    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${C}) {
      let col = colIn * ${l};
      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)
      {
      var value = valueIn;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${h}
      ${wf(i)}
      ${W}
      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
      }
    }`},X1=(e,t,n,r,i,a,s,o)=>{let l=t.format==="NHWC",d=l?e[0].dims[3]:e[0].dims[1],c=n[0],u=l?n[2]:n[3],f=l?n[1]:n[2],h=l?n[3]:n[1],g=l&&(d%4===0||d%3===0)&&h%4===0,$=l?h:u*f,E=l?u*f:h,y=[8,8,1],_=r<=8?[4,1,1]:[4,4,1],v=[Math.ceil($/y[0]/_[0]),Math.ceil(E/y[1]/_[1]),Math.ceil(c/y[2]/_[2])];$t("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${v}`);let b=g?l&&d%4!==0?3:4:1,C=y[1]*_[1],I=y[0]*_[0],O=Math.max(y[0]*b,y[1]),M=r%C===0,W=i%I===0,G=a%O===0,z=g?[b,4,4]:[1,1,1],K=wn(e[0].dataType),ae=g?4:1,ue=[{type:"int32",data:r},{type:"int32",data:i},{type:"int32",data:a}],R=te("x",e[0].dataType,e[0].dims.length,b===3?1:b),H=te("w",e[1].dataType,e[1].dims.length,ae),re=[R,H];ue.push(...le(e[0].dims)),ue.push(...le(e[1].dims));let L=`
      fn setOutputAtIndex(flatIndex : i32, value : ${g?`vec4<${K}>`:K}) {
        result[flatIndex] = ${g?`vec4<${K}>`:K}(value);
      }
      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${g?`vec4<${K}>`:K}) {
        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));
        setOutputAtIndex(flatIndex ${g?"/ 4":""}, value);
      }`;if(s){let Z=te("bias",e[2].dataType,e[2].dims.length,ae);re.push(Z),ue.push(...le(e[2].dims)),L+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${g?`vec4<${K}>`:K} {
          return bias[coords.${l?"w":"y"}${g?"/ 4":""}];
        }`}let X=ke("result",e[0].dataType,n.length,ae);return ue.push(...le(n)),{name:"Conv2DMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:v[0],y:v[1],z:v[2]},programUniforms:ue}),getShaderSource:Z=>`
        ${vf("uniforms.result_strides")}
        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,
        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,
        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };
        ${Z.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...re,X)}
        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[0]}, ${t.kernelShape[1]});
        const pad : vec2<i32> = vec2<i32>(${t.pads[0]}, ${t.pads[1]});
        const stride : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});
        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});
        ${L}
        ${Og(l,M,W,G,s,t,z[0],z[1],z[2],K)}
            ${g?Jl(_,y,K,void 0,!l,O):eu(_,y,K,void 0,!l,O,!1,void 0,o)}`}}}),Gc,hE=ee(()=>{ze(),Oe(),e2(),xi(),Gc=(e,t,n)=>{let r=e.length>2,i=r?"value += b[output_channel];":"",a=e[0].dims,s=e[1].dims,o=s[0]/t.group,l=t.format==="NHWC",d=qc(a,s,t.dilations,t.pads,t.strides,l),c=ne.size(d),u=ke("output",e[0].dataType,d),{activationFunction:f,applyActivation:h}=ya(t,u.type.value),g=te("x",e[0].dataType,a),$=te("w",e[1].dataType,s),E=[g,$];r&&E.push(te("b",e[2].dataType,e[2].dims));let y=_=>`
  const strides: vec2<u32> = vec2(${t.strides[0]}u, ${t.strides[1]}u);
  const pads: vec2<u32> = vec2(${t.pads[0]}u, ${t.pads[1]}u);

  ${_.declareVariables(...E,u)}

  ${f}

  ${_.mainStart()}
    ${_.guardAgainstOutOfBoundsWorkgroupSizes(c)}

    let outputIndices = ${u.offsetToIndices("global_idx")};
    let batch: u32 = outputIndices[0];
    let output_channel: u32 = outputIndices[${l?3:1}];
    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${l?1:2}], outputIndices[${l?2:3}]) * strides - pads;
    let group_id: u32 = output_channel / ${o}u;

    var value: ${u.type.value} = ${u.type.value}(0);
    for (var wInChannel: u32 = 0u; wInChannel < ${s[1]}u; wInChannel++) {
      let input_channel = group_id * ${s[1]}u + wInChannel;
      for (var wHeight: u32 = 0u; wHeight < ${s[2]}u; wHeight++) {
        let xHeight = xRCCorner.x + wHeight * ${t.dilations[0]}u;

        if (xHeight < 0u || xHeight >= ${a[l?1:2]}u) {
          continue;
        }

        for (var wWidth: u32 = 0u; wWidth < ${s[3]}u; wWidth++) {
          let xWidth = xRCCorner.y + wWidth * ${t.dilations[1]}u;
          if (xWidth < 0u || xWidth >= ${a[l?2:3]}u) {
            continue;
          }

          let xVal = ${l?g.get("batch","xHeight","xWidth","input_channel"):g.get("batch","input_channel","xHeight","xWidth")};
          let wVal = ${$.get("output_channel","wInChannel","wHeight","wWidth")};
          value += xVal*wVal;
        }
      }
    }
    ${i}
    ${h}
    ${u.setByOffset("global_idx","value")}
  }`;return{name:"GroupedConv",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:n?n(d):d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)}}),getShaderSource:y}}}),Hc,Mg,Z1,J1=ee(()=>{ze(),xu(),Oe(),xi(),Hc=(e,t,n,r,i=!1)=>{let a=e[0].dims,s=e[1].dims,o=a[a.length-2],l=s[s.length-1],d=a[a.length-1],c=rn(l),u=rn(d),f=rn(o),h=ne.size(n)/c/f,g=e.length>2,$=r?r.slice(0,-2):n.slice(0,-2),E=[ne.size($),o,l],y=[{type:"uint32",data:h},{type:"uint32",data:o},{type:"uint32",data:l},{type:"uint32",data:d},...le($),...le(a),...le(s)];g&&y.push(...le(e[2].dims)),y.push(...le(E));let _=v=>{let b=mf("batch_dims",e[0].dataType,$.length),C=te("a",e[0].dataType,a.length,u),I=te("b",e[1].dataType,s.length,c),O=ke("output",e[0].dataType,E.length,c),{activationFunction:M,applyActivation:W}=ya(t,O.type.value),G=[C,I],z="";if(g){let L=i?c:1;G.push(te("bias",e[2].dataType,e[2].dims.length,L)),z=`${i?`value += bias[col / ${L}];`:`value += ${O.type.value}(bias[row + i]);`}`}let K=a.slice(0,-2),ae=s.slice(0,-2),ue=Ns(K,$),R=Ns(ae,$),H=(L,X)=>{let Z=L.rank,V=L.name;if(Z===2)return`var ${V}_indices = ${L.type.indices}(0u, 0u);`;let ie=b.rank,xe=`var ${V}_indices: ${L.type.indices};`;for(let Re=Z-2-1,He=ie-1;Re>=0;Re--,He--)xe+=`
${V}_indices[${Re}] = ${ie>1?`batch_indices[${He}]`:"batch_indices"};`;return X.forEach(Re=>{xe+=`
${V}_indices[${Re}] = 0;`}),xe+=`${V}_indices[${Z-2}] = 0u;
                     ${V}_indices[${Z-1}] = 0u;`,xe},re=()=>{let L=`var a_data: ${C.type.value};`;for(let X=0;X<u;X++)L+=`
              let b_data${X} = b[(b_offset + (k + ${X}) * uniforms.N + col) / ${c}];`;for(let X=0;X<f;X++){L+=`a_data = a[(a_offset + (row + ${X}) * uniforms.K + k) / ${u}];`;for(let Z=0;Z<u;Z++)L+=`
            values[${X}] = fma(${I.type.value}(a_data${u===1?"":`[${Z}]`}), b_data${Z}, values[${X}]);
`}return L};return`
  ${v.registerUniform("outputSize","u32").registerUniform("M","u32").registerUniform("N","u32").registerUniform("K","u32").registerInternalVariables(b).declareVariables(...G,O)}
  ${M}
  ${v.mainStart()}
    ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
    let col = (global_idx % (uniforms.N / ${c})) * ${c};
    var index1 = global_idx / (uniforms.N / ${c});
    let stride1 = uniforms.M / ${f};
    let row = (index1 % stride1) * ${f};
    let batch = index1 / stride1;

    ${n.length===2?"":`let batch_indices = ${b.offsetToIndices("batch")};`}
    ${H(C,ue)}
    let a_offset = ${C.indicesToOffset("a_indices")};
    ${H(I,R)}
    let b_offset = ${I.indicesToOffset("b_indices")};
    var values: array<${O.type.value}, ${f}>;
    for (var k: u32 = 0u; k < uniforms.K; k = k + ${u}) {
      ${re()}
    }
    for (var i = 0u; i < ${f}u; i++) {
      var value = values[i];
      ${z}
      ${W}
      let cur_indices = ${O.type.indices}(batch, row + i, col);
      let offset = ${O.indicesToOffset("cur_indices")};
      ${O.setByOffset(`offset / ${c}`,"value")};
    }
  }
  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activationCacheKey}_${c}_${u}_${f}_${i}`,inputDependencies:g?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:y}),getShaderSource:_}},Mg=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},Z1=e=>{Mg(e.inputs);let t=ca.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let n=t[t.length-1],r=e.inputs[0].dims[e.inputs[0].dims.length-1];n<8&&r<8?e.compute(Hc(e.inputs,{activation:"",activationCacheKey:""},t)):e.compute($f(e.inputs,{activation:"",activationCacheKey:""},t))}}),qc,bd,zg,xd,Kc,Rg,Pg,Yc,e2=ee(()=>{ze(),St(),fE(),xu(),hE(),xi(),J1(),qs(),qc=(e,t,n,r,i,a)=>{let s=e[0],o=e.slice(a?1:2,a?3:4),l=o.length,d=t[0],c=t.slice(2).map((f,h)=>f+(f-1)*(n[h]-1)),u=o.map((f,h)=>f+r[h]+r[h+l]).map((f,h)=>Math.floor((f-c[h]+i[h])/i[h]));return u.splice(0,0,s),u.splice(a?3:1,0,d),u},bd=[2,3,1,0],zg=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let n=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],r=e[1].dims[1]*t.group;if(n!==r)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let i=e[0].dims.length-2;if(t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},xd=(e,t)=>{let n=e.kernelShape.slice();for(let a=2;a<t[1].dims.length;++a)n[a-2]===0&&(n[a-2]=t[1].dims[a]);let r=e.pads.slice();Ql.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,n,r,e.format==="NHWC",e.autoPad);let i=Object.assign({},e);return Object.assign(i,{kernelShape:n,pads:r,cacheKey:e.cacheKey}),i},Kc=e=>{let t=yf(e),n=e.format,r=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],i=e.dilations,a=e.group,s=e.kernel_shape,o=e.pads,l=e.strides,d=e.w_is_const();return tt({autoPad:r,format:n,dilations:i,group:a,kernelShape:s,pads:o,strides:l,wIsConst:d,...t})},Rg=(e,t,n)=>{let r=xd(n,t),i=n.format==="NHWC";if(n.group!==1){e.compute(Gc(t,r));return}let a=t.length===3,s=t[0].dims[i?1:2],o=t[0].dims[i?2:3],l=t[0].dims[i?3:1],d=t[1].dims[2],c=t[1].dims[3],u=qc(t[0].dims,t[1].dims,n.dilations,r.pads,n.strides,i),f=u[i?1:2],h=u[i?2:3],g=u[i?3:1],$=i&&d===s&&c===o&&n.pads[0]===0&&n.pads[1]===0;if($||d===1&&c===1&&n.dilations[0]===1&&n.dilations[1]===1&&n.strides[0]===1&&n.strides[1]===1&&n.pads[0]===0&&n.pads[1]===0){let I=u[0],O,M,W,G=[];if(i){let ae=e.kernelCustomData.wT??e.compute(Gr(t[1],bd),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];if(n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=ae),$){let ue=s*o*l;O=t[0].reshape([1,I,ue]),M=ae.reshape([1,ue,g]),W=[1,I,g]}else O=t[0].reshape([I,s*o,l]),M=ae.reshape([1,l,g]),W=[I,f*h,g];G.push(O),G.push(M)}else O=t[0].reshape([I,l,s*o]),M=t[1].reshape([1,g,l]),W=[I,g,f*h],G.push(M),G.push(O);a&&G.push(t[2]);let z=W[2],K=G[0].dims[G[0].dims.length-1];z<8&&K<8?e.compute(Hc(G,r,u,W,i),{inputs:G}):e.compute($f(G,r,u,W,i),{inputs:G});return}let E=!0,y=e.kernelCustomData.wT??e.compute(Gr(t[1],bd),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=y);let _=[t[0],y];a&&_.push(t[2]);let v=i?f*h:g,b=i?g:f*h,C=d*c*l;e.compute(X1(_,r,u,v,b,C,a,E),{inputs:_})},Pg=(e,t)=>{let n=t.format==="NHWC",r=[e.inputs[0].reshape(n?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&r.push(e.inputs[2]);let i=[0,t.pads[0],0,t.pads[1]],a=[1].concat(t.strides),s=[1].concat(t.dilations),o=[1].concat(t.kernelShape),l=xd({...t,pads:i,strides:a,dilations:s,kernelShape:o},r);e.compute(Gc(r,l,d=>n?[d[0],d[2],d[3]]:[]))},Yc=(e,t)=>{zg(e.inputs,t),e.inputs[0].dims.length===3?Pg(e,t):Rg(e,e.inputs,t)}}),Bg,t2,mE=ee(()=>{bi(),Oe(),xi(),_f(),Q1(),xu(),Bg=(e,t=!1,n,r=4)=>{let i=Mt(r,"f32"),a=y=>{switch(y){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`
            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];
            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];
            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];
            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];
            return vec4<f32>(v0, v1, v2, v3);
            `;default:throw new Error(`innerElementSize ${y} is not supported.`)}},s=e?`
      let coord = vec4<i32>(batch, iXR, iXC, xCh);
      `:`
      let coord = vec4<i32>(batch, xCh, iXR, iXC);
      `,o=e?`
    let coords = vec4<i32>(
      batch,
      row / outWidth,
      row % outWidth,
      col);
    `:`
    let coords = vec4<i32>(
      batch,
      row,
      col / outWidth,
      col % outWidth);
    `,l=e?"outBackprop[1]":"outBackprop[2]",d=e?"outBackprop[2]":"outBackprop[3]",c=e?"row":"col",u=e?"col":"row",f=`
      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      let outRow = ${c} / outWidth;
      let outCol = ${c} % outWidth;

      let WRow = ${u} / (filterDims[1] * inChannels);
      let WCol = ${u} / inChannels % filterDims[1];
      let xR = f32(outRow - pads[0] + dilation[0] * WRow) / f32(strides[0]);
      let xC = f32(outCol - pads[1] + dilation[1] * WCol) / f32(strides[1]);
      if (xR < 0.0 || xR >= f32(${l}) || fract(xR) > 0.0) {
        return ${i}(0.0);
      }
      if (xC < 0.0 || xC >= f32(${d}) || fract(xC) > 0.0) {
        return ${i}(0.0);
      }
      let iXR = i32(xR);
      let iXC = i32(xC);
      let xCh = ${u} % inChannels;
      ${s}
      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${r}];`,h=e?`
      let col = colIn * ${r};
      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
        ${f}
      }
      return ${i}(0.0);`:`
      let col = colIn * ${r};
      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
        ${f}
      }
      return ${i}(0.0);`,g=`
      let col = colIn * ${r};
      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};
      let coordX = filterDims.x - 1 - row / (filterDims[1] * inChannels);
      let coordY = filterDims.y - 1 - (row / inChannels) % filterDims[1];
      if (${e?"row < uniforms.dimInner && col < uniforms.dimBOuter":"row < uniforms.dimInner && col < uniforms.dimAOuter"}  && coordX >= 0 && coordY >= 0) {
        let rowInner = row % inChannels;
        let coord = vec4<i32>(coordX, coordY, col, rowInner);
        ${a(r)}
      }
      return ${i}(0.0);
      `,{activationFunction:$,applyActivation:E}=ya(n,i);return`
      ${$}
  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${i} {
    ${e?h:g}
  }

  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${i} {
    ${e?g:h}
  }

  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${i}) {
    let col = colIn * ${r};
    if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {
      var value = valueInput;
      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};
      ${o}
      ${wf(t)}
      ${E}
      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${r}] = value;
    }
  }`},t2=(e,t,n,r,i,a,s,o)=>{let l=t.format==="NHWC",d=l?e[0].dims[3]:e[0].dims[1],c=n[0],u=l?n[2]:n[3],f=l?n[1]:n[2],h=l?n[3]:n[1],g=l?d%4===0&&h%4===0:u%4===0&&h%4===0,$=l?h:u*f,E=l?u*f:h,y=g?[8,8,1]:[$<=4||E<=4?4:16,$>4&&E<=4?4:16,1],_=g?[4,4,1]:[$<=4?1:4,$>4&&E<=4?1:4,1],v=[Math.ceil($/y[0]/_[0]),Math.ceil(E/y[1]/_[1]),Math.ceil(c/y[2]/_[2])];$t("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${v}`);let b=g?4:1,C=Math.max(y[0]*b,y[1]),I=g?4:1,O=[{type:"int32",data:r},{type:"int32",data:i},{type:"int32",data:a}],M=te("x",e[0].dataType,e[0].dims.length,I),W=te("w",e[1].dataType,e[1].dims.length,1),G=ke("result",e[0].dataType,n.length,I),z=[M,W];O.push(...le(e[0].dims)),O.push(...le(e[1].dims));let K="";if(s){let ae=te("bias",e[2].dataType,e[2].dims.length,I);z.push(ae),O.push(...le(e[2].dims)),K+=`
        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${g?"vec4<f32>":"f32"} {
          return bias[coords.${l?"w":"y"}${g?"/ 4":""}];
        }`}return O.push(...le(n)),{name:"Conv2DTransposeMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:v[0],y:v[1],z:v[2]},programUniforms:O}),getShaderSource:ae=>`
        ${vf("uniforms.result_strides")}
        ${ae.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...z,G)};
        const outBackprop : vec4<i32> = vec4<i32>(${e[0].dims.join(",")});
        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[l?1:2]}, ${t.kernelShape[l?2:3]});
        const effectiveFilterDims : vec2<i32> = filterDims + vec2<i32>(
              ${t.dilations[0]<=1?0:(t.kernelShape[l?1:2]-1)*(t.dilations[0]-1)},
              ${t.dilations[1]<=1?0:(t.kernelShape[l?2:3]-1)*(t.dilations[1]-1)});
        const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${t.pads[0]+t.pads[2]})/2,
                                         i32(effectiveFilterDims[1]) - 1 - (${t.pads[1]+t.pads[3]})/2);
        const strides : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});
        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});
        const dimAOuter : i32 = ${r};
        const dimBOuter : i32 = ${i};
        const dimInner : i32 = ${a};
        ${K}
        ${Bg(l,s,t,b)}
        ${g?Jl(_,y,"f32",void 0,!l,C):eu(_,y,"f32",void 0,!l,C,!1,void 0,o)}`}}}),Dg,Qc,gE=ee(()=>{bi(),ze(),Oe(),Dg=(e,t,n,r,i,a,s=!1,o)=>{let l=n.format==="NHWC",d=l?1:2,c=l?2:3,u=l?3:1,f=ne.size(r),h=s?2:1,g=n.group,$=t[1].dims,E=$[0]/g,y=$[1],_=`
  fn setOutputAtIndex(flatIndex : u32, value : ${s?`vec4<${o}>`:o}) {
    result[flatIndex] = ${s?`vec4<${o}>`:o}(value);
  }`;i&&(_+=`
    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${s?`vec4<${o}>`:o} {
      return bias[coords.${l?"w":"y"}${s?"/ 4":""}];
    }`);let v=s?4:1,b=te("W",t[1].dataType,t[1].dims,v),C=te("Dy",t[0].dataType,t[0].dims,v),I=[C,b];i&&I.push(te("bias",t[2].dataType,[r[u]],v));let O=ke("result",t[0].dataType,r,v),M=`{
        let batch: u32 = ${a?"global_id.z":"workgroup_id.z"} / outShape[1];
        let r = ${a?"global_id.z":"workgroup_id.z"} % outShape[1];
        let c = ${a?"global_id.y":"workgroup_id.y"} * ${h};
        let d1: u32 = ${a?"global_id.x":"workgroup_id.x"} * 4;

        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(pads);

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        var dotProd: array<vec4<${o}>, ${h}>;
        for (var i = 0; i < ${h}; i++) {
          dotProd[i] = vec4<${o}>(0.0);
        }
        for (var wR: u32 = 0; wR < filterDims[0]; wR = wR + 1) {
          var dyR = (${o}(dyCorner.x) + ${o}(wR)) / ${o}(strides.x);
          let wRPerm = filterDims[0] - 1 - wR;
          if (dyR < 0.0 || dyR >= ${o}(outBackprop[1]) ||
              fract(dyR) > 0.0 || wRPerm < 0) {
            continue;
          }
          let idyR: u32 = u32(dyR);

          for (var wC: u32 = 0; wC < filterDims[1]; wC = wC + 1) {
            let dyC = (${o}(dyCorner.y) + ${o}(wC)) / ${o}(strides.y);
            let dyC2 = (${o}(dyCorner.y) + 1.0 + ${o}(wC)) / ${o}(strides.y);
            let wCPerm = filterDims[1] - 1 - wC;
            if (wCPerm < 0) {
              continue;
            }
            var bDyCVal = true;
            var bDyCVal2 = true;
            if (dyC < 0.0 || dyC >= ${o}(outBackprop[2]) ||
                fract(dyC) > 0.0) {
              bDyCVal = false;
            }
            if (dyC2 < 0.0 || dyC2 >= ${o}(outBackprop[2]) ||
                fract(dyC2) > 0.0) {
              bDyCVal2 = false;
            }

            let idyC: u32 = u32(dyC);
            let idyC2: u32 = u32(dyC2);
            if (bDyCVal && bDyCVal2) {
              let d2Length = outBackprop[3];
              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {
                let wValue0 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

                var xValue = ${C.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${o}>(dot(xValue, wValue0),
                                      dot(xValue, wValue1),
                                      dot(xValue, wValue2),
                                      dot(xValue, wValue3));
                dotProd[0] = dotProd[0] + tmpval;

                xValue =  ${C.get("batch","idyR","idyC2","d2")};

                dotProd[1] = dotProd[1] + vec4<${o}>(dot(xValue, wValue0),
                                                    dot(xValue, wValue1),
                                                    dot(xValue, wValue2),
                                                    dot(xValue, wValue3));
              }
            } else if (bDyCVal) {
              let d2Length = outBackprop[${u}];
              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {
                let wValue0 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

                var xValue = ${C.get("batch","idyR","idyC","d2")};
                let tmpval = vec4<${o}>(dot(xValue, wValue0),
                                      dot(xValue, wValue1),
                                      dot(xValue, wValue2),
                                      dot(xValue, wValue3));
                dotProd[0] = dotProd[0] + tmpval;
              }
            } else if (bDyCVal2) {
              let d2Length = outBackprop[3];
              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {
                let wValue0 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};
                let wValue1 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};
                let wValue2 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};
                let wValue3 = ${b.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};

                var xValue = ${C.get("batch","idyR","idyC2","d2")};
                let tmpval = vec4<${o}>(dot(xValue, wValue0),
                                      dot(xValue, wValue1),
                                      dot(xValue, wValue2),
                                      dot(xValue, wValue3));
                dotProd[1] = dotProd[1] + tmpval;
              }
            }
          }
        }

        for (var i: u32 = 0; i < ${h}; i = i + 1) {
          let value = dotProd[i] + ${i?"bias[c+i]":`vec4<${o}>(0.0)`};
          ${O.set("batch","r","c + i","d1","value")};
        }
      }`,W=`
          let outputIndices = ${O.offsetToIndices("global_idx")};
          let batch = ${O.indicesGet("outputIndices",0)};
          let d1 = ${O.indicesGet("outputIndices",u)};
          let r = ${O.indicesGet("outputIndices",d)};
          let c = ${O.indicesGet("outputIndices",c)};
          let dyCorner = vec2<i32>(i32(r), i32(c)) - pads;
          let dyRCorner = dyCorner.x;
          let dyCCorner = dyCorner.y;
          let groupId = d1 / ${y};
          let wOutChannel = d1 - groupId * ${y};
          // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
          // ? = to be determined. : = across all values in that axis.
          var dotProd = ${o}(0.0);
          for (var wR: u32 = 0; wR < effectiveFilterDims.x; wR = wR + 1) {
            if (wR % dilations.x != 0) {
              continue;
            }
            let dyR = (${o}(dyRCorner) + ${o}(wR)) / ${o}(strides[0]);
            let wRPerm = filterDims.x - 1 - wR / dilations.x;
            if (dyR < 0.0 || dyR >= ${o}(outBackprop[${d}]) || fract(dyR) > 0.0 ||
                wRPerm < 0) {
              continue;
            }
            let idyR: u32 = u32(dyR);

            for (var wC: u32 = 0; wC < effectiveFilterDims.y; wC = wC + 1) {
              if (wC % dilations.y != 0) {
                continue;
              }
              let dyC = (${o}(dyCCorner) + ${o}(wC)) / ${o}(strides.y);
              let wCPerm = filterDims.y - 1 - wC / dilations.y;
              if (dyC < 0.0 || dyC >= ${o}(outBackprop[${c}]) ||
                  fract(dyC) > 0.0 || wCPerm < 0) {
                continue;
              }
              let idyC: u32 = u32(dyC);
              var inputChannel = groupId * ${E};
              for (var d2: u32 = 0; d2 < ${E}; d2 = d2 + 1) {
                let xValue = ${l?C.get("batch","idyR","idyC","inputChannel"):C.get("batch","inputChannel","idyR","idyC")};
                let wValue = ${b.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};
                dotProd = dotProd + xValue * wValue;
                inputChannel = inputChannel + 1;
              }
            }
          }
          let value = dotProd + ${i?"bias[d1]":`${o}(0.0)`};
          ${O.setByOffset("global_idx","value")};
        `;return`
  ${e.declareVariables(...I,O)}
  ${_}
  const outShape : vec4<u32> = vec4<u32>(${r.join(",")});
  const outBackprop : vec4<u32> = vec4<u32>(${t[0].dims.join(",")});
  const strides : vec2<u32> = vec2<u32>(${n.strides[0]}, ${n.strides[1]});
  const filterDims : vec2<u32> = vec2<u32>(${n.kernelShape[l?1:2]}, ${n.kernelShape[l?2:3]});
  const dilations : vec2<u32> = vec2<u32>(${n.dilations[0]}, ${n.dilations[1]});
  const effectiveFilterDims : vec2<u32> = filterDims + vec2<u32>(
          ${n.dilations[0]<=1?0:(n.kernelShape[l?1:2]-1)*(n.dilations[0]-1)},
          ${n.dilations[1]<=1?0:(n.kernelShape[l?2:3]-1)*(n.dilations[1]-1)});
  const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${n.pads[0]+n.pads[2]})/2,
                                     i32(effectiveFilterDims[1]) - 1 - (${n.pads[1]+n.pads[3]})/2);
    ${e.mainStart()}
    ${e.guardAgainstOutOfBoundsWorkgroupSizes(f)};
  ${s?M:W}}`},Qc=(e,t,n)=>{let r=e.length>2,i=t.outputShape,a=ne.size(i),s=[Math.ceil(a/64),1,1];$t("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${s}`);let o=wn(e[0].dataType);return{name:"ConvTranspose2D",shaderCache:{hint:t.cacheKey},getRunData:()=>({dispatchGroup:{x:s[0],y:s[1],z:s[2]},outputs:[{dims:n?n(i):i,dataType:e[0].dataType}]}),getShaderSource:l=>Dg(l,e,t,i,r,s[1]===1&&s[2]===1,!1,o)}}}),Ng,Lg,Fg,Sd,n2,Ug,Wg,Vg,jg,r2,yE=ee(()=>{St(),mE(),gE(),xi(),qs(),Ng=(e,t,n,r,i,a)=>(e-1)*t+n+(r-1)*i+1-a,Lg=(e,t,n,r,i)=>{let a=Math.floor(e/2);t==="SAME_UPPER"?(n[r]=a,n[i]=e-a):t==="SAME_LOWER"&&(n[r]=e-a,n[i]=a)},Fg=(e,t,n,r,i,a,s,o,l,d)=>{let c=e.length-2,u=d.length===0;if(l.length===0)for(let g=0;g<c;++g)l.push(0);let f=e[0],h=t[o?3:1]*i;for(let g=0,$=e.length-c-(o?1:0);g<c;++g,++$){let E=e[$],y=u?E*s[g]:d[g],_=Ng(E,s[g],a[g],t[$],n[g],y);Lg(_,r,a,g,g+c),u&&d.push(s[g]*(E-1)+l[g]+(t[$]-1)*n[g]+1-a[g]-a[g+c])}d.splice(0,0,f),d.splice(o?3:1,0,h)},Sd=(e,t)=>{let n=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((f,h)=>f*h,1)===0){n.length=0;for(let f=2;f<t[1].dims.length;++f)n.push(t[1].dims[f])}let r=e.format==="NHWC";n.splice(0,0,t[1].dims[0]),n.splice(r?3:1,0,t[1].dims[1]);let i=e.pads.slice(),a=e.outputShape.slice(),s=e.outputPadding.slice(),o=t[0].dims,l=e.dilations.slice();if(l.reduce((f,h)=>f+h,0)===0){let f=t[0].dims.length-2;l=new Array(f).fill(1)}let d=e.strides.slice();if(d.reduce((f,h)=>f+h,0)===0){let f=t[0].dims.length-2;d=new Array(f).fill(1)}Fg(o,n,l,e.autoPad,e.group,i,d,r,s,a);let c=Object.assign({},e),u=e.cacheKey+[n.join("n,"),i.join(","),d.join(","),s.join(","),a.join(","),l.join(",")].join("_");return Object.assign(c,{kernelShape:n,pads:i,outputPadding:s,outputShape:a,dilations:l,strides:d,cacheKey:u}),c},n2=e=>{let t=yf(e),n=e.format,r=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],i=e.dilations,a=e.group,s=e.kernelShape,o=e.pads,l=e.strides,d=e.wIsConst(),c=e.outputPadding,u=e.outputShape;return tt({autoPad:r,format:n,dilations:i,group:a,kernelShape:s,outputPadding:c,outputShape:u,pads:o,strides:l,wIsConst:d,...t})},Ug=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let n=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],r=e[1].dims[0];if(n!==r)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let i=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==i))throw new Error("invalid bias");let a=e[0].dims.length-2;if(t.dilations.reduce((s,o)=>s+o,0)>0&&t.dilations.length!==a)throw new Error(`dilations should be ${a}D`);if(t.strides.reduce((s,o)=>s+o,0)>0&&t.strides.length!==a)throw new Error(`strides should be ${a}D`);if(t.pads.reduce((s,o)=>s+o,0)>0&&t.pads.length!==a*2)throw new Error(`pads should be ${a*2}D`);if(t.outputPadding.length!==a&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${a}D`);if(t.kernelShape.reduce((s,o)=>s+o,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},Wg=[2,3,1,0],Vg=(e,t,n)=>{let r=Sd(n,t),i=n.format==="NHWC",a=r.outputShape,s=a[i?3:1],o=t[0].dims[i?3:1];if(r.group!==1||s===1&&o===1){e.compute(Qc(t,r));return}let l=a[i?1:2],d=a[i?2:3],c=t[1].dims[2],u=t[1].dims[3],f=i?l*d:s,h=i?s:l*d,g=c*u*o,$=!0,E=e.kernelCustomData.wT??e.compute(Gr(t[1],Wg),{inputs:[1],outputs:[n.wIsConst?-2:-1]})[0];n.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=E);let y=[t[0],E],_=t.length===3;_&&(!i&&t[2].dims.length===1?y.push(t[2].reshape([t[2].dims[0],1,1])):y.push(t[2])),e.compute(t2(y,r,a,f,h,g,_,$),{inputs:y})},jg=(e,t)=>{let n=t.format==="NHWC",r=[e.inputs[0].reshape(n?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];r.length===3&&r.push(e.inputs[2]);let i=t.kernelShape;(i.length===0||i[0]===0)&&(i=[e.inputs[1].dims[2]]);let a=t.dilations;(a.length===0||a[0]===0)&&(a=[1]);let s=t.strides;(s.length===0||s[0]===0)&&(s=[1]);let o=t.pads;o.length===0&&(o=[0,0]),o=[0,o[0],0,o[1]],s=[1].concat(s),a=[1].concat(a),i=[1].concat(i);let l=Sd({...t,pads:o,strides:s,dilations:a,kernelShape:i},r);e.compute(Qc(r,l,d=>n?[d[0],d[2],d[3]]:[d[0],d[1],d[3]]))},r2=(e,t)=>{Ug(e.inputs,t),e.inputs[0].dims.length===3?jg(e,t):Vg(e,e.inputs,t)}}),Gg,i2,a2,wE=ee(()=>{lt(),ze(),St(),Oe(),Gg=(e,t,n,r)=>{let i=ne.size(t),a=t.length,s=te("input",e,a),o=ke("output",e,a),l=n.dataType===6?n.getInt32Array()[0]:Number(n.getBigInt64Array()[0]),d=ne.normalizeAxis(l,a),c=u=>{let f=` i32(${s.indicesGet("inputIndices","uniforms.axis")}) `,h=Ce("uniforms.input_shape","uniforms.axis",a),g=r.reverse?f+(r.exclusive?" + 1":""):"0",$=r.reverse?h:f+(r.exclusive?"":" + 1");return`
                ${u.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(s,o)}
                ${u.mainStart()}
                  ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
                  var inputIndices = ${o.offsetToIndices("global_idx")};
                  var sum = ${o.type.value}(0);
                  let first : i32 = ${g};
                  let last : i32 = ${$};
                  for (var i : i32 = first; i < last; i++) {
                    ${s.indicesSet("inputIndices","uniforms.axis","u32(i)")};
                    sum = sum + ${s.getByIndices("inputIndices")};
                  }
                  ${o.setByOffset("global_idx","sum")};
                }`};return{name:"CumSum",shaderCache:{hint:r.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:"uint32",data:i},{type:"int32",data:d},...le(t),...le(t)]}),getShaderSource:c}},i2=(e,t)=>{let n=e.inputs[0].dims,r=e.inputs[0].dataType,i=e.inputs[1];e.compute(Gg(r,n,i,t),{inputs:[0]})},a2=e=>{let t=e.exclusive===1,n=e.reverse===1;return tt({exclusive:t,reverse:n})}}),Yo,Ka,Ed,Hg,qg,Kg,Yg,kd,Qg,s2,o2,_E=ee(()=>{ze(),St(),Oe(),Yo="[a-zA-Z]|\\.\\.\\.",Ka="("+Yo+")+",Ed="^"+Ka+"$",Hg="("+Ka+",)*"+Ka,qg="^"+Hg+"$",Kg=class{constructor(e=-1){this.symbolToIndices=new Map,this.inputIndex=e}addSymbol(e,t){let n=this.symbolToIndices.get(e);n===void 0?n=[t]:n.push(t),this.symbolToIndices.set(e,n)}},Yg=class{constructor(e,t){var i;this.equation=t,this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[n,r]=t.includes("->")?t.split("->",2):[t,""];if(!n.match(RegExp(qg)))throw new Error("Invalid LHS term");if(n.split(",").forEach((a,s)=>{let o=e[s].dims.slice();if(!a.match(RegExp(Ed)))throw new Error("Invalid LHS term");let l=this.processTerm(a,!0,o,s);this.lhs.push(l)}),r==="")r+=[...this.symbolToInfo.entries()].filter(([a,s])=>s.count===1||a==="...").map(([a])=>a).join("");else if(!r.match(RegExp(Ka)))throw new Error("Invalid RHS");(i=r.match(RegExp(Yo,"g")))==null||i.forEach(a=>{if(a==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let s=this.symbolToInfo.get(a);if(s===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(s.dimValue)}}),this.rhs=this.processTerm(r,!1,this.outputDims)}addSymbol(e,t,n){let r=this.symbolToInfo.get(e);if(r!==void 0){if(r.dimValue!==t&&r.count!==1)throw new Error("Dimension mismatch");r.count++,r.inputIndices.push(n)}else r={count:1,dimValue:t,inputIndices:[n]};this.symbolToInfo.set(e,r)}processTerm(e,t,n,r=-1){let i=n.length,a=!1,s=[],o=0;if(!e.match(RegExp(Ed))&&!t&&e!=="")throw new Error("Invalid LHS term");let l=e.match(RegExp(Yo,"g")),d=new Kg(r);return l==null||l.forEach((c,u)=>{if(c==="..."){if(a)throw new Error("Only one ellipsis is allowed per input term");a=!0;let f=i-l.length+1;if(f<0)throw new Error("Ellipsis out of bounds");if(s=n.slice(o,o+f),this.hasEllipsis){if(this.ellipsisDims.length!==s.length||this.ellipsisDims.toString()!==s.toString())throw new Error("Ellipsis dimensions mismatch")}else if(t)this.hasEllipsis=!0,this.ellipsisDims=s;else throw new Error("Ellipsis must be specified in the LHS");for(let h=0;h<s.length;h++){let g=String.fromCharCode(48+h);d.addSymbol(g,u+h),this.addSymbol(g,n[o++],r)}}else d.addSymbol(c,u+(this.hasEllipsis?this.ellipsisDims.length-1:0)),this.addSymbol(c,n[o++],r)}),d}},kd=e=>e+"_max",Qg=(e,t,n,r,i)=>{let a=t.map((f,h)=>e[h]?f.length:f).map((f,h)=>te(`input${h}`,n,f)),s=ne.size(i),o=xt(i.length),l=o?i.length:i,d=ke("output",n,l),c=[...r.symbolToInfo.keys()].filter(f=>!r.rhs.symbolToIndices.has(f)),u=f=>{let h=[],g="var prod = 1.0;",$="var sum = 0.0;",E="sum += prod;",y=[],_=[],v=[],b=[],C=r.symbolToInfo.size===r.rhs.symbolToIndices.size;r.symbolToInfo.forEach((O,M)=>{var W;if(r.rhs.symbolToIndices.has(M)){let G=(W=r.rhs.symbolToIndices.get(M))==null?void 0:W[0];G!==void 0&&r.lhs.forEach((z,K)=>{if(O.inputIndices.includes(K)){let ae=z.symbolToIndices.get(M);if(ae===void 0)throw new Error("Invalid symbol error");ae.forEach(ue=>{h.push(`${a[K].indicesSet(`input${K}Indices`,ue,d.indicesGet("outputIndices",G))}`)})}})}else r.lhs.forEach((G,z)=>{if(O.inputIndices.includes(z)){let K=G.symbolToIndices.get(M);if(K===void 0)throw new Error("Invalid symbol error");K.forEach(ae=>{y.push(`${a[z].indicesSet(`input${z}Indices`,ae,`${M}`)}`)}),b.push(`prod *= ${a[z].getByIndices(`input${z}Indices`)};`)}}),_.push(`for(var ${M}: u32 = 0; ${M} < uniforms.${kd(M)}; ${M}++) {`),v.push("}")});let I=C?[...h,`let sum = ${a.map((O,M)=>O.getByIndices(`input${M}Indices`)).join(" * ")};`]:[...h,$,..._,...y,g,...b,E,...v];return`
            ${f.registerUniforms(c.map(O=>({name:`${kd(O)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...a,d)}

            ${f.mainStart()}
            ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
            var outputIndices = ${d.offsetToIndices("global_idx")};
            ${a.map((O,M)=>`var input${M}Indices: ${a[M].type.indices};`).join(`
`)}
            ${I.join(`
`)};
            ${d.setByOffset("global_idx","sum")};
          }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:e.map(f=>f?"rank":"dims")},getRunData:()=>{let f=c.filter(g=>r.symbolToInfo.has(g)).map(g=>{var $;return{type:"uint32",data:(($=r.symbolToInfo.get(g))==null?void 0:$.dimValue)||0}});f.push({type:"uint32",data:s});let h=t.filter((g,$)=>e[$]).map((g,$)=>[...le(g)]).reduce((g,$)=>g.concat($),f);return o&&h.push(...le(i)),{outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:h}},getShaderSource:u}},s2=(e,t)=>{let n=new Yg(e.inputs,t.equation),r=e.inputs.map((s,o)=>xt(s.dims.length)),i=n.outputDims,a=e.inputs.map((s,o)=>s.dims);e.compute(Qg(r,a,e.inputs[0].dataType,n,i))},o2=e=>{let t=e.equation.replace(/\s+/g,"");return tt({equation:t})}}),Xg,Cd,Zg,Jg,l2,vE=ee(()=>{lt(),ze(),Oe(),Xg=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,n=Array.from(e[1].getBigInt64Array(),Number),r=n.length<t.length?0:n.length-t.length,i=t.length<n.length?0:t.length-n.length;for(;r<n.length&&i<t.length;++r,++i)if(n[r]!==t[i]&&n[r]!==1&&t[i]!==1)throw new Error("Expand requires shape to be broadcastable to input")},Cd=(e,t)=>{let n=e.length-t.length,r=[];for(let i=0;i<n;++i)r.push(e[i]);for(let i=0;i<t.length;++i)r.push(t[i]===1?e[i+n]:t[i]);return r},Zg=(e,t)=>e.length>t.length?Cd(e,t):Cd(t,e),Jg=e=>{let t=e[0].dims,n=Array.from(e[1].getBigInt64Array(),Number),r=Zg(t,n),i=e[0].dataType,a=i===9?4:1,s=Math.ceil(ne.size(r)/a),o=xt(t.length),l=xt(r.length),d=u=>{let f=o?t.length:t,h=l?r.length:r,g=te("input",i,f,a),$=ke("output",i,h,a),E;if(i===9){let y=(_,v,b="")=>`
          let outputIndices${v} = ${$.offsetToIndices(`outputOffset + ${v}u`)};
          let offset${v} = ${g.broadcastedIndicesToOffset(`outputIndices${v}`,$)};
          let index${v} = offset${v} / 4u;
          let component${v} = offset${v} % 4u;
          ${_}[${v}] = ${b}(${g.getByOffset(`index${v}`)}[component${v}]);
        `;E=`
        let outputOffset = global_idx * ${a};
        var data = vec4<u32>(0);
        ${y("data",0,"u32")}
        ${y("data",1,"u32")}
        ${y("data",2,"u32")}
        ${y("data",3,"u32")}
        ${$.setByOffset("global_idx","data")}
      }`}else E=`
        let outputIndices = ${$.offsetToIndices("global_idx")};
        let inputOffset = ${g.broadcastedIndicesToOffset("outputIndices",$)};
        ${$.setByOffset("global_idx",g.getByOffset("inputOffset"))}
      }`;return`
    ${u.registerUniform("vec_size","u32").declareVariables(g,$)}
    ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}
    ${E}`},c=[{type:"uint32",data:s}];return o&&c.push(...le(t)),l&&c.push(...le(r)),{name:"Expand",shaderCache:{hint:`${r.length}`,inputDependencies:[o?"rank":"dims"]},getShaderSource:d,getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:c})}},l2=e=>{Xg(e.inputs),e.compute(Jg(e.inputs),{inputs:[0]})}}),e0,t0,u2,d2,$E=ee(()=>{lt(),ze(),St(),Oe(),e0=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},t0=(e,t)=>{let n=e[0].dims,r=e[1].dims,i=n.length,a=ne.normalizeAxis(t.axis,i),s=n.slice(0);s.splice(a,1,...r);let o=n[a],l=e[0].dataType===9?4:1,d=Math.ceil(ne.size(s)/l),c=xt(e[0].dims.length),u=c?e[0].dims.length:e[0].dims,f=xt(e[1].dims.length),h=f?e[1].dims.length:e[1].dims,g=xt(s.length),$=g?s.length:s,E=[{type:"uint32",data:d},{type:"int32",data:o},{type:"uint32",data:a}];c&&E.push(...le(e[0].dims)),f&&E.push(...le(e[1].dims)),g&&E.push(...le(s));let y=[];y.push(c?"rank":"dims"),y.push(f?"rank":"dims");let _=v=>{let b=te("data",e[0].dataType,u,l),C=te("inputIndices",e[1].dataType,h),I=ke("output",e[0].dataType,$,l),O=W=>{let G=r.length,z=`var indicesIndices${W}  = ${C.type.indices}(0);`;for(let K=0;K<G;K++)z+=`${G>1?`indicesIndices${W}[${K}]`:`indicesIndices${W}`} = ${s.length>1?`outputIndices${W}[uniforms.axis + ${K}]`:`outputIndices${W}`};`;z+=`
          var idx${W} = ${C.getByIndices(`indicesIndices${W}`)};
          if (idx${W} < 0) {
            idx${W} = idx${W} + uniforms.axisDimLimit;
          }
          var dataIndices${W} = ${b.type.indices}(0);
        `;for(let K=0,ae=0;K<i;K++)K===a?(z+=`${i>1?`dataIndices${W}[${K}]`:`dataIndices${W}`} = u32(idx${W});`,ae+=G):(z+=`${i>1?`dataIndices${W}[${K}]`:`dataIndices${W}`} = ${s.length>1?`outputIndices${W}[${ae}]`:`outputIndices${W}`};`,ae++);return z},M;if(e[0].dataType===9){let W=(G,z,K="")=>`
          let outputIndices${z} = ${I.offsetToIndices(`outputOffset + ${z}u`)};
          ${O(z)};
          let offset${z} = ${b.indicesToOffset(`dataIndices${z}`)};
          let index${z} = offset${z} / 4u;
          let component${z} = offset${z} % 4u;
          ${G}[${z}] = ${K}(${b.getByOffset(`index${z}`)}[component${z}]);
        `;M=`
        let outputOffset = global_idx * ${l};
        var value = vec4<u32>(0);
        ${W("value",0,"u32")}
        ${W("value",1,"u32")}
        ${W("value",2,"u32")}
        ${W("value",3,"u32")}
        ${I.setByOffset("global_idx","value")}
      `}else M=`
      let outputIndices = ${I.offsetToIndices("global_idx")};
      ${O("")};
      let value = ${b.getByIndices("dataIndices")};
      ${I.setByOffset("global_idx","value")};
      `;return`
      ${v.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(b,C,I)}
      ${v.mainStart()}
        ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        ${M}
      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:y},getRunData:()=>({outputs:[{dims:s,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:E}),getShaderSource:_}},u2=e=>tt({axis:e.axis}),d2=(e,t)=>{let n=e.inputs;e0(n),e.compute(t0(e.inputs,t))}}),n0,r0,c2,p2,bE=ee(()=>{ze(),St(),Oe(),n0=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and
                     indices input tensors be of same rank.`)},r0=(e,t)=>{let n=e[0].dims,r=e[0].dataType,i=n.length,a=e[1].dims,s=e[1].dataType,o=ne.normalizeAxis(t.axis,i),l=n[o],d=a.slice(0),c=ne.size(d),u=te("input",r,i),f=te("indicesInput",s,a.length),h=ke("output",r,d.length),g=[{type:"uint32",data:c},{type:"int32",data:l},{type:"uint32",data:o}];return g.push(...le(n)),g.push(...le(a)),g.push(...le(d)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:d,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:g}),getShaderSource:$=>`
      ${$.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(u,f,h)}
      ${$.mainStart()}
      ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

      let outputIndices = ${h.offsetToIndices("global_idx")};

      var idx = ${f.getByOffset("global_idx")};
      if (idx < 0) {
        idx = idx + uniforms.axisDimLimit;
      }
      var inputIndices = ${u.type.indices}(outputIndices);
      ${u.indicesSet("inputIndices","uniforms.axis","u32(idx)")};
      let value = ${u.getByIndices("inputIndices")};

      ${h.setByOffset("global_idx","value")};
  }`}},c2=e=>tt({axis:e.axis}),p2=(e,t)=>{let n=e.inputs;n0(n),e.compute(r0(e.inputs,t))}}),i0,a0,f2,h2,xE=ee(()=>{ze(),Oe(),i0=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},a0=(e,t)=>{let n=e[0].dims.slice(),r=e[1].dims.slice(),[i,a,s]=Iv.getShapeOfGemmResult(n,t.transA,r,t.transB,e.length===3?e[2].dims:void 0),o=[i,a];if(!o)throw new Error("Can't use gemm on the given tensors");let l=ne.size(o),d=[{type:"uint32",data:l},{type:"uint32",data:i},{type:"uint32",data:a},{type:"uint32",data:s},{type:"float32",data:t.alpha},{type:"float32",data:t.beta}],c=["type","type"];e.length===3&&(d.push(...le(e[2].dims)),c.push("rank")),d.push(...le(o));let u=f=>{let h="";t.transA&&t.transB?h="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?h="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?h="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(h="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let g=t.alpha===1?"":"value *= uniforms.alpha;",$=te("a",e[0].dataType,e[0].dims),E=te("b",e[1].dataType,e[1].dims),y=$.type.value,_=null,v=[$,E];e.length===3&&(_=te("c",e[2].dataType,e[2].dims.length),v.push(_));let b=ke("output",e[0].dataType,o.length);v.push(b);let C=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`
  ${f.registerUniforms(C).declareVariables(...v)}

  ${f.mainStart()}
    ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

    let m = global_idx / uniforms.N;
    let n = global_idx % uniforms.N;

    var value = ${y}(0);
    for (var k: u32 = 0u; k < uniforms.K; k++) {
      ${h}
    }

    ${g}
    ${_!=null?`let cOffset = ${_.broadcastedIndicesToOffset("vec2(m, n)",b)}; value += ${y}(uniforms.beta) * ${_.getByOffset("cOffset")};`:""}
    output[global_idx] = value;
  }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:u}},f2=e=>{let t=e.transA,n=e.transB,r=e.alpha,i=e.beta;return{transA:t,transB:n,alpha:r,beta:i,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},h2=(e,t)=>{i0(e.inputs),e.compute(a0(e.inputs,t))}}),s0,o0,l0,m2,SE=ee(()=>{lt(),ze(),Oe(),s0=(e,t)=>{let n=e[0].dims,r=n,i=2,a=ne.sizeToDimension(n,i),s=ne.sizeFromDimension(n,i),o=rn(s),l=s/o,d=[n[0],n[1],l],c=["rank","type","type"],u=[{type:"uint32",data:s},{type:"uint32",data:l}];u.push(...le(d),...le(d));let f=h=>{let g=te("x",e[0].dataType,d.length,o),$=te("scale",e[1].dataType,e[1].dims),E=te("bias",e[2].dataType,e[2].dims),y=ke("output",e[0].dataType,d.length,o),_=[g,$,E,y],v=g.type.value,b=o===1?"f32":`vec${o}<f32>`,C=64,I=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`
  var<workgroup> meanShared : f32;
  var<workgroup> squaredNormShared : f32;
  var<workgroup> workgroupShared : array<${b}, ${C}>;
  const workgroupSize = ${C}u;
  ${h.registerUniforms(I).declareVariables(..._)}
  ${h.mainStart(C)}
    let norm = global_idx / workgroupSize;
    let batch = norm / uniforms.x_shape[1];
    let channel = norm % uniforms.x_shape[1];
    let localIndex = local_id.x;

    // initialize workgroup memory
    var initial = ${b}(0);
    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
      initial = initial + ${b}(${g.get("batch","channel","h")});
    }
    workgroupShared[localIndex] = initial;
    workgroupBarrier();

    // Calculate the mean of current channel data.
    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {
      if (localIndex < currSize) {
        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];
      }
      workgroupBarrier();
    }
    if (localIndex == 0) {
      meanShared = ${Zn("workgroupShared[0]",o)} / f32(uniforms.normSize);
    }
    workgroupBarrier();

    // reinitialize workgroup memory.
    initial = ${b}(0);
    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
      let deviation =  ${b}(${g.get("batch","channel","h")}) - ${b}(meanShared);
      initial = initial + deviation * deviation;
    }
    workgroupShared[localIndex] = initial;
    workgroupBarrier();

    // Calculate the sum of square of deviation of current channel data.
    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {
      if (localIndex < currSize) {
        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];
      }
      workgroupBarrier();
    }
    if (localIndex == 0) {
      squaredNormShared = ${Zn("workgroupShared[0]",o)};
    }
    workgroupBarrier();

    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));
    let channelScale = invStdDev * f32(${$.getByOffset("channel")});
    let channelShift = f32(${E.getByOffset("channel")}) - meanShared * channelScale;
    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
      let value = ${g.get("batch","channel","h")} * ${v}(${b}(channelScale)) + ${v}(${b}(channelShift));
      ${y.set("batch","channel","h","value")};
    }
  }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${o}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:a},programUniforms:u}),getShaderSource:f}},o0=(e,t,n,r,i,a,s,o)=>{let l=rn(s),d=64,c=l===1?"vec2f":`mat2x${l}f`,u=l===1?"f32":`vec${l}f`,f=(I,O)=>`${c}(${I}, ${O})`,h=i*s/l,g=Math.ceil(a/d),$=["type"],E=[{type:"uint32",data:g},{type:"uint32",data:a},{type:"uint32",data:Math.floor(s/l)},{type:"uint32",data:Math.floor(a*s/l)}],y=I=>{let O=te("input",t.dataType,t.dims,l);return`
  ${I.declareVariables(O)}
  @group(0) @binding(1) var<storage, read_write> output : array<${c}>;
  struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};
  @group(0) @binding(2) var<uniform> uniforms: Uniforms;

  ${I.mainStart(d)}
    let currentImageNumber = global_idx / ${d} / uniforms.C;
    let currentChannelNumber = (global_idx / ${d}) % uniforms.C;
    let wgId = global_idx % ${d};
    let wgOffset = wgId * uniforms.wg_size;
    if (wgOffset >= uniforms.H) {
        return;
    }
    let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);

    let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;
    var sum = ${tn("f32",l)};
    var squaredSum = ${tn("f32",l)};
    for (var i: u32 = wgOffset; i < wgMax; i++) {
        let value = ${u}(input[offset + i * uniforms.C]);
        sum += value;
        squaredSum += value * value;
    }
    output[global_idx] = ${f("sum","squaredSum")};
  }`},_=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${l}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:[i,s,d,2],dataType:1}],dispatchGroup:{x:i*s/l},programUniforms:E}),getShaderSource:y},{inputs:[t],outputs:[-1]})[0],v=[{type:"uint32",data:h},{type:"uint32",data:a},{type:"uint32",data:Math.floor(s/l)},{type:"uint32",data:Math.floor(d*s/l)}],b=["type","type","type"],C=I=>{let O=te("scale",n.dataType,n.dims,l),M=te("bias",r.dataType,r.dims,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${c}>;
  @group(0) @binding(1) var<storage, read> scale : array<${O.type.storage}>;
  @group(0) @binding(2) var<storage, read> bias : array<${M.type.storage}>;
  @group(0) @binding(3) var<storage, read_write> output : array<${c}>;
  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};
  @group(0) @binding(4) var<uniform> uniforms: Uniforms;

  ${I.mainStart()}
    ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}
    let currentImageNumber = global_idx / uniforms.C;
    let currentChannelNumber = global_idx % uniforms.C;

    let offset = currentImageNumber * uniforms.image_size;
    var sum = ${tn("f32",l)};
    var squaredSum = ${tn("f32",l)};
    for (var i: u32 = 0; i < ${d}; i++) {
        let value = input[offset + i + currentChannelNumber * ${d}];
        sum += value[0];
        squaredSum += value[1];
    }
    sum = sum / f32(uniforms.H);
    squaredSum = squaredSum / f32(uniforms.H);
    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${o}));
    let channelScale = invStdDev * ${u}(scale[currentChannelNumber]);
    let channelShift = ${u}(bias[currentChannelNumber]) - sum * channelScale;

    output[global_idx] = ${f("channelScale","channelShift")};
  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${l};${o}`,inputDependencies:b},getRunData:()=>({outputs:[{dims:[i,s,2],dataType:1}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:v}),getShaderSource:C},{inputs:[_,n,r],outputs:[-1]})[0]},l0=(e,t,n)=>{let r=t[0].dims,i=r,a=r[0],s=r[r.length-1],o=ne.sizeFromDimension(r,1)/s,l=rn(s),d=ne.size(i)/l,c=[{type:"uint32",data:o},{type:"uint32",data:Math.floor(s/l)}],u=["type","type"],f=o0(e,t[0],t[1],t[2],a,o,s,n.epsilon),h=g=>{let $=wn(t[0].dataType),E=l===1?"vec2f":`mat2x${l}f`,y=l===1?$:`vec${l}<${$}>`,_=te("input",t[0].dataType,t[0].dims,l),v=ke("output",t[0].dataType,i,l);return`
  @group(0) @binding(0) var<storage, read> input : array<${_.type.storage}>;
  @group(0) @binding(1) var<storage, read> scaleInput : array<${E}>;
  @group(0) @binding(2) var<storage, read_write> output : array<${v.type.storage}>;
  struct Uniforms {H: u32, C : u32};
  @group(0) @binding(3) var<uniform> uniforms: Uniforms;

  ${g.mainStart()}
    let currentImageNumber = global_idx / (uniforms.C * uniforms.H);
    let currentChannelNumber = global_idx % uniforms.C;

    let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;
    let scale = scaleInput[scaleOffset];
    output[global_idx] = fma(input[global_idx], ${y}(scale[0]), ${y}(scale[1]));
  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${l}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:i,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:h},{inputs:[t[0],f]})},m2=(e,t)=>{t.format==="NHWC"?l0(e,e.inputs,t):e.compute(s0(e.inputs,t))}}),u0,d0,g2,EE=ee(()=>{lt(),ze(),Oe(),u0=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},d0=(e,t,n)=>{let r=e[0].dims,i=e[1],a=e[2],s=r,o=ne.normalizeAxis(t.axis,r.length),l=ne.sizeToDimension(r,o),d=ne.sizeFromDimension(r,o),c=ne.size(i.dims),u=a?ne.size(a.dims):0;if(c!==d||a&&u!==d)throw new Error(`Size of X.shape()[axis:] == ${d}.
       Size of scale and bias (if provided) must match this.
       Got scale size of ${c} and bias size of ${u}`);let f=[];for(let b=0;b<r.length;++b)b<o?f.push(r[b]):f.push(1);let h=rn(d),g=["type","type"],$=[{type:"uint32",data:l},{type:"float32",data:d},{type:"uint32",data:Math.floor(d/h)},{type:"float32",data:t.epsilon}];a&&g.push("type");let E=n>1,y=n>2,_=b=>{let C=wn(e[0].dataType),I=[te("x",e[0].dataType,e[0].dims,h),te("scale",i.dataType,i.dims,h)];a&&I.push(te("bias",a.dataType,a.dims,h)),I.push(ke("output",e[0].dataType,s,h)),E&&I.push(ke("mean_data_output",1,f)),y&&I.push(ke("inv_std_output",1,f));let O=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`
  ${b.registerUniforms(O).declareVariables(...I)}
  ${b.mainStart()}
    ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}
    let offset = global_idx * uniforms.norm_size_vectorized;
    var meanVector = ${tn("f32",h)};
    var meanSquareVector = ${tn("f32",h)};

    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {
      let value = ${ur(C,h,"x[h + offset]")};
      meanVector += value;
      meanSquareVector += value * value;
    }
    let mean = ${Zn("meanVector",h)} / uniforms.norm_size;
    let invStdDev =
        inverseSqrt(${Zn("meanSquareVector",h)} / uniforms.norm_size - mean * mean + uniforms.epsilon);

    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {
      let f32input = ${ur(C,h,"x[j + offset]")};
      let f32scale = ${ur(C,h,"scale[j]")};
      output[j + offset] = ${I[0].type.value}((f32input - mean) * invStdDev * f32scale
        ${a?`+ ${ur(C,h,"bias[j]")}`:""}
      );
    }

    ${E?"mean_data_output[global_idx] = mean":""};
    ${y?"inv_std_output[global_idx] = invStdDev":""};
  }`},v=[{dims:s,dataType:e[0].dataType}];return E&&v.push({dims:f,dataType:1}),y&&v.push({dims:f,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${h};${n}`,inputDependencies:g},getRunData:()=>({outputs:v,dispatchGroup:{x:Math.ceil(l/64)},programUniforms:$}),getShaderSource:_}},g2=(e,t)=>{u0(e.inputs),e.compute(d0(e.inputs,t,e.outputCount))}}),c0,y2,Td,p0,Qo,w2,kE=ee(()=>{ze(),St(),pf(),n1(),Oe(),qs(),c0=(e,t)=>{let n=e[0],r=e[1],i=e[2],a=e[3],s=e[4],o=e[5],l=e[6],d=e[7];if(n.dims.length!==3&&n.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let c=!1,u=n.dims[0],f=n.dims[1],h=n.dims.length===3?c?n.dims[2]/3:n.dims[2]:t.numHeads*n.dims[4],g=f,$=0,E=0,y=Math.floor(h/t.numHeads);if(l&&d){if(l.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');$=l.dims[2],E=l.dims[2]}else if(l||d)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let _;if(r){if(n.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(r.dims.length<3||r.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(n.dims[0]!==r.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(r.dims.length===3){if(r.dims[2]!==n.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');_=2,g=r.dims[1]}else if(r.dims.length===5){if(r.dims[2]!==t.numHeads||r.dims[3]!==2||r.dims[4]!==y)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(i)throw new Error('Expect "value" be none when "key" has packed kv format.');_=5,g=r.dims[1]}else{if(r.dims[1]!==t.numHeads||r.dims[3]!==y)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');_=0,g=r.dims[2]}}else{if(n.dims.length!==3&&n.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(n.dims.length===5&&(n.dims[2]!==t.numHeads||n.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');_=3}if(a){if(a.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(i&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let v=0;if(s){v=8;let M=s.dims;throw M.length===1?M[0]===u?v=1:M[0]===3*u+2&&(v=3):M.length===2&&M[0]===u&&M[1]===g&&(v=5),v===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)'):new Error("Mask not supported")}let b=!1,C=h;if(i){if(i.dims.length!==3&&i.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(n.dims[0]!==i.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(i.dims.length===3){if(g!==i.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');C=i.dims[2]}else{if(g!==i.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');C=i.dims[1]*i.dims[3],b=!0}}let I=$+g,O=!1;if(s)throw new Error("Key padding mask is not supported");if(o)throw new Error("extraAddQk is not supported");if(l)throw new Error("pastKey is not supported");if(d)throw new Error("pastValue is not supported");return{batchSize:u,sequenceLength:f,pastSequenceLength:$,kvSequenceLength:g,totalSequenceLength:I,maxSequenceLength:E,inputHiddenSize:0,hiddenSize:h,vHiddenSize:C,headSize:y,vHeadSize:Math.floor(C/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:v,scale:t.scale,broadcastResPosBias:O,passPastInKv:b,qkvFormat:_}},y2=e=>tt({...e}),Td=tt({perm:[0,2,1,3]}),p0=(e,t,n,r,i,a,s)=>{let o=[r,i,a],l=ne.size(o),d=[{type:"uint32",data:l},{type:"uint32",data:s},{type:"uint32",data:a}],c=u=>{let f=ke("qkv_with_bias",t.dataType,o),h=te("qkv",t.dataType,o),g=te("bias",n.dataType,o),$=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`
  ${u.registerUniforms($).declareVariables(h,g,f)}
  ${u.mainStart()}
    ${u.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;

    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];
  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:o,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(l/64)},programUniforms:d}),getShaderSource:c},{inputs:[t,n],outputs:[-1]})[0]},Qo=(e,t,n,r,i,a,s,o)=>{let l=a;if(s){if(r===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return l=p0(e,a,s,t,r,n*i,o),l=l.reshape([t,r,n,i]),e.compute(Gr(l,Td.perm),{inputs:[l],outputs:[-1]})[0]}else return a.dims.length===3&&(l=a.reshape([t,r,n,i])),e.compute(Gr(l,Td.perm),{inputs:[l],outputs:[-1]})[0]},w2=(e,t)=>{var o;let n=c0(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(((o=e.inputs[1])==null?void 0:o.dims.length)===5)throw new Error("Packed KV is not implemented");let r=e.inputs[1]&&e.inputs[2]&&e.inputs[1].dims.length===4&&e.inputs[2].dims.length===4,i=Qo(e,n.batchSize,n.numHeads,n.sequenceLength,n.headSize,e.inputs[0],e.inputs[3],0);if(r)return Zl(e,i,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],n,t);let a=Qo(e,n.batchSize,n.numHeads,n.kvSequenceLength,n.headSize,e.inputs[1],e.inputs[3],n.hiddenSize),s=Qo(e,n.batchSize,n.numHeads,n.kvSequenceLength,n.vHeadSize,e.inputs[2],e.inputs[3],2*n.hiddenSize);Zl(e,i,a,s,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],n,t)}}),f0,h0,m0,g0,y0,w0,_0,v0,_2,CE=ee(()=>{lt(),ze(),Oe(),f0=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1)throw new Error("Input type must be float.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},h0=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`
            k = i32(${e.indicesGet("indices",i)}) - ${Ce("uniforms.pads",i,n)};
            if (k < 0) {
              break;
            }
            if (k >= i32(${Ce("uniforms.x_shape",i,t)})) {
              break;
            }
            offset += k * i32(${Ce("uniforms.x_strides",i,t)});
        `;return`
          value = ${e.type.value}(uniforms.constant_value);
          for (var i = 0; i < 1; i++) {
            var offset = 0;
            var k = 0;
            ${r}
            value = x[offset];
          }
      `},m0=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`
                k = i32(${e.indicesGet("indices",i)}) - ${Ce("uniforms.pads",i,n)};
                if (k < 0) {
                  k = -k;
                }
                {
                  let _2n_1 = 2 * (i32(${Ce("uniforms.x_shape",i,t)}) - 1);
                  k = k % _2n_1;
                  if(k >= i32(${Ce("uniforms.x_shape",i,t)})) {
                    k = _2n_1 - k;
                  }
                }
                offset += k * i32(${Ce("uniforms.x_strides",i,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${r}
              value = x[offset];
          `},g0=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`
                k = i32(${e.indicesGet("indices",i)}) - ${Ce("uniforms.pads",i,n)};
                if (k < 0) {
                  k = 0;
                }
                if (k >= i32(${Ce("uniforms.x_shape",i,t)})) {
                  k = i32(${Ce("uniforms.x_shape",i,t)}) - 1;
                }
                offset += k * i32(${Ce("uniforms.x_strides",i,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${r}
              value = x[offset];
          `},y0=(e,t,n)=>{let r="";for(let i=t-1;i>=0;--i)r+=`
                k = i32(${e.indicesGet("indices",i)}) - ${Ce("uniforms.pads",i,n)};
                if (k < 0)  {
                  k += i32(${Ce("uniforms.x_shape",i,t)}]);
                }
                if (k >= i32(${Ce("uniforms.x_shape",i,t)})) {
                  k -= i32(${Ce("uniforms.x_shape",i,t)});
                }
                offset += k * i32(${Ce("uniforms.x_strides",i,t)});
            `;return`
              var offset = 0;
              var k = 0;
              ${r}
              value = x[offset];
          `},w0=(e,t,n)=>{switch(n.mode){case 0:return h0(e,t,n.pads.length);case 1:return m0(e,t,n.pads.length);case 2:return g0(e,t,n.pads.length);case 3:return y0(e,t,n.pads.length);default:throw new Error("Invalid mode")}},_0=(e,t)=>{let n=ne.padShape(e[0].dims.slice(),t.pads),r=e[0].dims,i=[{type:"uint32",data:ne.size(n)},{type:"uint32",data:t.pads}];if(t.mode===0){let o=Kn(e[0].dataType);i.push({type:o,data:t.value})}i.push(...le(e[0].dims),...le(n));let a=["rank"],s=o=>{let l=ke("output",e[0].dataType,n.length),d=te("x",e[0].dataType,r.length),c=d.type.value,u=w0(l,r.length,t),f=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&f.push({name:"constant_value",type:c}),`
            ${o.registerUniforms(f).declareVariables(d,l)}
            ${o.mainStart()}
            ${o.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}

            let indices = ${l.offsetToIndices("global_idx")};

            var value = ${c}(0);
            ${u}
            output[global_idx] = value;
        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:a},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(ne.size(n)/64)},programUniforms:i}),getShaderSource:s}},v0=(e,t)=>{if(e.length>1){let n=e[1].getBigInt64Array(),r=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,i=e[0].dims.length,a=new Int32Array(2*i).fill(0);if(e.length>=4){let o=e[3].getBigInt64Array();for(let l=0;l<o.length;l++)a[Number(o[l])]=Number(n[l]),a[Number(o[l])+i]=Number(n[l+o.length])}else n.forEach((o,l)=>a[Number(l)]=Number(o));let s=[];return a.forEach(o=>s.push(o)),{mode:t.mode,value:r,pads:s}}else return t},_2=(e,t)=>{f0(e.inputs);let n=v0(e.inputs,t);e.compute(_0(e.inputs,n),{inputs:[0]})}}),Ya,Id,Ad,Od,Md,$0,b0,zd,Rd,v2,$2,Pd,b2,x2,Bd,S2,E2,k2,C2,TE=ee(()=>{Fn(),ze(),Oe(),Ya=e=>{if(je.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Id=(e,t,n)=>{let r=t.format==="NHWC",i=e.dims.slice();r&&i.splice(1,0,i.pop());let a=Object.hasOwnProperty.call(t,"dilations"),s=t.kernelShape.slice(),o=t.strides.slice(),l=a?t.dilations.slice():[],d=t.pads.slice();Ql.adjustPoolAttributes(n,i,s,o,l,d);let c=Ql.computePoolOutputShape(n,i,o,l,s,d,t.autoPad),u=Object.assign({},t);a?Object.assign(u,{kernelShape:s,strides:o,pads:d,dilations:l,cacheKey:t.cacheKey}):Object.assign(u,{kernelShape:s,strides:o,pads:d,cacheKey:t.cacheKey});let f=c.slice();return f.push(f.splice(1,1)[0]),[u,r?f:c]},Ad=(e,t)=>{let n=t.format==="NHWC",r=ne.size(e),i=ne.size(t.kernelShape),a=[{type:"uint32",data:r},{type:"uint32",data:i}],s=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let o=t.kernelShape[t.kernelShape.length-1],l=t.strides[t.strides.length-1],d=t.pads[t.pads.length/2-1],c=t.pads[t.pads.length-1],u=!!(d+c);a.push({type:"uint32",data:o},{type:"uint32",data:l},{type:"uint32",data:d},{type:"uint32",data:c}),s.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let f=!1;if(t.kernelShape.length===2){let h=t.kernelShape[t.kernelShape.length-2],g=t.strides[t.strides.length-2],$=t.pads[t.pads.length/2-2],E=t.pads[t.pads.length-2];f=!!($+E),a.push({type:"uint32",data:h},{type:"uint32",data:g},{type:"uint32",data:$},{type:"uint32",data:E}),s.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[a,s,!0,u,f]}else{if(n)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let o=ne.computeStrides(t.kernelShape);a.push({type:"uint32",data:o},{type:"uint32",data:t.pads},{type:"uint32",data:t.strides}),s.push({name:"kernelStrides",type:"u32",length:o.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let l=t.pads.reduce((d,c)=>d+c);return[a,s,!!l,!1,!1]}},Od=(e,t,n,r,i,a,s,o,l,d,c,u)=>{let f=i.format==="NHWC",h=t.type.value,g=ke("output",t.type.tensor,r);if(i.kernelShape.length<=2){let $="",E="",y="",_=n-(f?2:1);if(c?$=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${_}] = indices[${_}] * uniforms.sw - uniforms.pwStart + i;
                  if (xIndices[${_}] < 0 || xIndices[${_}]
                      >= uniforms.x_shape[${_}]) {
                    pad++;
                    continue;
                  }
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`:$=`
                for (var i: u32 = 0u; i < uniforms.kw; i++) {
                  xIndices[${_}] = indices[${_}] * uniforms.sw - uniforms.pwStart + i;
                  let x_val = x[${t.indicesToOffset("xIndices")}];
                  ${a}
                }`,i.kernelShape.length===2){let v=n-(f?3:2);u?E=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${v}] = indices[${v}] * uniforms.sh - uniforms.phStart + j;
                  if (xIndices[${v}] < 0 || xIndices[${v}] >= uniforms.x_shape[${v}]) {
                    pad += i32(uniforms.kw);
                    continue;
                  }
              `:E=`
                for (var j: u32 = 0u; j < uniforms.kh; j++) {
                  xIndices[${v}] = indices[${v}] * uniforms.sh - uniforms.phStart + j;
                `,y=`
              }
            `}return`
            ${e.registerUniforms(l).declareVariables(t,g)}

            ${e.mainStart()}
              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}

              let indices = ${g.offsetToIndices("global_idx")};
              var xIndices = ${g.offsetToIndices("global_idx")};

              var value = ${h}(${o});
              var pad = 0;
              ${E}
              ${$}
              ${y}
              ${s}

              output[global_idx] = value;
            }`}else{if(f)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let $=i.kernelShape.length,E=i.pads.length,y="";return d?y=`
                if (xIndices[j] >= uniforms.x_shape[j]) {
                  pad++;
                  isPad = true;
                  break;
                }
              }
              if (!isPad) {
                let x_val = x[${t.indicesToOffset("xIndices")}];
                ${a}
              }`:y=`
              }
              let x_val = x[${t.indicesToOffset("xIndices")}];
              ${a}
            `,`
            ${e.registerUniforms(l).declareVariables(t,g)}

            ${e.mainStart()}
              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
              let indices = ${g.offsetToIndices("global_idx")};
              var xIndices = ${g.offsetToIndices("global_idx")};

              var offsets: array<u32, ${$}>;

              var value = ${h}(${o});
              var pad = 0;
              var isPad = false;

              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {
                var offset = i;
                for (var j = 0u; j < ${$-1}u; j++) {
                  offsets[j] = offset / ${Ce("uniforms.kernelStrides","j",$)};
                  offset -= offsets[j] * ${Ce("uniforms.kernelStrides","j",$)};
                }
                offsets[${$-1}] = offset;

                isPad = false;
                for (var j = ${n-$}u; j < ${n}u; j++) {
                  xIndices[j] = indices[j] * ${Ce("uniforms.strides",`j - ${n-$}u`,$)}
                    + offsets[j - ${n-$}u] - ${Ce("uniforms.pads","j - 2u",E)};
                  ${y}
              }
              ${s}

              output[global_idx] = value;
            }`}},Md=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,$0=e=>`${Md(e)};${e.countIncludePad}`,b0=e=>`${Md(e)};${e.storageOrder};${e.dilations}`,zd=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),Rd=(e,t,n,r)=>{let[i,a]=Id(t,r,n),s=te("x",t.dataType,t.dims.length),o=s.type.value,l="value += x_val;",d="";i.countIncludePad?d+=`value /= ${o}(uniforms.kernelSize);`:d+=`value /= ${o}(i32(uniforms.kernelSize) - pad);`;let[c,u,f,h,g]=Ad(a,i);c.push(...le(t.dims),...le(a));let $=["rank"];return{name:e,shaderCache:{hint:`${r.cacheKey};${f};${h};${g}`,inputDependencies:$},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(ne.size(a)/64)},programUniforms:c}),getShaderSource:E=>Od(E,s,t.dims.length,a.length,i,l,d,0,u,f,h,g)}},v2=e=>{let t=e.count_include_pad!==0,n=zd(e);if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let r={countIncludePad:t,...n,cacheKey:""};return{...r,cacheKey:$0(r)}},$2=(e,t)=>{Ya(e.inputs),e.compute(Rd("AveragePool",e.inputs[0],!1,t))},Pd={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},b2=e=>{let t=e.format;return{format:t,...Pd,cacheKey:t}},x2=(e,t)=>{Ya(e.inputs),e.compute(Rd("GlobalAveragePool",e.inputs[0],!0,t))},Bd=(e,t,n,r)=>{let[i,a]=Id(t,r,n),s=`
      value = max(x_val, value);
    `,o="",l=te("x",t.dataType,t.dims.length),d=["rank"],[c,u,f,h,g]=Ad(a,i);return c.push(...le(t.dims),...le(a)),{name:e,shaderCache:{hint:`${r.cacheKey};${f};${h};${g}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:a,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(ne.size(a)/64)},programUniforms:c}),getShaderSource:$=>Od($,l,t.dims.length,a.length,i,s,o,-1e5,u,f,h,g)}},S2=(e,t)=>{Ya(e.inputs),e.compute(Bd("MaxPool",e.inputs[0],!1,t))},E2=e=>{let t=e.storage_order,n=e.dilations,r=zd(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let i={storageOrder:t,dilations:n,...r,cacheKey:""};return{...i,cacheKey:b0(i)}},k2=e=>{let t=e.format;return{format:t,...Pd,cacheKey:t}},C2=(e,t)=>{Ya(e.inputs),e.compute(Bd("GlobalMaxPool",e.inputs[0],!0,t))}}),x0,S0,T2,IE=ee(()=>{Fn(),lt(),Oe(),x0=(e,t,n)=>{let r=e===t,i=e<t&&n<0,a=e>t&&n>0;if(r||i||a)throw new Error("Range these inputs' contents are invalid.")},S0=(e,t,n,r)=>{let i=Math.abs(Math.ceil((t-e)/n)),a=[i],s=i,o=Kn(r),l=[{type:"uint32",data:s},{type:o,data:e},{type:o,data:n},...le(a)],d=c=>{let u=ke("output",r,a.length),f=u.type.value,h=[{name:"outputSize",type:"u32"},{name:"start",type:f},{name:"delta",type:f}];return`
        ${c.registerUniforms(h).declareVariables(u)}
        ${c.mainStart()}
        ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
        output[global_idx] = uniforms.start + ${f}(global_idx) * uniforms.delta;
      }`};return{name:"Range",shaderCache:{hint:`${r}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:a,dataType:r}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:l})}},T2=e=>{let t=0,n=0,r=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],n=e.inputs[1].getInt32Array()[0],r=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],n=e.inputs[1].getFloat32Array()[0],r=e.inputs[2].getFloat32Array()[0]),je.webgpu.validateInputContent&&x0(t,n,r),e.compute(S0(t,n,r,e.inputs[0].dataType),{inputs:[]})}}),E0,k0,C0,T0,I0,A0,O0,M0,z0,R0,P0,Dd,B0,D0,N0,L0,F0,I2,A2,AE=ee(()=>{ze(),St(),Oe(),E0=(e,t)=>{if(e.every(n=>n>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and
            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},k0=(e,t,n)=>{t.every(i=>i>=0&&i<n||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let r=new Array(n).fill(1);return t.forEach((i,a)=>r[i]=e[a]),r},C0=(e,t,n,r,i,a)=>{let[s,o,l]=n>10?[1,2,3]:[-1,e.length>1?1:-1,-1],d=e[0].dims.length;if(s>0&&e.length>s&&e[s].dims.length>0)e[s].getFloat32Array().forEach(c=>a.push(c));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(o>0&&e.length>o&&e[o].dims.length>0){if(e[o].getFloat32Array().forEach(c=>r.push(c)),r.length!==0&&r.length!==d&&n>=18&&r.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");E0(r,t),t.axes.length>0&&k0(r,t.axes,d).forEach((c,u)=>r[u]=c)}if(l>0&&e.length>l&&(e[l].getBigInt64Array().forEach(c=>i.push(Number(c))),i.length!==d||n>=18&&i.length===t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(r.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(i.length!==t.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof r<"u"&&typeof i<"u"&&r.length>0&&i.length>d)throw new Error("Resize requires only of scales or sizes to be specified")},T0=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,
     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {
                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;
                  } else {
                    return 0.0;
                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {
                    return 0.0;
                  } else {
                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating
                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an
                    // offset-by-one error later in floor().
                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));
                    let fract =
                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);
                    return whole + fract;
                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {
                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +
                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /
                        ${t}(lengthResized - 1);
                  } else {
                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);
                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);
                  const adjustment = ${t}(lengthResized) / outputWidth;
                  const center = ${t}(lengthOriginal) / 2;
                  const offset = center * (1 - adjustment);
                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",I0=(e,t,n)=>`fn getNearestPixelFromOriginal(xOriginal: ${n}, isDownSample: bool) -> ${n} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",A0=(e,t,n)=>{let r=new Array(n).fill(0).concat(new Array(n).fill(1)),i=e.length===0?r:e.slice();return t.length>0?(t.forEach((a,s)=>{r[a]=i[s],r[s+n]=i[t.length+s]}),r):i},O0=(e,t,n,r)=>{let i=[];if(n.length>0)if(r.length>0){if(e.forEach(a=>i.push(a)),Math.max(...r)>e.length)throw new Error("axes is out of bound");r.forEach((a,s)=>i[a]=n[s])}else n.forEach(a=>i.push(a));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");i=e.map((a,s)=>Math.round(a*t[s]))}return i},M0=(e,t,n)=>{let r=(()=>{switch(n.keepAspectRatioPolicy){case"not_larger":return n.axes.length>0?Math.min(...n.axes.map(a=>t[a]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return n.axes.length>0?Math.max(...n.axes.map(a=>t[a]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${n.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let i=e.slice();return n.axes.length>0?(n.axes.forEach(a=>t[a]=r),n.axes.forEach(a=>i[a]=Math.round(e[a]*t[a]))):(t.fill(r,0,t.length),i.forEach((a,s)=>i[s]=Math.round(a*t[s]))),i},z0=(e,t,n,r,i)=>`
    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${n.length}> {
      var original_indices: array<${e.type.value}, ${n.length}>;
      for (var i:u32 = 0; i < ${n.length}; i++) {
        var output_index = ${e.indicesGet("output_indices","i")};
        var scale = ${Ce("uniforms.scales","i",r)};
        var roi_low = ${Ce("uniforms.roi","i",i)};
        var roi_hi = ${Ce("uniforms.roi",`i + ${t.length}`,i)};
        if (scale == 1.0) {
          original_indices[i] = ${e.type.value}(output_index);
        } else {
          var input_shape_i = ${Ce("uniforms.input_shape","i",t.length)};
          var output_shape_i = ${Ce("uniforms.output_shape","i",n.length)};
          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                           input_shape_i, roi_low, roi_hi);
        }
      }
      return original_indices;
    }`,R0=(e,t,n,r,i,a,s)=>`
    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
      var input_indices: ${e.type.indices};
      for (var i:u32 = 0; i < ${r.length}; i++) {
        var output_index = ${t.indicesGet("output_indices","i")};
        var input_index: u32;
        var scale = ${Ce("uniforms.scales","i",i)};
        if (scale == 1.0) {
          input_index = output_index;
        } else {
          var roi_low = ${Ce("uniforms.roi","i",a)};
          var roi_hi = ${Ce("uniforms.roi",`i + ${n.length}`,a)};
          var input_shape_i = ${Ce("uniforms.input_shape","i",n.length)};
          var output_shape_i = ${Ce("uniforms.output_shape","i",r.length)};
          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,
                                                                        input_shape_i, roi_low, roi_hi);
          if (!${s} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {
            if (original_idx < 0) {
              input_index = 0;
            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {
              input_index = input_shape_i - 1;
            } else {
              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));
            }
          } else {
            input_index = u32(original_idx);
          }
        }
        ${e.indicesSet("input_indices","i"," input_index")}
      }
      return input_indices;
    }`,P0=(e,t)=>`
    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {
      for (var i:u32 = 0; i < ${t.length}; i++) {
        var input_index = ${e.indicesGet("input_indices","i")};
        if (input_index < 0 || input_index >= ${Ce("uniforms.input_shape","i",t.length)}) {
          return false;
        }
      }
      return true;
    }`,Dd=(e,t,n,r)=>e.rank>r?`
    ${e.indicesSet("input_indices",t,"channel")};
    ${e.indicesSet("input_indices",n,"batch")};
`:"",B0=(e,t,n,r,i)=>{let[a,s,o,l]=n.length===2?[-1,0,1,-1]:[0,2,3,1],d=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${d} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",s,`max(0, min(row, ${n[s]} - 1))`)};
      ${e.indicesSet("input_indices",o,`max(0, min(col, ${n[o]} - 1))`)};
      ${Dd(e,l,a,2)}
      return ${e.getByIndices("input_indices")};
    }

    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${d} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var row:${d} = originalIndices[${s}];
      var col:${d} = originalIndices[${o}];
      ${r?`if (row < 0 || row > (${n[s]} - 1) || col < 0 || col > (${n[o]} - 1)) {
        return ${i};
      }`:""};
      row = max(0, min(row, ${n[s]} - 1));
      col = max(0, min(col, ${n[o]} - 1));
      var row1: u32 = u32(row);
      var col1: u32 = u32(col);
      var row2: u32 = u32(row + 1);
      var col2: u32 = u32(col + 1);
      var channel: u32 = ${n.length>2?`u32(originalIndices[${l}])`:"0"};
      var batch: u32 =  ${n.length>2?`u32(originalIndices[${a}])`:"0"};
      var x11: ${d} = getInputValue(batch, channel, row1, col1);
      var x12: ${d} = getInputValue(batch, channel, row1, col2);
      var x21: ${d} = getInputValue(batch, channel, row2, col1);
      var x22: ${d} = getInputValue(batch, channel, row2, col2);
      var dx1: ${d} = abs(row - ${d}(row1));
      var dx2: ${d} = abs(${d}(row2) - row);
      var dy1: ${d} = abs(col - ${d}(col1));
      var dy2: ${d} = abs(${d}(col2) - col);
      if (row1 == row2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (col1 == col2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);
    }`},D0=(e,t,n,r,i,a,s,o,l,d)=>{let c=n.length===2,[u,f]=c?[0,1]:[2,3],h=e.type.value,g=$=>{let E=$===u?"row":"col";return`
      fn ${E}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${h} {
        var output_index = ${t.indicesGet("output_indices",$)};
        var originalIdx: ${h} = getOriginalCoordinateFromResizedCoordinate(output_index, ${i[$]},
        ${r[$]}, ${n[$]}, ${a[$]}, ${a[$]} + ${n.length});
        var fractOriginalIdx: ${h} = originalIdx - floor(originalIdx);
        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);

        if (${o} && (originalIdx < 0 || originalIdx > (${n[$]} - 1))) {
          return ${l};
        }
        var data: array<${h}, 4> = array<${h}, 4>(0.0, 0.0, 0.0, 0.0);
        for (var i: i32 = -1; i < 3; i++) {
          var ${E}: ${h} = originalIdx + ${h}(i);
          if (${E} < 0 || ${E} >= ${n[$]}) {
            ${d?`coefs[i + 1] = 0.0;
                        continue;`:o?`return ${l};`:`${E} = max(0, min(${E}, ${n[$]} - 1));`};
          }
        var input_indices_copy: ${e.type.indices} = input_indices;
          ${e.indicesSet("input_indices_copy",$,`u32(${E})`)};
          data[i + 1] = ${$===u?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};
        }
        return cubicInterpolation1D(data, coefs);
      }`};return`
    ${g(u)};
    ${g(f)};
  fn getCubicInterpolationCoefs(s: ${h}) -> array<${h}, 4> {
    var absS = abs(s);
    var coeffs: array<${h}, 4> = array<${h}, 4>(0.0, 0.0, 0.0, 0.0);
    var oneMinusAbsS: ${h} = 1.0 - absS;
    var twoMinusAbsS: ${h} = 2.0 - absS;
    var onePlusAbsS: ${h} = 1.0 + absS;
    coeffs[0] = ((${s} * onePlusAbsS - 5 * ${s}) * onePlusAbsS + 8 * ${s}) * onePlusAbsS - 4 * ${s};
    coeffs[1] = ((${s} + 2) * absS - (${s} + 3)) * absS * absS + 1;
    coeffs[2] = ((${s} + 2) * oneMinusAbsS - (${s} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;
    coeffs[3] = ((${s} * twoMinusAbsS - 5 * ${s}) * twoMinusAbsS + 8 * ${s}) * twoMinusAbsS - 4 * ${s};
    return coeffs;
  }

  fn cubicInterpolation1D(x: array<${h}, 4>, coefs: array<${h}, 4>) -> ${h} {
    var coefsSum: ${h} = coefs[0] + coefs[1] + coefs[2] + coefs[3];
    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;
  }

  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${h} {
    var input_indices: ${e.type.indices} = output_indices;
    return colCubicInterpolation(input_indices, output_indices);
  }
    `},N0=(e,t,n,r,i)=>{let[a,s,o,l,d]=n.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],c=e.type.value;return`
    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${c} {
      var input_indices: ${e.type.indices};
      ${e.indicesSet("input_indices",s,`max(0, min(depth, ${n[s]} - 1))`)};
      ${e.indicesSet("input_indices",o,`max(0, min(height, ${n[o]} - 1))`)};
      ${e.indicesSet("input_indices",l,`max(0, min(width, ${n[l]} - 1))`)};
      ${Dd(e,d,a,3)}
      return ${e.getByIndices("input_indices")};
    }

    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${c} {
      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);
      var depth:${c} = originalIndices[${s}];
      var height:${c} = originalIndices[${o}];
      var width:${c} = originalIndices[${l}];
      ${r?`if (depth < 0 || depth > (${n[s]} - 1) || height < 0 || height > (${n[o]} - 1) || width < 0 || (width > ${n[l]} - 1)) {
      return ${i};
        }`:""};

    depth = max(0, min(depth, ${n[s]} - 1));
      height = max(0, min(height, ${n[o]} - 1));
      width = max(0, min(width, ${n[l]} - 1));
      var depth1: u32 = u32(depth);
      var height1: u32 = u32(height);
      var width1: u32 = u32(width);
      var depth2: u32 = u32(depth + 1);
      var height2: u32 = u32(height + 1);
      var width2: u32 = u32(width + 1);
      var channel: u32 = ${n.length>3?`u32(originalIndices[${d}])`:"0"};
      var batch: u32 =  ${n.length>3?`u32(originalIndices[${a}])`:"0"};

      var x111: ${c} = getInputValue(batch, channel, depth1, height1, width1);
      var x112: ${c} = getInputValue(batch, channel, depth1, height1, width2);
      var x121: ${c} = getInputValue(batch, channel, depth1, height2, width1);
      var x122: ${c} = getInputValue(batch, channel, depth1, height2, width2);
      var x211: ${c} = getInputValue(batch, channel, depth2, height1, width1);
      var x212: ${c} = getInputValue(batch, channel, depth2, height1, width2);
      var x221: ${c} = getInputValue(batch, channel, depth2, height2, width1);
      var x222: ${c} = getInputValue(batch, channel, depth2, height2, width2);
      var dx1: ${c} = abs(depth - ${c}(depth1));
      var dx2: ${c} = abs(${c}(depth2) - depth);
      var dy1: ${c} = abs(height - ${c}(height1));
      var dy2: ${c} = abs(${c}(height2) - height);
      var dz1: ${c} = abs(width - ${c}(width1));
      var dz2: ${c} = abs(${c}(width2) - width);
      if (depth1 == depth2) {
        dx1 = 0.5;
        dx2 = 0.5;
      }
      if (height1 == height2) {
        dy1 = 0.5;
        dy2 = 0.5;
      }
      if (width1 == width2) {
        dz1 = 0.5;
        dz2 = 0.5;
      }
      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +
              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);
    }`},L0=(e,t,n,r,i,a)=>{let s=e.dims,o=A0(a,t.axes,s.length),l=O0(s,r,i,t.axes),d=r.slice();r.length===0&&(d=s.map((_,v)=>_===0?1:l[v]/_),t.keepAspectRatioPolicy!=="stretch"&&(l=M0(s,d,t)));let c=ke("output",e.dataType,l.length),u=te("input",e.dataType,s.length),f=ne.size(l),h=s.length===l.length&&s.every((_,v)=>_===l[v]),g=t.coordinateTransformMode==="tf_crop_and_resize",$=t.extrapolationValue,E=u.type.value,y=_=>`
      ${h?"":`
      ${T0(t.coordinateTransformMode,E)};
      ${(()=>{switch(t.mode){case"nearest":return`
              ${P0(u,s)};
              ${I0(t.nearestMode,n,E)};
              ${R0(u,c,s,l,d.length,o.length,g)};
              `;case"linear":return`
              ${z0(c,s,l,d.length,o.length)};
              ${(()=>{if(s.length===2||s.length===4)return`${B0(u,c,s,g,$)}`;if(s.length===3||s.length===5)return`${N0(u,c,s,g,$)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};
            `;case"cubic":return`
            ${(()=>{if(s.length===2||s.length===4)return`${D0(u,c,s,l,d,o,t.cubicCoeffA,g,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};
            `;default:throw Error("Invalid resize mode")}})()};
      `}
      ${_.registerUniform("output_size","u32").registerUniform("scales","f32",d.length).registerUniform("roi","f32",o.length).declareVariables(u,c)}
      ${_.mainStart()}
        ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}
        ${h?"output[global_idx] = input[global_idx];":`
        let output_indices = ${c.offsetToIndices("global_idx")};
        var input_indices: ${u.type.indices};
        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);
                if (checkInputIndices(input_indices)) {
                  output[global_idx] = ${u.getByIndices("input_indices")};
                } else {
                  output[global_idx] = ${t.extrapolationValue};
                }`;case"linear":return`output[global_idx] = ${s.length===2||s.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};
`}
      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${n}|${d.length>0?d:""}|${i.length>0?i:""}|${o.length>0?o:""}|${h}|${s}`,inputDependencies:["rank"]},getShaderSource:y,getRunData:()=>({outputs:[{dims:l,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:[{type:"uint32",data:f},{type:"float32",data:d},{type:"float32",data:o},...le(s),...le(l)]})}},F0=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},I2=(e,t)=>{let n=[],r=[],i=[],a=F0(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");C0(e.inputs,t,a,n,r,i),e.compute(L0(e.inputs[0],t,a,n,r,i),{inputs:[0]})},A2=e=>{let t=e.antialias,n=e.axes,r=e.coordinateTransformMode,i=e.cubicCoeffA,a=e.excludeOutside!==0,s=e.extrapolationValue,o=e.keepAspectRatioPolicy,l=e.mode,d=e.nearestMode===""?"simple":e.nearestMode;return tt({antialias:t,axes:n,coordinateTransformMode:r,cubicCoeffA:i,excludeOutside:a,extrapolationValue:s,keepAspectRatioPolicy:o,mode:l,nearestMode:d})}}),U0,W0,O2,M2,OE=ee(()=>{lt(),ze(),St(),Oe(),U0=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],n=e[1],r=e[2];if(t.dataType!==n.dataType||t.dataType!==r.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(n.dims.length!==3&&n.dims.length!==2)throw new Error("Skip must be 2D or 3D");let i=t.dims[t.dims.length-1],a=t.dims[t.dims.length-2];if(n.dims[n.dims.length-1]!==i)throw new Error("Skip must have the same hidden size as input");if(n.dims[n.dims.length-2]!==a)throw new Error("Skip must have the same sequence length as input");if(r.dims.length!==1)throw new Error("Gamma must be 1D");if(r.dims[r.dims.length-1]!==i)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let s=e[3];if(s.dims.length!==1)throw new Error("Beta must be 1D");if(s.dims[s.dims.length-1]!==i)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let s=e[4];if(s.dims.length!==1)throw new Error("Bias must be 1D");if(s.dims[s.dims.length-1]!==i)throw new Error("Bias must have the same hidden size as input")}},W0=(e,t,n,r)=>{let i=e[0].dims,a=ne.size(i),s=i,o=a,l=i.slice(-1)[0],d=r?i.slice(0,-1).concat(1):[],c=e.length>3,u=e.length>4,f=r&&n>1,h=r&&n>2,g=n>3,$=rn(l),E=[te("x",e[0].dataType,e[0].dims,$),te("skip",e[1].dataType,e[1].dims,$),te("gamma",e[2].dataType,e[2].dims,$)];c&&E.push(te("beta",e[3].dataType,e[3].dims,$)),u&&E.push(te("bias",e[4].dataType,e[4].dims,$)),E.push(ke("output",e[0].dataType,s,$)),f&&E.push(ke("meanOutput",1,d)),h&&E.push(ke("invStdOutput",1,d)),g&&E.push(ke("inputSkipBiasSum",e[0].dataType,s,$));let y=wn(e[0].dataType),_=b=>`
      const hiddenSize: f32 = ${l};
      const hiddenSizeVectorized: u32 = ${l/$};
      const epsilon: f32 = ${t.epsilon};

      ${b.declareVariables(...E)}

      ${b.mainStart()}
        ${b.guardAgainstOutOfBoundsWorkgroupSizes(o/l)}
        let offset = global_idx * hiddenSizeVectorized;
        var sum = ${tn("f32",$)};
        var squareSum = ${tn("f32",$)};
        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {
          let skipValue = skip[offset + i];
          let biasValue = ${u?"bias[i]":"0.0"};
          let inputValue = x[offset + i];
          let value = inputValue + skipValue + biasValue;
          ${g?"inputSkipBiasSum[offset + i] = value;":""}
          output[offset + i] = value;
          let f32Value = ${ur(y,$,"value")};
          sum += f32Value;
          squareSum += f32Value * f32Value;
        }
        let mean = ${Zn("sum",$)} / hiddenSize;
        let invStdDev = inverseSqrt(${Zn("squareSum",$)} / hiddenSize - mean * mean + epsilon);
        ${f?"meanOutput[global_idx] = mean;":""}
        ${h?"invStdOutput[global_idx] = invStdDev;":""}
        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {
          output[offset + i] = (output[offset + i] - ${y}(mean)) * ${y}(invStdDev) * gamma[i]
           + ${c?"beta[i]":"0.0"};
        }
      }`,v=[{dims:s,dataType:e[0].dataType}];return n>1&&v.push({dims:d,dataType:1}),n>2&&v.push({dims:d,dataType:1}),n>3&&v.push({dims:i,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:t.cacheKey},getShaderSource:_,getRunData:()=>({outputs:v,dispatchGroup:{x:Math.ceil(o/l/64)}})}},O2=(e,t)=>{U0(e.inputs);let n=[0];e.outputCount>1&&n.push(-3),e.outputCount>2&&n.push(-3),e.outputCount>3&&n.push(3),e.compute(W0(e.inputs,t,e.outputCount,!1),{outputs:n})},M2=e=>{let t=e.epsilon;return tt({epsilon:t})}}),V0,Qa,j0,Nd,G0,H0,z2,R2,ME=ee(()=>{lt(),ze(),St(),Oe(),V0=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((n,r)=>{if(e[r+1].dataType!==6&&e[r+1].dataType!==7)throw new Error(`Input ${r} must be an array of int32 or int64`)})},Qa=(e,t)=>{let n=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(r=>n.push(Number(r)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(r=>n.push(Number(r)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return n},j0=(e,t)=>{if(e.length>1){let n=Qa(e,1),r=Qa(e,2),i=Qa(e,3);return i.length===0&&(i=[...Array(e[0].dims.length).keys()]),tt({starts:n,ends:r,axes:i})}else return t},Nd=(e,t,n,r,i)=>{let a=e;return e<0&&(a+=n[r[t]]),i[t]<0?Math.max(0,Math.min(a,n[r[t]]-1)):Math.max(0,Math.min(a,n[r[t]]))},G0=(e,t,n)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {
          var input_indices: ${e.type.indices};
          var carry = 0u;
          for (var i = ${n.length}; i >= 0; i--) {
            let input_shape_i = ${Ce("uniforms.input_shape","i",n.length)};
            let steps_i = ${Ce("uniforms.steps","i",n.length)};
            let signs_i = ${Ce("uniforms.signs","i",n.length)};
            let starts_i = ${Ce("uniforms.starts","i",n.length)};
            var output_index = ${t.indicesGet("output_indices","i")};
            var input_index = output_index * steps_i + starts_i + carry;
            carry = input_index / input_shape_i;
            input_index = input_index % input_shape_i;
            if (signs_i < 0) {
              input_index = input_shape_i - input_index - 1u + starts_i;
            }
            ${e.indicesSet("input_indices","i","input_index")};
          }
          return input_indices;
      }`,H0=(e,t)=>{let n=e[0].dims,r=ne.size(n),i=t.axes.length>0?ne.normalizeAxes(t.axes,n.length):[...Array(n.length).keys()],a=Qa(e,4);a.forEach(y=>y!==0||(()=>{throw new Error("step cannot be 0")})),a.length===0&&(a=Array(i.length).fill(1));let s=t.starts.map((y,_)=>Nd(y,_,n,i,a)),o=t.ends.map((y,_)=>Nd(y,_,n,i,a));if(i.length!==s.length||i.length!==o.length)throw new Error("start, ends and axes should have the same number of elements");if(i.length!==n.length)for(let y=0;y<n.length;++y)i.includes(y)||(s.splice(y,0,0),o.splice(y,0,n[y]),a.splice(y,0,1));let l=a.map(y=>Math.sign(y));a.forEach((y,_,v)=>{if(y<0){let b=(o[_]-s[_])/y,C=s[_],I=C+b*a[_];s[_]=I,o[_]=C,v[_]=-y}});let d=n.slice(0);i.forEach((y,_)=>{d[y]=Math.ceil((o[y]-s[y])/a[y])});let c={dims:d,dataType:e[0].dataType},u=ke("output",e[0].dataType,d.length),f=te("input",e[0].dataType,e[0].dims.length),h=ne.size(d),g=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:s.length},{name:"signs",type:"i32",length:l.length},{name:"steps",type:"u32",length:a.length}],$=[{type:"uint32",data:h},{type:"uint32",data:s},{type:"int32",data:l},{type:"uint32",data:a},...le(e[0].dims),...le(d)],E=y=>`
      ${y.registerUniforms(g).declareVariables(f,u)}
        ${G0(f,u,n)}
        ${y.mainStart()}
          ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}
          let output_indices = ${u.offsetToIndices("global_idx")};
          let input_indices = calculateInputIndices(output_indices);
          ${u.setByOffset("global_idx",f.getByIndices("input_indices"))}
      }`;return{name:"Slice",shaderCache:{hint:`${l.length}_${s.length}_${a.length}`,inputDependencies:["rank"]},getShaderSource:E,getRunData:()=>({outputs:[c],dispatchGroup:{x:Math.ceil(r/64)},programUniforms:$})}},z2=(e,t)=>{V0(e.inputs,t);let n=j0(e.inputs,t);e.compute(H0(e.inputs,n),{inputs:[0]})},R2=e=>{let t=e.starts,n=e.ends,r=e.axes;return tt({starts:t,ends:n,axes:r})}}),q0,K0,P2,B2,zE=ee(()=>{ze(),St(),Oe(),q0=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},K0=(e,t)=>{let n=e.dims,r=ne.size(n),i=64,a=t.axis;if(a<0&&(a=n.length+a),a<n.length-1)throw new Error("softmax only supports last axis for now.");let s=n[a],o=r/s,l=rn(s),d=s/l,c=(E,y)=>y===4?`max(max(${E}.x, ${E}.y), max(${E}.z, ${E}.w))`:y===2?`max(${E}.x, ${E}.y)`:y===3?`max(max(${E}.x, ${E}.y), ${E}.z)`:E,u=te("x",e.dataType,e.dims,l),f=ke("result",e.dataType,e.dims,l),h=u.type.value,g=wn(e.dataType)==="f32"?`var threadMax = ${h}(-3.402823e+38f);`:`var threadMax = ${h}(-65504.0h);`,$=E=>`
      var<workgroup> rowMaxShared : ${h};
      var<workgroup> rowSumShared : ${h};
      var<workgroup> threadShared : array<${h}, ${i}>;

      fn getValue(row: i32, col: i32, row_stride: i32) -> ${h} {
        let index = row * row_stride + col;
        return x[index];
      }

      fn setValue(row: i32, col: i32, row_stride: i32, value: ${h}) {
        let index = row * row_stride + col;
        result[index] = value;
      }
      ${E.registerUniform("packedCols","i32").declareVariables(u,f)}
      ${E.mainStart()}
        let gindex = i32(global_idx);
        let lindex = i32(local_idx);
        const wg = ${i};
        let row = gindex / wg;
        let cols = uniforms.packedCols;
        let row_stride : i32 = uniforms.packedCols;

        // find the rows max
        ${g}
        for (var col = lindex; col < cols; col += wg) {
          let value = getValue(row, col, row_stride);
          threadMax = max(threadMax, value);
        }
        if (lindex < cols) {
          threadShared[lindex] = threadMax;
        }
        workgroupBarrier();

        var reduceSize = min(cols, wg);
        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {
          reduceSize = currSize + (reduceSize & 1);
          if (lindex < currSize) {
            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
          rowMaxShared = ${h}(${c("threadShared[0]",l)});
        }
        workgroupBarrier();

        // find the rows sum
        var threadSum = ${h}(0.0);
        for (var col = lindex; col < cols; col += wg) {
          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);
          threadSum += subExp;
        }
        threadShared[lindex] = threadSum;
        workgroupBarrier();

        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {
          if (lindex < currSize) {
            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];
          }
          workgroupBarrier();
        }
        if (lindex == 0) {
          rowSumShared = ${h}(${Zn("threadShared[0]",l)});
        }
        workgroupBarrier();

        // calculate final value for each element in the row
        for (var col = lindex; col < cols; col += wg) {
          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;
          setValue(row, col, row_stride, value);
        }
      }`;return{name:"Softmax",shaderCache:{hint:`${l}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:n,dataType:e.dataType}],dispatchGroup:{x:o},programUniforms:[{type:"uint32",data:d}]}),getShaderSource:$}},P2=(e,t)=>{q0(e.inputs),e.compute(K0(e.inputs[0],t))},B2=e=>tt({axis:e.axis})}),Y0,Q0,X0,Z0,J0,D2,N2,RE=ee(()=>{ze(),St(),Oe(),Y0=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Q0=(e,t)=>{let n=[],r=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(i=>n.push(Number(i))),r=n.length),tt({numOutputs:r,axis:t.axis,splitSizes:n})},X0=e=>`
fn calculateOutputIndex(index: u32) -> u32 {
    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {
    if (index < ${Ce("uniforms.size_in_split_axis","i",e)}) {
        return i;
    }
    }
    return ${e}u;
}`,Z0=e=>{let t=e.length,n=[];for(let r=0;r<t;++r){let i=e[r].setByIndices("indices","input[global_idx]");t===1?n.push(i):r===0?n.push(`if (output_number == ${r}u) { ${i} }`):r===t-1?n.push(`else { ${i} }`):n.push(`else if (output_number == ${r}) { ${i} }`)}return`
      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {
        ${n.join(`
`)}
      }`},J0=(e,t)=>{let n=e[0].dims,r=ne.size(n),i=e[0].dataType,a=ne.normalizeAxis(t.axis,n.length),s=new Array(t.numOutputs),o=te("input",i,n),l=new Array(t.numOutputs),d=[],c=[],u=0,f=[{type:"uint32",data:r}];for(let g=0;g<t.numOutputs;g++){u+=t.splitSizes[g],l[g]=u;let $=n.slice();$[t.axis]=t.splitSizes[g],c.push($),s[g]=ke(`output${g}`,i,$),d.push({dims:c[g],dataType:e[0].dataType})}f.push({type:"uint32",data:l}),f.push(...le(n)),c.forEach(g=>f.push(...le(g)));let h=g=>`
  ${g.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",l.length).declareVariables(o,...s)}
  ${X0(l.length)}
  ${Z0(s)}

  ${g.mainStart()}
    ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}

    var indices = ${o.offsetToIndices("global_idx")};
    var index = ${o.indicesGet("indices",a)};
    let output_number = calculateOutputIndex(index);
    if (output_number != 0) {
      index -= ${Ce("uniforms.size_in_split_axis","output_number - 1u",l.length)};
      ${o.indicesSet("indices",a,"index")};
    }
    writeBufferData(output_number, indices, global_idx);
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      var input_indices: ${s.type.indices};
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            ${u("data",2,"u32")}
            ${u("data",3,"u32")}
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            ${u("output_data[global_idx]",2)}
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e;this.queryType!=="none"&&(this.commandEncoder.resolveQuerySet(this.querySet,0,this.pendingDispatchNumber*2,this.queryResolveBuffer,0),e=this.device.createBuffer({size:this.pendingDispatchNumber*2*8,usage:GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST}),this.pendingQueries.set(e,this.pendingKernels),this.pendingKernels=[],this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.pendingDispatchNumber*2*8)),this.device.queue.submit([this.commandEncoder.finish()]),this.gpuDataManager.refreshPendingBuffers(),this.commandEncoder=null,this.pendingDispatchNumber=0,this.queryType!=="none"&&e.mapAsync(GPUMapMode.READ).then(()=>{var r;let t=new BigUint64Array(e.getMappedRange()),n=this.pendingQueries.get(e);for(let i=0;i<t.length/2;i++){let a=n[i],s=a.kernelId,o=this.kernels.get(s),l=o.kernelType,d=o.kernelName,c=a.programName,u=a.inputTensorViews,f=a.outputTensorViews,h=t[i*2],g=t[i*2+1];typeof this.queryTimeBase>"u"&&(this.queryTimeBase=h);let 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  ${I.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n          var input_indices: ${x.type.indices};\n          let output_indices = ${E.offsetToIndices("global_idx")};\n\n          ${$.join(`\n`)}\n          ${A[0]}       // init ops for reduce max/min\n          ${A[1]}\n          ${z}\n          ${A[3]}\n          ${A.length===4?E.setByOffset("global_idx","value"):A.slice(4).join(`\n`)}\n        }`},getRunData:()=>({outputs:[{dims:a,dataType:s}],dispatchGroup:{x:Math.ceil(y/64)},programUniforms:[{type:"uint32",data:y},...L(p),...L(a)]})}},Mn=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(o=>r.push(Number(o))),ge({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},mt=(e,t,r,o)=>{let n=e.inputs,s=n.length===1?r:Mn(n,r);e.compute(qr(t,{hint:s.cacheKey,inputDependencies:["rank"]},[n[0]],s.noopWithEmptyAxes&&s.axes.length===0?Ad:o,s.axes,n[0].dataType,s.keepDims,s.noopWithEmptyAxes),{inputs:[0]})},Td=(e,t)=>{pt(e.inputs),mt(e,"ReduceLogSum",t,(o,n)=>[`var value = ${n.type.storage}(0);`,"",`value += ${o.getByIndices("input_indices")};`,"value = log(value);"])},Ed=(e,t)=>{pt(e.inputs),mt(e,"ReduceL1",t,(o,n)=>[`var value = ${n.type.storage}(0);`,"",`value += abs(${o.getByIndices("input_indices")});`,""])},Od=(e,t)=>{pt(e.inputs),mt(e,"ReduceL2",t,(o,n)=>[`var t = ${n.type.value}(0); var value = ${n.type.value}(0);`,"",`t = ${o.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},Pd=(e,t)=>{pt(e.inputs),mt(e,"ReduceLogSumExp",t,(o,n)=>[`var value = ${n.type.storage}(0);`,"",`value += exp(${o.getByIndices("input_indices")});`,"value = log(value);"])},kd=(e,t)=>{pt(e.inputs),mt(e,"ReduceMax",t,(o,n,s)=>{let u=[];for(let l=0;l<o.rank;l++)(s.indexOf(l)>=0||s.length===0)&&u.push(o.indicesSet("input_indices",l,0));return[`${u.join(`\n`)}`,`var value = ${o.getByIndices("input_indices")};`,`value = max(value, ${o.getByIndices("input_indices")});`,""]})},Rd=(e,t)=>{pt(e.inputs),mt(e,"ReduceMean",t,(o,n,s)=>{let u=1;for(let l=0;l<o.rank;l++)(s.indexOf(l)>=0||s.length===0)&&(u*=e.inputs[0].dims[l]);return["var sum = f32(0);","",`sum += f32(${o.getByIndices("input_indices")});`,`let value = ${n.type.value}(sum / ${u});`]})},Bd=(e,t)=>{pt(e.inputs),mt(e,"ReduceMin",t,(o,n,s)=>{let u=[];for(let l=0;l<o.rank;l++)(s.indexOf(l)>=0||s.length===0)&&u.push(`input_indices[${l}] = 0;`);return[`${u.join(`\n`)}`,`var value = ${o.getByIndices("input_indices")};`,`value = min(value, ${o.getByIndices("input_indices")});`,""]})},Dd=(e,t)=>{pt(e.inputs),mt(e,"ReduceProd",t,(o,n)=>[`var value = ${n.type.storage}(1);`,"",`value *= ${o.getByIndices("input_indices")};`,""])},Md=(e,t)=>{pt(e.inputs),mt(e,"ReduceSum",t,(o,n)=>[`var 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n<32&&o>1024},Ta=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Rd(e,t):ya(e,t)},Ea=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Ed(e,t):ba(e,t)},Oa=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Od(e,t):wa(e,t)},Pa=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Pd(e,t):va(e,t)},ka=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?kd(e,t):$a(e,t)},Ra=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Bd(e,t):Sa(e,t)},Ba=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Dd(e,t):xa(e,t)},Da=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Md(e,t):_a(e,t)},Ma=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?zd(e,t):Ca(e,t)},za=(e,t)=>{ft(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Td(e,t):Ia(e,t)}});var Ua,Va,Na,zn,Wa=j(()=>{"use strict";Ne();je();jr();Ua=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Va=(e,t)=>{Ua(e.inputs);let r=(o,n,s)=>{let u=[];for(let l=0;l<o.rank;l++)(s.indexOf(l)>=0||s.length===0)&&u.push(`input_indices[${l}] = 0;`);return[`${u.join(`\n`)}`,`var value = ${o.getByIndices("input_indices")};\nvar best_index : i32 = 0;`,`if (${o.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) {\n         value = ${o.getByIndices("input_indices")};\n         best_index = i32(last_index);\n       }`,"",n.setByOffset("global_idx","best_index")]};e.compute(qr("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},Na=(e,t)=>{Ua(e.inputs);let r=(o,n,s)=>{let u=[];for(let l=0;l<o.rank;l++)(s.indexOf(l)>=0||s.length===0)&&u.push(`input_indices[${l}] = 0;`);return[`${u.join(`\n`)}`,`var value = ${o.getByIndices("input_indices")};\nvar best_index : i32 = 0;`,`if (${o.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) {\n         value = ${o.getByIndices("input_indices")};\n         best_index = i32(last_index);\n       }`,"",n.setByOffset("global_idx","best_index")]};e.compute(qr("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},zn=e=>ge(e)});var Ud,Vd,Nd,Wd,Kr,Hd,Ha,Un=j(()=>{"use strict";Ne();Nr();ve();Ud=(e,t)=>{let r=e[0],o=e[1],n=e[2],s=e[3],u=e[4],l=e[5];if(u&&l)throw new Error("Attention cannot have both past and relative_position_bias");if(r.dims.length!==3)throw new Error(\'Input "input" must have 3 dimensions\');let a=r.dims[0],p=r.dims[1],h=r.dims[2];if(n.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimensions\');if(o.dims.length!==2)throw new Error(\'Input "weights" is expected to have 2 dimensions\');if(o.dims[0]!==h)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(n.dims[0]!==o.dims[1])throw new Error(\'Input "bias" dimension 0 should have same length as dimension 1 of input "weights"\');let g=n.dims[0]/3,b=g,w=b;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let E of t.qkvHiddenSizes)if(E%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");g=t.qkvHiddenSizes[0],b=t.qkvHiddenSizes[1],w=t.qkvHiddenSizes[2]}let y=p;if(g!==b)throw new Error("qkv_hidden_sizes first element should be same as the second");if(n.dims[0]!==g+b+w)throw new Error(\'Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes\');let _=0;if(u){if(b!==w)throw new Error(\'Input "past" expect k_hidden_size == v_hidden_size\');if(u.dims.length!==5)throw new Error(\'Input "past" must have 5 dimensions\');if(u.dims[0]!==2)throw new Error(\'Input "past" first dimension must be 2\');if(u.dims[1]!==a)throw new Error(\'Input "past" second dimension must be batch_size\');if(u.dims[2]!==t.numHeads)throw new Error(\'Input "past" third dimension must be 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w=F("x",t.dataType,t.dims,n),y="thread_max_vector";n===2?y="max(thread_max_vector.x, thread_max_vector.y)":n===4&&(y="max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))");let _=lt(t.dataType),I=[{name:"d_inv",type:_},{name:"d_comp",type:"u32"},{name:"elements_per_wg",type:"u32"}];return`\n  var<workgroup> wgMax: array<f32, ${s}>;\n  var<workgroup> wgSum: array<f32, ${s}>;\n  ${b.registerUniforms(I).declareVariables(w)}\n  ${b.mainStart([s,1,1])}\n    let localOffset = local_idx * uniforms.elements_per_wg;\n    let offset: u32 = workgroup_id.x * uniforms.d_comp + localOffset;\n\n    var thread_max_vector = ${Ze("f32",n,"-3.402823e+38f")};\n    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n      thread_max_vector = max(${at(_,n,"x[offset + i]")}, thread_max_vector);\n    }\n    wgMax[local_idx] = ${y};\n    workgroupBarrier();\n\n    var maxValue = -3.402823e+38f;\n    for (var i = 0u; i < ${s}; i++) {\n      maxValue = max(wgMax[i], maxValue);\n    }\n\n    var sumVector = ${Ze("f32",n,"0")};\n    for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n      sumVector += exp(${at(_,n,"x[offset + i]")} - maxValue);\n    }\n    wgSum[local_idx] = ${Je("sumVector",n)};\n    workgroupBarrier();\n\n    var sum: f32 = 0;\n    for (var i = 0u; i < ${s}; i++) {\n      sum += wgSum[i];\n    }\n\n    if (sum == 0) {\n      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n        x[offset + i] = ${Ze("f32",n,"uniforms.d_inv")};\n      }\n    } else {\n      for (var i: u32 = 0; i < uniforms.elements_per_wg && i + localOffset < uniforms.d_comp; i++) {\n        let f32input = ${at(_,n,"x[offset + i]")};\n        x[offset + i] = ${w.type.value}(exp(f32input - maxValue) / sum);\n      }\n    }\n  }`};e.compute({name:"AttentionProbsSoftmax",shaderCache:{hint:`${s};${h};${n}`},getShaderSource:g,getRunData:()=>({outputs:[],dispatchGroup:{x:r},programUniforms:p})},{inputs:[t],outputs:[]})},Nd=(e,t,r,o,n,s)=>{let u=[n.batchSize,n.numHeads,n.sequenceLength,n.kvSequenceLength+n.pastSequenceLength],l=s.scale===0?1/Math.sqrt(n.headSize):s.scale,a=Fe(n.headSize),p=n.headSize/a,h=12,g={x:Math.ceil(n.totalSequenceLength/h),y:Math.ceil(n.sequenceLength/h),z:n.batchSize*n.numHeads},b=Xe(t.dataType),w=[{type:"uint32",data:n.sequenceLength},{type:"uint32",data:p},{type:"uint32",data:n.totalSequenceLength},{type:"uint32",data:n.kvSequenceLength},{type:b,data:l}],y=[t,r],_=$=>{let x=M("q",t.dataType,t.dims,a),E=M("key",r.dataType,r.dims,a),A=F("output",t.dataType,u),z=Le(t.dataType),R=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"alpha",type:z}];return`\n  const beta: ${z} = 1.0;\n  const TILE_SIZE = ${h}u;\n\n  var<workgroup> tileQ: array<${x.type.storage}, ${h*h}>;\n  var<workgroup> tileK: array<${x.type.storage}, ${h*h}>;\n  ${$.registerUniforms(R).declareVariables(x,E,A)}\n  ${$.mainStart([h,h,1])}\n    // x holds the N and y holds the M\n    let headIdx = workgroup_id.z;\n    let m = workgroup_id.y * TILE_SIZE;\n    let n = workgroup_id.x * TILE_SIZE;\n    let lm = m + local_id.y;\n    let ln = n + local_id.x;\n\n    let qOffset = uniforms.M * uniforms.K * headIdx + m * uniforms.K;\n    let kOffset = uniforms.kv_sequence_length * uniforms.K * headIdx + n * uniforms.K;\n\n    var value = ${Ze(z,a)};\n    for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n      if (m + local_id.y < uniforms.M && w + local_id.x < uniforms.K) {\n        tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) {\n        tileK[TILE_SIZE * local_id.y + local_id.x] = key[kOffset + local_id.y * uniforms.K + w + local_id.x];\n      }\n      workgroupBarrier();\n\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = headIdx * uniforms.M * uniforms.N;\n    if (lm < uniforms.M && ln < uniforms.N) {\n      let outputIdx = headOffset + lm * uniforms.N + ln;\n      output[outputIdx] = ${Je("value",a)} * uniforms.alpha;\n    }\n  }`},I=e.compute({name:"AttentionProbs",shaderCache:{hint:`${a}`,inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:g,programUniforms:w}),getShaderSource:_},{inputs:y,outputs:[-1]})[0];return Vd(e,I,n.batchSize*n.numHeads*n.sequenceLength,n.totalSequenceLength),I},Wd=(e,t,r,o)=>{let n=[o.batchSize,o.sequenceLength,o.vHiddenSize],s=12,u={x:Math.ceil(o.vHeadSize/s),y:Math.ceil(o.sequenceLength/s),z:o.batchSize*o.numHeads},l=[{type:"uint32",data:o.sequenceLength},{type:"uint32",data:o.totalSequenceLength},{type:"uint32",data:o.vHeadSize},{type:"uint32",data:o.numHeads},{type:"uint32",data:o.vHiddenSize}],a=p=>{let h=M("probs",t.dataType,t.dims),g=M("v",r.dataType,r.dims),b=F("output",t.dataType,n),w=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"v_hidden_size",type:"u32"}];return`\n  const TILE_SIZE = ${s}u;\n  var<workgroup> tileQ: array<${h.type.value}, ${s*s}>;\n  var<workgroup> tileK: array<${h.type.value}, ${s*s}>;\n  ${p.registerUniforms(w).declareVariables(h,g,b)}\n  ${p.mainStart([s,s,1])}\n   let headIdx = workgroup_id.z;\n   let m = workgroup_id.y * TILE_SIZE + local_id.y;\n   let n = workgroup_id.x * TILE_SIZE + local_id.x;\n\n   let offsetA = headIdx * (uniforms.M * uniforms.K) + m * uniforms.K;\n   let offsetB = headIdx * (uniforms.N * uniforms.K) + n;\n\n   var value = ${h.type.storage}(0);\n   for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) {\n     if (m < uniforms.M && w + local_id.x < uniforms.K) {\n       tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x];\n     }\n     if (n < uniforms.N && w + local_id.y < uniforms.K) {\n       tileK[TILE_SIZE * local_id.y + local_id.x] = v[offsetB + (w + local_id.y) * uniforms.N];\n     }\n     workgroupBarrier();\n     for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n       value += tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * k + local_id.x];\n     }\n     workgroupBarrier();\n   }\n\n   // we need to transpose output from BNSH_v to BSND_v\n   let batchIdx = workgroup_id.z / uniforms.num_heads;\n   let currentBatchHeadNumber = workgroup_id.z % uniforms.num_heads;\n   let headOffset = (batchIdx * uniforms.M * uniforms.num_heads + currentBatchHeadNumber) * uniforms.N;\n   if (m < uniforms.M && n < uniforms.N) {\n     let outputIdx = batchIdx * uniforms.M *uniforms.v_hidden_size + m * uniforms.v_hidden_size\n       + currentBatchHeadNumber * uniforms.N + n;\n     output[outputIdx] = value;\n   }\n  }`};return e.compute({name:"AttentionScore",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:n,dataType:t.dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:l}),getShaderSource:a},{inputs:[t,r],outputs:[0]})[0]},Kr=(e,t,r,o,n,s,u,l,a,p,h)=>{let g=Nd(e,t,r,a,p,h);Wd(e,g,o,p)},Hd=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],o=t.sequenceLength,n=t.inputHiddenSize,s=t.headSize,u=12,l={x:Math.ceil(t.headSize/u),y:Math.ceil(t.sequenceLength/u),z:t.batchSize*t.numHeads},a=[e.inputs[0],e.inputs[1],e.inputs[2]],p=[{type:"uint32",data:o},{type:"uint32",data:n},{type:"uint32",data:s},{type:"uint32",data:t.numHeads},{type:"uint32",data:t.headSize},{type:"uint32",data:t.hiddenSize},{type:"uint32",data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],h=g=>{let b=F("output_q",a[0].dataType,r),w=F("output_k",a[0].dataType,r),y=F("output_v",a[0].dataType,r),_=M("input",a[0].dataType,a[0].dims),I=M("weight",a[1].dataType,a[1].dims),$=M("bias",a[2].dataType,a[2].dims),x=_.type.storage,E=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return`\n  const TILE_SIZE = ${u}u;\n  var<workgroup> tileInput: array<${x}, ${u*u}>;\n  var<workgroup> 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local_id.x];\n      }\n      if (n < uniforms.N && w + local_id.y < uniforms.K) {\n        let offset = n + (w + local_id.y) * uniforms.ldb;\n        tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset];\n        tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset];\n        tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset];\n      }\n      workgroupBarrier();\n      for (var k: u32 = 0u; k<TILE_SIZE && w+k < uniforms.K; k++) {\n        let inputTileOffset = TILE_SIZE * local_id.y + k;\n        let weightTileOffset = TILE_SIZE * k + local_id.x;\n        valueQ += tileInput[inputTileOffset] * tileWeightQ[weightTileOffset];\n        valueK += tileInput[inputTileOffset] * tileWeightK[weightTileOffset];\n        valueV += tileInput[inputTileOffset] * tileWeightV[weightTileOffset];\n      }\n\n      workgroupBarrier();\n    }\n\n    let headOffset = (m * uniforms.N + n) % uniforms.head_size;\n    valueQ += bias[headOffset + biasOffsetQ];\n    valueK += bias[headOffset + biasOffsetK];\n    valueV += bias[headOffset + biasOffsetV];\n\n    let offset = workgroup_id.z * uniforms.M * uniforms.N;\n    if (m < uniforms.M && n < uniforms.N) {\n      let outputIdx = offset + m * uniforms.N + n;\n      output_q[outputIdx] = valueQ;\n      output_k[outputIdx] = valueK;\n      output_v[outputIdx] = valueV;\n    }\n  }`};return e.compute({name:"AttentionPrepare",shaderCache:{inputDependencies:["type","type","type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:l,programUniforms:p}),getShaderSource:h},{inputs:a,outputs:[-1,-1,-1]})},Ha=(e,t)=>{let r=Ud(e.inputs,t),[o,n,s]=Hd(e,r);return Kr(e,o,n,s,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t)}});var Gd,Ld,Fd,Ga,La=j(()=>{"use strict";Lt();$e();je();ve();Gd=(e,t)=>{if(!e||e.length!==5)throw new 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0},getShaderSource:x,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p?[{type:"uint32",data:a},...L(s)]:[{type:"uint32",data:a}]})}},Fd=e=>ge(e),Ga=(e,t)=>{let{inputs:r,outputCount:o}=e,n=Fd({...t,outputCount:o});if(Gt.webgpu.validateInputContent&&Gd(r,n),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Ld(r,n))}});var jd,qd,Fa,ja=j(()=>{"use strict";$e();ve();jd=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},qd=e=>{let t=e[0].dims,r=e[0].dims[2],o=U.size(t)/4,n=e[0].dataType,s=M("input",n,t,4),u=M("bias",n,[r],4),l=M("residual",n,t,4),a=F("output",n,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(o/64)}}),getShaderSource:h=>`\n  const channels = ${r}u / 4;\n  ${h.declareVariables(s,u,l,a)}\n\n  ${h.mainStart()}\n    ${h.guardAgainstOutOfBoundsWorkgroupSizes(o)}\n    let value = ${s.getByOffset("global_idx")}\n      + ${u.getByOffset("global_idx % channels")} + ${l.getByOffset("global_idx")};\n    ${a.setByOffset("global_idx","value")}\n  }`}},Fa=e=>{jd(e.inputs),e.compute(qd(e.inputs))}});var Kd,Ae,qa,Ka,Ya,Za,Qa,Xa,Ja,ei,ti,Yd,ri,ni,oi,ai,Yr,ii,Zr,si,ui,di,li,ci,pi,mi,fi,hi,gi,yi,bi,wi,vi,$i,Si,xi,Vn=j(()=>{"use strict";Ne();$e();je();ve();Kd=(e,t,r,o,n,s)=>{let u=Math.ceil(t/4),l="";typeof n=="string"?l=`${n}(a)`:l=n("a");let a=M("inputData",r,[u],4),p=F("outputData",o,[u],4);return`\n      ${e.registerUniform("vec_size","u32").declareVariables(a,p)}\n\n  ${s??""}\n\n  ${e.mainStart()}\n    ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n\n    let a = ${a.getByOffset("global_idx")};\n    ${p.setByOffset("global_idx",l)}\n  }`},Ae=(e,t,r,o,n,s=e.dataType)=>({name:t,shaderCache:{hint:n,inputDependencies:["type"]},getShaderSource:u=>Kd(u,U.size(e.dims),e.dataType,s,r,o),getRunData:u=>({outputs:[{dims:e.dims,dataType:s}],dispatchGroup:{x:Math.ceil(U.size(u[0].dims)/64/4)},programUniforms:[{type:"uint32",data:Math.ceil(U.size(e.dims)/4)}]})}),qa=e=>{e.compute(Ae(e.inputs[0],"Abs","abs"))},Ka=e=>{e.compute(Ae(e.inputs[0],"Acos","acos"))},Ya=e=>{e.compute(Ae(e.inputs[0],"Acosh","acosh"))},Za=e=>{e.compute(Ae(e.inputs[0],"Asin","asin"))},Qa=e=>{e.compute(Ae(e.inputs[0],"Asinh","asinh"))},Xa=e=>{e.compute(Ae(e.inputs[0],"Atan","atan"))},Ja=e=>{e.compute(Ae(e.inputs[0],"Atanh","atanh"))},ei=e=>ge(e),ti=(e,t)=>{let r;switch(t.to){case 10:r="vec4<f16>";break;case 1:r="vec4<f32>";break;case 12:r="vec4<u32>";break;case 6:r="vec4<i32>";break;case 9:r="vec4<bool>";break;default:throw new RangeError(`not supported type (specified in attribute \'to\' from \'Cast\' operator): ${t.to}`)}e.compute(Ae(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},Yd=e=>{let t=e.length>=2&&e[1].data!==0?e[1].getFloat32Array()[0]:Gr,r=e.length>=3&&e[2].data!==0?e[2].getFloat32Array()[0]:Lr;return ge({min:t,max:r})},ri=(e,t)=>{let r=e.inputs.length===1?t:Yd(e.inputs),o=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"Clip",n=>`clamp(${n}, clip_min_, clip_max_)`,`\n    const clip_min_: vec4<${o}> = vec4(${o}(${r.min}));\n    const clip_max_: vec4<${o}> = vec4(${o}(${r.max}));\n`,r.cacheKey),{inputs:[0]})},ni=e=>{e.compute(Ae(e.inputs[0],"Ceil","ceil"))},oi=e=>{e.compute(Ae(e.inputs[0],"Cos","cos"))},ai=e=>{e.compute(Ae(e.inputs[0],"Cosh","cosh"))},Yr=e=>ge(e),ii=(e,t)=>{let r=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"Elu",o=>`elu_vf32(${o})`,`\n  const elu_alpha_ 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0.7071067811865475))`,Zr(`vec4<${t}>`,t)))},ci=(e,t)=>{let r=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"LeakyRelu",o=>`select(leaky_relu_alpha_ * ${o}, ${o}, ${o} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},pi=e=>{e.compute(Ae(e.inputs[0],"Not",t=>`!${t}`))},mi=e=>{e.compute(Ae(e.inputs[0],"Neg",t=>`-${t}`))},fi=e=>{e.compute(Ae(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},hi=e=>{let t=lt(e.inputs[0].dataType);e.compute(Ae(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},gi=e=>{e.compute(Ae(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},yi=e=>{e.compute(Ae(e.inputs[0],"Sin","sin"))},bi=e=>{e.compute(Ae(e.inputs[0],"Sinh","sinh"))},wi=e=>{e.compute(Ae(e.inputs[0],"Sqrt","sqrt"))},vi=e=>{e.compute(Ae(e.inputs[0],"Tan","tan"))},$i=e=>{e.compute(Ae(e.inputs[0],"Tanh","tanh"))},Si=(e,t)=>{let r=lt(e.inputs[0].dataType);return e.compute(Ae(e.inputs[0],"ThresholdedRelu",o=>`select(vec4<${r}>(0.0), ${o}, ${o} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},xi=e=>{e.compute(Ae(e.inputs[0],"Log","log"))}});var Qd,Xd,_i,Ci=j(()=>{"use strict";$e();ve();Vn();Qd=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Xd=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=M("input",e[0].dataType,e[0].dims,4),o=M("bias",e[0].dataType,[e[0].dims[2]],4),n=F("output",e[0].dataType,t,4),s=U.size(t)/4,u=Le(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)}}),getShaderSource:a=>`\n  const M_SQRT2 = sqrt(2.0);\n  const halfChannels = ${e[0].dims[2]/4/2}u;\n\n  ${a.declareVariables(r,o,n)}\n\n  ${Zr(`vec4<${u}>`,u)}\n\n  ${a.mainStart()}\n    ${a.guardAgainstOutOfBoundsWorkgroupSizes(s)}\n    let biasIdx = global_idx % halfChannels;\n    let batchIndex = global_idx / halfChannels;\n    let inputOffset = biasIdx + batchIndex * halfChannels * 2;\n    let valueLeft = input[inputOffset] + bias[biasIdx];\n    let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels];\n    let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1);\n\n    ${n.setByOffset("global_idx","valueLeft * geluRight")}\n  }`}},_i=e=>{Qd(e.inputs),e.compute(Xd(e.inputs))}});var Jd,el,ht,Ii,Ai,Ti,Ei,Oi,Pi,ki,Ri,Bi,Di,Mi=j(()=>{"use strict";Ne();$e();ve();Jd=(e,t,r,o,n,s,u,l,a,p,h,g,b)=>{let w,y;typeof l=="string"?w=y=(R,V)=>`${l}((${R}),(${V}))`:typeof l=="function"?w=y=l:(w=l.scalar,y=l.vector);let _=g?t.length:t,I=g?r.length:r,$=g?o.length:o,x=F("outputData",h,$,4),E=M("aData",a,_,4),A=M("bData",p,I,4),z;if(n)if(s){let R=U.size(t)===1,V=U.size(r)===1,T=t.length>0&&t[t.length-1]%4===0,N=r.length>0&&r[r.length-1]%4===0;R||V?z=x.setByOffset("global_idx",y(R?`${E.type.value}(${E.getByOffset("0")}.x)`:E.getByOffset("global_idx"),V?`${A.type.value}(${A.getByOffset("0")}.x)`:A.getByOffset("global_idx"))):z=`\n            let outputIndices = ${x.offsetToIndices("global_idx * 4u")};\n            let offsetA = ${E.broadcastedIndicesToOffset("outputIndices",x)};\n            let offsetB = ${A.broadcastedIndicesToOffset("outputIndices",x)};\n            ${x.setByOffset("global_idx",y(u||T?E.getByOffset("offsetA / 4u"):`${E.type.value}(${E.getByOffset("offsetA / 4u")}[offsetA % 4u])`,u||N?A.getByOffset("offsetB / 4u"):`${A.type.value}(${A.getByOffset("offsetB / 4u")}[offsetB % 4u])`))}\n          `}else z=x.setByOffset("global_idx",y(E.getByOffset("global_idx"),A.getByOffset("global_idx")));else{if(!s)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let R=(V,T,N="")=>{let te=`aData[indexA${T}][componentA${T}]`,Y=`bData[indexB${T}][componentB${T}]`;return`\n            let outputIndices${T} = ${x.offsetToIndices(`global_idx * 4u + ${T}u`)};\n            let offsetA${T} = ${E.broadcastedIndicesToOffset(`outputIndices${T}`,x)};\n            let offsetB${T} = ${A.broadcastedIndicesToOffset(`outputIndices${T}`,x)};\n            let indexA${T} = offsetA${T} / 4u;\n            let indexB${T} = offsetB${T} / 4u;\n            let componentA${T} = offsetA${T} % 4u;\n            let componentB${T} = offsetB${T} % 4u;\n            ${V}[${T}] = ${N}(${w(te,Y)});\n          `};h===9?z=`\n            var data = vec4<u32>(0);\n            ${R("data",0,"u32")}\n            ${R("data",1,"u32")}\n            ${R("data",2,"u32")}\n            ${R("data",3,"u32")}\n            outputData[global_idx] = dot(vec4<u32>(0x1, 0x100, 0x10000, 0x1000000), vec4<u32>(data));`:z=`\n            ${R("outputData[global_idx]",0)}\n            ${R("outputData[global_idx]",1)}\n            ${R("outputData[global_idx]",2)}\n            ${R("outputData[global_idx]",3)}\n          `}return`\n        ${e.registerUniform("vec_size","u32").declareVariables(E,A,x)}\n\n        ${b??""}\n\n        ${e.mainStart()}\n        ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")}\n        ${z}\n      }`},el=(e,t,r,o,n,s,u=r.dataType)=>{let l=!U.areEqual(r.dims,o.dims),a=r.dims,p=U.size(r.dims),h=!1,g=!1,b=[l];if(l){let y=dt.calcShape(r.dims,o.dims,!1);if(!y)throw new Error("Can\'t perform binary op on the given tensors");a=y,p=U.size(a);let _=U.size(r.dims)===1,I=U.size(o.dims)===1,$=r.dims.length>0&&r.dims[r.dims.length-1]%4===0,x=o.dims.length>0&&o.dims[o.dims.length-1]%4===0;b.push(_),b.push(I),b.push($),b.push(x);let E=1;for(let A=1;A<a.length;A++){let z=r.dims[r.dims.length-A]??1,R=o.dims[o.dims.length-A]??1;if(z===R)E*=z;else break}E%4===0?(g=!0,h=!0):(_||I||$||x)&&(h=!0)}else h=!0;b.push(h);let w=Re(r.dims.length)&&Re(o.dims.length)&&Re(a.length);return{name:e,shaderCache:{hint:t+b.map(y=>y.toString()).join("_"),inputDependencies:w?["rank","rank"]:["dims","dims"]},getShaderSource:y=>Jd(y,r.dims,o.dims,a,h,l,g,n,r.dataType,o.dataType,u,w,s),getRunData:()=>({outputs:[{dims:a,dataType:u}],dispatchGroup:{x:Math.ceil(p/64/4)},programUniforms:w?[{type:"uint32",data:Math.ceil(U.size(a)/4)},...L(r.dims),...L(o.dims),...L(a)]:[{type:"uint32",data:Math.ceil(U.size(a)/4)}]})}},ht=(e,t,r,o,n,s)=>{e.compute(el(t,n??"",e.inputs[0],e.inputs[1],r,o,s))},Ii=e=>{ht(e,"Add",(t,r)=>`${t}+${r}`)},Ai=e=>{ht(e,"Div",(t,r)=>`${t}/${r}`)},Ti=e=>{ht(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4<u32>(${t}==${r})`},void 0,void 0,9)},Ei=e=>{ht(e,"Mul",(t,r)=>`${t}*${r}`)},Oi=e=>{let t=M("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;ht(e,"Pow",{scalar:(o,n)=>`pow_custom(${o},${n})`,vector:(o,n)=>`pow_vector_custom(${o},${n})`},`\n    fn pow_custom(a : ${t}, b : ${t}) -> ${t} {\n      if (b == ${t}(0.0)) {\n        return ${t}(1.0);\n      } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) {\n        return ${t}(pow(f32(a), f32(b))); // NaN\n      }\n      return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b))));\n    }\n    fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> {\n      // TODO: implement vectorized pow\n      return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w));\n    }\n      `)},Pi=e=>{ht(e,"Sub",(t,r)=>`${t}-${r}`)},ki=e=>{ht(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4<u32>(${t}>${r})`},void 0,void 0,9)},Ri=e=>{ht(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4<u32>(${t}<${r})`},void 0,void 0,9)},Bi=e=>{ht(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4<u32>(${t}>=${r})`},void 0,void 0,9)},Di=e=>{ht(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4<u32>(${t}<=${r})`},void 0,void 0,9)}});var rl,nl,ol,al,zi,Ui,Vi=j(()=>{"use strict";$e();je();ve();rl=e=>{if(!e||e.length<1)throw new Error("too few inputs");let t=e[0].dataType,r=e[0].dims.length;for(let o of e){if(o.dataType!==t)throw new Error("input tensors should be one type");if(o.dims.length!==r)throw new Error("input tensors should have the same shape")}},nl=(e,t)=>`\n  fn calculateInputIndex(index: u32) -> u32 {\n    let sizeInConcatAxis = array<u32, ${e}u>(${t});\n    for (var i: u32 = 0u; i < ${e}; i += 1u ) {\n      if (index < sizeInConcatAxis[i]) {\n        return i;\n      }\n    }\n    return ${e}u;\n  }`,ol=(e,t)=>{let r=e.length,o=[];for(let n=0;n<r;++n){let s=t.setByOffset("global_idx",e[n].getByIndices("indices"));r===1?o.push(s):n===0?o.push(`if (inputIndex == ${n}u) { ${s} }`):n===r-1?o.push(`else { ${s} }`):o.push(`else if (inputIndex == ${n}) { ${s} }`)}return o.join(`\n`)},al=(e,t)=>{let r=e[0].dims.slice();if(t>=r.length||t<-1*r.length)throw new Error("axis specified for concat doesn\'t match input dimensionality");let o=t<0?r.length+t:t,n=r.slice(0);for(let A=1;A<e.length;A++){let z=e[A].dims.slice();for(let R=0;R<r.length;R++)if(R===o)n[o]+=z[R];else if(r[R]!==z[R])throw new Error("non concat dimensions must match")}let s=U.size(n),u=new Array(e.length),l=new Array(e.length),a=e[0].dataType,p=0,h=[],g=[],b=[],w=[{type:"uint32",data:s}];for(let A=0;A<e.length;++A)p+=e[A].dims[o],u[A]=p,b.push(Re(e[A].dims.length)),g.push(b[A]?e[A].dims.length:e[A].dims),l[A]=M(`input${A}`,a,g[A]),h.push(b[A]?"rank":"dims"),w.push({type:"uint32",data:u[A]});for(let A=0;A<e.length;++A)b[A]&&w.push(...L(e[A].dims));let y=Re(n.length);y&&w.push(...L(n));let _=y?n.length:n,I=F("output",a,_),$=I.indicesGet("indices",o),x=Array.from(Array(u.length).keys()).map(A=>`uniforms.sizeInConcatAxis${A}`).join(","),E=A=>`\n\n  ${(()=>{A.registerUniform("outputSize","u32");for(let z=0;z<e.length;z++)A.registerUniform(`sizeInConcatAxis${z}`,"u32");return A.declareVariables(...l,I)})()}\n\n  ${nl(u.length,x)}\n\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n    var indices = ${I.offsetToIndices("global_idx")};\n\n    let inputIndex = calculateInputIndex(${$});\n    if (inputIndex != 0u) {\n      let sizeInConcatAxis = array<u32, ${u.length}u>(${x});\n      ${$} -= sizeInConcatAxis[inputIndex - 1u];\n    }\n\n    ${ol(l,I)}\n  }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(s/64)},programUniforms:w}),getShaderSource:E}},zi=(e,t)=>{rl(e.inputs),e.compute(al(e.inputs,t.axis))},Ui=e=>ge({axis:e.axis})});var gt,Qr,It=j(()=>{"use strict";$e();gt=(e,t)=>{switch(e.activation){case"Relu":return{activationFunction:"",applyActivation:`value = max(value, ${t}(0.0));`};case"Sigmoid":return{activationFunction:"",applyActivation:`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`};case"Clip":return{activationFunction:`const clip_min_=${t}(${e.clipMin});const clip_max_=${t}(${e.clipMax});`,applyActivation:"value = clamp(value, clip_min_, clip_max_);"};default:return{activationFunction:"",applyActivation:""}}},Qr=e=>{let t=e?.activation||"";if(t==="Clip"){let[r,o]=e?.activation_params||[Gr,Lr];return{activation:t,clipMax:o,clipMin:r,activationCacheKey:`${t}:${r},${o}`}}return{activation:t,activationCacheKey:t}}});var Ke,Xr,Jr=j(()=>{"use strict";Ke=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Xr=e=>`\n      ${e?"value = value + getBiasByOutputCoords(coords);":""}\n      `});var en,Nn=j(()=>{"use strict";en=e=>`\nfn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n      shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n}\nfn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n  return dot(coords, vec4<i32>(\n    i32(${e}.x), i32(${e}.y), i32(${e}.z), 1));\n}\n`});var il,sl,mr,Ni,ul,fr,dl,tn,hr=j(()=>{"use strict";$e();ve();It();Jr();il=(e,t)=>e?`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `:`\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / innerElementSize + inputCol${t?", batchIndices":""});\n        `,sl=(e,t)=>e?`\n        let ACached0 = mm_Asub[k * innerElementSize][localRow];\n        let ACached1 = mm_Asub[k * innerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * innerElementSize + 2][localRow];\n        ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"}\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"}\n        }`:`\n        for (var i = 0; i < rowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"}\n        }`,mr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32)=>{let a=t[1]*e[1],p=t[0]*e[0],h=n?a:s,g=n?s:a,b=h/t[0],w=s/t[1];if(!((n&&b===4&&e[1]===4||!n&&(b===3||b===4))&&h%t[0]===0&&s%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${n} is true, innerElementSize ${b} and workPerThread[1] ${e[1]} must be 4.\n      Otherwise, innerElementSize ${b} must be 3 or 4.\n  tileAWidth ${h} must be divisible by workgroupSize[0]${t[0]}. tileInner ${s} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return`\nvar<workgroup> mm_Asub: array<array<vec${b}<${r}>, ${h/b}>, ${g}>;\nvar<workgroup> mm_Bsub: array<array<vec4<${r}>, ${p/e[0]}>, ${s}>;\n\nconst rowPerThread = ${e[1]};\nconst colPerThread = ${e[0]};\nconst innerElementSize = ${b};\nconst tileInner = ${s};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n  let localRow = i32(localId.y);\n  let tileRow = localRow * rowPerThread;\n  let tileCol = i32(localId.x);\n\n  let globalRow =i32(globalId.y) * rowPerThread;\n  let globalCol = i32(globalId.x);\n  let batch = ${u?"0":"i32(globalId.z)"};\n  ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n  let globalRowStart = i32(workgroupId.y) * ${a};\n\n  let numTiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n  var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n  var acc: array<vec4<${r}>, rowPerThread>;\n\n  // Loop over shared dimension.\n  let tileRowB = localRow * ${w};\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          ${il(n,o)}\n      }\n\n      // Load one tile of B into local memory.\n      for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {\n          let inputRow = tileRowB + innerRow;\n          let inputCol = tileCol;\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${o?", batchIndices":""});\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      for (var k = 0; k < tileInner / innerElementSize; k = k + 1) {\n          let BCached0 = mm_Bsub[k * innerElementSize][tileCol];\n          let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol];\n          let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol];\n          ${b===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"}\n\n          ${sl(n,b)}\n      }\n\n      workgroupBarrier();\n  }\n\n  for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n  }\n}`},Ni=(e,t)=>e?`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              kStart + inputRow,\n              globalRowStart + inputCol${t?", batchIndices":""});\n            `:`\n            mm_Asub[inputRow][inputCol] = mm_readA(batch,\n              globalRowStart + inputRow,\n              kStart + inputCol${t?", batchIndices":""});\n            `,ul=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",fr=(e,t,r="f32",o,n=!1,s=32,u=!1,l=32,a=!1)=>{let p=e[1]*t[1],h=e[0]*t[0],g=n?p:s,b=n?s:p;if(!(b%t[1]===0&&g%t[0]===0&&s%t[1]===0))throw new Error(`tileAHight ${b} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${g} must be divisible by workgroupSize[0]${t[0]}, tileInner ${s} must be divisible by workgroupSize[1]${t[1]}`);let w=b/t[1],y=g/t[0],_=s/t[1],I=a?`\n    let localRow = i32(localId.y);\n    let localCol = i32(localId.x);\n    let globalRowStart = i32(workgroupId.y) * ${p};\n    let globalColStart = i32(workgroupId.x) * ${h};\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 inputRow = localRow; inputRow < ${b}; inputRow = inputRow + ${t[1]}) {\n        for (var inputCol = localCol; inputCol < ${g}; inputCol = inputCol + ${t[0]}) {\n          ${Ni(n,o)}\n        }\n      }\n      // Load one tile of B into local memory.\n      for (var inputRow = localRow; inputRow < ${s}; inputRow = inputRow + ${t[1]}) {\n            for (var inputCol = localCol; inputCol < ${h}; inputCol = inputCol + ${t[0]}) {\n          mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n            kStart + inputRow,\n            globalColStart + inputCol${o?", batchIndices":""});\n        }\n      }\n      kStart = kStart + tileInner;\n      workgroupBarrier();\n\n      // Compute acc values for a single thread.\n      var BCached : array<${r}, colPerThread>;\n      for (var k = 0; k < tileInner; k = k + 1) {\n        for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n          BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}];\n        }\n        for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n          let ACached = ${n?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`}\n          for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n            acc[innerRow][innerCol] = acc[innerRow][innerCol] +\n                ACached * BCached[innerCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      let gRow = globalRowStart + localRow + innerRow * ${t[1]};\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        let gCol = globalColStart + localCol + innerCol * ${t[0]};\n        mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n      }\n    }\n    `:`\nlet tileRow = i32(localId.y) * rowPerThread;\nlet tileCol = i32(localId.x) * colPerThread;\n\nlet globalRow = i32(globalId.y) * rowPerThread;\nlet globalCol = i32(globalId.x) * colPerThread;\nlet globalRowStart = i32(workgroupId.y) * ${p};\n\nlet tileRowA = i32(localId.y) * ${w};\nlet tileColA = i32(localId.x) * ${y};\nlet tileRowB = i32(localId.y) * ${_};\n// Loop over shared dimension.\nfor (var t = 0; t < numTiles; t = t + 1) {\n  // Load one tile of A into local memory.\n  for (var innerRow = 0; innerRow < ${w}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < ${y}; innerCol = innerCol + 1) {\n      let inputRow = tileRowA + innerRow;\n      let inputCol = tileColA + innerCol;\n      ${Ni(n,o)}\n    }\n  }\n\n  // Load one tile of B into local memory.\n  for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) {\n    for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n      let inputRow = tileRowB + innerRow;\n      let inputCol = tileCol + innerCol;\n      mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n        kStart + inputRow,\n        globalCol + innerCol${o?", batchIndices":""});\n    }\n  }\n  kStart = kStart + tileInner;\n  workgroupBarrier();\n\n  // Compute acc values for a single thread.\n  var BCached : array<${r}, colPerThread>;\n  for (var k = 0; k < tileInner; k = k + 1) {\n    for (var inner = 0; inner < colPerThread; inner = inner + 1) {\n      BCached[inner] = mm_Bsub[k][tileCol + inner];\n    }\n\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      ${ul(n)}\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n      }\n    }\n  }\n\n  workgroupBarrier();\n}\n\nfor (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n  for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n    mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n        acc[innerRow][innerCol]);\n  }\n}\n`;return`\n  var<workgroup> mm_Asub : array<array<${r}, ${g}>, ${b}>;\n  var<workgroup> mm_Bsub : array<array<${r}, ${h}>, ${s}>;\n  const rowPerThread = ${e[1]};\n  const colPerThread = ${e[0]};\n  const tileInner = ${s};\n\n@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]})\nfn main(@builtin(local_invocation_id) localId : vec3<u32>,\n        @builtin(global_invocation_id) globalId : vec3<u32>,\n        @builtin(workgroup_id) workgroupId : vec3<u32>) {\n    let batch = ${u?"0":"i32(globalId.z)"};\n    ${o?`let batchIndices = ${o.offsetToIndices("u32(batch)")};`:""}\n    let numTiles = ${u?`${Math.ceil(l/s)}`:"(uniforms.dimInner - 1) / tileInner + 1"};\n    var kStart = ${u?`i32(globalId.z) * ${l}`:"0"};\n\n    var acc : array<array<${r}, colPerThread>, rowPerThread>;\n\n    // Without this initialization strange values show up in acc.\n    for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) {\n      for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) {\n        acc[innerRow][innerCol] = 0.0;\n      }\n    }\n    ${I}\n  }\n`},dl=(e,t,r,o,n,s=!1)=>{let[u,l,a]=n,[p,h,g,b]=o,w=Ft(u,a),y=Ft(l,a),_=Le(o[0].type.tensor),I=()=>{let E=h.rank,A=p.rank,z=`var aIndices: ${h.type.indices};`;for(let R=E-2-1,V=A-1;R>=0;R--,V--)z+=`\naIndices[${R}] = ${A>1?`batchIndices[${V}]`:"batchIndices"};`;return w.forEach(R=>{z+=`\naIndices[${R}] = 0;`}),z+=`\naIndices[${E-2}] = u32(row);\n                   aIndices[${E-1}] = u32(colIn);`,z},$=()=>{let E=g.rank,A=p.rank,z=`var bIndices: ${g.type.indices};`;for(let R=E-2-1,V=A-1;R>=0;R--,V--)z+=`\nbIndices[${R}] = ${A>1?`batchIndices[${V}]`:"batchIndices"};`;return y.forEach(R=>{z+=`\nbIndices[${R}] = 0;`}),z+=`\nbIndices[${E-2}] = u32(row);\n                   bIndices[${E-1}] = u32(colIn);`,z};return`\n    fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ke(e,_)} {\n      var value = ${Ke(e,_)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimAOuter && col < uniforms.dimInner)\n      {\n        ${I()}\n        value = ${h.getByIndices("aIndices")};\n      }\n      return value;\n    }\n\n    fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${p.type.indices}) -> ${Ke(e,_)} {\n      var value = ${Ke(e,_)}(0.0);\n      let col = colIn * ${e};\n      if(row < uniforms.dimInner && col < uniforms.dimBOuter)\n      {\n        ${$()}\n        value = ${g.getByIndices("bIndices")};\n      }\n      return value;\n    }\n\n    fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ke(e,_)}) {\n      let col = colIn * ${e};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n        var value = valueIn;\n        let coords = vec3<i32>(batch, row, colIn);\n        ${t?`value = value + ${s?"bias[colIn]":`${Ke(e,_)}(bias[row])`};`:""}\n        ${r}\n        ${b.setByIndices("vec3<u32>(coords)","value")}\n      }\n    }\n    `},tn=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s.slice(0,-2),a=u.slice(0,-2),p=o?o.slice(0,-2):r.slice(0,-2),h=Re(p.length),g=h?p.length:p,b=Fr("batchDims",e[0].dataType,g,1),w=U.size(p),y=s[s.length-2],_=s[s.length-1],I=u[u.length-1],$=_%4===0&&I%4===0,x=y<=8?[4,1,1]:[4,4,1],E=[8,8,1],A=[Math.ceil(I/E[0]/x[0]),Math.ceil(y/E[1]/x[1]),Math.ceil(w/E[2]/x[2])],z=Le(e[0].dataType),R=$?4:1,V=[...l,y,_/R],T=Re(V.length),N=T?V.length:V,te=[...a,_,I/R],Y=Re(te.length),K=Y?te.length:te,Q=[w,y,I/R],Z=M("a",e[0].dataType,N,R),Ee=M("b",e[1].dataType,K,R),Pe=F("result",e[0].dataType,Q.length,R),fe=[Z,Ee],Ie=[{type:"int32",data:y},{type:"int32",data:I},{type:"int32",data:_}];h&&Ie.push(...L(p)),T&&Ie.push(...L(V)),Y&&Ie.push(...L(te));let he=[];he.push(T?"rank":"dims"),he.push(Y?"rank":"dims");let ye=e.length>2,{activationFunction:We,applyActivation:De}=gt(t,Pe.type.value),Ge=dl(R,ye,De,[b,Z,Ee,Pe],[l,a,p],n);if(ye){let ee=n?R:1;fe.push(M("bias",e[2].dataType,e[2].dims.length,ee)),Ie.push(...L(e[2].dims)),he.push("rank")}Ie.push(...L(Q));let G=ee=>`\n  ${ee.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").registerInternalVariables(b).declareVariables(...fe,Pe)}\n  ${We}\n  ${Ge}\n  ${$?mr(x,E,z,b):fr(x,E,z,b)}\n                   `;return{name:"MatMul",shaderCache:{hint:t.activationCacheKey+`${x}${$}${n}`,inputDependencies:he},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:A[0],y:A[1],z:A[2]},programUniforms:Ie}),getShaderSource:G}}});var ll,Wi,Hi=j(()=>{"use strict";Ct();ve();It();Jr();Nn();hr();ll=(e,t,r,o,n=!1,s,u=4,l=4,a=4,p="f32")=>{let h=Y=>{switch(Y){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${p}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${Y} is not supported.`)}},g=Y=>{switch(Y){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${Y} is not supported.`)}},b=e?`\n    let coord = vec4<i32>(batch, xRow, xCol, xCh);\n    `:`\n    let coord = vec4<i32>(batch, xCh, xRow, xCol);\n    `,w=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,y=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",_=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",I=e?"row":"col",$=e?"col":"row",x=`\n    let inChannels = i32(uniforms.w_shape[2]);\n    let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n    let outRow = ${I} / outWidth;\n    let outCol = ${I} % outWidth;\n\n    let WRow = ${$} / (filterDims[1] * inChannels);\n    let WCol = ${$} / inChannels % filterDims[1];\n    let xRow = outRow * stride[0] + dilation[0] * WRow - pad[0];\n    let xCol = outCol * stride[1] + dilation[1] * WCol - pad[1];\n    let xCh = ${$} % inChannels;\n    var resData = ${Ke(u,p)}(0.0);\n    // The bounds checking is always needed since we use it to pad zero for\n    // the \'same\' padding type.\n    if (xRow >= 0 && xRow < ${y} && xCol >= 0 && xCol < ${_}) {\n      ${b}\n      let xIndex = getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape));\n      ${h(u)}\n    }\n    return resData;`,E=e?t&&o?`\n    let col = colIn * ${u};\n    ${x}`:`\n    let col = colIn * ${u};\n    if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${x}\n    }\n    return ${Ke(u,p)}(0.0);`:o&&r?`\n    let col = colIn * ${u};\n    ${x}`:`\n    let col = colIn * ${u};\n    if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n      ${x}\n    }\n    return ${Ke(u,p)}(0.0);`,A=`${g(l)}`,z=Ke(a,p),R=e?Ke(u,p):Ke(l,p),V=e?Ke(l,p):Ke(u,p),{activationFunction:T,applyActivation:N}=gt(s,z);return`\n    ${T}\n    fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${R} {\n      ${e?E:A}\n    }\n\n    fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${V} {\n      ${e?A:E}\n    }\n\n    fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${z}) {\n      let col = colIn * ${a};\n      if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n      {\n      var value = valueIn;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${w}\n      ${Xr(n)}\n      ${N}\n      setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n      }\n    }`},Wi=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],h=r[0],g=a?r[2]:r[3],b=a?r[1]:r[2],w=a?r[3]:r[1],y=a&&(p%4===0||p%3===0)&&w%4===0,_=a?w:g*b,I=a?g*b:w,$=[8,8,1],x=o<=8?[4,1,1]:[4,4,1],E=[Math.ceil(_/$[0]/x[0]),Math.ceil(I/$[1]/x[1]),Math.ceil(h/$[2]/x[2])];Be("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${E}`);let A=y?a&&p%4!==0?3:4:1,z=$[1]*x[1],R=$[0]*x[0],V=Math.max($[0]*A,$[1]),T=o%z===0,N=n%R===0,te=s%V===0,Y=y?[A,4,4]:[1,1,1],K=Le(e[0].dataType),Q=y?4:1,Z=[{type:"int32",data:o},{type:"int32",data:n},{type:"int32",data:s}],Ee=M("x",e[0].dataType,e[0].dims.length,A===3?1:A),Pe=M("w",e[1].dataType,e[1].dims.length,Q),fe=[Ee,Pe];Z.push(...L(e[0].dims)),Z.push(...L(e[1].dims));let Ie=`\n      fn setOutputAtIndex(flatIndex : i32, value : ${y?`vec4<${K}>`:K}) {\n        result[flatIndex] = ${y?`vec4<${K}>`:K}(value);\n      }\n      fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${y?`vec4<${K}>`:K}) {\n        let flatIndex = getOutputIndexFromCoords(vec4<i32>(d0, d1, d2, d3));\n        setOutputAtIndex(flatIndex ${y?"/ 4":""}, value);\n      }`;if(u){let ye=M("bias",e[2].dataType,e[2].dims.length,Q);fe.push(ye),Z.push(...L(e[2].dims)),Ie+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${y?`vec4<${K}>`:K} {\n          return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n        }`}let he=F("result",e[0].dataType,r.length,Q);return Z.push(...L(r)),{name:"Conv2DMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:Z}),getShaderSource:ye=>`\n        ${en("uniforms.result_strides")}\n        //struct Uniforms { xShape : vec4<i32>, wShape : vec4<i32>, outShape : vec4<i32>,\n        //  outShapeStrides: vec3<i32>, filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>,\n        //  dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32 };\n        ${ye.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...fe,he)}\n        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[0]}, ${t.kernelShape[1]});\n        const pad : vec2<i32> = vec2<i32>(${t.pads[0]}, ${t.pads[1]});\n        const stride : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});\n        ${Ie}\n        ${ll(a,T,N,te,u,t,Y[0],Y[1],Y[2],K)}\n            ${y?mr(x,$,K,void 0,!a,V):fr(x,$,K,void 0,!a,V,!1,void 0,l)}`}}});var Wn,Gi=j(()=>{"use strict";$e();ve();Gn();It();Wn=(e,t,r)=>{let o=e.length>2,n=o?"value += b[output_channel];":"",s=e[0].dims,u=e[1].dims,l=u[0]/t.group,a=t.format==="NHWC",p=Hn(s,u,t.dilations,t.pads,t.strides,a),h=U.size(p),g=F("output",e[0].dataType,p),{activationFunction:b,applyActivation:w}=gt(t,g.type.value),y=M("x",e[0].dataType,s),_=M("w",e[1].dataType,u),I=[y,_];o&&I.push(M("b",e[2].dataType,e[2].dims));let $=x=>`\n  const strides: vec2<u32> = vec2(${t.strides[0]}u, ${t.strides[1]}u);\n  const pads: vec2<u32> = vec2(${t.pads[0]}u, ${t.pads[1]}u);\n\n  ${x.declareVariables(...I,g)}\n\n  ${b}\n\n  ${x.mainStart()}\n    ${x.guardAgainstOutOfBoundsWorkgroupSizes(h)}\n\n    let outputIndices = ${g.offsetToIndices("global_idx")};\n    let batch: u32 = outputIndices[0];\n    let output_channel: u32 = outputIndices[${a?3:1}];\n    let xRCCorner: vec2<u32> = vec2<u32>(outputIndices[${a?1:2}], outputIndices[${a?2:3}]) * strides - pads;\n    let group_id: u32 = output_channel / ${l}u;\n\n    var value: ${g.type.value} = ${g.type.value}(0);\n    for (var wInChannel: u32 = 0u; wInChannel < ${u[1]}u; wInChannel++) {\n      let input_channel = group_id * ${u[1]}u + wInChannel;\n      for (var wHeight: u32 = 0u; wHeight < ${u[2]}u; wHeight++) {\n        let xHeight = xRCCorner.x + wHeight * ${t.dilations[0]}u;\n\n        if (xHeight < 0u || xHeight >= ${s[a?1:2]}u) {\n          continue;\n        }\n\n        for (var wWidth: u32 = 0u; wWidth < ${u[3]}u; wWidth++) {\n          let xWidth = xRCCorner.y + wWidth * ${t.dilations[1]}u;\n          if (xWidth < 0u || xWidth >= ${s[a?2:3]}u) {\n            continue;\n          }\n\n          let xVal = ${a?y.get("batch","xHeight","xWidth","input_channel"):y.get("batch","input_channel","xHeight","xWidth")};\n          let wVal = ${_.get("output_channel","wInChannel","wHeight","wWidth")};\n          value += xVal*wVal;\n        }\n      }\n    }\n    ${n}\n    ${w}\n    ${g.setByOffset("global_idx","value")}\n  }`;return{name:"GroupedConv",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r?r(p):p,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)}}),getShaderSource:$}}});var Ln,cl,Li,Fn=j(()=>{"use strict";$e();hr();ve();It();Ln=(e,t,r,o,n=!1)=>{let s=e[0].dims,u=e[1].dims,l=s[s.length-2],a=u[u.length-1],p=s[s.length-1],h=Fe(a),g=Fe(p),b=Fe(l),w=U.size(r)/h/b,y=e.length>2,_=o?o.slice(0,-2):r.slice(0,-2),$=[U.size(_),l,a],x=[{type:"uint32",data:w},{type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:p},...L(_),...L(s),...L(u)];y&&x.push(...L(e[2].dims)),x.push(...L($));let E=A=>{let z=Fr("batch_dims",e[0].dataType,_.length),R=M("a",e[0].dataType,s.length,g),V=M("b",e[1].dataType,u.length,h),T=F("output",e[0].dataType,$.length,h),{activationFunction:N,applyActivation:te}=gt(t,T.type.value),Y=[R,V],K="";if(y){let he=n?h:1;Y.push(M("bias",e[2].dataType,e[2].dims.length,he)),K=`${n?`value += bias[col / ${he}];`:`value += ${T.type.value}(bias[row + i]);`}`}let Q=s.slice(0,-2),Z=u.slice(0,-2),Ee=Ft(Q,_),Pe=Ft(Z,_),fe=(he,ye)=>{let We=he.rank,De=he.name;if(We===2)return`var ${De}_indices = ${he.type.indices}(0u, 0u);`;let Ge=z.rank,G=`var ${De}_indices: ${he.type.indices};`;for(let ee=We-2-1,be=Ge-1;ee>=0;ee--,be--)G+=`\n${De}_indices[${ee}] = ${Ge>1?`batch_indices[${be}]`:"batch_indices"};`;return ye.forEach(ee=>{G+=`\n${De}_indices[${ee}] = 0;`}),G+=`${De}_indices[${We-2}] = 0u;\n                     ${De}_indices[${We-1}] = 0u;`,G},Ie=()=>{let he=`var a_data: ${R.type.value};`;for(let ye=0;ye<g;ye++)he+=`\n              let b_data${ye} = b[(b_offset + (k + ${ye}) * uniforms.N + col) / ${h}];`;for(let ye=0;ye<b;ye++){he+=`a_data = a[(a_offset + (row + ${ye}) * uniforms.K + k) / ${g}];`;for(let We=0;We<g;We++)he+=`\n            values[${ye}] = fma(${V.type.value}(a_data${g===1?"":`[${We}]`}), b_data${We}, values[${ye}]);\n`}return he};return`\n  ${A.registerUniform("outputSize","u32").registerUniform("M","u32").registerUniform("N","u32").registerUniform("K","u32").registerInternalVariables(z).declareVariables(...Y,T)}\n  ${N}\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n    let col = (global_idx % (uniforms.N / ${h})) * ${h};\n    var index1 = global_idx / (uniforms.N / ${h});\n    let stride1 = uniforms.M / ${b};\n    let row = (index1 % stride1) * ${b};\n    let batch = index1 / stride1;\n\n    ${r.length===2?"":`let batch_indices = ${z.offsetToIndices("batch")};`}\n    ${fe(R,Ee)}\n    let a_offset = ${R.indicesToOffset("a_indices")};\n    ${fe(V,Pe)}\n    let b_offset = ${V.indicesToOffset("b_indices")};\n    var values: array<${T.type.value}, ${b}>;\n    for (var k: u32 = 0u; k < uniforms.K; k = k + ${g}) {\n      ${Ie()}\n    }\n    for (var i = 0u; i < ${b}u; i++) {\n      var value = values[i];\n      ${K}\n      ${te}\n      let cur_indices = ${T.type.indices}(batch, row + i, col);\n      let offset = ${T.indicesToOffset("cur_indices")};\n      ${T.setByOffset(`offset / ${h}`,"value")};\n    }\n  }\n  `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activationCacheKey}_${h}_${g}_${b}_${n}`,inputDependencies:y?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:x}),getShaderSource:E}},cl=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},Li=e=>{cl(e.inputs);let t=dt.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can\'t use matmul on the given tensors");let r=t[t.length-1],o=e.inputs[0].dims[e.inputs[0].dims.length-1];r<8&&o<8?e.compute(Ln(e.inputs,{activation:"",activationCacheKey:""},t)):e.compute(tn(e.inputs,{activation:"",activationCacheKey:""},t))}});var Hn,Fi,pl,ji,jn,ml,fl,qn,Gn=j(()=>{"use strict";$e();je();Hi();hr();Gi();It();Fn();jt();Hn=(e,t,r,o,n,s)=>{let u=e[0],l=e.slice(s?1:2,s?3:4),a=l.length,p=t[0],g=t.slice(2).map((y,_)=>y+(y-1)*(r[_]-1)),w=l.map((y,_)=>y+o[_]+o[_+a]).map((y,_)=>Math.floor((y-g[_]+n[_])/n[_]));return w.splice(0,0,u),w.splice(s?3:1,0,p),w},Fi=[2,3,1,0],pl=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support conv 1D and 2D");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],o=e[1].dims[1]*t.group;if(r!==o)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let n=e[0].dims.length-2;if(t.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(t.strides.length!==n)throw new Error(`strides should be ${n}D`);if(t.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},ji=(e,t)=>{let r=e.kernelShape.slice();for(let s=2;s<t[1].dims.length;++s)r[s-2]===0&&(r[s-2]=t[1].dims[s]);let o=e.pads.slice();Bt.adjustPadsBasedOnAutoPad(t[0].dims,e.strides,e.dilations,r,o,e.format==="NHWC",e.autoPad);let n=Object.assign({},e);return Object.assign(n,{kernelShape:r,pads:o,cacheKey:e.cacheKey}),n},jn=e=>{let t=Qr(e),r=e.format,o=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],n=e.dilations,s=e.group,u=e.kernel_shape,l=e.pads,a=e.strides,p=e.w_is_const();return ge({autoPad:o,format:r,dilations:n,group:s,kernelShape:u,pads:l,strides:a,wIsConst:p,...t})},ml=(e,t,r)=>{let o=ji(r,t),n=r.format==="NHWC";if(r.group!==1){e.compute(Wn(t,o));return}let s=t.length===3,u=t[0].dims[n?1:2],l=t[0].dims[n?2:3],a=t[0].dims[n?3:1],p=t[1].dims[2],h=t[1].dims[3],g=Hn(t[0].dims,t[1].dims,r.dilations,o.pads,r.strides,n),b=g[n?1:2],w=g[n?2:3],y=g[n?3:1],_=n&&p===u&&h===l&&r.pads[0]===0&&r.pads[1]===0;if(_||p===1&&h===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let R=g[0],V,T,N,te=[];if(n){let Q=e.kernelCustomData.wT??e.compute(it(t[1],Fi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=Q),_){let Z=u*l*a;V=t[0].reshape([1,R,Z]),T=Q.reshape([1,Z,y]),N=[1,R,y]}else V=t[0].reshape([R,u*l,a]),T=Q.reshape([1,a,y]),N=[R,b*w,y];te.push(V),te.push(T)}else V=t[0].reshape([R,a,u*l]),T=t[1].reshape([1,y,a]),N=[R,y,b*w],te.push(T),te.push(V);s&&te.push(t[2]);let Y=N[2],K=te[0].dims[te[0].dims.length-1];Y<8&&K<8?e.compute(Ln(te,o,g,N,n),{inputs:te}):e.compute(tn(te,o,g,N,n),{inputs:te});return}let I=!0,$=e.kernelCustomData.wT??e.compute(it(t[1],Fi),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=$);let x=[t[0],$];s&&x.push(t[2]);let E=n?b*w:y,A=n?y:b*w,z=p*h*a;e.compute(Wi(x,o,g,E,A,z,s,I),{inputs:x})},fl=(e,t)=>{let r=t.format==="NHWC",o=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&o.push(e.inputs[2]);let n=[0,t.pads[0],0,t.pads[1]],s=[1].concat(t.strides),u=[1].concat(t.dilations),l=[1].concat(t.kernelShape),a=ji({...t,pads:n,strides:s,dilations:u,kernelShape:l},o);e.compute(Wn(o,a,p=>r?[p[0],p[2],p[3]]:[]))},qn=(e,t)=>{pl(e.inputs,t),e.inputs[0].dims.length===3?fl(e,t):ml(e,e.inputs,t)}});var hl,qi,Ki=j(()=>{"use strict";Ct();ve();It();Jr();Nn();hr();hl=(e,t=!1,r,o=4)=>{let n=Ke(o,"f32"),s=x=>{switch(x){case 1:return"return w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];";case 4:return`\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = w[getIndexFromCoords4D(coord, vec4<i32>(uniforms.w_shape))];\n            let v1 = w[getIndexFromCoords4D(coord1, vec4<i32>(uniforms.w_shape))];\n            let v2 = w[getIndexFromCoords4D(coord2, vec4<i32>(uniforms.w_shape))];\n            let v3 = w[getIndexFromCoords4D(coord3, vec4<i32>(uniforms.w_shape))];\n            return vec4<f32>(v0, v1, v2, v3);\n            `;default:throw new Error(`innerElementSize ${x} is not supported.`)}},u=e?`\n      let coord = vec4<i32>(batch, iXR, iXC, xCh);\n      `:`\n      let coord = vec4<i32>(batch, xCh, iXR, iXC);\n      `,l=e?`\n    let coords = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    `:`\n    let coords = vec4<i32>(\n      batch,\n      row,\n      col / outWidth,\n      col % outWidth);\n    `,a=e?"outBackprop[1]":"outBackprop[2]",p=e?"outBackprop[2]":"outBackprop[3]",h=e?"row":"col",g=e?"col":"row",b=`\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      let outRow = ${h} / outWidth;\n      let outCol = ${h} % outWidth;\n\n      let WRow = ${g} / (filterDims[1] * inChannels);\n      let WCol = ${g} / inChannels % filterDims[1];\n      let xR = f32(outRow - pads[0] + dilation[0] * WRow) / f32(strides[0]);\n      let xC = f32(outCol - pads[1] + dilation[1] * WCol) / f32(strides[1]);\n      if (xR < 0.0 || xR >= f32(${a}) || fract(xR) > 0.0) {\n        return ${n}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(${p}) || fract(xC) > 0.0) {\n        return ${n}(0.0);\n      }\n      let iXR = i32(xR);\n      let iXC = i32(xC);\n      let xCh = ${g} % inChannels;\n      ${u}\n      return x[getIndexFromCoords4D(coord, vec4<i32>(uniforms.x_shape))/${o}];`,w=e?`\n      let col = colIn * ${o};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${b}\n      }\n      return ${n}(0.0);`:`\n      let col = colIn * ${o};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${b}\n      }\n      return ${n}(0.0);`,y=`\n      let col = colIn * ${o};\n      let inChannels = ${e?"outBackprop[3]":"outBackprop[1]"};\n      let coordX = filterDims.x - 1 - row / (filterDims[1] * inChannels);\n      let coordY = filterDims.y - 1 - (row / inChannels) % filterDims[1];\n      if (${e?"row < uniforms.dimInner && col < uniforms.dimBOuter":"row < uniforms.dimInner && col < uniforms.dimAOuter"}  && coordX >= 0 && coordY >= 0) {\n        let rowInner = row % inChannels;\n        let coord = vec4<i32>(coordX, coordY, col, rowInner);\n        ${s(o)}\n      }\n      return ${n}(0.0);\n      `,{activationFunction:_,applyActivation:I}=gt(r,n);return`\n      ${_}\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${n} {\n    ${e?w:y}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${n} {\n    ${e?y:w}\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${n}) {\n    let col = colIn * ${o};\n    if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n      var value = valueInput;\n      let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"};\n      ${l}\n      ${Xr(t)}\n      ${I}\n      result[getIndexFromCoords4D(coords, vec4<i32>(uniforms.result_shape))/${o}] = value;\n    }\n  }`},qi=(e,t,r,o,n,s,u,l)=>{let a=t.format==="NHWC",p=a?e[0].dims[3]:e[0].dims[1],h=r[0],g=a?r[2]:r[3],b=a?r[1]:r[2],w=a?r[3]:r[1],y=a?p%4===0&&w%4===0:g%4===0&&w%4===0,_=a?w:g*b,I=a?g*b:w,$=y?[8,8,1]:[_<=4||I<=4?4:16,_>4&&I<=4?4:16,1],x=y?[4,4,1]:[_<=4?1:4,_>4&&I<=4?1:4,1],E=[Math.ceil(_/$[0]/x[0]),Math.ceil(I/$[1]/x[1]),Math.ceil(h/$[2]/x[2])];Be("verbose",()=>`[conv_backprop_mm_webgpu] dispatch = ${E}`);let A=y?4:1,z=Math.max($[0]*A,$[1]),R=y?4:1,V=[{type:"int32",data:o},{type:"int32",data:n},{type:"int32",data:s}],T=M("x",e[0].dataType,e[0].dims.length,R),N=M("w",e[1].dataType,e[1].dims.length,1),te=F("result",e[0].dataType,r.length,R),Y=[T,N];V.push(...L(e[0].dims)),V.push(...L(e[1].dims));let K="";if(u){let Q=M("bias",e[2].dataType,e[2].dims.length,R);Y.push(Q),V.push(...L(e[2].dims)),K+=`\n        fn getBiasByOutputCoords(coords : vec4<i32>) -> ${y?"vec4<f32>":"f32"} {\n          return bias[coords.${a?"w":"y"}${y?"/ 4":""}];\n        }`}return V.push(...L(r)),{name:"Conv2DTransposeMatMul",shaderCache:{hint:t.cacheKey},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:E[0],y:E[1],z:E[2]},programUniforms:V}),getShaderSource:Q=>`\n        ${en("uniforms.result_strides")}\n        ${Q.registerUniform("dimAOuter","i32").registerUniform("dimBOuter","i32").registerUniform("dimInner","i32").declareVariables(...Y,te)};\n        const outBackprop : vec4<i32> = vec4<i32>(${e[0].dims.join(",")});\n        const filterDims : vec2<i32> = vec2<i32>(${t.kernelShape[a?1:2]}, ${t.kernelShape[a?2:3]});\n        const effectiveFilterDims : vec2<i32> = filterDims + vec2<i32>(\n              ${t.dilations[0]<=1?0:(t.kernelShape[a?1:2]-1)*(t.dilations[0]-1)},\n              ${t.dilations[1]<=1?0:(t.kernelShape[a?2:3]-1)*(t.dilations[1]-1)});\n        const pads : vec2<i32> = vec2<i32>(i32(effectiveFilterDims[0]) - 1 - (${t.pads[0]+t.pads[2]})/2,\n                                         i32(effectiveFilterDims[1]) - 1 - (${t.pads[1]+t.pads[3]})/2);\n        const strides : vec2<i32> = vec2<i32>(${t.strides[0]}, ${t.strides[1]});\n        const dilation : vec2<i32> = vec2<i32>(${t.dilations[0]}, ${t.dilations[1]});\n        const dimAOuter : i32 = ${o};\n        const dimBOuter : i32 = ${n};\n        const dimInner : i32 = ${s};\n        ${K}\n        ${hl(a,u,t,A)}\n        ${y?mr(x,$,"f32",void 0,!a,z):fr(x,$,"f32",void 0,!a,z,!1,void 0,l)}`}}});var gl,Kn,Yi=j(()=>{"use strict";Ct();$e();ve();gl=(e,t,r,o,n,s,u=!1,l)=>{let a=r.format==="NHWC",p=a?1:2,h=a?2:3,g=a?3:1,b=U.size(o),w=u?2:1,y=r.group,_=t[1].dims,I=_[0]/y,$=_[1],x=`\n  fn setOutputAtIndex(flatIndex : u32, value : ${u?`vec4<${l}>`:l}) {\n    result[flatIndex] = ${u?`vec4<${l}>`:l}(value);\n  }`;n&&(x+=`\n    fn getBiasByOutputCoords(coords : vec4<u32>) -> ${u?`vec4<${l}>`:l} {\n      return bias[coords.${a?"w":"y"}${u?"/ 4":""}];\n    }`);let E=u?4:1,A=M("W",t[1].dataType,t[1].dims,E),z=M("Dy",t[0].dataType,t[0].dims,E),R=[z,A];n&&R.push(M("bias",t[2].dataType,[o[g]],E));let V=F("result",t[0].dataType,o,E),T=`{\n        let batch: u32 = ${s?"global_id.z":"workgroup_id.z"} / outShape[1];\n        let r = ${s?"global_id.z":"workgroup_id.z"} % outShape[1];\n        let c = ${s?"global_id.y":"workgroup_id.y"} * ${w};\n        let d1: u32 = ${s?"global_id.x":"workgroup_id.x"} * 4;\n\n        let dyCorner = vec2<i32>(i32(r), i32(c)) - vec2<i32>(pads);\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd: array<vec4<${l}>, ${w}>;\n        for (var i = 0; i < ${w}; i++) {\n          dotProd[i] = vec4<${l}>(0.0);\n        }\n        for (var wR: u32 = 0; wR < filterDims[0]; wR = wR + 1) {\n          var dyR = (${l}(dyCorner.x) + ${l}(wR)) / ${l}(strides.x);\n          let wRPerm = filterDims[0] - 1 - wR;\n          if (dyR < 0.0 || dyR >= ${l}(outBackprop[1]) ||\n              fract(dyR) > 0.0 || wRPerm < 0) {\n            continue;\n          }\n          let idyR: u32 = u32(dyR);\n\n          for (var wC: u32 = 0; wC < filterDims[1]; wC = wC + 1) {\n            let dyC = (${l}(dyCorner.y) + ${l}(wC)) / ${l}(strides.y);\n            let dyC2 = (${l}(dyCorner.y) + 1.0 + ${l}(wC)) / ${l}(strides.y);\n            let wCPerm = filterDims[1] - 1 - wC;\n            if (wCPerm < 0) {\n              continue;\n            }\n            var bDyCVal = true;\n            var bDyCVal2 = true;\n            if (dyC < 0.0 || dyC >= ${l}(outBackprop[2]) ||\n                fract(dyC) > 0.0) {\n              bDyCVal = false;\n            }\n            if (dyC2 < 0.0 || dyC2 >= ${l}(outBackprop[2]) ||\n                fract(dyC2) > 0.0) {\n              bDyCVal2 = false;\n            }\n\n            let idyC: u32 = u32(dyC);\n            let idyC2: u32 = u32(dyC2);\n            if (bDyCVal && bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2 :u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${z.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${l}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n\n                xValue =  ${z.get("batch","idyR","idyC2","d2")};\n\n                dotProd[1] = dotProd[1] + vec4<${l}>(dot(xValue, wValue0),\n                                                    dot(xValue, wValue1),\n                                                    dot(xValue, wValue2),\n                                                    dot(xValue, wValue3));\n              }\n            } else if (bDyCVal) {\n              let d2Length = outBackprop[${g}];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${z.get("batch","idyR","idyC","d2")};\n                let tmpval = vec4<${l}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[0] = dotProd[0] + tmpval;\n              }\n            } else if (bDyCVal2) {\n              let d2Length = outBackprop[3];\n              for (var d2: u32 = 0; d2 < d2Length; d2 = d2 + 4) {\n                let wValue0 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1","d2")};\n                let wValue1 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 1","d2")};\n                let wValue2 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 2","d2")};\n                let wValue3 = ${A.get("u32(wRPerm)","u32(wCPerm)","d1 + 3","d2")};\n\n                var xValue = ${z.get("batch","idyR","idyC2","d2")};\n                let tmpval = vec4<${l}>(dot(xValue, wValue0),\n                                      dot(xValue, wValue1),\n                                      dot(xValue, wValue2),\n                                      dot(xValue, wValue3));\n                dotProd[1] = dotProd[1] + tmpval;\n              }\n            }\n          }\n        }\n\n        for (var i: u32 = 0; i < ${w}; i = i + 1) {\n          let value = dotProd[i] + ${n?"bias[c+i]":`vec4<${l}>(0.0)`};\n          ${V.set("batch","r","c + i","d1","value")};\n        }\n      }`,N=`\n          let outputIndices = ${V.offsetToIndices("global_idx")};\n          let batch = ${V.indicesGet("outputIndices",0)};\n          let d1 = ${V.indicesGet("outputIndices",g)};\n          let r = ${V.indicesGet("outputIndices",p)};\n          let c = ${V.indicesGet("outputIndices",h)};\n          let dyCorner = vec2<i32>(i32(r), i32(c)) - pads;\n          let dyRCorner = dyCorner.x;\n          let dyCCorner = dyCorner.y;\n          let groupId = d1 / ${$};\n          let wOutChannel = d1 - groupId * ${$};\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 = ${l}(0.0);\n          for (var wR: u32 = 0; wR < effectiveFilterDims.x; wR = wR + 1) {\n            if (wR % dilations.x != 0) {\n              continue;\n            }\n            let dyR = (${l}(dyRCorner) + ${l}(wR)) / ${l}(strides[0]);\n            let wRPerm = filterDims.x - 1 - wR / dilations.x;\n            if (dyR < 0.0 || dyR >= ${l}(outBackprop[${p}]) || fract(dyR) > 0.0 ||\n                wRPerm < 0) {\n              continue;\n            }\n            let idyR: u32 = u32(dyR);\n\n            for (var wC: u32 = 0; wC < effectiveFilterDims.y; wC = wC + 1) {\n              if (wC % dilations.y != 0) {\n                continue;\n              }\n              let dyC = (${l}(dyCCorner) + ${l}(wC)) / ${l}(strides.y);\n              let wCPerm = filterDims.y - 1 - wC / dilations.y;\n              if (dyC < 0.0 || dyC >= ${l}(outBackprop[${h}]) ||\n                  fract(dyC) > 0.0 || wCPerm < 0) {\n                continue;\n              }\n              let idyC: u32 = u32(dyC);\n              var inputChannel = groupId * ${I};\n              for (var d2: u32 = 0; d2 < ${I}; d2 = d2 + 1) {\n                let xValue = ${a?z.get("batch","idyR","idyC","inputChannel"):z.get("batch","inputChannel","idyR","idyC")};\n                let wValue = ${A.get("inputChannel","wOutChannel","u32(wRPerm)","u32(wCPerm)")};\n                dotProd = dotProd + xValue * wValue;\n                inputChannel = inputChannel + 1;\n              }\n            }\n          }\n          let value = dotProd + ${n?"bias[d1]":`${l}(0.0)`};\n          ${V.setByOffset("global_idx","value")};\n        `;return`\n  ${e.declareVariables(...R,V)}\n  ${x}\n  const outShape : vec4<u32> = vec4<u32>(${o.join(",")});\n  const outBackprop : vec4<u32> = vec4<u32>(${t[0].dims.join(",")});\n  const strides : vec2<u32> = vec2<u32>(${r.strides[0]}, ${r.strides[1]});\n  const filterDims : vec2<u32> = vec2<u32>(${r.kernelShape[a?1:2]}, ${r.kernelShape[a?2:3]});\n  const dilations : vec2<u32> = vec2<u32>(${r.dilations[0]}, ${r.dilations[1]});\n  const effectiveFilterDims : vec2<u32> = filterDims + vec2<u32>(\n          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${A.indicesToOffset(`dataIndices${Y}`)};\n          let index${Y} = offset${Y} / 4u;\n          let component${Y} = offset${Y} % 4u;\n          ${te}[${Y}] = ${K}(${A.getByOffset(`index${Y}`)}[component${Y}]);\n        `;T=`\n        let outputOffset = global_idx * ${a};\n        var value = vec4<u32>(0);\n        ${N("value",0,"u32")}\n        ${N("value",1,"u32")}\n        ${N("value",2,"u32")}\n        ${N("value",3,"u32")}\n        ${R.setByOffset("global_idx","value")}\n      `}else T=`\n      let outputIndices = ${R.offsetToIndices("global_idx")};\n      ${V("")};\n      let value = ${A.getByIndices("dataIndices")};\n      ${R.setByOffset("global_idx","value")};\n      `;return`\n      ${E.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(A,z,R)}\n      ${E.mainStart()}\n        ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        ${T}\n      }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:$},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:I}),getShaderSource:x}},cs=e=>ge({axis:e.axis}),ps=(e,t)=>{let r=e.inputs;Pl(r),e.compute(kl(e.inputs,t))}});var Rl,Bl,fs,hs,gs=j(()=>{"use strict";$e();je();ve();Rl=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and\n                     indices input tensors be of same rank.`)},Bl=(e,t)=>{let r=e[0].dims,o=e[0].dataType,n=r.length,s=e[1].dims,u=e[1].dataType,l=U.normalizeAxis(t.axis,n),a=r[l],p=s.slice(0),h=U.size(p),g=M("input",o,n),b=M("indicesInput",u,s.length),w=F("output",o,p.length),y=[{type:"uint32",data:h},{type:"int32",data:a},{type:"uint32",data:l}];return y.push(...L(r)),y.push(...L(s)),y.push(...L(p)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(h/64)},programUniforms:y}),getShaderSource:$=>`\n      ${$.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(g,b,w)}\n      ${$.mainStart()}\n      ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n      let outputIndices = ${w.offsetToIndices("global_idx")};\n\n      var idx = ${b.getByOffset("global_idx")};\n      if (idx < 0) {\n        idx = idx + uniforms.axisDimLimit;\n      }\n      var inputIndices = ${g.type.indices}(outputIndices);\n      ${g.indicesSet("inputIndices","uniforms.axis","u32(idx)")};\n      let value = ${g.getByIndices("inputIndices")};\n\n      ${w.setByOffset("global_idx","value")};\n  }`}},fs=e=>ge({axis:e.axis}),hs=(e,t)=>{let r=e.inputs;Rl(r),e.compute(Bl(e.inputs,t))}});var Dl,Ml,ys,bs,ws=j(()=>{"use strict";$e();ve();Dl=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},Ml=(e,t)=>{let r=e[0].dims.slice(),o=e[1].dims.slice(),[n,s,u]=Hr.getShapeOfGemmResult(r,t.transA,o,t.transB,e.length===3?e[2].dims:void 0),l=[n,s];if(!l)throw new Error("Can\'t use gemm on the given tensors");let a=U.size(l),p=[{type:"uint32",data:a},{type:"uint32",data:n},{type:"uint32",data:s},{type:"uint32",data:u},{type:"float32",data:t.alpha},{type:"float32",data:t.beta}],h=["type","type"];e.length===3&&(p.push(...L(e[2].dims)),h.push("rank")),p.push(...L(l));let g=b=>{let w="";t.transA&&t.transB?w="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?w="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?w="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(w="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let y=t.alpha===1?"":"value *= uniforms.alpha;",_=M("a",e[0].dataType,e[0].dims),I=M("b",e[1].dataType,e[1].dims),$=_.type.value,x=null,E=[_,I];e.length===3&&(x=M("c",e[2].dataType,e[2].dims.length),E.push(x));let A=F("output",e[0].dataType,l.length);E.push(A);let z=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return`\n  ${b.registerUniforms(z).declareVariables(...E)}\n\n  ${b.mainStart()}\n    ${b.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n    let m = global_idx / uniforms.N;\n    let n = global_idx % uniforms.N;\n\n    var value = ${$}(0);\n    for (var k: u32 = 0u; k < uniforms.K; k++) {\n      ${w}\n    }\n\n    ${y}\n    ${(()=>x!=null?`let cOffset = ${x.broadcastedIndicesToOffset("vec2(m, n)",A)}; value += ${$}(uniforms.beta) * ${x.getByOffset("cOffset")};`:"")()}\n    output[global_idx] = value;\n  }`};return{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:l,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:g}},ys=e=>{let t=e.transA,r=e.transB,o=e.alpha,n=e.beta;return{transA:t,transB:r,alpha:o,beta:n,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},bs=(e,t)=>{Dl(e.inputs),e.compute(Ml(e.inputs,t))}});var zl,Ul,Vl,vs,$s=j(()=>{"use strict";Ne();$e();ve();zl=(e,t)=>{let r=e[0].dims,o=r,n=2,s=U.sizeToDimension(r,n),u=U.sizeFromDimension(r,n),l=Fe(u),a=u/l,p=[r[0],r[1],a],h=["rank","type","type"],g=[{type:"uint32",data:u},{type:"uint32",data:a}];g.push(...L(p),...L(p));let b=w=>{let y=M("x",e[0].dataType,p.length,l),_=M("scale",e[1].dataType,e[1].dims),I=M("bias",e[2].dataType,e[2].dims),$=F("output",e[0].dataType,p.length,l),x=[y,_,I,$],E=y.type.value,A=l===1?"f32":`vec${l}<f32>`,z=64,R=[{name:"normSize",type:"u32"},{name:"normPackedSize",type:"u32"}];return`\n  var<workgroup> meanShared : f32;\n  var<workgroup> squaredNormShared : f32;\n  var<workgroup> workgroupShared : array<${A}, ${z}>;\n  const workgroupSize = ${z}u;\n  ${w.registerUniforms(R).declareVariables(...x)}\n  ${w.mainStart(z)}\n    let norm = global_idx / workgroupSize;\n    let batch = norm / uniforms.x_shape[1];\n    let channel = norm % uniforms.x_shape[1];\n    let localIndex = local_id.x;\n\n    // initialize workgroup memory\n    var initial = ${A}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      initial = initial + ${A}(${y.get("batch","channel","h")});\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the mean of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      meanShared = ${Je("workgroupShared[0]",l)} / f32(uniforms.normSize);\n    }\n    workgroupBarrier();\n\n    // reinitialize workgroup memory.\n    initial = ${A}(0);\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let deviation =  ${A}(${y.get("batch","channel","h")}) - ${A}(meanShared);\n      initial = initial + deviation * deviation;\n    }\n    workgroupShared[localIndex] = initial;\n    workgroupBarrier();\n\n    // Calculate the sum of square of deviation of current channel data.\n    for (var currSize = workgroupSize >> 1;  currSize > 0; currSize = currSize >> 1) {\n      if (localIndex < currSize) {\n        workgroupShared[localIndex] = workgroupShared[localIndex] + workgroupShared[localIndex + currSize];\n      }\n      workgroupBarrier();\n    }\n    if (localIndex == 0) {\n      squaredNormShared = ${Je("workgroupShared[0]",l)};\n    }\n    workgroupBarrier();\n\n    let invStdDev = inverseSqrt(squaredNormShared / f32(uniforms.normSize) + f32(${t.epsilon}));\n    let channelScale = invStdDev * f32(${_.getByOffset("channel")});\n    let channelShift = f32(${I.getByOffset("channel")}) - meanShared * channelScale;\n    for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {\n      let value = ${y.get("batch","channel","h")} * ${E}(${A}(channelScale)) + ${E}(${A}(channelShift));\n      ${$.set("batch","channel","h","value")};\n    }\n  }`};return{name:"InstanceNormalization",shaderCache:{hint:`${t.epsilon};${l}`,inputDependencies:h},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:s},programUniforms:g}),getShaderSource:b}},Ul=(e,t,r,o,n,s,u,l)=>{let a=Fe(u),p=64,h=a===1?"vec2f":`mat2x${a}f`,g=a===1?"f32":`vec${a}f`,b=(R,V)=>`${h}(${R}, ${V})`,w=n*u/a,y=Math.ceil(s/p),_=["type"],I=[{type:"uint32",data:y},{type:"uint32",data:s},{type:"uint32",data:Math.floor(u/a)},{type:"uint32",data:Math.floor(s*u/a)}],$=R=>{let V=M("input",t.dataType,t.dims,a);return`\n  ${R.declareVariables(V)}\n  @group(0) @binding(1) var<storage, read_write> output : array<${h}>;\n  struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};\n  @group(0) @binding(2) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart(p)}\n    let currentImageNumber = global_idx / ${p} / uniforms.C;\n    let currentChannelNumber = (global_idx / ${p}) % uniforms.C;\n    let wgId = global_idx % ${p};\n    let wgOffset = wgId * uniforms.wg_size;\n    if (wgOffset >= uniforms.H) {\n        return;\n    }\n    let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);\n\n    let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;\n    var sum = ${Ze("f32",a)};\n    var squaredSum = ${Ze("f32",a)};\n    for (var i: u32 = wgOffset; i < wgMax; i++) {\n        let value = ${g}(input[offset + i * uniforms.C]);\n        sum += value;\n        squaredSum += value * value;\n    }\n    output[global_idx] = ${b("sum","squaredSum")};\n  }`},x=e.compute({name:"InstanceNormComputeMean",shaderCache:{hint:`${a}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:[n,u,p,2],dataType:1}],dispatchGroup:{x:n*u/a},programUniforms:I}),getShaderSource:$},{inputs:[t],outputs:[-1]})[0],E=[{type:"uint32",data:w},{type:"uint32",data:s},{type:"uint32",data:Math.floor(u/a)},{type:"uint32",data:Math.floor(p*u/a)}],A=["type","type","type"],z=R=>{let V=M("scale",r.dataType,r.dims,a),T=M("bias",o.dataType,o.dims,a);return`\n  @group(0) @binding(0) var<storage, read> input : array<${h}>;\n  @group(0) @binding(1) var<storage, read> scale : array<${V.type.storage}>;\n  @group(0) @binding(2) var<storage, read> bias : array<${T.type.storage}>;\n  @group(0) @binding(3) var<storage, read_write> output : array<${h}>;\n  struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};\n  @group(0) @binding(4) var<uniform> uniforms: Uniforms;\n\n  ${R.mainStart()}\n    ${R.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.units_of_work")}\n    let currentImageNumber = global_idx / uniforms.C;\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let offset = currentImageNumber * uniforms.image_size;\n    var sum = ${Ze("f32",a)};\n    var squaredSum = ${Ze("f32",a)};\n    for (var i: u32 = 0; i < ${p}; i++) {\n        let value = input[offset + i + currentChannelNumber * ${p}];\n        sum += value[0];\n        squaredSum += value[1];\n    }\n    sum = sum / f32(uniforms.H);\n    squaredSum = squaredSum / f32(uniforms.H);\n    let invStdDev = inverseSqrt(squaredSum - sum * sum + f32(${l}));\n    let channelScale = invStdDev * ${g}(scale[currentChannelNumber]);\n    let channelShift = ${g}(bias[currentChannelNumber]) - sum * channelScale;\n\n    output[global_idx] = ${b("channelScale","channelShift")};\n  }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${a};${l}`,inputDependencies:A},getRunData:()=>({outputs:[{dims:[n,u,2],dataType:1}],dispatchGroup:{x:Math.ceil(w/64)},programUniforms:E}),getShaderSource:z},{inputs:[x,r,o],outputs:[-1]})[0]},Vl=(e,t,r)=>{let o=t[0].dims,n=o,s=o[0],u=o[o.length-1],l=U.sizeFromDimension(o,1)/u,a=Fe(u),p=U.size(n)/a,h=[{type:"uint32",data:l},{type:"uint32",data:Math.floor(u/a)}],g=["type","type"],b=Ul(e,t[0],t[1],t[2],s,l,u,r.epsilon),w=y=>{let _=Le(t[0].dataType),I=a===1?"vec2f":`mat2x${a}f`,$=a===1?_:`vec${a}<${_}>`,x=M("input",t[0].dataType,t[0].dims,a),E=F("output",t[0].dataType,n,a);return`\n  @group(0) @binding(0) var<storage, read> input : array<${x.type.storage}>;\n  @group(0) @binding(1) var<storage, read> scaleInput : array<${I}>;\n  @group(0) @binding(2) var<storage, read_write> output : array<${E.type.storage}>;\n  struct Uniforms {H: u32, C : u32};\n  @group(0) @binding(3) var<uniform> uniforms: Uniforms;\n\n  ${y.mainStart()}\n    let currentImageNumber = global_idx / (uniforms.C * uniforms.H);\n    let currentChannelNumber = global_idx % uniforms.C;\n\n    let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;\n    let scale = scaleInput[scaleOffset];\n    output[global_idx] = fma(input[global_idx], ${$}(scale[0]), ${$}(scale[1]));\n  }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${a}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:n,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:h}),getShaderSource:w},{inputs:[t[0],b]})},vs=(e,t)=>{t.format==="NHWC"?Vl(e,e.inputs,t):e.compute(zl(e.inputs,t))}});var Nl,Wl,Ss,xs=j(()=>{"use strict";Ne();$e();ve();Nl=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},Wl=(e,t,r)=>{let o=e[0].dims,n=e[1],s=e[2],u=o,l=U.normalizeAxis(t.axis,o.length),a=U.sizeToDimension(o,l),p=U.sizeFromDimension(o,l),h=U.size(n.dims),g=s?U.size(s.dims):0;if(h!==p||s&&g!==p)throw new Error(`Size of X.shape()[axis:] == ${p}.\n       Size of scale and bias (if provided) must match this.\n       Got scale size of ${h} and bias size of ${g}`);let b=[];for(let A=0;A<o.length;++A)A<l?b.push(o[A]):b.push(1);let w=Fe(p),y=["type","type"],_=[{type:"uint32",data:a},{type:"float32",data:p},{type:"uint32",data:Math.floor(p/w)},{type:"float32",data:t.epsilon}];s&&y.push("type");let I=r>1,$=r>2,x=A=>{let z=Le(e[0].dataType),R=[M("x",e[0].dataType,e[0].dims,w),M("scale",n.dataType,n.dims,w)];s&&R.push(M("bias",s.dataType,s.dims,w)),R.push(F("output",e[0].dataType,u,w)),I&&R.push(F("mean_data_output",1,b)),$&&R.push(F("inv_std_output",1,b));let V=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return`\n  ${A.registerUniforms(V).declareVariables(...R)}\n  ${A.mainStart()}\n    ${A.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")}\n    let offset = global_idx * uniforms.norm_size_vectorized;\n    var meanVector = ${Ze("f32",w)};\n    var meanSquareVector = ${Ze("f32",w)};\n\n    for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) {\n      let value = ${at(z,w,"x[h + offset]")};\n      meanVector += value;\n      meanSquareVector += value * value;\n    }\n    let mean = ${Je("meanVector",w)} / uniforms.norm_size;\n    let invStdDev =\n        inverseSqrt(${Je("meanSquareVector",w)} / uniforms.norm_size - mean * mean + uniforms.epsilon);\n\n    for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) {\n      let f32input = ${at(z,w,"x[j + offset]")};\n      let f32scale = ${at(z,w,"scale[j]")};\n      output[j + offset] = ${R[0].type.value}((f32input - mean) * invStdDev * f32scale\n        ${s?`+ ${at(z,w,"bias[j]")}`:""}\n      );\n    }\n\n    ${I?"mean_data_output[global_idx] = mean":""};\n    ${$?"inv_std_output[global_idx] = invStdDev":""};\n  }`},E=[{dims:u,dataType:e[0].dataType}];return I&&E.push({dims:b,dataType:1}),$&&E.push({dims:b,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${w};${r}`,inputDependencies:y},getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(a/64)},programUniforms:_}),getShaderSource:x}},Ss=(e,t)=>{Nl(e.inputs),e.compute(Wl(e.inputs,t,e.outputCount))}});var Hl,Cs,_s,Gl,Xn,Is,As=j(()=>{"use strict";$e();je();Nr();Un();ve();jt();Hl=(e,t)=>{let r=e[0],o=e[1],n=e[2],s=e[3],u=e[4],l=e[5],a=e[6],p=e[7];if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let h=!1,g=r.dims[0],b=r.dims[1],w=r.dims.length===3?h?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],y=b,_=0,I=0,$=Math.floor(w/t.numHeads);if(a&&p){if(a.dims.length!==4)throw new Error(\'Input "past_key" is expected to have 4 dimensions\');if(p.dims.length!==4)throw new Error(\'Input "past_value" is expected to have 4 dimensions\');_=a.dims[2],I=a.dims[2]}else if(a||p)throw new Error(\'Input "past_key" and "past_value" shall be both present or both absent\');let x;if(o){if(r.dims.length!==3)throw new Error(\'Input "query" is expected to have 3 dimensions when key is given\');if(o.dims.length<3||o.dims.length>5)throw new Error(\'Input "key" is expected to have 3, 4, or 5 dimensions\');if(r.dims[0]!==o.dims[0])throw new Error(\'Input "query" and "key" shall have same dim 0 (batch size)\');if(o.dims.length===3){if(o.dims[2]!==r.dims[2])throw new Error(\'Input "query" and "key" shall have same dim 2 (hidden_size)\');x=2,y=o.dims[1]}else if(o.dims.length===5){if(o.dims[2]!==t.numHeads||o.dims[3]!==2||o.dims[4]!==$)throw new Error(\'Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv\');if(n)throw new Error(\'Expect "value" be none when "key" has packed kv format.\');x=5,y=o.dims[1]}else{if(o.dims[1]!==t.numHeads||o.dims[3]!==$)throw new Error(\'Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key\');x=0,y=o.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error(\'Input "query" is expected to have 3 or 5 dimensions when key is empty\');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error(\'Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv\');x=3}if(s){if(s.dims.length!==1)throw new Error(\'Input "bias" is expected to have 1 dimension\');if(n&&r.dims.length===5&&r.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let E=0;if(u){E=8;let T=u.dims;throw T.length===1?T[0]===g?E=1:T[0]===3*g+2&&(E=3):T.length===2&&T[0]===g&&T[1]===y&&(E=5),E===8?new Error(\'Input "key_padding_mask" shape shall be (batch_size) or (batch_size, kv_sequence_length)\'):new Error("Mask not supported")}let A=!1,z=w;if(n){if(n.dims.length!==3&&n.dims.length!==4)throw new Error(\'Input "value" is expected to have 3 or 4 dimensions\');if(r.dims[0]!==n.dims[0])throw new Error(\'Input "query" and "value" shall have same dim 0 (batch_size)\');if(n.dims.length===3){if(y!==n.dims[1])throw new Error(\'Input "key" and "value" shall have the same dim 1 (kv_sequence_length)\');z=n.dims[2]}else{if(y!==n.dims[2])throw new Error(\'Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)\');z=n.dims[1]*n.dims[3],A=!0}}let R=_+y,V=!1;if(u)throw new Error("Key padding mask is not supported");if(l)throw new Error("extraAddQk is not supported");if(a)throw new Error("pastKey is not supported");if(p)throw new Error("pastValue is not supported");return{batchSize:g,sequenceLength:b,pastSequenceLength:_,kvSequenceLength:y,totalSequenceLength:R,maxSequenceLength:I,inputHiddenSize:0,hiddenSize:w,vHiddenSize:z,headSize:$,vHeadSize:Math.floor(z/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:E,scale:t.scale,broadcastResPosBias:V,passPastInKv:A,qkvFormat:x}},Cs=e=>ge({...e}),_s=ge({perm:[0,2,1,3]}),Gl=(e,t,r,o,n,s,u)=>{let l=[o,n,s],a=U.size(l),p=[{type:"uint32",data:a},{type:"uint32",data:u},{type:"uint32",data:s}],h=g=>{let b=F("qkv_with_bias",t.dataType,l),w=M("qkv",t.dataType,l),y=M("bias",r.dataType,l),_=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return`\n  ${g.registerUniforms(_).declareVariables(w,y,b)}\n  ${g.mainStart()}\n    ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n    let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset;\n\n    qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx];\n  }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:l,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:h},{inputs:[t,r],outputs:[-1]})[0]},Xn=(e,t,r,o,n,s,u,l)=>{let a=s;if(u){if(o===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return a=Gl(e,s,u,t,o,r*n,l),a=a.reshape([t,o,r,n]),e.compute(it(a,_s.perm),{inputs:[a],outputs:[-1]})[0]}else return s.dims.length===3&&(a=s.reshape([t,o,r,n])),e.compute(it(a,_s.perm),{inputs:[a],outputs:[-1]})[0]},Is=(e,t)=>{let r=Hl(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let o=e.inputs[1]&&e.inputs[2]&&e.inputs[1].dims.length===4&&e.inputs[2].dims.length===4,n=Xn(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,e.inputs[0],e.inputs[3],0);if(o)return Kr(e,n,e.inputs[1],e.inputs[2],e.inputs[4],void 0,void 0,void 0,e.inputs[5],r,t);let s=Xn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,e.inputs[1],e.inputs[3],r.hiddenSize),u=Xn(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,e.inputs[2],e.inputs[3],2*r.hiddenSize);Kr(e,n,s,u,e.inputs[4],void 0,e.inputs[6],e.inputs[7],e.inputs[5],r,t)}});var Ll,Fl,jl,ql,Kl,Yl,Zl,Ql,Ts,Es=j(()=>{"use strict";Ne();$e();ve();Ll=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1)throw new Error("Input type must be float.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},Fl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n            k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n            if (k < 0) {\n              break;\n            }\n            if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n              break;\n            }\n            offset += k * i32(${ce("uniforms.x_strides",n,t)});\n        `;return`\n          value = ${e.type.value}(uniforms.constant_value);\n          for (var i = 0; i < 1; i++) {\n            var offset = 0;\n            var k = 0;\n            ${o}\n            value = x[offset];\n          }\n      `},jl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n                if (k < 0) {\n                  k = -k;\n                }\n                {\n                  let _2n_1 = 2 * (i32(${ce("uniforms.x_shape",n,t)}) - 1);\n                  k = k % _2n_1;\n                  if(k >= i32(${ce("uniforms.x_shape",n,t)})) {\n                    k = _2n_1 - k;\n                  }\n                }\n                offset += k * i32(${ce("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},ql=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n                if (k < 0) {\n                  k = 0;\n                }\n                if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n                  k = i32(${ce("uniforms.x_shape",n,t)}) - 1;\n                }\n                offset += k * i32(${ce("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},Kl=(e,t,r)=>{let o="";for(let n=t-1;n>=0;--n)o+=`\n                k = i32(${e.indicesGet("indices",n)}) - ${ce("uniforms.pads",n,r)};\n                if (k < 0)  {\n                  k += i32(${ce("uniforms.x_shape",n,t)}]);\n                }\n                if (k >= i32(${ce("uniforms.x_shape",n,t)})) {\n                  k -= i32(${ce("uniforms.x_shape",n,t)});\n                }\n                offset += k * i32(${ce("uniforms.x_strides",n,t)});\n            `;return`\n              var offset = 0;\n              var k = 0;\n              ${o}\n              value = x[offset];\n          `},Yl=(e,t,r)=>{switch(r.mode){case 0:return Fl(e,t,r.pads.length);case 1:return jl(e,t,r.pads.length);case 2:return ql(e,t,r.pads.length);case 3:return Kl(e,t,r.pads.length);default:throw new Error("Invalid mode")}},Zl=(e,t)=>{let r=U.padShape(e[0].dims.slice(),t.pads),o=e[0].dims,s=[{type:"uint32",data:U.size(r)},{type:"uint32",data:t.pads}];if(t.mode===0){let a=Xe(e[0].dataType);s.push({type:a,data:t.value})}s.push(...L(e[0].dims),...L(r));let u=["rank"],l=a=>{let p=F("output",e[0].dataType,r.length),h=M("x",e[0].dataType,o.length),g=h.type.value,b=Yl(p,o.length,t),w=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&w.push({name:"constant_value",type:g}),`\n            ${a.registerUniforms(w).declareVariables(h,p)}\n            ${a.mainStart()}\n            ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n\n            let indices = ${p.offsetToIndices("global_idx")};\n\n            var value = ${g}(0);\n            ${b}\n            output[global_idx] = value;\n        }`};return{name:"Pad",shaderCache:{hint:`${t.mode}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(U.size(r)/64)},programUniforms:s}),getShaderSource:l}},Ql=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),o=e.length>=3&&e[2].data?e[2].getFloat32Array()[0]:0,n=e[0].dims.length,s=new Int32Array(2*n).fill(0);if(e.length>=4){let l=e[3].getBigInt64Array();for(let a=0;a<l.length;a++)s[Number(l[a])]=Number(r[a]),s[Number(l[a])+n]=Number(r[a+l.length])}else r.forEach((l,a)=>s[Number(a)]=Number(l));let u=[];return s.forEach(l=>u.push(l)),{mode:t.mode,value:o,pads:u}}else return t},Ts=(e,t)=>{Ll(e.inputs);let r=Ql(e.inputs,t);e.compute(Zl(e.inputs,r),{inputs:[0]})}});var nn,Os,Ps,ks,Rs,Xl,Jl,Bs,Ds,Ms,zs,Us,Vs,Ns,Ws,Hs,Gs,Ls,Fs,js=j(()=>{"use strict";Lt();$e();ve();nn=e=>{if(Gt.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},Os=(e,t,r)=>{let o=t.format==="NHWC",n=e.dims.slice();o&&n.splice(1,0,n.pop());let s=Object.hasOwnProperty.call(t,"dilations"),u=t.kernelShape.slice(),l=t.strides.slice(),a=s?t.dilations.slice():[],p=t.pads.slice();Bt.adjustPoolAttributes(r,n,u,l,a,p);let h=Bt.computePoolOutputShape(r,n,l,a,u,p,t.autoPad),g=Object.assign({},t);s?Object.assign(g,{kernelShape:u,strides:l,pads:p,dilations:a,cacheKey:t.cacheKey}):Object.assign(g,{kernelShape:u,strides:l,pads:p,cacheKey:t.cacheKey});let b=h.slice();return b.push(b.splice(1,1)[0]),[g,o?b:h]},Ps=(e,t)=>{let r=t.format==="NHWC",o=U.size(e),n=U.size(t.kernelShape),s=[{type:"uint32",data:o},{type:"uint32",data:n}],u=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let l=t.kernelShape[t.kernelShape.length-1],a=t.strides[t.strides.length-1],p=t.pads[t.pads.length/2-1],h=t.pads[t.pads.length-1],g=!!(p+h);s.push({type:"uint32",data:l},{type:"uint32",data:a},{type:"uint32",data:p},{type:"uint32",data:h}),u.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let b=!1;if(t.kernelShape.length===2){let w=t.kernelShape[t.kernelShape.length-2],y=t.strides[t.strides.length-2],_=t.pads[t.pads.length/2-2],I=t.pads[t.pads.length-2];b=!!(_+I),s.push({type:"uint32",data:w},{type:"uint32",data:y},{type:"uint32",data:_},{type:"uint32",data:I}),u.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[s,u,!0,g,b]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let l=U.computeStrides(t.kernelShape);s.push({type:"uint32",data:l},{type:"uint32",data:t.pads},{type:"uint32",data:t.strides}),u.push({name:"kernelStrides",type:"u32",length:l.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let a=t.pads.reduce((p,h)=>p+h);return[s,u,!!a,!1,!1]}},ks=(e,t,r,o,n,s,u,l,a,p,h,g)=>{let b=n.format==="NHWC",w=t.type.value,y=F("output",t.type.tensor,o);if(n.kernelShape.length<=2){let _="",I="",$="",x=r-(b?2:1);if(h?_=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  if (xIndices[${x}] < 0 || xIndices[${x}]\n                      >= uniforms.x_shape[${x}]) {\n                    pad++;\n                    continue;\n                  }\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${s}\n                }`:_=`\n                for (var i: u32 = 0u; i < uniforms.kw; i++) {\n                  xIndices[${x}] = indices[${x}] * uniforms.sw - uniforms.pwStart + i;\n                  let x_val = x[${t.indicesToOffset("xIndices")}];\n                  ${s}\n                }`,n.kernelShape.length===2){let A=r-(b?3:2);g?I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;\n                  if (xIndices[${A}] < 0 || xIndices[${A}] >= uniforms.x_shape[${A}]) {\n                    pad += i32(uniforms.kw);\n                    continue;\n                  }\n              `:I=`\n                for (var j: u32 = 0u; j < uniforms.kh; j++) {\n                  xIndices[${A}] = indices[${A}] * uniforms.sh - uniforms.phStart + j;\n                `,$=`\n              }\n            `}return`\n            ${e.registerUniforms(a).declareVariables(t,y)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n\n              let indices = ${y.offsetToIndices("global_idx")};\n              var xIndices = ${y.offsetToIndices("global_idx")};\n\n              var value = ${w}(${l});\n              var pad = 0;\n              ${I}\n              ${_}\n              ${$}\n              ${u}\n\n              output[global_idx] = value;\n            }`}else{if(b)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let _=n.kernelShape.length,I=n.pads.length,$="";return p?$=`\n                if (xIndices[j] >= uniforms.x_shape[j]) {\n                  pad++;\n                  isPad = true;\n                  break;\n                }\n              }\n              if (!isPad) {\n                let x_val = x[${t.indicesToOffset("xIndices")}];\n                ${s}\n              }`:$=`\n              }\n              let x_val = x[${t.indicesToOffset("xIndices")}];\n              ${s}\n            `,`\n            ${e.registerUniforms(a).declareVariables(t,y)}\n\n            ${e.mainStart()}\n              ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n              let indices = ${y.offsetToIndices("global_idx")};\n              var xIndices = ${y.offsetToIndices("global_idx")};\n\n              var offsets: array<u32, ${_}>;\n\n              var value = ${w}(${l});\n              var pad = 0;\n              var isPad = false;\n\n              for (var i: u32 = 0u; i < uniforms.kernelSize; i++) {\n                var offset = i;\n                for (var j = 0u; j < ${_-1}u; j++) {\n                  offsets[j] = offset / ${ce("uniforms.kernelStrides","j",_)};\n                  offset -= offsets[j] * ${ce("uniforms.kernelStrides","j",_)};\n                }\n                offsets[${_-1}] = offset;\n\n                isPad = false;\n                for (var j = ${r-_}u; j < ${r}u; j++) {\n                  xIndices[j] = indices[j] * ${ce("uniforms.strides",`j - ${r-_}u`,_)}\n                    + offsets[j - ${r-_}u] - ${ce("uniforms.pads","j - 2u",I)};\n                  ${$}\n              }\n              ${u}\n\n              output[global_idx] = value;\n            }`}},Rs=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Xl=e=>`${Rs(e)};${e.countIncludePad}`,Jl=e=>`${Rs(e)};${e.storageOrder};${e.dilations}`,Bs=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),Ds=(e,t,r,o)=>{let[n,s]=Os(t,o,r),u=M("x",t.dataType,t.dims.length),l=u.type.value,a="value += x_val;",p="";n.countIncludePad?p+=`value /= ${l}(uniforms.kernelSize);`:p+=`value /= ${l}(i32(uniforms.kernelSize) - pad);`;let[h,g,b,w,y]=Ps(s,n);h.push(...L(t.dims),...L(s));let _=["rank"];return{name:e,shaderCache:{hint:`${o.cacheKey};${b};${w};${y}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(U.size(s)/64)},programUniforms:h}),getShaderSource:I=>ks(I,u,t.dims.length,s.length,n,a,p,0,g,b,w,y)}},Ms=e=>{let t=e.count_include_pad!==0,r=Bs(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let o={countIncludePad:t,...r,cacheKey:""};return{...o,cacheKey:Xl(o)}},zs=(e,t)=>{nn(e.inputs),e.compute(Ds("AveragePool",e.inputs[0],!1,t))},Us={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},Vs=e=>{let t=e.format;return{format:t,...Us,cacheKey:t}},Ns=(e,t)=>{nn(e.inputs),e.compute(Ds("GlobalAveragePool",e.inputs[0],!0,t))},Ws=(e,t,r,o)=>{let[n,s]=Os(t,o,r),u=`\n      value = max(x_val, value);\n    `,l="",a=M("x",t.dataType,t.dims.length),p=["rank"],[h,g,b,w,y]=Ps(s,n);return h.push(...L(t.dims),...L(s)),{name:e,shaderCache:{hint:`${o.cacheKey};${b};${w};${y}`,inputDependencies:p},getRunData:()=>({outputs:[{dims:s,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(U.size(s)/64)},programUniforms:h}),getShaderSource:_=>ks(_,a,t.dims.length,s.length,n,u,l,-1e5,g,b,w,y)}},Hs=(e,t)=>{nn(e.inputs),e.compute(Ws("MaxPool",e.inputs[0],!1,t))},Gs=e=>{let t=e.storage_order,r=e.dilations,o=Bs(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(o.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let n={storageOrder:t,dilations:r,...o,cacheKey:""};return{...n,cacheKey:Jl(n)}},Ls=e=>{let t=e.format;return{format:t,...Us,cacheKey:t}},Fs=(e,t)=>{nn(e.inputs),e.compute(Ws("GlobalMaxPool",e.inputs[0],!0,t))}});var tc,rc,qs,Ks=j(()=>{"use strict";Lt();Ne();ve();tc=(e,t,r)=>{let o=e===t,n=e<t&&r<0,s=e>t&&r>0;if(o||n||s)throw new Error("Range these inputs\' contents are invalid.")},rc=(e,t,r,o)=>{let n=Math.abs(Math.ceil((t-e)/r)),s=[n],u=n,l=Xe(o),a=[{type:"uint32",data:u},{type:l,data:e},{type:l,data:r},...L(s)],p=h=>{let g=F("output",o,s.length),b=g.type.value,w=[{name:"outputSize",type:"u32"},{name:"start",type:b},{name:"delta",type:b}];return`\n        ${h.registerUniforms(w).declareVariables(g)}\n        ${h.mainStart()}\n        ${h.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n        output[global_idx] = uniforms.start + ${b}(global_idx) * uniforms.delta;\n      }`};return{name:"Range",shaderCache:{hint:`${o}`},getShaderSource:p,getRunData:()=>({outputs:[{dims:s,dataType:o}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:a})}},qs=e=>{let t=0,r=0,o=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],o=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],o=e.inputs[2].getFloat32Array()[0]),Gt.webgpu.validateInputContent&&tc(t,r,o),e.compute(rc(t,r,o,e.inputs[0].dataType),{inputs:[]})}});var nc,oc,ac,ic,sc,uc,dc,lc,cc,pc,mc,Ys,fc,hc,gc,yc,bc,Zs,Qs,Xs=j(()=>{"use strict";$e();je();ve();nc=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and\n            one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},oc=(e,t,r)=>{t.every(n=>n>=0&&n<r||(()=>{throw new Error("Resize requires axes input values to be positive and less than rank")}));let o=new Array(r).fill(1);return t.forEach((n,s)=>o[n]=e[s]),o},ac=(e,t,r,o,n,s)=>{let[u,l,a]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],p=e[0].dims.length;if(u>0&&e.length>u&&e[u].dims.length>0)e[u].getFloat32Array().forEach(h=>s.push(h));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(l>0&&e.length>l&&e[l].dims.length>0){if(e[l].getFloat32Array().forEach(h=>o.push(h)),o.length!==0&&o.length!==p&&r>=18&&o.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");nc(o,t),t.axes.length>0&&oc(o,t.axes,p).forEach((h,g)=>o[g]=h)}if(a>0&&e.length>a&&(e[a].getBigInt64Array().forEach(h=>n.push(Number(h))),n.length!==p||r>=18&&n.length===t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(o.length!==t.axes.length)throw new Error(\'Resize requires "scales" input size to be of axes rank when axes attributes is specified\');if(n.length!==t.axes.length)throw new Error(\'Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified\')}if(typeof o<"u"&&typeof n<"u"&&o.length>0&&n.length>p)throw new Error("Resize requires only of scales or sizes to be specified")},ic=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32,\n     lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return`return ${t}(xResized) / ${t}(xScale);`;case"pytorch_half_pixel":return`if (lengthResized > 1) {\n                    return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5;\n                  } else {\n                    return 0.0;\n                  }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) {\n                    return 0.0;\n                  } else {\n                    // The whole part and the fractional part are calculated separately due to inaccuracy of floating\n                    // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an\n                    // offset-by-one error later in floor().\n                    let whole = ${t}(xResized * (lengthOriginal - 1) / (lengthResized - 1));\n                    let fract =\n                        ${t}(xResized * (lengthOriginal - 1) % (lengthResized - 1)) / ${t}(lengthResized - 1);\n                    return whole + fract;\n                  }`;case"tf_crop_and_resize":return`if (lengthResized > 1) {\n                    return ${t}(roiStart) * ${t}(lengthOriginal - 1) +\n                        (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) /\n                        ${t}(lengthResized - 1);\n                  } else {\n                    return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1);\n                  }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized);\n                  const adjustment = ${t}(lengthResized) / outputWidth;\n                  const center = ${t}(lengthOriginal) / 2;\n                  const offset = center * (1 - adjustment);\n                  return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",sc=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) {             return ceil(xOriginal);           } else {             return round(xOriginal);           }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) {                     return floor(xOriginal);                   } else {                     return round(xOriginal);                   }";case"simple":default:if(t<11)return"if (isDownSample)                     {                       return ceil(xOriginal);                     } else {                       return xOriginal;                     }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",uc=(e,t,r)=>{let o=new Array(r).fill(0).concat(new Array(r).fill(1)),n=e.length===0?o:e.slice();return t.length>0?(t.forEach((s,u)=>{o[s]=n[u],o[u+r]=n[t.length+u]}),o):n},dc=(e,t,r,o)=>{let n=[];if(r.length>0)if(o.length>0){if(e.forEach(s=>n.push(s)),Math.max(...o)>e.length)throw new Error("axes is out of bound");o.forEach((s,u)=>n[s]=r[u])}else r.forEach(s=>n.push(s));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");n=e.map((s,u)=>Math.round(s*t[u]))}return n},lc=(e,t,r)=>{let o=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(s=>t[s]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(s=>t[s]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let n=e.slice();return r.axes.length>0?(r.axes.forEach(s=>t[s]=o),r.axes.forEach(s=>n[s]=Math.round(e[s]*t[s]))):(t.fill(o,0,t.length),n.forEach((s,u)=>n[u]=Math.round(s*t[u]))),n},cc=(e,t,r,o,n)=>`\n    fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> {\n      var original_indices: array<${e.type.value}, ${r.length}>;\n      for (var i:u32 = 0; i < ${r.length}; i++) {\n        var output_index = ${e.indicesGet("output_indices","i")};\n        var scale = ${ce("uniforms.scales","i",o)};\n        var roi_low = ${ce("uniforms.roi","i",n)};\n        var roi_hi = ${ce("uniforms.roi",`i + ${t.length}`,n)};\n        if (scale == 1.0) {\n          original_indices[i] = ${e.type.value}(output_index);\n        } else {\n          var input_shape_i = ${ce("uniforms.input_shape","i",t.length)};\n          var output_shape_i = ${ce("uniforms.output_shape","i",r.length)};\n          original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                           input_shape_i, roi_low, roi_hi);\n        }\n      }\n      return original_indices;\n    }`,pc=(e,t,r,o,n,s,u)=>`\n    fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n      var input_indices: ${e.type.indices};\n      for (var i:u32 = 0; i < ${o.length}; i++) {\n        var output_index = ${t.indicesGet("output_indices","i")};\n        var input_index: u32;\n        var scale = ${ce("uniforms.scales","i",n)};\n        if (scale == 1.0) {\n          input_index = output_index;\n        } else {\n          var roi_low = ${ce("uniforms.roi","i",s)};\n          var roi_hi = ${ce("uniforms.roi",`i + ${r.length}`,s)};\n          var input_shape_i = ${ce("uniforms.input_shape","i",r.length)};\n          var output_shape_i = ${ce("uniforms.output_shape","i",o.length)};\n          var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i,\n                                                                        input_shape_i, roi_low, roi_hi);\n          if (!${u} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) {\n            if (original_idx < 0) {\n              input_index = 0;\n            } else if (original_idx > ${t.type.value}(input_shape_i - 1)) {\n              input_index = input_shape_i - 1;\n            } else {\n              input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1));\n            }\n          } else {\n            input_index = u32(original_idx);\n          }\n        }\n        ${e.indicesSet("input_indices","i"," input_index")}\n      }\n      return input_indices;\n    }`,mc=(e,t)=>`\n    fn checkInputIndices(input_indices: ${e.type.indices}) -> bool {\n      for (var i:u32 = 0; i < ${t.length}; i++) {\n        var input_index = ${e.indicesGet("input_indices","i")};\n        if (input_index < 0 || input_index >= ${ce("uniforms.input_shape","i",t.length)}) {\n          return false;\n        }\n      }\n      return true;\n    }`,Ys=(e,t,r,o)=>e.rank>o?`\n    ${e.indicesSet("input_indices",t,"channel")};\n    ${e.indicesSet("input_indices",r,"batch")};\n`:"",fc=(e,t,r,o,n)=>{let[u,l,a,p]=r.length===2?[-1,0,1,-1]:[0,2,3,1],h=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${h} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",l,`max(0, min(row, ${r[l]} - 1))`)};\n      ${e.indicesSet("input_indices",a,`max(0, min(col, ${r[a]} - 1))`)};\n      ${Ys(e,p,u,2)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${h} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var row:${h} = originalIndices[${l}];\n      var col:${h} = originalIndices[${a}];\n      ${o?`if (row < 0 || row > (${r[l]} - 1) || col < 0 || col > (${r[a]} - 1)) {\n        return ${n};\n      }`:""};\n      row = max(0, min(row, ${r[l]} - 1));\n      col = max(0, min(col, ${r[a]} - 1));\n      var row1: u32 = u32(row);\n      var col1: u32 = u32(col);\n      var row2: u32 = u32(row + 1);\n      var col2: u32 = u32(col + 1);\n      var channel: u32 = ${r.length>2?`u32(originalIndices[${p}])`:"0"};\n      var batch: u32 =  ${r.length>2?`u32(originalIndices[${u}])`:"0"};\n      var x11: ${h} = getInputValue(batch, channel, row1, col1);\n      var x12: ${h} = getInputValue(batch, channel, row1, col2);\n      var x21: ${h} = getInputValue(batch, channel, row2, col1);\n      var x22: ${h} = getInputValue(batch, channel, row2, col2);\n      var dx1: ${h} = abs(row - ${h}(row1));\n      var dx2: ${h} = abs(${h}(row2) - row);\n      var dy1: ${h} = abs(col - ${h}(col1));\n      var dy2: ${h} = abs(${h}(col2) - col);\n      if (row1 == row2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (col1 == col2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1);\n    }`},hc=(e,t,r,o,n,s,u,l,a,p)=>{let h=r.length===2,g=!0,[b,w]=h?[0,1]:g?[2,3]:[1,2],y=e.type.value,_=I=>{let $=I===b?"row":"col";return`\n      fn ${$}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${y} {\n        var output_index = ${t.indicesGet("output_indices",I)};\n        var originalIdx: ${y} = getOriginalCoordinateFromResizedCoordinate(output_index, ${n[I]},\n        ${o[I]}, ${r[I]}, ${s[I]}, ${s[I]} + ${r.length});\n        var fractOriginalIdx: ${y} = originalIdx - floor(originalIdx);\n        var coefs = getCubicInterpolationCoefs(fractOriginalIdx);\n\n        if (${l} && (originalIdx < 0 || originalIdx > (${r[I]} - 1))) {\n          return ${a};\n        }\n        var data: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n        for (var i: i32 = -1; i < 3; i++) {\n          var ${$}: ${y} = originalIdx + ${y}(i);\n          if (${$} < 0 || ${$} >= ${r[I]}) {\n            ${(()=>p?`coefs[i + 1] = 0.0;\n                        continue;`:l?`return ${a};`:`${$} = max(0, min(${$}, ${r[I]} - 1));`)()};\n          }\n        var input_indices_copy: ${e.type.indices} = input_indices;\n          ${e.indicesSet("input_indices_copy",I,`u32(${$})`)};\n          data[i + 1] = ${I===b?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"};\n        }\n        return cubicInterpolation1D(data, coefs);\n      }`};return`\n    ${_(b)};\n    ${_(w)};\n  fn getCubicInterpolationCoefs(s: ${y}) -> array<${y}, 4> {\n    var absS = abs(s);\n    var coeffs: array<${y}, 4> = array<${y}, 4>(0.0, 0.0, 0.0, 0.0);\n    var oneMinusAbsS: ${y} = 1.0 - absS;\n    var twoMinusAbsS: ${y} = 2.0 - absS;\n    var onePlusAbsS: ${y} = 1.0 + absS;\n    coeffs[0] = ((${u} * onePlusAbsS - 5 * ${u}) * onePlusAbsS + 8 * ${u}) * onePlusAbsS - 4 * ${u};\n    coeffs[1] = ((${u} + 2) * absS - (${u} + 3)) * absS * absS + 1;\n    coeffs[2] = ((${u} + 2) * oneMinusAbsS - (${u} + 3)) * oneMinusAbsS * oneMinusAbsS + 1;\n    coeffs[3] = ((${u} * twoMinusAbsS - 5 * ${u}) * twoMinusAbsS + 8 * ${u}) * twoMinusAbsS - 4 * ${u};\n    return coeffs;\n  }\n\n  fn cubicInterpolation1D(x: array<${y}, 4>, coefs: array<${y}, 4>) -> ${y} {\n    var coefsSum: ${y} = coefs[0] + coefs[1] + coefs[2] + coefs[3];\n    return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum;\n  }\n\n  fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${y} {\n    var input_indices: ${e.type.indices} = output_indices;\n    return colCubicInterpolation(input_indices, output_indices);\n  }\n    `},gc=(e,t,r,o,n)=>{let[u,l,a,p,h]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],g=e.type.value;return`\n    fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${g} {\n      var input_indices: ${e.type.indices};\n      ${e.indicesSet("input_indices",l,`max(0, min(depth, ${r[l]} - 1))`)};\n      ${e.indicesSet("input_indices",a,`max(0, min(height, ${r[a]} - 1))`)};\n      ${e.indicesSet("input_indices",p,`max(0, min(width, ${r[p]} - 1))`)};\n      ${Ys(e,h,u,3)}\n      return ${e.getByIndices("input_indices")};\n    }\n\n    fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${g} {\n      var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices);\n      var depth:${g} = originalIndices[${l}];\n      var height:${g} = originalIndices[${a}];\n      var width:${g} = originalIndices[${p}];\n      ${o?`if (depth < 0 || depth > (${r[l]} - 1) || height < 0 || height > (${r[a]} - 1) || width < 0 || (width > ${r[p]} - 1)) {\n      return ${n};\n        }`:""};\n\n    depth = max(0, min(depth, ${r[l]} - 1));\n      height = max(0, min(height, ${r[a]} - 1));\n      width = max(0, min(width, ${r[p]} - 1));\n      var depth1: u32 = u32(depth);\n      var height1: u32 = u32(height);\n      var width1: u32 = u32(width);\n      var depth2: u32 = u32(depth + 1);\n      var height2: u32 = u32(height + 1);\n      var width2: u32 = u32(width + 1);\n      var channel: u32 = ${r.length>3?`u32(originalIndices[${h}])`:"0"};\n      var batch: u32 =  ${r.length>3?`u32(originalIndices[${u}])`:"0"};\n\n      var x111: ${g} = getInputValue(batch, channel, depth1, height1, width1);\n      var x112: ${g} = getInputValue(batch, channel, depth1, height1, width2);\n      var x121: ${g} = getInputValue(batch, channel, depth1, height2, width1);\n      var x122: ${g} = getInputValue(batch, channel, depth1, height2, width2);\n      var x211: ${g} = getInputValue(batch, channel, depth2, height1, width1);\n      var x212: ${g} = getInputValue(batch, channel, depth2, height1, width2);\n      var x221: ${g} = getInputValue(batch, channel, depth2, height2, width1);\n      var x222: ${g} = getInputValue(batch, channel, depth2, height2, width2);\n      var dx1: ${g} = abs(depth - ${g}(depth1));\n      var dx2: ${g} = abs(${g}(depth2) - depth);\n      var dy1: ${g} = abs(height - ${g}(height1));\n      var dy2: ${g} = abs(${g}(height2) - height);\n      var dz1: ${g} = abs(width - ${g}(width1));\n      var dz2: ${g} = abs(${g}(width2) - width);\n      if (depth1 == depth2) {\n        dx1 = 0.5;\n        dx2 = 0.5;\n      }\n      if (height1 == height2) {\n        dy1 = 0.5;\n        dy2 = 0.5;\n      }\n      if (width1 == width2) {\n        dz1 = 0.5;\n        dz2 = 0.5;\n      }\n      return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 +\n              x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1);\n    }`},yc=(e,t,r,o,n,s)=>{let u=e.dims,l=uc(s,t.axes,u.length),a=dc(u,o,n,t.axes),p=o.slice();o.length===0&&(p=u.map((x,E)=>x===0?1:a[E]/x),t.keepAspectRatioPolicy!=="stretch"&&(a=lc(u,p,t)));let h=F("output",e.dataType,a.length),g=M("input",e.dataType,u.length),b=U.size(a),w=u.length===a.length&&u.every((x,E)=>x===a[E]),y=t.coordinateTransformMode==="tf_crop_and_resize",_=t.extrapolationValue,I=g.type.value,$=x=>`\n      ${w?"":`\n      ${ic(t.coordinateTransformMode,I)};\n      ${(()=>{switch(t.mode){case"nearest":return`\n              ${mc(g,u)};\n              ${sc(t.nearestMode,r,I)};\n              ${pc(g,h,u,a,p.length,l.length,y)};\n              `;case"linear":return`\n              ${cc(h,u,a,p.length,l.length)};\n              ${(()=>{if(u.length===2||u.length===4)return`${fc(g,h,u,y,_)}`;if(u.length===3||u.length===5)return`${gc(g,h,u,y,_)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()};\n            `;case"cubic":return`\n            ${(()=>{if(u.length===2||u.length===4)return`${hc(g,h,u,a,p,l,t.cubicCoeffA,y,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()};\n            `;default:throw Error("Invalid resize mode")}})()};\n      `}\n      ${x.registerUniform("output_size","u32").registerUniform("scales","f32",p.length).registerUniform("roi","f32",l.length).declareVariables(g,h)}\n      ${x.mainStart()}\n        ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n        ${w?"output[global_idx] = input[global_idx];":`\n        let output_indices = ${h.offsetToIndices("global_idx")};\n        var input_indices: ${g.type.indices};\n        ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices);\n                if (checkInputIndices(input_indices)) {\n                  output[global_idx] = ${g.getByIndices("input_indices")};\n                } else {\n                  output[global_idx] = ${t.extrapolationValue};\n                }`;case"linear":return`output[global_idx] = ${u.length===2||u.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()};\n`}\n      }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${p.length>0?p:""}|${n.length>0?n:""}|${l.length>0?l:""}|${w}|${u}`,inputDependencies:["rank"]},getShaderSource:$,getRunData:()=>({outputs:[{dims:a,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(b/64)},programUniforms:[{type:"uint32",data:b},{type:"float32",data:p},{type:"float32",data:l},...L(u),...L(a)]})}},bc=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Zs=(e,t)=>{let r=[],o=[],n=[],s=bc(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");ac(e.inputs,t,s,r,o,n),e.compute(yc(e.inputs[0],t,s,r,o,n),{inputs:[0]})},Qs=e=>{let t=e.antialias,r=e.axes,o=e.coordinateTransformMode,n=e.cubicCoeffA,s=e.excludeOutside!==0,u=e.extrapolationValue,l=e.keepAspectRatioPolicy,a=e.mode,p=e.nearestMode===""?"simple":e.nearestMode;return ge({antialias:t,axes:r,coordinateTransformMode:o,cubicCoeffA:n,excludeOutside:s,extrapolationValue:u,keepAspectRatioPolicy:l,mode:a,nearestMode:p})}});var wc,vc,Js,eu,tu=j(()=>{"use strict";Ne();$e();je();ve();wc=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],o=e[2];if(t.dataType!==r.dataType||t.dataType!==o.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let n=t.dims[t.dims.length-1],s=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==n)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==s)throw new Error("Skip must have the same sequence length as input");if(o.dims.length!==1)throw new Error("Gamma must be 1D");if(o.dims[o.dims.length-1]!==n)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let u=e[3];if(u.dims.length!==1)throw new Error("Beta must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let u=e[4];if(u.dims.length!==1)throw new Error("Bias must be 1D");if(u.dims[u.dims.length-1]!==n)throw new Error("Bias must have the same hidden size as input")}},vc=(e,t,r,o)=>{let n=e[0].dims,s=U.size(n),u=n,l=s,a=n.slice(-1)[0],p=o?n.slice(0,-1).concat(1):[],h=e.length>3,g=e.length>4,b=o&&r>1,w=o&&r>2,y=r>3,_=Fe(a),I=[M("x",e[0].dataType,e[0].dims,_),M("skip",e[1].dataType,e[1].dims,_),M("gamma",e[2].dataType,e[2].dims,_)];h&&I.push(M("beta",e[3].dataType,e[3].dims,_)),g&&I.push(M("bias",e[4].dataType,e[4].dims,_)),I.push(F("output",e[0].dataType,u,_)),b&&I.push(F("meanOutput",1,p)),w&&I.push(F("invStdOutput",1,p)),y&&I.push(F("inputSkipBiasSum",e[0].dataType,u,_));let $=Le(e[0].dataType),x=A=>`\n      const hiddenSize: f32 = ${a};\n      const hiddenSizeVectorized: u32 = ${a/_};\n      const epsilon: f32 = ${t.epsilon};\n\n      ${A.declareVariables(...I)}\n\n      ${A.mainStart()}\n        ${A.guardAgainstOutOfBoundsWorkgroupSizes(l/a)}\n        let offset = global_idx * hiddenSizeVectorized;\n        var sum = ${Ze("f32",_)};\n        var squareSum = ${Ze("f32",_)};\n        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n          let skipValue = skip[offset + i];\n          let biasValue = ${g?"bias[i]":"0.0"};\n          let inputValue = x[offset + i];\n          let value = inputValue + skipValue + biasValue;\n          ${y?"inputSkipBiasSum[offset + i] = value;":""}\n          output[offset + i] = value;\n          let f32Value = ${at($,_,"value")};\n          sum += f32Value;\n          squareSum += f32Value * f32Value;\n        }\n        let mean = ${Je("sum",_)} / hiddenSize;\n        let invStdDev = inverseSqrt(${Je("squareSum",_)} / hiddenSize - mean * mean + epsilon);\n        ${b?"meanOutput[global_idx] = mean;":""}\n        ${w?"invStdOutput[global_idx] = invStdDev;":""}\n        for (var i: u32 = 0; i < hiddenSizeVectorized; i++) {\n          output[offset + i] = (output[offset + i] - ${$}(mean)) * ${$}(invStdDev) * gamma[i]\n           + ${h?"beta[i]":"0.0"};\n        }\n      }`,E=[{dims:u,dataType:e[0].dataType}];return r>1&&E.push({dims:p,dataType:1}),r>2&&E.push({dims:p,dataType:1}),r>3&&E.push({dims:n,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:t.cacheKey},getShaderSource:x,getRunData:()=>({outputs:E,dispatchGroup:{x:Math.ceil(l/a/64)}})}},Js=(e,t)=>{wc(e.inputs);let o=[0];e.outputCount>1&&o.push(-3),e.outputCount>2&&o.push(-3),e.outputCount>3&&o.push(3),e.compute(vc(e.inputs,t,e.outputCount,!1),{outputs:o})},eu=e=>{let t=e.epsilon;return ge({epsilon:t})}});var $c,on,Sc,ru,xc,_c,nu,ou,au=j(()=>{"use strict";Ne();$e();je();ve();$c=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,o)=>{if(e[o+1].dataType!==6&&e[o+1].dataType!==7)throw new Error(`Input ${o} must be an array of int32 or int64`)})},on=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(o=>r.push(Number(o)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(o=>r.push(Number(o)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},Sc=(e,t)=>{if(e.length>1){let r=on(e,1),o=on(e,2),n=on(e,3);return n.length===0&&(n=[...Array(e[0].dims.length).keys()]),ge({starts:r,ends:o,axes:n})}else return t},ru=(e,t,r,o,n)=>{let s=e;return e<0&&(s+=r[o[t]]),n[t]<0?Math.max(0,Math.min(s,r[o[t]]-1)):Math.max(0,Math.min(s,r[o[t]]))},xc=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} {\n          var input_indices: ${e.type.indices};\n          var carry = 0u;\n          for (var i = ${r.length}; i >= 0; i--) {\n            let input_shape_i = ${ce("uniforms.input_shape","i",r.length)};\n            let steps_i = ${ce("uniforms.steps","i",r.length)};\n            let signs_i = ${ce("uniforms.signs","i",r.length)};\n            let starts_i = ${ce("uniforms.starts","i",r.length)};\n            var output_index = ${t.indicesGet("output_indices","i")};\n            var input_index = output_index * steps_i + starts_i + carry;\n            carry = input_index / input_shape_i;\n            input_index = input_index % input_shape_i;\n            if (signs_i < 0) {\n              input_index = input_shape_i - input_index - 1u + starts_i;\n            }\n            ${e.indicesSet("input_indices","i","input_index")};\n          }\n          return input_indices;\n      }`,_c=(e,t)=>{let r=e[0].dims,o=U.size(r),n=t.axes.length>0?U.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],s=on(e,4);s.forEach($=>$!==0||(()=>{throw new Error("step cannot be 0")})),s.length===0&&(s=Array(n.length).fill(1));let u=t.starts.map(($,x)=>ru($,x,r,n,s)),l=t.ends.map(($,x)=>ru($,x,r,n,s));if(n.length!==u.length||n.length!==l.length)throw new Error("start, ends and axes should have the same number of elements");if(n.length!==r.length)for(let $=0;$<r.length;++$)n.includes($)||(u.splice($,0,0),l.splice($,0,r[$]),s.splice($,0,1));let a=s.map($=>Math.sign($));s.forEach(($,x,E)=>{if($<0){let A=(l[x]-u[x])/$,z=u[x],R=z+A*s[x];u[x]=R,l[x]=z,E[x]=-$}});let p=r.slice(0);n.forEach(($,x)=>{p[$]=Math.ceil((l[$]-u[$])/s[$])});let h={dims:p,dataType:e[0].dataType},g=F("output",e[0].dataType,p.length),b=M("input",e[0].dataType,e[0].dims.length),w=U.size(p),y=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:u.length},{name:"signs",type:"i32",length:a.length},{name:"steps",type:"u32",length:s.length}],_=[{type:"uint32",data:w},{type:"uint32",data:u},{type:"int32",data:a},{type:"uint32",data:s},...L(e[0].dims),...L(p)],I=$=>`\n      ${$.registerUniforms(y).declareVariables(b,g)}\n        ${xc(b,g,r)}\n        ${$.mainStart()}\n          ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")}\n          let output_indices = ${g.offsetToIndices("global_idx")};\n          let input_indices = calculateInputIndices(output_indices);\n          ${g.setByOffset("global_idx",b.getByIndices("input_indices"))}\n      }`;return{name:"Slice",shaderCache:{hint:`${a.length}_${u.length}_${s.length}`,inputDependencies:["rank"]},getShaderSource:I,getRunData:()=>({outputs:[h],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:_})}},nu=(e,t)=>{$c(e.inputs,t);let r=Sc(e.inputs,t);e.compute(_c(e.inputs,r),{inputs:[0]})},ou=e=>{let t=e.starts,r=e.ends,o=e.axes;return ge({starts:t,ends:r,axes:o})}});var Cc,Ic,iu,su,uu=j(()=>{"use strict";$e();je();ve();Cc=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},Ic=(e,t)=>{let r=e.dims,o=U.size(r),n=64,s=t.axis;if(s<0&&(s=r.length+s),s<r.length-1)throw new Error("softmax only supports last axis for now.");let u=r[s],l=o/u,a=Fe(u),p=u/a,h=(I,$)=>$===4?`max(max(${I}.x, ${I}.y), max(${I}.z, ${I}.w))`:$===2?`max(${I}.x, ${I}.y)`:$===3?`max(max(${I}.x, ${I}.y), ${I}.z)`:I,g=M("x",e.dataType,e.dims,a),b=F("result",e.dataType,e.dims,a),w=g.type.value,y=Le(e.dataType)==="f32"?`var threadMax = ${w}(-3.402823e+38f);`:`var threadMax = ${w}(-65504.0h);`,_=I=>`\n      var<workgroup> rowMaxShared : ${w};\n      var<workgroup> rowSumShared : ${w};\n      var<workgroup> threadShared : array<${w}, ${n}>;\n\n      fn getValue(row: i32, col: i32, row_stride: i32) -> ${w} {\n        let index = row * row_stride + col;\n        return x[index];\n      }\n\n      fn setValue(row: i32, col: i32, row_stride: i32, value: ${w}) {\n        let index = row * row_stride + col;\n        result[index] = value;\n      }\n      ${I.registerUniform("packedCols","i32").declareVariables(g,b)}\n      ${I.mainStart()}\n        let gindex = i32(global_idx);\n        let lindex = i32(local_idx);\n        const wg = ${n};\n        let row = gindex / wg;\n        let cols = uniforms.packedCols;\n        let row_stride : i32 = uniforms.packedCols;\n\n        // find the rows max\n        ${y}\n        for (var col = lindex; col < cols; col += wg) {\n          let value = getValue(row, col, row_stride);\n          threadMax = max(threadMax, value);\n        }\n        if (lindex < cols) {\n          threadShared[lindex] = threadMax;\n        }\n        workgroupBarrier();\n\n        var reduceSize = min(cols, wg);\n        for (var currSize = reduceSize >> 1;  currSize > 0; currSize = reduceSize >> 1) {\n          reduceSize = currSize + (reduceSize & 1);\n          if (lindex < currSize) {\n            threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]);\n          }\n          workgroupBarrier();\n        }\n        if (lindex == 0) {\n          rowMaxShared = ${w}(${h("threadShared[0]",a)});\n        }\n        workgroupBarrier();\n\n        // find the rows sum\n        var threadSum = ${w}(0.0);\n        for (var col = lindex; col < cols; col += wg) {\n          let subExp = exp(getValue(row, col, row_stride) - rowMaxShared);\n          threadSum += subExp;\n        }\n        threadShared[lindex] = threadSum;\n        workgroupBarrier();\n\n        for (var currSize = wg >> 1;  currSize > 0; currSize = currSize >> 1) {\n          if (lindex < currSize) {\n            threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize];\n          }\n          workgroupBarrier();\n        }\n        if (lindex == 0) {\n          rowSumShared = ${w}(${Je("threadShared[0]",a)});\n        }\n        workgroupBarrier();\n\n        // calculate final value for each element in the row\n        for (var col = lindex; col < cols; col += wg) {\n          let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared;\n          setValue(row, col, row_stride, value);\n        }\n      }`;return{name:"Softmax",shaderCache:{hint:`${a}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:r,dataType:e.dataType}],dispatchGroup:{x:l},programUniforms:[{type:"uint32",data:p}]}),getShaderSource:_}},iu=(e,t)=>{Cc(e.inputs),e.compute(Ic(e.inputs[0],t))},su=e=>ge({axis:e.axis})});var Ac,Tc,Ec,Oc,Pc,du,lu,cu=j(()=>{"use strict";$e();je();ve();Ac=e=>{if(!e||e.length<1)throw new Error("too few inputs")},Tc=(e,t)=>{let r=[],o=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),o=r.length),ge({numOutputs:o,axis:t.axis,splitSizes:r})},Ec=e=>`\nfn calculateOutputIndex(index: u32) -> u32 {\n    for (var i: u32 = 0u; i < ${e}u; i += 1u ) {\n    if (index < ${ce("uniforms.size_in_split_axis","i",e)}) {\n        return i;\n    }\n    }\n    return ${e}u;\n}`,Oc=e=>{let t=e.length,r=[];for(let o=0;o<t;++o){let n=e[o].setByIndices("indices","input[global_idx]");t===1?r.push(n):o===0?r.push(`if (output_number == ${o}u) { ${n} }`):o===t-1?r.push(`else { ${n} }`):r.push(`else if (output_number == ${o}) { ${n} }`)}return`\n      fn writeBufferData(output_number: u32, indices: ${e[0].type.indices}, global_idx: u32) {\n        ${r.join(`\n`)}\n      }`},Pc=(e,t)=>{let r=e[0].dims,o=U.size(r),n=e[0].dataType,s=U.normalizeAxis(t.axis,r.length),u=new Array(t.numOutputs),l=M("input",n,r),a=new Array(t.numOutputs),p=[],h=[],g=0,b=[{type:"uint32",data:o}];for(let y=0;y<t.numOutputs;y++){g+=t.splitSizes[y],a[y]=g;let _=r.slice();_[t.axis]=t.splitSizes[y],h.push(_),u[y]=F(`output${y}`,n,_),p.push({dims:h[y],dataType:e[0].dataType})}b.push({type:"uint32",data:a}),b.push(...L(r)),h.forEach(y=>b.push(...L(y)));let w=y=>`\n  ${y.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",a.length).declareVariables(l,...u)}\n  ${Ec(a.length)}\n  ${Oc(u)}\n\n  ${y.mainStart()}\n    ${y.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")}\n\n    var indices = ${l.offsetToIndices("global_idx")};\n    var index = ${l.indicesGet("indices",s)};\n    let output_number = calculateOutputIndex(index);\n    if (output_number != 0) {\n      index -= ${ce("uniforms.size_in_split_axis","output_number - 1u",a.length)};\n      ${l.indicesSet("indices",s,"index")};\n    }\n    writeBufferData(output_number, indices, global_idx);\n  }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:w,getRunData:()=>({outputs:p,dispatchGroup:{x:Math.ceil(o/64)},programUniforms:b})}},du=(e,t)=>{Ac(e.inputs);let r=e.inputs.length===1?t:Tc(e.inputs,t);e.compute(Pc(e.inputs,r),{inputs:[0]})},lu=e=>{let t=e.axis,r=e.splitSizes,o=e.numOutputs<0?r.length:e.numOutputs;if(o!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return ge({axis:t,numOutputs:o,splitSizes:r})}});var pu,kc,Rc,Bc,mu,fu=j(()=>{"use strict";Ne();$e();ve();pu=e=>Array.from(e.getBigInt64Array(),Number),kc=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(pu(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},Rc=(e,t)=>{let r=[];for(let o=0;o<e.length;++o)r.push(e[o]*t[o]);return r},Bc=e=>{let t=e[0].dims,r=pu(e[1]),o=Rc(t,r),n=U.size(o),s=e[0].dataType,u=M("input",s,t.length),l=F("output",s,o.length),a=p=>`\n      const inputShape = ${u.indices(...t)};\n      ${p.registerUniform("output_size","u32").declareVariables(u,l)}\n      ${p.mainStart()}\n      ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}\n      let output_indices = ${l.offsetToIndices("global_idx")};\n      var input_indices: ${u.type.indices};\n      for (var i = 0; i < ${t.length}; i++) {\n        let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")};\n        let input_dim_value = ${l.indicesGet("output_indices","i")}  % input_dim_i;\n\n        ${u.indicesSet("input_indices","i","input_dim_value")}\n      }\n      ${l.setByOffset("global_idx",u.getByIndices("input_indices"))}\n    }`;return{name:"Tile",shaderCache:{hint:`${r}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:[{type:"uint32",data:n},...L(e[0].dims),...L(o)]}),getShaderSource:a}},mu=e=>{kc(e.inputs),e.compute(Bc(e.inputs),{inputs:[0]})}});var Dc,Mc,hu,gu=j(()=>{"use strict";Ne();$e();ve();Dc=(e,t,r,o,n)=>{let s=F("output_data",n,r.length,4),u=M("a_data",t[1].dataType,t[1].dims.length,4),l=M("b_data",t[2].dataType,t[2].dims.length,4),a=M("c_data",t[0].dataType,t[0].dims.length,4),p,h=(g,b,w)=>`select(${b}, ${g}, ${w})`;if(!o)p=s.setByOffset("global_idx",h(u.getByOffset("global_idx"),l.getByOffset("global_idx"),a.getByOffset("global_idx")));else{let g=(b,w,y="")=>{let _=`a_data[index_a${w}][component_a${w}]`,I=`b_data[index_b${w}][component_b${w}]`,$=`bool(c_data[index_c${w}] & ${4278190080>>>(3-w)*8}u)`;return`\n            let output_indices${w} = ${s.offsetToIndices(`global_idx * 4u + ${w}u`)};\n            let offset_a${w} = ${u.broadcastedIndicesToOffset(`output_indices${w}`,s)};\n            let offset_b${w} = ${l.broadcastedIndicesToOffset(`output_indices${w}`,s)};\n            let offset_c${w} = ${a.broadcastedIndicesToOffset(`output_indices${w}`,s)};\n            let index_a${w} = offset_a${w} / 4u;\n            let index_b${w} = offset_b${w} / 4u;\n            let index_c${w} = offset_c${w} / 4u;\n            let component_a${w} = offset_a${w} % 4u;\n            let component_b${w} = offset_b${w} % 4u;\n            ${b}[${w}] = ${y}(${h(_,I,$)});\n          `};n===9?p=`\n            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an,wu=j(()=>{"use strict";Lt();Ct();ve();an=class{constructor(t){this.backend=t;this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,r){this.repo.set(t,r)}run(t,r,o,n,s){kt(t.programInfo.name);let u=this.backend.device,l=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2),l.setPipeline(t.computePipeline);let a=[];for(let h of r)a.push({binding:a.length,resource:{buffer:h.buffer}});for(let h of o)a.push({binding:a.length,resource:{buffer:h.buffer}});s&&a.push({binding:a.length,resource:s});let 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A=16;x=Math.ceil(x/A)*A;let z=new ArrayBuffer(x);p.forEach((V,T)=>{let N=E[T],te=typeof V.data=="number"?[V.data]:V.data;V.type==="int32"?new Int32Array(z,N,te.length).set(te):V.type==="uint32"?new Uint32Array(z,N,te.length).set(te):new Float32Array(z,N,te.length).set(te)});let R=this.gpuDataManager.create(x,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.device.queue.writeBuffer(R.buffer,0,z,0,x),this.gpuDataManager.release(R.id),w={offset:0,size:x,buffer:R.buffer}}let y=this.programManager.normalizeDispatchGroupSize(a),_=y[1]===1&&y[2]===1,I=Uc(t,r,_),$=this.programManager.getArtifact(I);if($||($=this.programManager.build(t,y),this.programManager.setArtifact(I,$),Be("info",()=>`[artifact] key: ${I}, programName: ${t.name}`)),Be("info",()=>`[ProgramManager] run "${t.name}" (key=${I}) with ${y[0]}x${y[1]}x${y[2]}`),this.queryType!=="none"){let x={kernelId:this.currentKernelId,programName:$.programInfo.name,inputTensorViews:r,outputTensorViews:g};this.pendingKernels.push(x)}return this.programManager.run($,u,b,y,w),Rt(t.name),g}upload(t,r){this.gpuDataManager.upload(t,r)}memcpy(t,r){this.gpuDataManager.memcpy(t,r)}async download(t,r){await this.gpuDataManager.download(t,r)}alloc(t){return this.gpuDataManager.create(t).id}free(t){return this.gpuDataManager.release(t)}createKernel(t,r,o,n){let s=yu.get(t);if(!s)throw new Error(`kernel not implemented: ${t}`);let u={kernelType:t,kernelName:n,kernelEntry:s[0],attributes:[s[1],o]};this.kernels.set(r,u)}releaseKernel(t){let r=this.kernelPersistentData.get(t);if(r){for(let o of r)this.gpuDataManager.release(o.id);this.kernelPersistentData.delete(t)}this.kernelCustomData.delete(t),this.kernels.delete(t)}computeKernel(t,r,o){let n=this.kernels.get(t);if(!n)throw new Error(`kernel not created: ${t}`);let s=n.kernelType,u=n.kernelName,l=n.kernelEntry,a=n.attributes;if(this.currentKernelId!==null)throw new Error(`kernel "[${s}] ${u}" is not allowed to be called recursively`);this.currentKernelId=t,a[0]&&(a[1]=a[0](a[1]),a[0]=void 0),Be("info",()=>`[WebGPU] Start to run kernel "[${s}] ${u}"...`);let p=this.env.debug;this.temporaryData=[];try{return p&&this.device.pushErrorScope("validation"),l(r,a[1]),0}catch(h){return o.push(Promise.resolve(`[WebGPU] Kernel "[${s}] ${u}" failed. ${h}`)),1}finally{p&&o.push(this.device.popErrorScope().then(h=>h?`GPU validation error for kernel "[${s}] ${u}": ${h.message}`:null));for(let h of this.temporaryData)this.gpuDataManager.release(h.id);this.temporaryData=[],this.currentKernelId=null}}registerBuffer(t,r,o,n){let s=this.sessionExternalDataMapping.get(t);s||(s=new Map,this.sessionExternalDataMapping.set(t,s));let u=s.get(r),l=this.gpuDataManager.registerExternalBuffer(o,n,u?.[1]);return s.set(r,[l,o]),l}unregisterBuffers(t){let r=this.sessionExternalDataMapping.get(t);r&&(r.forEach(o=>this.gpuDataManager.unregisterExternalBuffer(o[1])),this.sessionExternalDataMapping.delete(t))}getBuffer(t){let r=this.gpuDataManager.get(t);if(!r)throw new Error(`no GPU data for buffer: ${t}`);return r.buffer}createDownloader(t,r,o){return async()=>{let n=await On(this,t,r);return sa(n.buffer,o)}}writeTimestamp(t){this.queryType==="inside-passes"&&this.computePassEncoder.writeTimestamp(this.querySet,t)}setQueryType(){this.queryType="none",(this.env.webgpu.profiling?.mode==="default"||this.env.wasm.trace)&&(this.device.features.has("chromium-experimental-timestamp-query-inside-passes")?this.queryType="inside-passes":this.device.features.has("timestamp-query")&&(this.queryType="at-passes"))}}});var $u={};Br($u,{init:()=>Vc});var gr,Jn,Vc,Su=j(()=>{"use strict";Ne();vu();Ct();$e();gr=class e{constructor(t,r,o,n){this.module=t;this.dataType=r;this.data=o;this.dims=n}getFloat32Array(){if(this.dataType!==1)throw new Error("Invalid data type");let t=U.size(this.dims);return t===0?new Float32Array:new Float32Array(this.module.HEAP8.buffer,this.data,t)}getBigInt64Array(){if(this.dataType!==7)throw new Error("Invalid data type");let t=U.size(this.dims);return t===0?new BigInt64Array:new BigInt64Array(this.module.HEAP8.buffer,this.data,t)}getInt32Array(){if(this.dataType!==6)throw new Error("Invalid data type");let t=U.size(this.dims);return t===0?new Int32Array:new Int32Array(this.module.HEAP8.buffer,this.data,t)}reshape(t){if(U.size(t)!==U.size(this.dims))throw new Error("Invalid new shape");return new e(this.module,this.dataType,this.data,t)}},Jn=class{constructor(t,r,o){this.module=t;this.backend=r;this.customDataOffset=0;this.customDataSize=0;let n=t.HEAPU32,s=o>>>2;this.opKernelContext=n[s++];let u=n[s++];this.outputCount=n[s++],this.customDataOffset=n[s++],this.customDataSize=n[s++];let l=[];for(let a=0;a<u;a++){let p=n[s++],h=n[s++],g=n[s++],b=[];for(let w=0;w<g;w++)b.push(n[s++]);l.push(new gr(t,p,h,b))}this.inputs=l}get kernelCustomData(){return this.backend.currentKernelCustomData}get customDataBuffer(){return this.module.HEAPU8.subarray(this.customDataOffset,this.customDataOffset+this.customDataSize)}compute(t,r){let o=r?.inputs?.map(l=>typeof l=="number"?this.inputs[l]:l)??this.inputs,n=r?.outputs??[],s=(l,a,p)=>new gr(this.module,a,this.output(l,p),p),u=(l,a)=>{let p=cr(l);if(!p)throw new Error(`Unsupported data type: ${l}`);let h=p*U.size(a);return new gr(this.module,l,this.backend.gpuDataManager.create(h).id,a)};return this.backend.run(t,o,n,s,u)}output(t,r){let o=this.module.stackSave();try{let n=this.module.stackAlloc((1+r.length)*4),s=n>>2;this.module.HEAPU32[s++]=r.length;for(let u=0;u<r.length;u++)this.module.HEAPU32[s++]=r[u];return this.module._JsepOutput(this.opKernelContext,t,n)}catch(n){throw new Error(`Failed to generate kernel\'s output[${t}] with dims [${r}]. If you are running with pre-allocated output, please make sure the output type/dims are correct. Error: ${n}`)}finally{this.module.stackRestore(o)}}},Vc=async(e,t,r)=>{let o=e.jsepInit;if(!o)throw new Error("Failed to initialize JSEP. 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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 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'}),kr,jt,Pi,Xa,Za,Ud,Zo,ti,ni,dy,Ja,e$,t$,n$,r$,i$,a$,s$,o$=ee(()=>{var e;Fn(),UE(),Hs(),kr=()=>!!je.wasm.proxy&&typeof document<"u",Pi=!1,Xa=!1,Za=!1,Zo=new Map,ti=(t,n)=>{let r=Zo.get(t);r?r.push(n):Zo.set(t,[n])},ni=()=>{if(Pi||!Xa||Za||!jt)throw new Error("worker not 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q2(je),Xa=!0}catch(t){throw Za=!0,t}finally{Pi=!1}}},t$=async t=>{if(kr())return ni(),new Promise((n,r)=>{ti("init-ep",[n,r]);let i={type:"init-ep",in:{epName:t,env:je}};jt.postMessage(i)});await K2(je,t)},n$=async t=>kr()?(ni(),new Promise((n,r)=>{ti("copy-from",[n,r]);let i={type:"copy-from",in:{buffer:t}};jt.postMessage(i,[t.buffer])})):Xc(t),r$=async(t,n)=>{if(kr()){if(n!=null&&n.preferredOutputLocation)throw new Error('session option "preferredOutputLocation" is not supported for proxy.');return ni(),new Promise((r,i)=>{ti("create",[r,i]);let a={type:"create",in:{model:t,options:n}},s=[];t instanceof Uint8Array&&s.push(t.buffer),jt.postMessage(a,s)})}else return Y2(t,n)},i$=async t=>{if(kr())return ni(),new Promise((n,r)=>{ti("release",[n,r]);let i={type:"release",in:t};jt.postMessage(i)});Q2(t)},a$=async(t,n,r,i,a,s)=>{if(kr()){if(r.some(o=>o[3]!=="cpu"))throw new Error("input tensor on GPU is not supported for proxy.");if(a.some(o=>o))throw new Error("pre-allocated output tensor 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fetchModelAndCopyToWasmMemory(e){return n$(await Yl(e))}async loadModel(e,t){Nn();let n;typeof e=="string"?typeof process<"u"&&process.versions&&process.versions.node?n=await Yl(e):n=await this.fetchModelAndCopyToWasmMemory(e):n=e,[this.sessionId,this.inputNames,this.outputNames]=await r$(n,t),Ln()}async dispose(){return i$(this.sessionId)}async run(e,t,n){Nn();let r=[],i=[];Object.entries(e).forEach(u=>{let f=u[0],h=u[1],g=this.inputNames.indexOf(f);if(g===-1)throw new Error(`invalid input '${f}'`);r.push(h),i.push(g)});let a=[],s=[];Object.entries(t).forEach(u=>{let f=u[0],h=u[1],g=this.outputNames.indexOf(f);if(g===-1)throw new Error(`invalid output '${f}'`);a.push(h),s.push(g)});let o=r.map((u,f)=>Wd(u,()=>`input "${this.inputNames[i[f]]}"`)),l=a.map((u,f)=>u?Wd(u,()=>`output "${this.outputNames[s[f]]}"`):null),d=await a$(this.sessionId,i,o,s,l,n),c={};for(let u=0;u<d.length;u++)c[this.outputNames[s[u]]]=a[u]??cy(d[u]);return Ln(),c}startProfiling(){}endProfiling(){s$(this.sessionId)}}}),py,u$,jE=ee(()=>{Fn(),o$(),VE(),py=()=>{if((typeof je.wasm.initTimeout!="number"||je.wasm.initTimeout<0)&&(je.wasm.initTimeout=0),typeof je.wasm.simd!="boolean"&&(je.wasm.simd=!0),typeof je.wasm.proxy!="boolean"&&(je.wasm.proxy=!1),typeof je.wasm.trace!="boolean"&&(je.wasm.trace=!1),typeof je.wasm.numThreads!="number"||!Number.isInteger(je.wasm.numThreads)||je.wasm.numThreads<=0){(typeof self<"u"&&!self.crossOriginIsolated||typeof process<"u"&&process.versions&&process.versions.node)&&(je.wasm.numThreads=1);let e=typeof navigator>"u"?(void 0)().length:navigator.hardwareConcurrency;je.wasm.numThreads=Math.min(4,Math.ceil((e||1)/2))}},u$=class{async init(e){py(),await e$(),await t$(e)}async createInferenceSessionHandler(e,t){let n=new l$;return await n.loadModel(e,t),Promise.resolve(n)}}}),d$={};ga(d$,{wasmBackend:()=>c$});var c$,GE=ee(()=>{jE(),c$=new u$});Fn();Fn();Fn();var HE="1.17.1",qE=fv;{let e=(GE(),fi(d$)).wasmBackend;na("webgpu",e,5),na("cpu",e,10),na("wasm",e,10)}Object.defineProperty(je.versions,"web",{value:HE,enumerable:!0});/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*//**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 *//**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */const KE=Object.freeze(Object.defineProperty({__proto__:null,get InferenceSession(){return af},get TRACE(){return Ds},get TRACE_FUNC_BEGIN(){return Nn},get TRACE_FUNC_END(){return Ln},get Tensor(){return Lt},get TrainingSession(){return sf},default:qE,get env(){return je},get registerBackend(){return na}},Symbol.toStringTag,{value:"Module"})),YE=(e,t)=>{const n=typeof document<"u"?document.createElement("canvas"):new OffscreenCanvas(1,1);n.width=e.dims[3],n.height=e.dims[2];const r=n.getContext("2d");if(r!=null){let i,a;(t==null?void 0:t.tensorLayout)!==void 0&&t.tensorLayout==="NHWC"?(i=e.dims[2],a=e.dims[3]):(i=e.dims[3],a=e.dims[2]);const s=(t==null?void 0:t.format)!==void 0?t.format:"RGB",o=t==null?void 0:t.norm;let l,d;o===void 0||o.mean===void 0?l=[255,255,255,255]:typeof o.mean=="number"?l=[o.mean,o.mean,o.mean,o.mean]:(l=[o.mean[0],o.mean[1],o.mean[2],0],o.mean[3]!==void 0&&(l[3]=o.mean[3])),o===void 0||o.bias===void 0?d=[0,0,0,0]:typeof 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Yn("float32",c,[1,3,n,r])},XE=async(e,t)=>{const n=typeof HTMLImageElement<"u"&&e instanceof HTMLImageElement,r=typeof ImageData<"u"&&e instanceof ImageData,i=typeof ImageBitmap<"u"&&e instanceof ImageBitmap,a=typeof e=="string";let s,o=t??{};const l=()=>{if(typeof document<"u")return document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},d=c=>c instanceof HTMLCanvasElement||c instanceof OffscreenCanvas?c.getContext("2d"):null;if(n){const c=l();c.width=e.width,c.height=e.height;const u=d(c);if(u!=null){let f=e.height,h=e.width;if(t!==void 0&&t.resizedHeight!==void 0&&t.resizedWidth!==void 0&&(f=t.resizedHeight,h=t.resizedWidth),t!==void 0){if(o=t,t.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");o.tensorFormat="RGBA",o.height=f,o.width=h}else o.tensorFormat="RGBA",o.height=f,o.width=h;u.drawImage(e,0,0),s=u.getImageData(0,0,h,f).data}else throw new Error("Can not access image data")}else if(r){let c,u;if(t!==void 0&&t.resizedWidth!==void 0&&t.resizedHeight!==void 0?(c=t.resizedHeight,u=t.resizedWidth):(c=e.height,u=e.width),t!==void 0&&(o=t),o.format="RGBA",o.height=c,o.width=u,t!==void 0){const f=l();f.width=u,f.height=c;const h=d(f);if(h!=null)h.putImageData(e,0,0),s=h.getImageData(0,0,u,c).data;else throw new Error("Can not access image data")}else s=e.data}else if(i){if(t===void 0)throw new Error("Please provide image config with format for Imagebitmap");const c=l();c.width=e.width,c.height=e.height;const u=d(c);if(u!=null){const f=e.height,h=e.width;return u.drawImage(e,0,0,h,f),s=u.getImageData(0,0,h,f).data,o.height=f,o.width=h,Vd(s,o)}else throw new Error("Can not access image data")}else{if(a)return new Promise((c,u)=>{const f=l(),h=d(f);if(!e||!h)return u();const g=new Image;g.crossOrigin="Anonymous",g.src=e,g.onload=()=>{f.width=g.width,f.height=g.height,h.drawImage(g,0,0,f.width,f.height);const $=h.getImageData(0,0,f.width,f.height);o.height=f.height,o.width=f.width,c(Vd($.data,o))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return Vd(s,o);throw new Error("Input data provided is not supported - aborted tensor creation")},ZE=(e,t)=>{const{width:n,height:r,download:i,dispose:a}=t,s=[1,r,n,4];return new Yn({location:"texture",type:"float32",texture:e,dims:s,download:i,dispose:a})},JE=(e,t)=>{const{dataType:n,dims:r,download:i,dispose:a}=t;return new Yn({location:"gpu-buffer",type:n??"float32",gpuBuffer:e,dims:r,download:i,dispose:a})},ek=(e,t,n)=>new Yn({location:"cpu-pinned",type:e,data:t,dims:n??[t.length]}),tu=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["float16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),Zc=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let fy=!1;const tk=()=>{if(!fy){fy=!0;const e=typeof BigInt64Array<"u"&&typeof BigInt64Array.from=="function",t=typeof BigUint64Array<"u"&&typeof BigUint64Array.from=="function";e&&(tu.set("int64",BigInt64Array),Zc.set(BigInt64Array,"int64")),t&&(tu.set("uint64",BigUint64Array),Zc.set(BigUint64Array,"uint64"))}},nk=e=>{let t=1;for(let n=0;n<e.length;n++){const r=e[n];if(typeof r!="number"||!Number.isSafeInteger(r))throw new TypeError(`dims[${n}] must be an integer, got: ${r}`);if(r<0)throw new RangeError(`dims[${n}] must be a non-negative integer, got: ${r}`);t*=r}return t},rk=(e,t)=>{switch(e.location){case"cpu":return new Yn(e.type,e.data,t);case"cpu-pinned":return new Yn({location:"cpu-pinned",data:e.data,type:e.type,dims:t});case"texture":return new Yn({location:"texture",texture:e.texture,type:e.type,dims:t});case"gpu-buffer":return new Yn({location:"gpu-buffer",gpuBuffer:e.gpuBuffer,type:e.type,dims:t});default:throw new Error(`tensorReshape: tensor location ${e.location} is not supported`)}};let Yn=class{constructor(t,n,r){tk();let i,a;if(typeof t=="object"&&"location"in t)switch(this.dataLocation=t.location,i=t.type,a=t.dims,t.location){case"cpu-pinned":{const o=tu.get(i);if(!o)throw new TypeError(`unsupported type "${i}" to create tensor from pinned buffer`);if(!(t.data instanceof o))throw new TypeError(`buffer should be of type ${o.name}`);this.cpuData=t.data;break}case"texture":{if(i!=="float32")throw new TypeError(`unsupported type "${i}" to create tensor from texture`);this.gpuTextureData=t.texture,this.downloader=t.download,this.disposer=t.dispose;break}case"gpu-buffer":{if(i!=="float32"&&i!=="float16"&&i!=="int32"&&i!=="int64"&&i!=="uint32"&&i!=="bool")throw new TypeError(`unsupported type "${i}" to create tensor from gpu buffer`);this.gpuBufferData=t.gpuBuffer,this.downloader=t.download,this.disposer=t.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let o,l;if(typeof t=="string")if(i=t,l=r,t==="string"){if(!Array.isArray(n))throw new TypeError("A string tensor's data must be a string array.");o=n}else{const d=tu.get(t);if(d===void 0)throw new TypeError(`Unsupported tensor type: ${t}.`);if(Array.isArray(n)){if(t==="float16")throw new TypeError("Creating a float16 tensor from number array is not supported. Please use Uint16Array as data.");t==="uint64"||t==="int64"?o=d.from(n,BigInt):o=d.from(n)}else if(n instanceof d)o=n;else throw new TypeError(`A ${i} tensor's data must be type of ${d}`)}else if(l=n,Array.isArray(t)){if(t.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");const d=typeof t[0];if(d==="string")i="string",o=t;else if(d==="boolean")i="bool",o=Uint8Array.from(t);else throw new TypeError(`Invalid element type of data array: ${d}.`)}else{const d=Zc.get(t.constructor);if(d===void 0)throw new TypeError(`Unsupported type for tensor data: ${t.constructor}.`);i=d,o=t}if(l===void 0)l=[o.length];else if(!Array.isArray(l))throw new TypeError("A tensor's dims must be a number array");a=l,this.cpuData=o,this.dataLocation="cpu"}const s=nk(a);if(this.cpuData&&s!==this.cpuData.length)throw new Error(`Tensor's size(${s}) does not match data length(${this.cpuData.length}).`);this.type=i,this.dims=a,this.size=s}static async fromImage(t,n){return XE(t,n)}static fromTexture(t,n){return ZE(t,n)}static fromGpuBuffer(t,n){return JE(t,n)}static fromPinnedBuffer(t,n,r){return ek(t,n,r)}toDataURL(t){return YE(this,t)}toImageData(t){return QE(this,t)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. 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e.args)if(a.type==="KeywordArgumentExpression"){const s=a;r.set(s.key.value,this.evaluate(s.value,t))}else n.push(this.evaluate(a,t));r.size>0&&n.push(new In(r));const i=this.evaluate(e.callee,t);if(i.type!=="FunctionValue")throw new Error(`Cannot call something that is not a function: got ${i.type}`);return i.value(n,t)}evaluateSliceExpression(e,t,n){if(!(e instanceof st||e instanceof Pe))throw new Error("Slice object must be an array or string");const r=this.evaluate(t.start,n),i=this.evaluate(t.stop,n),a=this.evaluate(t.step,n);if(!(r instanceof Xe||r instanceof Cn))throw new Error("Slice start must be numeric or undefined");if(!(i instanceof Xe||i instanceof Cn))throw new Error("Slice stop must be numeric or undefined");if(!(a instanceof Xe||a instanceof Cn))throw new Error("Slice step must be numeric or undefined");return e instanceof st?new st(xy(e.value,r.value,i.value,a.value)):new Pe(xy(Array.from(e.value),r.value,i.value,a.value).join(""))}evaluateMemberExpression(e,t){const n=this.evaluate(e.object,t);let r;if(e.computed){if(e.property.type==="SliceExpression")return this.evaluateSliceExpression(n,e.property,t);r=this.evaluate(e.property,t)}else r=new Pe(e.property.value);let i;if(n instanceof In){if(!(r instanceof Pe))throw new Error(`Cannot access property with non-string: got ${r.type}`);i=n.value.get(r.value)??n.builtins.get(r.value)}else if(n instanceof st||n instanceof Pe)if(r instanceof Xe)i=n.value.at(r.value),n instanceof Pe&&(i=new Pe(n.value.at(r.value)));else if(r instanceof Pe)i=n.builtins.get(r.value);else throw new Error(`Cannot access property with non-string/non-number: got ${r.type}`);else{if(!(r instanceof Pe))throw new Error(`Cannot access property with non-string: got ${r.type}`);i=n.builtins.get(r.value)}return i instanceof wr?i:new Cn}evaluateSet(e,t){const n=this.evaluate(e.value,t);if(e.assignee.type==="Identifier"){const r=e.assignee.value;t.setVariable(r,n)}else if(e.assignee.type==="MemberExpression"){const r=e.assignee,i=this.evaluate(r.object,t);if(!(i instanceof In))throw new Error("Cannot assign to member of non-object");if(r.property.type!=="Identifier")throw new Error("Cannot assign to member with non-identifier property");i.value.set(r.property.value,n)}else throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(e.assignee)}`);return new ws}evaluateIf(e,t){const n=this.evaluate(e.test,t);return this.evaluateBlock(n.__bool__().value?e.body:e.alternate,t)}evaluateFor(e,t){const n=new tp(t),r=this.evaluate(e.iterable,n);if(!(r instanceof st))throw new Error(`Expected iterable type in for loop: got ${r.type}`);let i="";for(let a=0;a<r.value.length;++a){const s=new Map([["index",new Xe(a+1)],["index0",new Xe(a)],["revindex",new Xe(r.value.length-a)],["revindex0",new Xe(r.value.length-a-1)],["first",new dt(a===0)],["last",new dt(a===r.value.length-1)],["length",new Xe(r.value.length)],["previtem",a>0?r.value[a-1]:new Cn],["nextitem",a<r.value.length-1?r.value[a+1]:new Cn]]);n.setVariable("loop",new In(s));const o=r.value[a];if(e.loopvar.type==="Identifier")n.setVariable(e.loopvar.value,o);else if(e.loopvar.type==="TupleLiteral"){const d=e.loopvar;if(o.type!=="ArrayValue")throw new Error(`Cannot unpack non-iterable type: ${o.type}`);const c=o;if(d.value.length!==c.value.length)throw new Error(`Too ${d.value.length>c.value.length?"few":"many"} items to unpack`);for(let u=0;u<d.value.length;++u){if(d.value[u].type!=="Identifier")throw new Error(`Cannot unpack non-identifier type: ${d.value[u].type}`);n.setVariable(d.value[u].value,c.value[u])}}const l=this.evaluateBlock(e.body,n);i+=l.value}return new Pe(i)}evaluate(e,t){if(e===void 0)return new Cn;switch(e.type){case"Program":return this.evalProgram(e,t);case"Set":return this.evaluateSet(e,t);case"If":return this.evaluateIf(e,t);case"For":return this.evaluateFor(e,t);case"NumericLiteral":return new Xe(Number(e.value));case"StringLiteral":return new Pe(e.value);case"BooleanLiteral":return new dt(e.value);case"ArrayLiteral":return new st(e.value.map(n=>this.evaluate(n,t)));case"TupleLiteral":return new Uk(e.value.map(n=>this.evaluate(n,t)));case"ObjectLiteral":{const n=new Map;for(const[r,i]of e.value){const a=this.evaluate(r,t);if(!(a instanceof Pe))throw new Error(`Object keys must be strings: got ${a.type}`);n.set(a.value,this.evaluate(i,t))}return new In(n)}case"Identifier":return this.evaluateIdentifier(e,t);case"CallExpression":return this.evaluateCallExpression(e,t);case"MemberExpression":return this.evaluateMemberExpression(e,t);case"UnaryExpression":return this.evaluateUnaryExpression(e,t);case"BinaryExpression":return this.evaluateBinaryExpression(e,t);case"FilterExpression":return this.evaluateFilterExpression(e,t);case"TestExpression":return this.evaluateTestExpression(e,t);default:throw new SyntaxError(`Unknown node type: ${e.type}`)}}};function _l(e){switch(typeof e){case"number":return new Xe(e);case"string":return new Pe(e);case"boolean":return new dt(e);case"object":return e===null?new ws:Array.isArray(e)?new st(e.map(_l)):new In(new Map(Object.entries(e).map(([t,n])=>[t,_l(n)])));case"function":return new dr((t,n)=>{const r=e(...t.map(i=>i.value))??null;return _l(r)});default:throw new Error(`Cannot convert to runtime value: ${e}`)}}var Vk=class{constructor(e){j(this,"parsed");const t=Ek(e,{lstrip_blocks:!0,trim_blocks:!0});this.parsed=Lk(t)}render(e){const t=new tp;t.set("false",!1),t.set("true",!0),t.set("raise_exception",i=>{throw new Error(i)}),t.set("range",Fk);for(const[i,a]of Object.entries(e))t.set(i,a);return new Wk(t).run(this.parsed).value}};async function h$(e,t){const n=await Promise.all([di(e,"tokenizer.json",!0,t),di(e,"tokenizer_config.json",!0,t)]);return t.legacy!==null&&(n[1].legacy=t.legacy),n}function jk(e,t){const n=[];let r=0;for(const i of e.matchAll(t)){const a=i[0];r<i.index&&n.push(e.slice(r,i.index)),a.length>0&&n.push(a),r=i.index+a.length}return r<e.length&&n.push(e.slice(r)),n}function Su(e,t=!0){if(e.Regex!==void 0){let n=e.Regex.replace(/\\([#&~])/g,"$1");for(const[r,i]of Kk)n=n.replaceAll(r,i);return new RegExp(n,"gu")}else if(e.String!==void 0){const n=K_(e.String);return new RegExp(t?n:`(${n})`,"gu")}else return console.warn("Unknown pattern type:",e),null}function Sf(e){return new Map(Object.entries(e))}function m$(e){const t=e.dims;switch(t.length){case 1:return e.tolist();case 2:if(t[0]!==1)throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");return e.tolist()[0];default:throw new Error(`Expected tensor to have 1-2 dimensions, got ${t.length}.`)}}function Ef(e){return e.replace(/ \./g,".").replace(/ \?/g,"?").replace(/ \!/g,"!").replace(/ ,/g,",").replace(/ \' /g,"'").replace(/ n\'t/g,"n't").replace(/ \'m/g,"'m").replace(/ \'s/g,"'s").replace(/ \'ve/g,"'ve").replace(/ \'re/g,"'re")}function g$(e){return e.replace(/[\u0300-\u036f]/g,"")}function Gk(e){return g$(e.toLowerCase())}function Hk(e,t,n){const r=[];let i=0;for(;i<e.length;){if(r.push(e[i]),(n.get(e[i])??t)!==t){++i;continue}for(;i<e.length&&(n.get(e[i])??t)===t;)++i}return r}function qk(e){return e.match(/\S+/g)||[]}const Ls="\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E",Kk=new Map([["(?i:'s|'t|'re|'ve|'m|'ll|'d)","(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]]);class Yk{constructor(t){this.content=t.content,this.id=t.id,this.single_word=t.single_word??!1,this.lstrip=t.lstrip??!1,this.rstrip=t.rstrip??!1,this.special=t.special??!1,this.normalized=t.normalized??null}}class Ys extends dn{constructor(t){super(),this.config=t,this.vocab=[],this.tokens_to_ids=new Map,this.unk_token_id=void 0,this.unk_token=void 0,this.end_of_word_suffix=void 0,this.fuse_unk=this.config.fuse_unk??!1}static fromConfig(t,...n){switch(t.type){case"WordPiece":return new Qk(t);case"Unigram":return new Xk(t,...n);case"BPE":return new Jk(t);default:if(t.vocab)return new eC(t,...n);throw new Error(`Unknown TokenizerModel type: ${t.type}`)}}_call(t){let n=this.encode(t);return this.fuse_unk&&(n=Hk(n,this.unk_token_id,this.tokens_to_ids)),n}encode(t){throw Error("encode should be implemented in subclass.")}convert_tokens_to_ids(t){return t.map(n=>this.tokens_to_ids.get(n)??this.unk_token_id)}convert_ids_to_tokens(t){return t.map(n=>this.vocab[n]??this.unk_token)}}class Qk extends Ys{constructor(t){super(t),this.tokens_to_ids=Sf(t.vocab),this.unk_token_id=this.tokens_to_ids.get(t.unk_token),this.unk_token=t.unk_token,this.max_input_chars_per_word=t.max_input_chars_per_word??100,this.vocab=new Array(this.tokens_to_ids.size);for(const[n,r]of this.tokens_to_ids)this.vocab[r]=n}encode(t){const n=[];for(const r of t){const i=[...r];if(i.length>this.max_input_chars_per_word){n.push(this.unk_token);continue}let a=!1,s=0;const o=[];for(;s<i.length;){let l=i.length,d=null;for(;s<l;){let c=i.slice(s,l).join("");if(s>0&&(c=this.config.continuing_subword_prefix+c),this.tokens_to_ids.has(c)){d=c;break}--l}if(d===null){a=!0;break}o.push(d),s=l}a?n.push(this.unk_token):n.push(...o)}return n}}class Xk extends Ys{constructor(t,n){super(t);const r=t.vocab.length;this.vocab=new Array(r),this.scores=new Array(r);for(let i=0;i<r;++i){const a=t.vocab[i];this.vocab[i]=a[0],this.scores[i]=a[1]}this.unk_token_id=t.unk_id,this.unk_token=this.vocab[t.unk_id],this.tokens_to_ids=new Map(this.vocab.map((i,a)=>[i,a])),this.bosToken=" ",this.bosTokenId=this.tokens_to_ids.get(this.bosToken),this.eosToken=n.eos_token,this.eosTokenId=this.tokens_to_ids.get(this.eosToken),this.unkToken=this.vocab[this.unk_token_id],this.minScore=MS(this.scores)[0],this.unkScore=this.minScore-10,this.scores[this.unk_token_id]=this.unkScore,this.trie=new vk,this.trie.extend(this.vocab),this.fuse_unk=!0}populateNodes(t){const n=t.sentence,r=n.length;let i=0;for(;i<r;){let s=!1;for(let o of this.trie.commonPrefixSearch(n.slice(i))){const l=this.tokens_to_ids.get(o),d=this.scores[l],c=o.length;t.insert(i,c,d,l),!s&&c===1&&(s=!0)}s||t.insert(i,1,this.unkScore,this.unk_token_id),i+=1}}tokenize(t){const n=new $k(t,this.bosTokenId,this.eosTokenId);return this.populateNodes(n),n.tokens()}encode(t){const n=[];for(const r of t){const i=this.tokenize(r);n.push(...i)}return n}}const y$=(()=>{const e=[...Array.from({length:94},(i,a)=>a+33),...Array.from({length:12},(i,a)=>a+161),...Array.from({length:82},(i,a)=>a+174)],t=e.slice();let n=0;for(let i=0;i<256;++i)e.includes(i)||(e.push(i),t.push(256+n),n+=1);const r=t.map(i=>String.fromCharCode(i));return Object.fromEntries(e.map((i,a)=>[i,r[a]]))})(),Zk=ES(y$);class Jk extends Ys{constructor(t){super(t),this.BPE_SPLIT_TOKEN=" ",this.tokens_to_ids=Sf(t.vocab),this.unk_token_id=this.tokens_to_ids.get(t.unk_token),this.unk_token=t.unk_token,this.vocab=new Array(this.tokens_to_ids.size);for(const[n,r]of this.tokens_to_ids)this.vocab[r]=n;this.bpe_ranks=new Map(t.merges.map((n,r)=>[n,r])),this.merges=t.merges.map(n=>n.split(this.BPE_SPLIT_TOKEN)),this.end_of_word_suffix=t.end_of_word_suffix,this.continuing_subword_suffix=t.continuing_subword_suffix??null,this.byte_fallback=this.config.byte_fallback??!1,this.byte_fallback&&(this.text_encoder=new TextEncoder),this.cache=new Map}bpe(t){if(t.length===0)return[];const n=this.cache.get(t);if(n!==void 0)return n;const r=Array.from(t);this.end_of_word_suffix&&(r[r.length-1]+=this.end_of_word_suffix);let i=[];if(r.length>1){const a=new _k((l,d)=>l.score<d.score);let s={token:r[0],bias:0,prev:null,next:null},o=s;for(let l=1;l<r.length;++l){const d={bias:l/r.length,token:r[l],prev:o,next:null};o.next=d,this._add_node(a,o),o=d}for(;!a.isEmpty();){const l=a.pop();if(l.deleted||!l.next||l.next.deleted)continue;if(l.deleted=!0,l.next.deleted=!0,l.prev){const c={...l.prev};l.prev.deleted=!0,l.prev=c,c.prev?c.prev.next=c:s=c}const d={token:l.token+l.next.token,bias:l.bias,prev:l.prev,next:l.next.next};d.prev?(d.prev.next=d,this._add_node(a,d.prev)):s=d,d.next&&(d.next.prev=d,this._add_node(a,d))}for(let l=s;l!==null;l=l.next)i.push(l.token)}else i=r;if(this.continuing_subword_suffix)for(let a=0;a<i.length-1;++a)i[a]+=this.continuing_subword_suffix;return this.cache.set(t,i),i}_add_node(t,n){const r=this.bpe_ranks.get(n.token+this.BPE_SPLIT_TOKEN+n.next.token);r!==void 0&&(n.score=r+n.bias,t.push(n))}encode(t){const n=[];for(const r of t){const i=this.bpe(r);for(const a of i)this.tokens_to_ids.has(a)?n.push(a):this.byte_fallback?n.push(...Array.from(this.text_encoder.encode(a)).map(s=>`<0x${s.toString(16).toUpperCase().padStart(2,"0")}>`)):n.push(this.unk_token)}return n}}class eC extends Ys{constructor(t,n){super(t),this.tokens_to_ids=Sf(n.target_lang?t.vocab[n.target_lang]:t.vocab),this.bos_token=n.bos_token,this.bos_token_id=this.tokens_to_ids.get(this.bos_token),this.eos_token=n.eos_token,this.eos_token_id=this.tokens_to_ids.get(this.eos_token),this.pad_token=n.pad_token,this.pad_token_id=this.tokens_to_ids.get(this.pad_token),this.unk_token=n.unk_token,this.unk_token_id=this.tokens_to_ids.get(this.unk_token),this.vocab=new Array(this.tokens_to_ids.size);for(const[r,i]of this.tokens_to_ids)this.vocab[i]=r}encode(t){return t}}class sn extends dn{constructor(t){super(),this.config=t}static fromConfig(t){if(t===null)return null;switch(t.type){case"BertNormalizer":return new dC(t);case"Precompiled":return new IC(t);case"Sequence":return new uC(t);case"Replace":return new tC(t);case"NFC":return new nC(t);case"NFKC":return new rC(t);case"NFKD":return new iC(t);case"Strip":return new aC(t);case"StripAccents":return new sC(t);case"Lowercase":return new oC(t);case"Prepend":return new lC(t);default:throw new Error(`Unknown Normalizer type: ${t.type}`)}}normalize(t){throw Error("normalize should be implemented in subclass.")}_call(t){return this.normalize(t)}}class tC extends sn{normalize(t){const n=Su(this.config.pattern);return n===null?t:t.replaceAll(n,this.config.content)}}class nC extends sn{normalize(t){return t=t.normalize("NFC"),t}}class rC extends sn{normalize(t){return t=t.normalize("NFKC"),t}}class iC extends sn{normalize(t){return t=t.normalize("NFKD"),t}}class aC extends sn{normalize(t){return this.config.strip_left&&this.config.strip_right?t=t.trim():(this.config.strip_left&&(t=t.trimStart()),this.config.strip_right&&(t=t.trimEnd())),t}}class sC extends sn{normalize(t){return t=g$(t),t}}class oC extends sn{normalize(t){return t=t.toLowerCase(),t}}class lC extends sn{normalize(t){return t=this.config.prepend+t,t}}class uC extends sn{constructor(t){super(t),this.normalizers=t.normalizers.map(n=>sn.fromConfig(n))}normalize(t){return this.normalizers.reduce((n,r)=>r.normalize(n),t)}}class dC extends sn{_tokenize_chinese_chars(t){const n=[];for(let r=0;r<t.length;++r){const i=t[r],a=i.charCodeAt(0);this._is_chinese_char(a)?(n.push(" "),n.push(i),n.push(" ")):n.push(i)}return n.join("")}_is_chinese_char(t){return t>=19968&&t<=40959||t>=13312&&t<=19903||t>=131072&&t<=173791||t>=173824&&t<=177983||t>=177984&&t<=178207||t>=178208&&t<=183983||t>=63744&&t<=64255||t>=194560&&t<=195103}stripAccents(t){return t.normalize("NFD").replace(/[\u0300-\u036f]/g,"")}_is_control(t){switch(t){case"	":case`
`:case"\r":return!1;default:return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$","u").test(t)}}_clean_text(t){const n=[];for(const r of t){const i=r.charCodeAt(0);i===0||i===65533||this._is_control(r)||(/^\s$/.test(r)?n.push(" "):n.push(r))}return n.join("")}normalize(t){return this.config.clean_text&&(t=this._clean_text(t)),this.config.handle_chinese_chars&&(t=this._tokenize_chinese_chars(t)),this.config.lowercase?(t=t.toLowerCase(),this.config.strip_accents!==!1&&(t=this.stripAccents(t))):this.config.strip_accents&&(t=this.stripAccents(t)),t}}class _n extends dn{static fromConfig(t){if(t===null)return null;switch(t.type){case"BertPreTokenizer":return new cC(t);case"Sequence":return new AC(t);case"Whitespace":return new OC(t);case"WhitespaceSplit":return new MC(t);case"Metaspace":return new v$(t);case"ByteLevel":return new pC(t);case"Split":return new fC(t);case"Punctuation":return new hC(t);case"Digits":return new mC(t);case"Replace":return new zC(t);default:throw new Error(`Unknown PreTokenizer type: ${t.type}`)}}pre_tokenize_text(t,n){throw Error("pre_tokenize_text should be implemented in subclass.")}pre_tokenize(t,n){return(Array.isArray(t)?t.map(r=>this.pre_tokenize_text(r,n)):this.pre_tokenize_text(t,n)).flat()}_call(t,n){return this.pre_tokenize(t,n)}}class cC extends _n{constructor(t){super(),this.pattern=new RegExp(`[^\\s${Ls}]+|[${Ls}]`,"gu")}pre_tokenize_text(t,n){return t.trim().match(this.pattern)||[]}}class pC extends _n{constructor(t){super(),this.config=t,this.add_prefix_space=this.config.add_prefix_space,this.trim_offsets=this.config.trim_offsets,this.use_regex=this.config.use_regex??!0,this.pattern=new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+","gu"),this.byte_encoder=y$,this.text_encoder=new TextEncoder}pre_tokenize_text(t,n){return this.add_prefix_space&&!t.startsWith(" ")&&(t=" "+t),(this.use_regex?t.match(this.pattern)||[]:[t]).map(i=>Array.from(this.text_encoder.encode(i),a=>this.byte_encoder[a]).join(""))}}class fC extends _n{constructor(t){super(),this.config=t,this.pattern=Su(this.config.pattern,this.config.invert)}pre_tokenize_text(t,n){return this.pattern===null?[]:this.config.invert?t.match(this.pattern)||[]:jk(t,this.pattern)}}class hC extends _n{constructor(t){super(),this.config=t,this.pattern=new RegExp(`[^${Ls}]+|[${Ls}]+`,"gu")}pre_tokenize_text(t,n){return t.match(this.pattern)||[]}}class mC extends _n{constructor(t){super(),this.config=t;const n=`[^\\d]+|\\d${this.config.individual_digits?"":"+"}`;this.pattern=new RegExp(n,"gu")}pre_tokenize_text(t,n){return t.match(this.pattern)||[]}}class Eu extends dn{constructor(t){super(),this.config=t}static fromConfig(t){if(t===null)return null;switch(t.type){case"TemplateProcessing":return new gC(t);case"ByteLevel":return new yC(t);case"RobertaProcessing":return new _$(t);case"BertProcessing":return new w$(t);default:throw new Error(`Unknown PostProcessor type: ${t.type}`)}}post_process(t,...n){throw Error("post_process should be implemented in subclass.")}_call(t,...n){return this.post_process(t,...n)}}class w$ extends Eu{constructor(t){super(t),this.cls=t.cls[0],this.sep=t.sep[0]}post_process(t,n=null,{add_special_tokens:r=!0}={}){r&&(t=kt([this.cls],t,[this.sep]));let i=new Array(t.length).fill(0);if(n!==null){const a=r&&this instanceof _$?[this.sep]:[],s=r?[this.sep]:[];t=kt(t,a,n,s),i=kt(i,new Array(n.length+a.length+s.length).fill(1))}return{tokens:t,token_type_ids:i}}}class _$ extends w${}class gC extends Eu{constructor(t){super(t),this.single=t.single,this.pair=t.pair}post_process(t,n=null,{add_special_tokens:r=!0}={}){const i=n===null?this.single:this.pair;let a=[],s=[];for(const o of i)"SpecialToken"in o?r&&(a.push(o.SpecialToken.id),s.push(o.SpecialToken.type_id)):"Sequence"in o&&(o.Sequence.id==="A"?(a=kt(a,t),s=kt(s,new Array(t.length).fill(o.Sequence.type_id))):o.Sequence.id==="B"&&(a=kt(a,n),s=kt(s,new Array(n.length).fill(o.Sequence.type_id))));return{tokens:a,token_type_ids:s}}}class yC extends Eu{post_process(t,n=null){return n&&(t=kt(t,n)),{tokens:t}}}class on extends dn{constructor(t){super(),this.config=t,this.added_tokens=[],this.end_of_word_suffix=null,this.trim_offsets=t.trim_offsets}static fromConfig(t){if(t===null)return null;switch(t.type){case"WordPiece":return new bC(t);case"Metaspace":return new TC(t);case"ByteLevel":return new xC(t);case"Replace":return new wC(t);case"ByteFallback":return new _C(t);case"Fuse":return new vC(t);case"Strip":return new $C(t);case"Sequence":return new EC(t);case"CTC":return new SC(t);case"BPEDecoder":return new kC(t);default:throw new Error(`Unknown Decoder type: ${t.type}`)}}_call(t){return this.decode(t)}decode(t){return this.decode_chain(t).join("")}decode_chain(t){throw Error("`decode_chain` should be implemented in subclass.")}}class wC extends on{decode_chain(t){const n=Su(this.config.pattern);return n===null?t:t.map(r=>r.replaceAll(n,this.config.content))}}class _C extends on{constructor(t){super(t),this.text_decoder=new TextDecoder}decode_chain(t){const n=[];let r=[];for(const i of t){let a=null;if(i.length===6&&i.startsWith("<0x")&&i.endsWith(">")){const s=parseInt(i.slice(3,5),16);isNaN(s)||(a=s)}if(a!==null)r.push(a);else{if(r.length>0){const s=this.text_decoder.decode(Uint8Array.from(r));n.push(s),r=[]}n.push(i)}}if(r.length>0){const i=this.text_decoder.decode(Uint8Array.from(r));n.push(i),r=[]}return n}}class vC extends on{decode_chain(t){return[t.join("")]}}class $C extends on{constructor(t){super(t),this.content=this.config.content,this.start=this.config.start,this.stop=this.config.stop}decode_chain(t){return t.map(n=>{let r=0;for(let a=0;a<this.start&&n[a]===this.content;++a){r=a+1;continue}let i=n.length;for(let a=0;a<this.stop;++a){const s=n.length-a-1;if(n[s]===this.content){i=s;continue}else break}return n.slice(r,i)})}}class bC extends on{constructor(t){super(t),this.cleanup=t.cleanup}decode_chain(t){return t.map((n,r)=>(r!==0&&(n.startsWith(this.config.prefix)?n=n.replace(this.config.prefix,""):n=" "+n),this.cleanup&&(n=Ef(n)),n))}}class xC extends on{constructor(t){super(t),this.byte_decoder=Zk,this.text_decoder=new TextDecoder("utf-8",{fatal:!1,ignoreBOM:!0}),this.end_of_word_suffix=null}convert_tokens_to_string(t){const n=t.join(""),r=new Uint8Array([...n].map(a=>this.byte_decoder[a]));return this.text_decoder.decode(r)}decode_chain(t){const n=[];let r=[];for(const i of t)this.added_tokens.find(a=>a.content===i)!==void 0?(r.length>0&&(n.push(this.convert_tokens_to_string(r)),r=[]),n.push(i)):r.push(i);return r.length>0&&n.push(this.convert_tokens_to_string(r)),n}}class SC extends on{constructor(t){super(t),this.pad_token=this.config.pad_token,this.word_delimiter_token=this.config.word_delimiter_token,this.cleanup=this.config.cleanup}convert_tokens_to_string(t){if(t.length===0)return"";const n=[t[0]];for(let a=1;a<t.length;++a)t[a]!==n.at(-1)&&n.push(t[a]);let i=n.filter(a=>a!==this.pad_token).join("");return this.cleanup&&(i=Ef(i).replaceAll(this.word_delimiter_token," ").trim()),i}decode_chain(t){return[this.convert_tokens_to_string(t)]}}class EC extends on{constructor(t){super(t),this.decoders=t.decoders.map(n=>on.fromConfig(n))}decode_chain(t){return this.decoders.reduce((n,r)=>r.decode_chain(n),t)}}class kC extends on{constructor(t){super(t),this.suffix=this.config.suffix}decode_chain(t){return t.map((n,r)=>n.replaceAll(this.suffix,r===t.length-1?"":" "))}}class CC extends on{decode_chain(t){let n="";for(let r=1;r<t.length;r+=2)n+=t[r];return[n]}}class v$ extends _n{constructor(t){super(),this.addPrefixSpace=t.add_prefix_space,this.replacement=t.replacement,this.strRep=t.str_rep||this.replacement,this.prepend_scheme=t.prepend_scheme??"always"}pre_tokenize_text(t,{section_index:n=void 0}={}){let r=t.replaceAll(" ",this.strRep);return this.addPrefixSpace&&!r.startsWith(this.replacement)&&(this.prepend_scheme==="always"||this.prepend_scheme==="first"&&n===0)&&(r=this.strRep+r),[r]}}class TC extends on{constructor(t){super(t),this.addPrefixSpace=t.add_prefix_space,this.replacement=t.replacement}decode_chain(t){const n=[];for(let r=0;r<t.length;++r){let i=t[r].replaceAll(this.replacement," ");this.addPrefixSpace&&r==0&&i.startsWith(" ")&&(i=i.substring(1)),n.push(i)}return n}}class IC extends sn{constructor(t){super(t),this.charsmap=t.precompiled_charsmap}normalize(t){return t=t.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm,""),t=t.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm," "),t.includes("～")?t=t.split("～").map(r=>r.normalize("NFKC")).join("～"):t=t.normalize("NFKC"),t}}class AC extends _n{constructor(t){super(),this.tokenizers=t.pretokenizers.map(n=>_n.fromConfig(n))}pre_tokenize_text(t,n){return this.tokenizers.reduce((r,i)=>i.pre_tokenize(r,n),[t])}}class OC extends _n{constructor(t){super()}pre_tokenize_text(t,n){return t.match(/\w+|[^\w\s]+/g)||[]}}class MC extends _n{constructor(t){super()}pre_tokenize_text(t,n){return qk(t)}}class zC extends _n{constructor(t){super(),this.config=t,this.pattern=Su(this.config.pattern),this.content=this.config.content}pre_tokenize_text(t,n){return this.pattern===null?[t]:[t.replaceAll(this.pattern,this.config.content)]}}const RC=["bos_token","eos_token","unk_token","sep_token","pad_token","cls_token","mask_token"];function PC(e,t,n,r){for(const i of Object.keys(e)){const a=t-e[i].length,s=n(i),o=new Array(a).fill(s);e[i]=r==="right"?kt(e[i],o):kt(o,e[i])}}function BC(e,t){for(const n of Object.keys(e))e[n].length=t}class Te extends dn{constructor(n,r){super();j(this,"return_token_type_ids",!1);j(this,"_default_chat_template",`{% for message in messages %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}`);j(this,"padding_side","right");this._tokenizer_config=r,this.normalizer=sn.fromConfig(n.normalizer),this.pre_tokenizer=_n.fromConfig(n.pre_tokenizer),this.model=Ys.fromConfig(n.model,r),this.post_processor=Eu.fromConfig(n.post_processor),this.decoder=on.fromConfig(n.decoder),this.special_tokens=[],this.all_special_ids=[],this.added_tokens=[];for(const i of n.added_tokens){const a=new Yk(i);this.added_tokens.push(a),this.model.tokens_to_ids.set(a.content,a.id),this.model.vocab[a.id]=a.content,a.special&&(this.special_tokens.push(a.content),this.all_special_ids.push(a.id))}if(this.additional_special_tokens=r.additional_special_tokens??[],this.special_tokens.push(...this.additional_special_tokens),this.special_tokens=[...new Set(this.special_tokens)],this.decoder&&(this.decoder.added_tokens=this.added_tokens,this.decoder.end_of_word_suffix=this.model.end_of_word_suffix),this.added_tokens_regex=this.added_tokens.length>0?new RegExp(this.added_tokens.map(i=>`${i.lstrip?"\\s*":""}(${K_(i.content)})${i.rstrip?"\\s*":""}`).join("|")):null,this.mask_token=this.getToken("mask_token"),this.mask_token_id=this.model.tokens_to_ids.get(this.mask_token),this.pad_token=this.getToken("pad_token","eos_token"),this.pad_token_id=this.model.tokens_to_ids.get(this.pad_token),this.sep_token=this.getToken("sep_token"),this.sep_token_id=this.model.tokens_to_ids.get(this.sep_token),this.unk_token=this.getToken("unk_token"),this.unk_token_id=this.model.tokens_to_ids.get(this.unk_token),this.model_max_length=r.model_max_length,this.remove_space=r.remove_space,this.clean_up_tokenization_spaces=r.clean_up_tokenization_spaces??!0,this.do_lowercase_and_remove_accent=r.do_lowercase_and_remove_accent??!1,r.padding_side&&(this.padding_side=r.padding_side),this.legacy=!1,this.chat_template=r.chat_template??null,Array.isArray(this.chat_template)){const i=Object.create(null);for(const{name:a,template:s}of this.chat_template){if(typeof a!="string"||typeof s!="string")throw new Error('Chat template must be a list of objects with "name" and "template" properties');i[a]=s}this.chat_template=i}this._compiled_template_cache=new Map}getToken(...n){for(const r of n){const i=this._tokenizer_config[r];if(i)if(typeof i=="object"){if(i.__type==="AddedToken")return i.content;throw Error(`Unknown token: ${i}`)}else return i}return null}static async from_pretrained(n,{progress_callback:r=null,config:i=null,cache_dir:a=null,local_files_only:s=!1,revision:o="main",legacy:l=null}={}){const d=await h$(n,{progress_callback:r,config:i,cache_dir:a,local_files_only:s,revision:o,legacy:l});return new this(...d)}_call(n,{text_pair:r=null,add_special_tokens:i=!0,padding:a=!1,truncation:s=null,max_length:o=null,return_tensor:l=!0}={}){const d=Array.isArray(n);let c;if(d){if(n.length===0)throw Error("text array must be non-empty");if(r!==null){if(Array.isArray(r)){if(n.length!==r.length)throw Error("text and text_pair must have the same length")}else throw Error("text_pair must also be an array");c=n.map((f,h)=>this._encode_plus(f,{text_pair:r[h],add_special_tokens:i}))}else c=n.map(f=>this._encode_plus(f,{add_special_tokens:i}))}else{if(n==null)throw Error("text may not be null or undefined");if(Array.isArray(r))throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");c=[this._encode_plus(n,{text_pair:r,add_special_tokens:i})]}if(o===null?a==="max_length"?o=this.model_max_length:o=Zp(c.map(f=>f.input_ids.length))[0]:s||console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=true` to explicitly truncate examples to max length."),o=Math.min(o,this.model_max_length),a||s)for(let f=0;f<c.length;++f)c[f].input_ids.length!==o&&(c[f].input_ids.length>o?s&&BC(c[f],o):a&&PC(c[f],o,h=>h==="input_ids"?this.pad_token_id:0,this.padding_side));const u={};if(l){if(!(a&&s)&&c.some(h=>{var g;for(const $ of Object.keys(h))if(h[$].length!==((g=c[0][$])==null?void 0:g.length))return!0;return!1}))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length.");const f=[c.length,c[0].input_ids.length];for(const h of Object.keys(c[0]))u[h]=new _e("int64",BigInt64Array.from(c.flatMap(g=>g[h]).map(BigInt)),f)}else{for(const f of Object.keys(c[0]))u[f]=c.map(h=>h[f]);if(!d)for(const f of Object.keys(u))u[f]=u[f][0]}return u}_encode_text(n){return n===null?null:(this.added_tokens_regex?n.split(this.added_tokens_regex).filter(a=>a):[n]).map((a,s)=>{if(this.added_tokens.find(l=>l.content===a)!==void 0)return a;{if(this.remove_space===!0&&(a=a.trim().split(/\s+/).join(" ")),this.do_lowercase_and_remove_accent&&(a=Gk(a)),this.normalizer!==null&&(a=this.normalizer(a)),a.length===0)return[];const l=this.pre_tokenizer!==null?this.pre_tokenizer(a,{section_index:s}):[a];return this.model(l)}}).flat()}_encode_plus(n,{text_pair:r=null,add_special_tokens:i=!0}={}){const{tokens:a,token_type_ids:s}=this._tokenize_helper(n,{pair:r,add_special_tokens:i}),o=this.model.convert_tokens_to_ids(a),l={input_ids:o,attention_mask:new Array(o.length).fill(1)};return this.return_token_type_ids&&s&&(l.token_type_ids=s),l}_tokenize_helper(n,{pair:r=null,add_special_tokens:i=!1}={}){const a=this._encode_text(n),s=this._encode_text(r);return this.post_processor?this.post_processor(a,s,{add_special_tokens:i}):{tokens:kt(a??[],s??[])}}tokenize(n,{pair:r=null,add_special_tokens:i=!1}={}){return this._tokenize_helper(n,{pair:r,add_special_tokens:i}).tokens}encode(n,{text_pair:r=null,add_special_tokens:i=!0}={}){return this._encode_plus(n,{text_pair:r,add_special_tokens:i}).input_ids}batch_decode(n,r={}){return n instanceof _e&&(n=n.tolist()),n.map(i=>this.decode(i,r))}decode(n,r={}){if(n instanceof _e&&(n=m$(n)),!Array.isArray(n)||n.length===0||!kS(n[0]))throw Error("token_ids must be a non-empty array of integers.");return this.decode_single(n,r)}decode_single(n,{skip_special_tokens:r=!1,clean_up_tokenization_spaces:i=null}){let a=this.model.convert_ids_to_tokens(n);r&&(a=a.filter(o=>!this.special_tokens.includes(o)));let s=this.decoder?this.decoder(a):a.join(" ");return this.decoder&&this.decoder.end_of_word_suffix&&(s=s.replaceAll(this.decoder.end_of_word_suffix," "),r&&(s=s.trim())),(i??this.clean_up_tokenization_spaces)&&(s=Ef(s)),s}get default_chat_template(){return this._warned_about_chat_template||(console.warn("No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format. If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information."),this._warned_about_chat_template=!0),this._default_chat_template}apply_chat_template(n,{chat_template:r=null,add_generation_prompt:i=!1,tokenize:a=!0,padding:s=!1,truncation:o=!1,max_length:l=null,return_tensor:d=!0,tokenizer_kwargs:c={},...u}={}){if(this.chat_template&&typeof this.chat_template=="object"||this.chat_template===null&&this.default_chat_template&&typeof this.default_chat_template=="object"){const $=this.chat_template??this.default_chat_template;if(r!==null&&Object.hasOwn($,r))r=$[r];else if(r===null&&"default"in $)r=$.default;else if(r===null)throw Error(`This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys($).sort()}.`)}else r??(r=this.chat_template??this.default_chat_template);if(typeof r!="string")throw Error(`chat_template must be a string, but got ${typeof r}`);let f=this._compiled_template_cache.get(r);f===void 0&&(f=new Vk(r),this._compiled_template_cache.set(r,f));const h=Object.create(null);for(const $ of RC){const E=this.getToken($);E&&(h[$]=E)}const g=f.render({messages:n,add_generation_prompt:i,...h,...u});return a?this._call(g,{add_special_tokens:!1,padding:s,truncation:o,max_length:l,return_tensor:d,...c}).input_ids:g}}class DC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class NC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class LC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class FC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class UC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class WC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class VC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class jC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class GC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class HC extends Te{}class qC extends Te{}class KC extends Te{constructor(n,r){super(n,r);j(this,"return_token_type_ids",!0);console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}}class YC extends Te{constructor(){super(...arguments);j(this,"return_token_type_ids",!0)}}class QC extends Te{}class $$ extends Te{constructor(){super(...arguments);j(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}}class XC extends Te{}class b$ extends Te{constructor(t,n){super(t,n),this.languageRegex=/^[a-z]{2}_[A-Z]{2}$/,this.language_codes=this.special_tokens.filter(r=>this.languageRegex.test(r)),this.lang_to_token=r=>r}_build_translation_inputs(t,n,r){return kf(this,t,n,r)}}class ZC extends b${}class JC extends Te{}class eT extends $${constructor(t,n){var a,s;const r=".,!?…。，、।۔،",i=(s=(a=t.pre_tokenizer)==null?void 0:a.pretokenizers[0])==null?void 0:s.pattern;i&&i.Regex===` ?[^(\\s|[${r}])]+`&&(i.Regex=` ?[^\\s${r}]+`),super(t,n)}}const Jo="▁";class x$ extends Te{constructor(n,r){super(n,r);j(this,"_default_chat_template",`{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>
' + system_message + '
<</SYS>>

' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>
' + content.strip() + '
<</SYS>>

' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}`);j(this,"DEFAULT_SYSTEM_PROMPT",`You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.`);j(this,"padding_side","left");this.use_default_system_prompt=r.use_default_system_prompt??!1,this.legacy=r.legacy??!0,this.legacy||(this.normalizer=null,this.pre_tokenizer=new v$({replacement:Jo,add_prefix_space:!0,prepend_scheme:"first"}))}_encode_text(n){if(n===null)return null;if(this.legacy||n.length===0)return super._encode_text(n);let r=super._encode_text(Jo+n.replaceAll(Jo," "));return r.length>1&&r[0]===Jo&&this.special_tokens.includes(r[1])&&(r=r.slice(1)),r}get default_chat_template(){return super.default_chat_template.replaceAll("USE_DEFAULT_PROMPT",this.use_default_system_prompt?"true":"false").replaceAll("DEFAULT_SYSTEM_MESSAGE",this.DEFAULT_SYSTEM_PROMPT.replaceAll(`
`,"\\n").replaceAll("'","\\'"))}}class tT extends x${}class nT extends Te{}class rT extends Te{}class iT extends Te{}class aT extends Te{}class sT extends Te{}class oT extends Te{}class lT extends Te{constructor(){super(...arguments);j(this,"_default_chat_template",`{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
' + message['content'] | trim + '<end_of_turn>
' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
'}}{% endif %}`)}}class uT extends Te{}function kf(e,t,n,r){if(!("language_codes"in e)||!Array.isArray(e.language_codes))throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");if(!("languageRegex"in e)||!(e.languageRegex instanceof RegExp))throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");if(!("lang_to_token"in e)||typeof e.lang_to_token!="function")throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");const i=r.src_lang,a=r.tgt_lang;if(!e.language_codes.includes(a))throw new Error(`Target language code "${a}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);if(i!==void 0){if(!e.language_codes.includes(i))throw new Error(`Source language code "${i}" is not valid. Must be one of: {${e.language_codes.join(", ")}}`);for(const s of e.post_processor.config.single)if("SpecialToken"in s&&e.languageRegex.test(s.SpecialToken.id)){s.SpecialToken.id=e.lang_to_token(i);break}}return r.forced_bos_token_id=e.model.convert_tokens_to_ids([e.lang_to_token(a)])[0],e._call(t,n)}class dT extends Te{constructor(t,n){super(t,n),this.languageRegex=/^[a-z]{3}_[A-Z][a-z]{3}$/,this.language_codes=this.special_tokens.filter(r=>this.languageRegex.test(r)),this.lang_to_token=r=>r}_build_translation_inputs(t,n,r){return kf(this,t,n,r)}}class cT extends Te{constructor(t,n){super(t,n),this.languageRegex=/^__[a-z]{2,3}__$/,this.language_codes=this.special_tokens.filter(r=>this.languageRegex.test(r)).map(r=>r.slice(2,-2)),this.lang_to_token=r=>`__${r}__`}_build_translation_inputs(t,n,r){return kf(this,t,n,r)}}const S$=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]],el=new Map(S$),pT=new Map([...S$.map(([e,t])=>[t,e]),["burmese","my"],["valencian","ca"],["flemish","nl"],["haitian","ht"],["letzeburgesch","lb"],["pushto","ps"],["panjabi","pa"],["moldavian","ro"],["moldovan","ro"],["sinhalese","si"],["castilian","es"]]);class fT extends Te{constructor(){super(...arguments);j(this,"_default_chat_template",'{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}')}_decode_asr(n,{return_timestamps:r=!1,return_language:i=!1,time_precision:a=null,force_full_sequences:s=!0}={}){if(a===null)throw Error("Must specify time_precision");let o=null;const l=r==="word";function d(){return{language:o,timestamp:[null,null],text:""}}const c=[];let u=d(),f=0;const h=this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0]+1;let g=[],$=[],E=!1,y=null;const _=new Set(this.all_special_ids);for(const C of n){const I=C.tokens,O=l?C.token_timestamps:null;let M=null,W=h;if("stride"in C){const[K,ae,ue]=C.stride;if(f-=ae,y=K-ue,ae&&(W=ae/a+h),ue)for(let R=I.length-1;R>=0;--R){const H=I[R];if(H>=h){if(M!==null&&(H-h)*a<y)break;M=H}}}let G=[],z=[];for(let K=0;K<I.length;++K){const ae=I[K];if(_.has(ae)){const ue=this.decode([ae]),R=el.get(ue.slice(2,-2));if(R!==void 0){if(o!==null&&R!==o&&!r){g.push(G);const H=this.findLongestCommonSequence(g)[0],re=this.decode(H);u.text=re,c.push(u),g=[],G=[],u=d()}o=u.language=R}}else if(ae>=h){const ue=(ae-h)*a+f,R=jo(ue,2);if(M!==null&&ae>=M)E=!0;else if(E||g.length>0&&ae<W)E=!1;else if(u.timestamp[0]===null)u.timestamp[0]=R;else if(R!==u.timestamp[0]){u.timestamp[1]=R,g.push(G),l&&$.push(z);const[H,re]=this.findLongestCommonSequence(g,$),L=this.decode(H);u.text=L,l&&(u.words=this.collateWordTimestamps(H,re,o)),c.push(u),g=[],G=[],$=[],z=[],u=d()}}else if(G.push(ae),l){let ue=jo(O[K]+f,2),R;K+1<O.length?R=jo(O[K+1]+f,2):R=null,z.push([ue,R])}}if("stride"in C){const[K,ae,ue]=C.stride;f+=K-ue}G.length>0?(g.push(G),l&&$.push(z)):g.every(K=>K.length===0)&&(u=d(),g=[],G=[],$=[],z=[])}if(g.length>0){if(s&&r)throw new Error("Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation.");const[C,I]=this.findLongestCommonSequence(g,$),O=this.decode(C);u.text=O,l&&(u.words=this.collateWordTimestamps(C,I,o)),c.push(u)}let v=Object.create(null);const b=c.map(C=>C.text).join("");if(r||i){for(let C=0;C<c.length;++C){const I=c[C];r||delete I.timestamp,i||delete I.language}if(l){const C=[];for(const I of c)for(const O of I.words)C.push(O);v={chunks:C}}else v={chunks:c}}return[b,v]}findLongestCommonSequence(n,r=null){let i=n[0],a=i.length,s=[];const o=Array.isArray(r)&&r.length>0;let l=o?[]:null,d=o?r[0]:null;for(let c=1;c<n.length;++c){const u=n[c];let f=0,h=[a,a,0,0];const g=u.length;for(let C=1;C<a+g;++C){const I=C/1e4,O=Math.max(0,a-C),M=Math.min(a,a+g-C),W=i.slice(O,M),G=Math.max(0,C-a),z=Math.min(g,C),K=u.slice(G,z);if(W.length!==K.length)throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");const ae=W.filter((R,H)=>R===K[H]).length,ue=ae/C+I;ae>1&&ue>f&&(f=ue,h=[O,M,G,z])}const[$,E,y,_]=h,v=Math.floor((E+$)/2),b=Math.floor((_+y)/2);s.push(...i.slice(0,v)),i=u.slice(b),a=i.length,o&&(l.push(...d.slice(0,v)),d=r[c].slice(b))}return s.push(...i),o?(l.push(...d),[s,l]):[s,[]]}collateWordTimestamps(n,r,i){const[a,s,o]=this.combineTokensIntoWords(n,i),l=[];for(let d=0;d<a.length;++d){const c=o[d];l.push({text:a[d],timestamp:[r[c.at(0)][0],r[c.at(-1)][1]]})}return l}combineTokensIntoWords(n,r,i=`"'“¡¿([{-`,a=`"'.。,，!！?？:：”)]}、`){r=r??"english";let s,o,l;return["chinese","japanese","thai","lao","myanmar"].includes(r)?[s,o,l]=this.splitTokensOnUnicode(n):[s,o,l]=this.splitTokensOnSpaces(n),this.mergePunctuations(s,o,l,i,a)}decode(n,r){let i;return r&&r.decode_with_timestamps?(n instanceof _e&&(n=m$(n)),i=this.decodeWithTimestamps(n,r)):i=super.decode(n,r),i}decodeWithTimestamps(n,r){const i=(r==null?void 0:r.time_precision)??.02,a=Array.from(this.all_special_ids).at(-1)+1;let s=[[]];for(const o of n)if(o>=a){const l=jo((o-a)*i,2);s.push(`<|${l}|>`),s.push([])}else s[s.length-1].push(o);return s=s.map(o=>typeof o=="string"?o:super.decode(o,r)),s.join("")}splitTokensOnUnicode(n){const r=this.decode(n,{decode_with_timestamps:!0}),i="�",a=[],s=[],o=[];let l=[],d=[],c=0;for(let u=0;u<n.length;++u){const f=n[u];l.push(f),d.push(u);const h=this.decode(l,{decode_with_timestamps:!0});(!h.includes(i)||r[c+h.indexOf(i)]===i)&&(a.push(h),s.push(l),o.push(d),l=[],d=[],c+=h.length)}return[a,s,o]}splitTokensOnSpaces(n){const[r,i,a]=this.splitTokensOnUnicode(n),s=[],o=[],l=[],d=new RegExp(`^[${Ls}]$`,"gu");for(let c=0;c<r.length;++c){const u=r[c],f=i[c],h=a[c],g=f[0]>=this.model.tokens_to_ids.get("<|endoftext|>"),$=u.startsWith(" "),E=u.trim(),y=d.test(E);if(g||$||y||s.length===0)s.push(u),o.push(f),l.push(h);else{const _=s.length-1;s[_]+=u,o[_].push(...f),l[_].push(...h)}}return[s,o,l]}mergePunctuations(n,r,i,a,s){const o=structuredClone(n),l=structuredClone(r),d=structuredClone(i);let c=o.length-2,u=o.length-1;for(;c>=0;)o[c].startsWith(" ")&&a.includes(o[c].trim())?(o[u]=o[c]+o[u],l[u]=kt(l[c],l[u]),d[u]=kt(d[c],d[u]),o[c]="",l[c]=[],d[c]=[]):u=c,--c;for(c=0,u=1;u<o.length;)!o[c].endsWith(" ")&&s.includes(o[u])?(o[c]+=o[u],l[c]=kt(l[c],l[u]),d[c]=kt(d[c],d[u]),o[u]="",l[u]=[],d[u]=[]):c=u,++u;return[o.filter(f=>f),l.filter(f=>f.length>0),d.filter(f=>f.length>0)]}get_decoder_prompt_ids({language:n=null,task:r=null,no_timestamps:i=!0}={}){const a=[];if(n){n=n.toLowerCase();let s=pT.get(n);if(s===void 0)if(el.has(n))s=n;else{const d=n.length===2?el.keys():el.values();throw new Error(`Language "${n}" is not supported. Must be one of: ${JSON.stringify(d)}`)}const o=this.model.tokens_to_ids.get(`<|${s}|>`);if(o===void 0)throw new Error(`Unable to find language "${s}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);a.push(o)}else a.push(null);if(r){if(r=r.toLowerCase(),r!=="transcribe"&&r!=="translate")throw new Error(`Task "${r}" is not supported. Must be one of: ["transcribe", "translate"]`);const s=this.model.tokens_to_ids.get(`<|${r}|>`);if(s===void 0)throw new Error(`Unable to find task "${r}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);a.push(s)}else a.push(null);if(i){const s=this.model.tokens_to_ids.get("<|notimestamps|>");if(s===void 0)throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');a.push(s)}return a.map((s,o)=>[o+1,s]).filter(s=>s[1]!==null)}}class hT extends Te{}class mT extends Te{}class gT extends Te{}class yT extends Te{constructor(t,n){super(t,n),this.languageRegex=/^(>>\w+<<)\s*/g,this.supported_language_codes=this.model.vocab.filter(r=>this.languageRegex.test(r)),console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.')}_encode_text(t){if(t===null)return null;const[n,...r]=t.trim().split(this.languageRegex);if(r.length===0)return super._encode_text(n);if(r.length===2){const[i,a]=r;return this.supported_language_codes.includes(i)||console.warn(`Unsupported language code "${i}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`),kt([i],super._encode_text(a))}}}class wT extends Te{}class E$ extends Te{constructor(){super(...arguments);j(this,"_default_chat_template","{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}")}}class _T extends E${}class vT extends Te{}class $T extends Te{}class bT extends Te{constructor(t,n){super(t,n),this.decoder=new CC({})}}class xT extends Te{}class k${static async from_pretrained(t,{progress_callback:n=null,config:r=null,cache_dir:i=null,local_files_only:a=!1,revision:s="main",legacy:o=null}={}){var f;const[l,d]=await h$(t,{progress_callback:n,config:r,cache_dir:i,local_files_only:a,revision:s,legacy:o}),c=((f=d.tokenizer_class)==null?void 0:f.replace(/Fast$/,""))??"PreTrainedTokenizer";let u=this.TOKENIZER_CLASS_MAPPING[c];return u||(console.warn(`Unknown tokenizer class "${c}", attempting to construct from base class.`),u=Te),new u(l,d)}}j(k$,"TOKENIZER_CLASS_MAPPING",{T5Tokenizer:QC,DistilBertTokenizer:HC,CamembertTokenizer:qC,DebertaTokenizer:UC,DebertaV2Tokenizer:WC,BertTokenizer:DC,HerbertTokenizer:VC,ConvBertTokenizer:jC,RoFormerTokenizer:GC,XLMTokenizer:KC,ElectraTokenizer:YC,MobileBertTokenizer:LC,SqueezeBertTokenizer:FC,AlbertTokenizer:NC,GPT2Tokenizer:$$,BartTokenizer:XC,MBartTokenizer:b$,MBart50Tokenizer:ZC,RobertaTokenizer:JC,WhisperTokenizer:fT,CodeGenTokenizer:hT,CLIPTokenizer:mT,SiglipTokenizer:gT,MarianTokenizer:yT,BloomTokenizer:eT,NllbTokenizer:dT,M2M100Tokenizer:cT,LlamaTokenizer:x$,CodeLlamaTokenizer:tT,XLMRobertaTokenizer:nT,MPNetTokenizer:rT,FalconTokenizer:iT,GPTNeoXTokenizer:aT,EsmTokenizer:sT,Wav2Vec2CTCTokenizer:wT,BlenderbotTokenizer:E$,BlenderbotSmallTokenizer:_T,SpeechT5Tokenizer:vT,NougatTokenizer:$T,VitsTokenizer:bT,Qwen2Tokenizer:oT,GemmaTokenizer:lT,Grok1Tokenizer:uT,CohereTokenizer:xT,PreTrainedTokenizer:Te});async function ST(e,t){return await di(e,"config.json",!0,t)}class ET{constructor(t){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,t)}static async from_pretrained(t,{progress_callback:n=null,config:r=null,cache_dir:i=null,local_files_only:a=!1,revision:s="main"}={}){let o=r??await ST(t,{progress_callback:n,config:r,cache_dir:i,local_files_only:a,revision:s});return new this(o)}}class Cr{static async from_pretrained(...t){return ET.from_pretrained(...t)}}const tl=Object.freeze({cpu:"cpu",gpu:"gpu",wasm:"wasm",webgpu:"webgpu"}),kT=function(){let e;return async function(){if(e===void 0)if(Gl.IS_NODE_ENV)e=!0;else if(!Gl.IS_WEBGPU_AVAILABLE)e=!1;else try{e=(await navigator.gpu.requestAdapter()).features.has("shader-f16")}catch{e=!1}return e}}(),Rn=Object.freeze({fp32:"fp32",fp16:"fp16",q8:"q8",int8:"int8",uint8:"uint8"}),CT=Object.freeze({[tl.cpu]:Rn.q8,[tl.gpu]:Rn.fp32,[tl.wasm]:Rn.q8,[tl.webgpu]:Rn.fp32}),Sy=Object.freeze({[Rn.fp32]:"",[Rn.fp16]:"_fp16",[Rn.int8]:"_int8",[Rn.uint8]:"_uint8",[Rn.q8]:"_quantized"});class _r extends dn{_call(t,n){throw Error("`_call` should be implemented in a subclass")}}class Cf extends dn{_call(t,n){throw Error("`_call` should be implemented in a subclass")}}class Ey extends dn{constructor(){super(),this.processors=[]}push(t){this.processors.push(t)}extend(t){this.processors.push(...t)}_call(t,n){let r=n;for(const i of this.processors)r=i(t,r);return r}[Symbol.iterator](){return this.processors.values()}}class TT extends _r{constructor(t){super(),this.bos_token_id=t}_call(t,n){for(let r=0;r<t.length;++r)if(t[r].length===1){const i=n[r];i.data.fill(-1/0),i.data[this.bos_token_id]=0}return n}}class IT extends _r{constructor(t,n){super(),this.max_length=t,this.forced_eos_token_id=n}_call(t,n){}}class AT extends _r{constructor(t,n){super(),this.begin_suppress_tokens=t,this.begin_index=n}_call(t,n){for(let r=0;r<t.length;++r)if(t[r].length===this.begin_index){const i=n[r];for(const a of this.begin_suppress_tokens)i.data[a]=-1/0}return n}}class OT extends _r{constructor(t){super(),this.no_repeat_ngram_size=t}getNgrams(t){const n=t.length,r=[];for(let a=0;a<n+1-this.no_repeat_ngram_size;++a){const s=[];for(let o=0;o<this.no_repeat_ngram_size;++o)s.push(t[a+o]);r.push(s)}const i=new Map;for(const a of r){const s=a.slice(0,a.length-1),o=JSON.stringify(s),l=i.get(o)??[];l.push(a[a.length-1]),i.set(o,l)}return i}getGeneratedNgrams(t,n){const r=n.slice(n.length+1-this.no_repeat_ngram_size,n.length);return t.get(JSON.stringify(r))??[]}calcBannedNgramTokens(t){const n=[];if(t.length+1<this.no_repeat_ngram_size)return n;{const r=this.getNgrams(t);return this.getGeneratedNgrams(r,t)}}_call(t,n){for(let r=0;r<t.length;++r){const i=n[r],a=this.calcBannedNgramTokens(t[r]);for(const s of a)i.data[s]=-1/0}return n}}class MT extends _r{constructor(t){super(),this.penalty=t}_call(t,n){for(let r=0;r<t.length;++r){const i=n[r];for(const a of t[r])i.data[a]<0?i.data[a]*=this.penalty:i.data[a]/=this.penalty}return n}}class zT extends _r{constructor(t,n){super(),this.min_length=t,this.eos_token_id=Array.isArray(n)?n:[n]}_call(t,n){for(let r=0;r<t.length;++r)if(t[r].length<this.min_length){const i=n[r];for(const a of this.eos_token_id)i.data[a]=-1/0}return n}}class RT extends _r{constructor(t,n,r){super(),this.prompt_length_to_skip=t,this.min_new_tokens=n,this.eos_token_id=Array.isArray(r)?r:[r]}_call(t,n){for(let r=0;r<t.length;++r)if(t[r].length-this.prompt_length_to_skip<this.min_new_tokens){const a=n[r];for(const s of this.eos_token_id)a[s]=-1/0}return n}}class PT extends _r{constructor(t,n){super(),this.bad_words_ids=t,this.eos_token_id=Array.isArray(n)?n:[n]}_call(t,n){for(let r=0;r<t.length;++r){const i=n[r];for(const a of this.bad_words_ids){let s=!0;for(let o=1;o<=a.length-1&&a.length<t[o].length;++o)if(a.at(-o-1)!==t[o].at(-o)){s=!1;break}s&&(i[a.at(-1)]=-1/0)}}return n}}class BT extends _r{constructor(t){if(super(),t<=1)throw new Error(`Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${t}.`);this.guidance_scale=t}_call(t,n){if(n.dims[0]!==2*t.length)throw new Error(`Logits should have twice the batch size of the input ids, the first half of batches corresponding to the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got batch size ${n.dims[0]} for the logits and ${t.length} for the input ids.`);const r=t.length,i=n.slice([0,r],null),a=n.slice([r,n.dims[0]],null);for(let s=0;s<a.data.length;++s)a.data[s]+=(i.data[s]-a.data[s])*this.guidance_scale;return a}}class DT extends Cf{constructor(t){super(),this.temperature=t}_call(t,n){const r=n.data;for(let i=0;i<r.length;++i)r[i]/=this.temperature;return n}}class NT extends Cf{constructor(t,{filter_value:n=-1/0,min_tokens_to_keep:r=1}={}){if(super(),t<0||t>1)throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${t}`);if(!Number.isInteger(r)||r<1)throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${r}`);this.top_p=t,this.filter_value=n,this.min_tokens_to_keep=r}}class LT extends Cf{constructor(t,{filter_value:n=-1/0,min_tokens_to_keep:r=1}={}){if(super(),!Number.isInteger(t)||t<0)throw new Error(`\`top_k\` must be a positive integer, but is ${t}`);this.top_k=Math.max(t,r),this.filter_value=n}}class FT{constructor(t){j(this,"max_length",20);j(this,"max_new_tokens",null);j(this,"min_length",0);j(this,"min_new_tokens",null);j(this,"early_stopping",!1);j(this,"max_time",null);j(this,"do_sample",!1);j(this,"num_beams",1);j(this,"num_beam_groups",1);j(this,"penalty_alpha",null);j(this,"use_cache",!0);j(this,"temperature",1);j(this,"top_k",50);j(this,"top_p",1);j(this,"typical_p",1);j(this,"epsilon_cutoff",0);j(this,"eta_cutoff",0);j(this,"diversity_penalty",0);j(this,"repetition_penalty",1);j(this,"encoder_repetition_penalty",1);j(this,"length_penalty",1);j(this,"no_repeat_ngram_size",0);j(this,"bad_words_ids",null);j(this,"force_words_ids",null);j(this,"renormalize_logits",!1);j(this,"constraints",null);j(this,"forced_bos_token_id",null);j(this,"forced_eos_token_id",null);j(this,"remove_invalid_values",!1);j(this,"exponential_decay_length_penalty",null);j(this,"suppress_tokens",null);j(this,"begin_suppress_tokens",null);j(this,"forced_decoder_ids",null);j(this,"guidance_scale",null);j(this,"num_return_sequences",1);j(this,"output_attentions",!1);j(this,"output_hidden_states",!1);j(this,"output_scores",!1);j(this,"return_dict_in_generate",!1);j(this,"pad_token_id",null);j(this,"bos_token_id",null);j(this,"eos_token_id",null);j(this,"encoder_no_repeat_ngram_size",0);j(this,"decoder_start_token_id",null);j(this,"generation_kwargs",{});Object.assign(this,ta(t,Object.getOwnPropertyNames(this)))}}class C$ extends dn{_call(t,n){throw Error("StoppingCriteria needs to be subclassed")}}class Tf extends dn{constructor(){super(),this.criteria=[]}push(t){this.criteria.push(t)}extend(t){t instanceof Tf&&(t=t.criteria),this.criteria.push(...t)}_call(t,n){const r=new Array(t.length).fill(!1);for(const i of this.criteria){const a=i(t,n);for(let s=0;s<r.length;++s)r[s]||(r[s]=a[s])}return r}[Symbol.iterator](){return this.criteria.values()}}class UT extends C${constructor(t,n=null){super(),this.max_length=t,this.max_position_embeddings=n}_call(t){return t.map(n=>n.length>=this.max_length)}}class WT extends C${constructor(t){super(),Array.isArray(t)||(t=[t]),this.eos_token_id=t}_call(t,n){return t.map(r=>this.eos_token_id.includes(r.at(-1)))}}class ku extends dn{constructor(t){super(),this.generation_config=t}_call(t,n=-1){return this.sample(t,n)}sample(t,n){throw Error("sample should be implemented in subclasses.")}getLogits(t,n){let r=t.dims.at(-1),i=t.data;if(n===-1)i=i.slice(-r);else{let a=n*r;i=i.slice(a,a+r)}return i}randomSelect(t){let n=t.reduce((i,a)=>i+a,0),r=Math.random()*n;for(let i=0;i<t.length;++i)if(r-=t[i],r<=0)return i;return 0}static getSampler(t){if(t.do_sample)return new jT(t);if(t.num_beams>1)return new GT(t);if(t.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${t.num_return_sequences}.`);return new VT(t)}}class VT extends ku{sample(t,n=-1){let r=this.getLogits(t,n);return[[Zp(r)[1],0]]}}class jT extends ku{sample(t,n=-1){let r=t.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const i=this.getLogits(t,n),a=Q_(i,r),s=Y_(a.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},()=>{const o=this.randomSelect(s);return[a[o][0],Math.log(s[o])]})}}class GT extends ku{sample(t,n=-1){let r=t.dims.at(-1);this.generation_config.top_k>0&&(r=Math.min(this.generation_config.top_k,r));const i=this.getLogits(t,n),a=Q_(i,r),s=Y_(a.map(o=>o[1]));return Array.from({length:this.generation_config.num_beams},(o,l)=>[a[l][0],Math.log(s[l])])}}const Ee={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5,ImageTextToText:6,Musicgen:7},iu=new Map,T$=new Map,_s=new Map;async function HT(e,t,n){let r=n.device;r&&typeof r!="string"&&(r.hasOwnProperty(t)?r=r[t]:(console.warn(`Device not specified for ${t}. Using the default device.`),r=null));const i=sk(r);let a=n.dtype;if(typeof a!="string"&&(a&&a.hasOwnProperty(t)?a=a[t]:(a=CT[i[0]],console.warn(`Dtype not specified for ${t}. Using the default dtype: ${a}.`))),Sy.hasOwnProperty(a)){if(a===Rn.fp16&&!await kT())throw new Error("The device does not support fp16.")}else throw new Error(`Invalid dtype: ${a}. Should be one of: ${Object.keys(Rn).join(", ")}`);const s=Sy[a],o=`${n.subfolder??""}/${t}${s}.onnx`,l=await Lc(e,o,!0,n),d={...n.session_options};if(d.executionProviders??(d.executionProviders=i),d.externalData!==void 0)for(let c=0;c<d.externalData.length;++c){const u=d.externalData[c];if(typeof u.data=="string"){const f=await Lc(e,u.data,!0,n);u.data=f}}return{buffer:l,session_options:d}}async function ii(e,t,n){const r=Object.keys(t),i=await Promise.all(r.map(async s=>HT(e,t[s],n))),a={};for(let s=0;s<r.length;++s){const{buffer:o,session_options:l}=i[s],d=await ok(o,l);a[r[s]]=d}return a}function qT(e,t){const n=Object.create(null),r=[];for(const s of e.inputNames){const o=t[s];if(!(o instanceof _e)){r.push(s);continue}n[s]=lk()?o.clone():o}if(r.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${r.join(", ")}.`);const i=Object.keys(t).length,a=e.inputNames.length;if(i>a){let s=Object.keys(t).filter(o=>!e.inputNames.includes(o));console.warn(`WARNING: Too many inputs were provided (${i} > ${a}). The following inputs will be ignored: "${s.join(", ")}".`)}return n}async function Hr(e,t){const n=qT(e,t);try{const r=Object.fromEntries(Object.entries(n).map(([a,s])=>[a,s.ort_tensor]));let i=await e.run(r);i=I$(i);for(const[a,s]of Object.entries(n))a.startsWith("past_key_values")&&s.dispose();return i}catch(r){throw console.error(`An error occurred during model execution: "${r}".`),console.error("Inputs given to model:",n),r}}function I$(e){for(let t in e)p$(e[t])?e[t]=new _e(e[t]):typeof e[t]=="object"&&I$(e[t]);return e}function KT(e){if(e instanceof _e)return e;if(e.length===0)throw Error("items must be non-empty");if(Array.isArray(e[0])){if(e.some(t=>t.length!==e[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new _e("int64",BigInt64Array.from(e.flat().map(t=>BigInt(t))),[e.length,e[0].length])}else return new _e("int64",BigInt64Array.from(e.map(t=>BigInt(t))),[1,e.length])}function A$(e){return new _e("bool",[e],[1])}async function ky(e,t){let{encoder_outputs:n,past_key_values:r}=t;if(!n){const l=ta(t,e.sessions.model.inputNames);n=(await Fs(e,l)).last_hidden_state}const{input_ids:i,decoder_input_ids:a,...s}=t;return s.input_ids=a,s.encoder_hidden_states=n,e.sessions.decoder_model_merged.inputNames.includes("encoder_attention_mask")&&(s.encoder_attention_mask=t.attention_mask),await If(e,s,!0)}async function Fs(e,t){const n=e.sessions.model,r=Object.create(null);for(const i of n.inputNames)r[i]=t[i];return n.inputNames.includes("token_type_ids")&&!r.token_type_ids&&(r.token_type_ids=new _e("int64",new BigInt64Array(r.input_ids.data.length),r.input_ids.dims)),await Hr(n,r)}async function If(e,t,n=!1){const r=e.sessions[n?"decoder_model_merged":"model"],{past_key_values:i,...a}=t;r.inputNames.includes("use_cache_branch")&&(a.use_cache_branch=A$(!!i)),e.addPastKeyValues(a,i);const s=ta(a,r.inputNames);return await Hr(r,s)}function YT(e,t,n,r){if(e.sessions.model.inputNames.includes("position_ids")&&n.attention_mask&&!n.position_ids){const[a,s]=n.attention_mask.dims,o=new BigInt64Array(n.attention_mask.data.length);for(let l=0;l<a;++l){const d=l*s;let c=BigInt(0);for(let u=0;u<s;++u){const f=d+u;n.attention_mask.data[f]===0n?o[f]=BigInt(1):(o[f]=c,c+=n.attention_mask.data[f])}}n.position_ids=new _e("int64",o,n.attention_mask.dims),n.past_key_values&&(n.position_ids=n.position_ids.slice(null,-1).unsqueeze_(-1))}return n}function QT(e,t,n,r){const{...i}=n;return n.past_key_values&&(t=t.map(s=>[s.at(-1)])),i.decoder_input_ids=KT(t),i}class J extends dn{constructor(n,r){super();j(this,"main_input_name","input_ids");j(this,"forward_params",["input_ids","attention_mask"]);this.config=n,this.sessions=r;const i=_s.get(this.constructor),a=iu.get(i);this.can_generate=!1,this._forward=null,this._prepare_inputs_for_generation=null,a===Ee.DecoderOnly?(this.can_generate=!0,this._forward=If,this._prepare_inputs_for_generation=YT):a===Ee.Seq2Seq||a===Ee.Vision2Seq||a===Ee.Musicgen?(this.can_generate=!0,this._forward=ky,this._prepare_inputs_for_generation=QT):a===Ee.EncoderDecoder?this._forward=ky:a===Ee.ImageTextToText?(this.can_generate=!0,console.warn("TODO: Implement visionDecoderForward")):this._forward=Fs}async dispose(){var r;const n=[];for(let i of Object.keys(this)){let a=this[i];((r=a==null?void 0:a.handler)==null?void 0:r.dispose)!==void 0&&n.push(a.handler.dispose())}return await Promise.all(n)}static async from_pretrained(n,{progress_callback:r=null,config:i=null,cache_dir:a=null,local_files_only:s=!1,revision:o="main",model_file_name:l=null,subfolder:d="onnx",device:c=null,dtype:u=null,session_options:f={}}={}){let h={progress_callback:r,config:i,cache_dir:a,local_files_only:s,revision:o,model_file_name:l,subfolder:d,device:c,dtype:u,session_options:f};const g=_s.get(this),$=iu.get(g);let E;return $===Ee.DecoderOnly?E=await Promise.all([Cr.from_pretrained(n,h),ii(n,{model:h.model_file_name??"model"},h),di(n,"generation_config.json",!1,h)]):$===Ee.Seq2Seq||$===Ee.Vision2Seq?E=await Promise.all([Cr.from_pretrained(n,h),ii(n,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},h),di(n,"generation_config.json",!1,h)]):$===Ee.MaskGeneration?E=await Promise.all([Cr.from_pretrained(n,h),ii(n,{model:"vision_encoder",prompt_encoder_mask_decoder:"prompt_encoder_mask_decoder"},h)]):$===Ee.EncoderDecoder?E=await Promise.all([Cr.from_pretrained(n,h),ii(n,{model:"encoder_model",decoder_model_merged:"decoder_model_merged"},h)]):$===Ee.ImageTextToText?E=await Promise.all([Cr.from_pretrained(n,h),ii(n,{embed_tokens:"embed_tokens",vision_encoder:"vision_encoder",decoder_model_merged:"decoder_model_merged"},h),di(n,"generation_config.json",!1,h)]):$===Ee.Musicgen?E=await Promise.all([Cr.from_pretrained(n,h),ii(n,{model:"text_encoder",decoder_model_merged:"decoder_model_merged",encodec_decode:"encodec_decode"},h),di(n,"generation_config.json",!1,h)]):($!==Ee.EncoderOnly&&console.warn(`Model type for '${g??(i==null?void 0:i.model_type)}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),E=await Promise.all([Cr.from_pretrained(n,h),ii(n,{model:h.model_file_name??"model"},h)])),new this(...E)}async _call(n){return await this.forward(n)}async forward(n){return await this._forward(this,n)}_get_logits_warper(n){const r=new Ey;return n.temperature!==null&&n.temperature!==1&&r.push(new DT(n.temperature)),n.top_k!==null&&n.top_k!==0&&r.push(new LT(n.top_k)),n.top_p!==null&&n.top_p<1&&r.push(new NT(n.top_p)),r}_get_logits_processor(n,r,i=null){const a=new Ey;if(n.repetition_penalty!==null&&n.repetition_penalty!==1&&a.push(new MT(n.repetition_penalty)),n.no_repeat_ngram_size!==null&&n.no_repeat_ngram_size>0&&a.push(new OT(n.no_repeat_ngram_size)),n.bad_words_ids!==null&&a.push(new PT(n.bad_words_ids,n.eos_token_id)),n.min_length!==null&&n.eos_token_id!==null&&n.min_length>0&&a.push(new zT(n.min_length,n.eos_token_id)),n.min_new_tokens!==null&&n.eos_token_id!==null&&n.min_new_tokens>0&&a.push(new RT(r,n.min_new_tokens,n.eos_token_id)),n.forced_bos_token_id!==null&&a.push(new TT(n.forced_bos_token_id)),n.forced_eos_token_id!==null&&a.push(new IT(n.max_length,n.forced_eos_token_id)),n.begin_suppress_tokens!==null){let s=r>1||n.forced_bos_token_id===null?r:r+1;n.forced_decoder_ids!==null&&(s+=n.forced_decoder_ids[n.forced_decoder_ids.length-1][0]),a.push(new AT(n.begin_suppress_tokens,s))}return n.guidance_scale!==null&&n.guidance_scale>1&&a.push(new BT(n.guidance_scale)),i!==null&&a.extend(i),a}_prepare_generation_config(n,r){const i=new FT(this.config);return"generation_config"in this&&Object.assign(i,this.generation_config),n&&Object.assign(i,n),r&&Object.assign(i,ta(r,Object.getOwnPropertyNames(i))),i}_get_stopping_criteria(n,r=null){const i=new Tf;return n.max_length!==null&&i.push(new UT(n.max_length,this.config.max_position_embeddings??null)),n.eos_token_id!==null&&i.push(new WT(n.eos_token_id)),r&&i.extend(r),i}_validate_model_class(){if(!this.can_generate){const n=[Bf,Tb,Cb,kb],r=_s.get(this.constructor),i=new Set,a=this.config.model_type;for(const o of n){const l=o.get(a);l&&i.add(l[0])}let s=`The current model class (${r}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;throw i.size>0&&(s+=` Please use the following class instead: ${[...i].join(", ")}`),Error(s)}}prepare_inputs_for_generation(...n){return this._prepare_inputs_for_generation(this,...n)}_update_model_kwargs_for_generation({generated_input_ids:n,outputs:r,model_inputs:i,is_encoder_decoder:a}){return i.past_key_values=this.getPastKeyValues(r,i.past_key_values),i.input_ids=new _e("int64",n,[n.length,1]),a||(i.attention_mask=pr([i.attention_mask,nu([i.attention_mask.dims[0],1])],1)),i.position_ids=null,i}_prepare_model_inputs({inputs:n,bos_token_id:r,model_kwargs:i}){const a=ta(i,this.forward_params),s=this.main_input_name;if(s in a){if(n)throw new Error("`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. Make sure to either pass {inputs} or {input_name}=...")}else a[s]=n;return{inputs_tensor:a[s],model_inputs:a,model_input_name:s}}async _prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:n,model_inputs:r,model_input_name:i,generation_config:a}){const s=ta(r,this.sessions.model.inputNames);let{last_hidden_state:o}=await Fs(this,s);return a.guidance_scale!==null&&a.guidance_scale>1&&(o=pr([o,mk(o,0)],0),"attention_mask"in r&&(r.attention_mask=pr([r.attention_mask,wk(r.attention_mask)],0))),r.encoder_outputs=o,r}_prepare_decoder_input_ids_for_generation({batch_size:n,model_input_name:r,model_kwargs:i,decoder_start_token_id:a,bos_token_id:s,generation_config:o}){a=a??s;let l;if(this.config.model_type==="musicgen")l=new Array(n*this.config.decoder.num_codebooks).fill(a);else if(Array.isArray(a)){if(a.length!==n)throw new Error(`\`decoder_start_token_id\` expcted to have length ${n} but got ${a.length}`);l=a}else l=new Array(n).fill(a);const c=new _e("int64",l,[l.length,1]);return i.decoder_attention_mask=gk(c),{input_ids:c,model_inputs:i}}async generate({inputs:n=null,generation_config:r=null,logits_processor:i=null,stopping_criteria:a=null,streamer:s=null,...o}){this._validate_model_class(),r=this._prepare_generation_config(r,o);let{inputs_tensor:l,model_inputs:d,model_input_name:c}=this._prepare_model_inputs({inputs:n,model_kwargs:o});const u=this.config.is_encoder_decoder;u&&("encoder_outputs"in d||(d=await this._prepare_encoder_decoder_kwargs_for_generation({inputs_tensor:l,model_inputs:d,model_input_name:c,generation_config:r})));let f;u?{input_ids:f,model_inputs:d}=this._prepare_decoder_input_ids_for_generation({batch_size:d[c].dims.at(0),model_input_name:c,model_kwargs:d,decoder_start_token_id:r.decoder_start_token_id,bos_token_id:r.bos_token_id,generation_config:r}):f=d[c];let h=f.dims.at(-1);r.max_new_tokens!==null&&(r.max_length=h+r.max_new_tokens);const g=this._get_logits_processor(r,h,i),$=this._get_stopping_criteria(r,a),E=d[c].dims.at(0),y=ku.getSampler(r),_=new Array(E).fill(0),v=f.tolist();for(s&&s.put(v);;){d=this.prepare_inputs_for_generation(v,d,r);const b=await this.forward(d),C=b.logits.slice(null,-1,null),I=g(v,C),O=[];for(let W=0;W<I.dims.at(0);++W){const G=I[W],z=y(G);for(const[K,ae]of z){const ue=BigInt(K);_[W]+=ae,v[W].push(ue),O.push(ue)}}if(s&&s.put(v),$(v).every(W=>W))break;d=this._update_model_kwargs_for_generation({generated_input_ids:O,outputs:b,model_inputs:d,is_encoder_decoder:u})}return s&&s.end(),new _e("int64",v.flat(),[v.length,v[0].length])}addAttentionsToBeam(n,r){if(this.config.is_encoder_decoder){if(!r.cross_attentions||r.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.cross_attentions||(n.cross_attentions=[]),n.cross_attentions.push(r.cross_attentions)}if(!r.decoder_attentions||r.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");n.decoder_attentions||(n.decoder_attentions=[]),n.decoder_attentions.push(r.decoder_attentions)}groupBeams(n){const r=Object.create(null);for(const i of n)r[i.id]===void 0?r[i.id]=[i]:r[i.id].push(i);return Object.values(r)}getPastKeyValues(n,r){const i=Object.create(null);for(const a in n)if(a.startsWith("present")){let s=a.replace("present","past_key_values");r&&a.includes("encoder")?i[s]=r[s]:i[s]=n[a]}return i}getAttentions(n){const r=Object.create(null);for(const i of["cross_attentions","decoder_attentions"]){const a=[];for(const s in n)if(s.startsWith(i)){const o=s.split(".").pop();a[o]=n[s]}r[i]=a}return r}addPastKeyValues(n,r){if(r)Object.assign(n,r);else{const a="float32",s=[];if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??!0)){let o=[1,this.num_encoder_heads,0,this.encoder_dim_kv],l=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let d=0;d<this.num_decoder_layers;++d)n[`past_key_values.${d}.encoder.key`]=new _e(a,s,o),n[`past_key_values.${d}.encoder.value`]=new _e(a,s,o),n[`past_key_values.${d}.decoder.key`]=new _e(a,s,l),n[`past_key_values.${d}.decoder.value`]=new _e(a,s,l)}else if(this.config.model_type==="falcon"){let o=[1*this.num_heads,0,this.dim_kv];for(let l=0;l<this.num_layers;++l)n[`past_key_values.${l}.key`]=new _e(a,s,o),n[`past_key_values.${l}.value`]=new _e(a,s,o)}else if(this.config.multi_query){let o=[1*this.num_heads,0,2*this.dim_kv];for(let l=0;l<this.num_layers;++l)n[`past_key_values.${l}.key_value`]=new _e(a,s,o)}else if(this.config.model_type==="bloom"){let o=[1*this.num_heads,this.dim_kv,0],l=[1*this.num_heads,0,this.dim_kv];for(let d=0;d<this.num_layers;++d)n[`past_key_values.${d}.key`]=new _e(a,s,o),n[`past_key_values.${d}.value`]=new _e(a,s,l)}else{let o=[1,this.num_heads,0,this.dim_kv];for(let l=0;l<this.num_layers;++l)n[`past_key_values.${l}.key`]=new _e(a,s,o),n[`past_key_values.${l}.value`]=new _e(a,s,o)}}}}class vn{}class Qs extends J{}class XT extends Qs{}class ZT extends Qs{async _call(t){return new Bt(await super._call(t))}}class JT extends Qs{async _call(t){return new Be(await super._call(t))}}class eI extends Qs{async _call(t){return new Pt(await super._call(t))}}class tI extends Qs{async _call(t){return new Vt(await super._call(t))}}class nI extends J{}class rI extends nI{}class Xs extends J{}class iI extends Xs{}class aI extends Xs{async _call(t){return new Bt(await super._call(t))}}class sI extends Xs{async _call(t){return new Be(await super._call(t))}}class oI extends Xs{async _call(t){return new Pt(await super._call(t))}}class lI extends Xs{async _call(t){return new Vt(await super._call(t))}}class Zs extends J{}class uI extends Zs{}class dI extends Zs{async _call(t){return new Bt(await super._call(t))}}class cI extends Zs{async _call(t){return new Be(await super._call(t))}}class pI extends Zs{async _call(t){return new Pt(await super._call(t))}}class fI extends Zs{async _call(t){return new Vt(await super._call(t))}}class Js extends J{}class hI extends Js{}class mI extends Js{async _call(t){return new Bt(await super._call(t))}}class gI extends Js{async _call(t){return new Be(await super._call(t))}}class yI extends Js{async _call(t){return new Pt(await super._call(t))}}class wI extends Js{async _call(t){return new Vt(await super._call(t))}}class eo extends J{}class _I extends eo{}class vI extends eo{async _call(t){return new Bt(await super._call(t))}}class $I extends eo{async _call(t){return new Be(await super._call(t))}}class bI extends eo{async _call(t){return new Pt(await super._call(t))}}class xI extends eo{async _call(t){return new Vt(await super._call(t))}}class to extends J{}class SI extends to{}class EI extends to{async _call(t){return new Bt(await super._call(t))}}class kI extends to{async _call(t){return new Be(await super._call(t))}}class CI extends to{async _call(t){return new Pt(await super._call(t))}}class TI extends to{async _call(t){return new Vt(await super._call(t))}}class no extends J{}class II extends no{}class AI extends no{async _call(t){return new Bt(await super._call(t))}}class OI extends no{async _call(t){return new Be(await super._call(t))}}class MI extends no{async _call(t){return new Pt(await super._call(t))}}class zI extends no{async _call(t){return new Vt(await super._call(t))}}class ro extends J{}class RI extends ro{}class PI extends ro{async _call(t){return new Be(await super._call(t))}}class BI extends ro{async _call(t){return new Pt(await super._call(t))}}class DI extends ro{async _call(t){return new Vt(await super._call(t))}}class NI extends ro{async _call(t){return new Bt(await super._call(t))}}class Cu extends J{}class LI extends Cu{}class FI extends Cu{async _call(t){return new Bt(await super._call(t))}}class UI extends Cu{async _call(t){return new Be(await super._call(t))}}class WI extends Cu{async _call(t){return new Pt(await super._call(t))}}class Tu extends J{}class VI extends Tu{}class jI extends Tu{async _call(t){return new Bt(await super._call(t))}}class GI extends Tu{async _call(t){return new Be(await super._call(t))}}class HI extends Tu{async _call(t){return new Vt(await super._call(t))}}class io extends J{}class qI extends io{}class KI extends io{async _call(t){return new Bt(await super._call(t))}}class YI extends io{async _call(t){return new Be(await super._call(t))}}class QI extends io{async _call(t){return new Pt(await super._call(t))}}class XI extends io{async _call(t){return new Vt(await super._call(t))}}class Iu extends J{}class ZI extends Iu{}class JI extends Iu{async _call(t){return new Bt(await super._call(t))}}class eA extends Iu{async _call(t){return new Be(await super._call(t))}}class tA extends Iu{async _call(t){return new Vt(await super._call(t))}}class Au extends J{}class nA extends Au{}class rA extends Au{async _call(t){return new Be(await super._call(t))}}class iA extends Au{async _call(t){return new Vt(await super._call(t))}}class aA extends Au{async _call(t){return new Bt(await super._call(t))}}class O$ extends J{constructor(n,r,i){super(n,r);j(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=i,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class sA extends O${}class oA extends O${}class M$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class lA extends M${}class uA extends M${}class z$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class dA extends z${}class cA extends z${}class Af extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class pA extends Af{}class fA extends Af{}class hA extends Af{async _call(t){return new Be(await super._call(t))}}class Ou extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class mA extends Ou{}class gA extends Ou{}class yA extends Ou{async _call(t){return new Be(await super._call(t))}}class wA extends Ou{constructor(t,n,r){super(t,n,r),this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class R$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class _A extends R${}class vA extends R${}class P$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class $A extends P${}class bA extends P${}class ao extends J{}class xA extends ao{}class SA extends ao{async _call(t){return new Bt(await super._call(t))}}class EA extends ao{async _call(t){return new Be(await super._call(t))}}class kA extends ao{async _call(t){return new Pt(await super._call(t))}}class CA extends ao{async _call(t){return new Vt(await super._call(t))}}class so extends J{}class TA extends so{}class IA extends so{async _call(t){return new Bt(await super._call(t))}}class AA extends so{async _call(t){return new Be(await super._call(t))}}class OA extends so{async _call(t){return new Pt(await super._call(t))}}class MA extends so{async _call(t){return new Vt(await super._call(t))}}class oo extends J{}class zA extends oo{}class RA extends oo{async _call(t){return new Bt(await super._call(t))}}class PA extends oo{async _call(t){return new Be(await super._call(t))}}class BA extends oo{async _call(t){return new Pt(await super._call(t))}}class DA extends oo{async _call(t){return new Vt(await super._call(t))}}class B$ extends J{}class NA extends B${}class LA extends B${}class D$ extends J{constructor(n,r,i){super(n,r);j(this,"requires_attention_mask",!1);j(this,"main_input_name","input_features");j(this,"forward_params",["input_features","attention_mask","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=i,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class FA extends D${}class UA extends D${_retrieve_init_tokens(t){throw t.decoder_start_token_id,new Error("Not implemented yet")}async generate({inputs:t=null,generation_config:n=null,logits_processor:r=null,stopping_criteria:i=null,language:a=null,task:s=null,...o}){throw new Error("WhisperForConditionalGeneration.generate is not yet in Transformers.js v3.")}_extract_token_timestamps(t,n,r=null,i=.02){if(!t.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let a=this.config.median_filter_width;a===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),a=7);const s=t.cross_attentions.map(d=>{let c=Array.from({length:this.config.decoder_layers},(E,y)=>pr(d.map(_=>_[y]),2)),u=ep(n.map(([E,y])=>r?c[E].slice(null,y,null,[0,r]):c[E].slice(null,y)));u=u.transpose(1,0,2,3);let[f,h]=ck(u,-2,0,!0),g=u.clone();for(let E=0;E<g.dims[0];++E){let y=g[E];for(let _=0;_<y.dims[0];++_){let v=y[_];const b=f[E][_][0],C=h[E][_][0];for(let I=0;I<v.dims[0];++I){let O=v[I];for(let M=0;M<O.data.length;++M)O.data[M]=(O.data[M]-C.data[M])/b.data[M];O.data.set(zS(O.data,a))}}}return bf(g,1)}),o=[t.sequences.length,t.sequences[0].length],l=new _e("float32",new Float32Array(o[0]*o[1]),o);for(let d=0;d<o[0];++d){const c=s[d].neg().squeeze_(0);let[u,f]=pk(c),h=Array.from({length:u.length-1},(E,y)=>u[y+1]-u[y]),g=kt([1],h).map(E=>!!E),$=[];for(let E=0;E<g.length;++E)g[E]&&$.push(f[E]*i);l[d].data.set($,1)}return l}}class WA extends J{constructor(n,r,i){super(n,r);j(this,"main_input_name","pixel_values");this.generation_config=i;const a=this.config.encoder,s=this.config.decoder,o=a.model_type;(Sb.get(o)??Eb.get(o))||console.warn(`Model type for encoder '${o}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);const d=Bf.get(s.model_type);if(!d)throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);const c=d[1],u=new c(s,{},i);this.add_encoder_pkv="num_decoder_layers"in u,this.add_encoder_pkv?(this.num_decoder_layers=u.num_decoder_layers,this.num_decoder_heads=u.num_decoder_heads,this.decoder_dim_kv=u.decoder_dim_kv,this.num_encoder_layers=u.num_encoder_layers,this.num_encoder_heads=u.num_encoder_heads,this.encoder_dim_kv=u.encoder_dim_kv):(this.num_layers=u.num_layers,this.num_heads=u.num_heads,this.dim_kv=u.dim_kv)}}class VA extends J{constructor(n,r,i){super(n,r);j(this,"forward_params",["input_ids","past_key_values","pixel_values","attention_mask"]);this.generation_config=i;const a=this.config.text_config;this.config.pad_token_id=a.eos_token_id,this.num_heads=a.num_attention_heads,this.num_layers=a.num_hidden_layers,this.dim_kv=a.hidden_size/this.num_heads}}class jA extends VA{async encode_image({pixel_values:t}){return(await Hr(this.sessions.vision_encoder,{pixel_values:t})).image_features}async encode_text({input_ids:t}){return(await Hr(this.sessions.embed_tokens,{input_ids:t})).inputs_embeds}_merge_input_ids_with_image_features({inputs_embeds:t,image_features:n,input_ids:r,attention_mask:i}){const a=this.config.image_token_index,o=r.tolist().map(f=>f.findIndex(h=>h==a)),l=o.every(f=>f===-1),d=o.every(f=>f!==-1);if(!l&&!d)throw new Error("Every input should contain either 0 or 1 image token.");if(l)return{inputs_embeds:t,attention_mask:i,position_ids:null};let c=[],u=[];for(let f=0;f<o.length;++f){const h=o[f],g=t[f],$=n[f],E=i[f];c.push(pr([g.slice([0,h]),$,g.slice([h+1,g.dims[0]])],0)),u.push(pr([E.slice([0,h]),nu([$.dims[0]]),E.slice([h+1,E.dims[0]])],0))}return{inputs_embeds:ep(c,0),attention_mask:ep(u,0),position_ids:null}}prepare_inputs_for_generation(t,n,r){return n}async forward({input_ids:t=null,attention_mask:n=null,pixel_values:r=null,position_ids:i=null,inputs_embeds:a=null,past_key_values:s=null,generation_config:o=null,logits_processor:l=null,...d}){if(!a){if(a=await this.encode_text({input_ids:t}),r&&t.dims[1]!==1){const u=await this.encode_image({pixel_values:r});({inputs_embeds:a,inputs_embeds:a,attention_mask:n,position_ids:i}=this._merge_input_ids_with_image_features({image_features:u,inputs_embeds:a,input_ids:t,attention_mask:n}))}else if(s&&r&&t.dims[1]===1){const u=t.dims[1],f=Object.values(s)[0].dims.at(-2);n=pr([nu([t.dims[0],f]),n.slice(null,[n.dims[1]-u,n.dims[1]])],1)}}return await If(this,{inputs_embeds:a,past_key_values:s,attention_mask:n,generation_config:o,logits_processor:l},!0)}}class Mu extends J{}class GA extends Mu{}class HA extends Mu{static async from_pretrained(t,n={}){return n.model_file_name??(n.model_file_name="text_model"),super.from_pretrained(t,n)}}class qA extends Mu{static async from_pretrained(t,n={}){return n.model_file_name??(n.model_file_name="vision_model"),super.from_pretrained(t,n)}}class N$ extends J{}class KA extends N${}class YA extends N${static async from_pretrained(t,n={}){return n.model_file_name??(n.model_file_name="text_model"),super.from_pretrained(t,n)}}class QA extends Mu{static async from_pretrained(t,n={}){return n.model_file_name??(n.model_file_name="vision_model"),super.from_pretrained(t,n)}}class XA extends J{}class ZA extends XA{}class L$ extends J{}class JA extends L${}class eO extends L${}class F$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class tO extends F${}class nO extends F${}class U$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_heads,this.num_layers=this.config.num_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class rO extends U${}class iO extends U${}class W$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class aO extends W${}class sO extends W${}class V$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class oO extends V${}class lO extends V${}class j$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class uO extends j${}class dO extends j${}class G$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class cO extends G${}class pO extends G${}class H$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_key_value_heads??this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class fO extends H${}class hO extends H${}class q$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_key_value_heads??this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class mO extends q${}class gO extends q${}class K$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class yO extends K${}class wO extends K${}class Y$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.hidden_size/this.num_heads}}class _O extends Y${}class vO extends Y${}class Q$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.n_heads,this.num_layers=this.config.n_layers,this.dim_kv=this.config.d_model/this.num_heads}}class $O extends Q${}class bO extends Q${}class X$ extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class xO extends X${}class SO extends X${}class Z$ extends J{}class EO extends Z${}class kO extends Z${async _call(t){return new Be(await super._call(t))}}class CO extends J{}class TO extends CO{async _call(t){return new Pz(await super._call(t))}}class J$ extends J{}class IO extends J${}class AO extends J${async _call(t){return new Be(await super._call(t))}}class eb extends J{}class OO extends eb{}class MO extends eb{}class tb extends J{}class zO extends tb{}class RO extends tb{}class nb extends J{}class PO extends nb{}class BO extends nb{async _call(t){return new Be(await super._call(t))}}class Of extends J{}class DO extends Of{}class NO extends Of{async _call(t){return new rb(await super._call(t))}}class LO extends Of{async _call(t){return new FO(await super._call(t))}}class rb extends vn{constructor({logits:t,pred_boxes:n}){super(),this.logits=t,this.pred_boxes=n}}class FO extends vn{constructor({logits:t,pred_boxes:n,pred_masks:r}){super(),this.logits=t,this.pred_boxes=n,this.pred_masks=r}}class ib extends J{}class UO extends ib{}class WO extends ib{async _call(t){return new VO(await super._call(t))}}class VO extends rb{}class ab extends J{}class jO extends ab{}class GO extends ab{async _call(t){return new Be(await super._call(t))}}class sb extends J{}class HO extends sb{}class qO extends sb{async _call(t){return new Be(await super._call(t))}}class ob extends J{}class KO extends ob{}class YO extends ob{async _call(t){return new Be(await super._call(t))}}class lb extends J{}class QO extends lb{}class XO extends lb{}class ub extends J{}class ZO extends ub{}class JO extends ub{}class eM extends J{}class tM extends eM{}class db extends J{}class nM extends db{}class rM extends db{}class iM extends J{}class aM extends iM{}class cb extends J{}class sM extends cb{}class oM extends cb{async _call(t){return new Be(await super._call(t))}}class pb extends J{}class lM extends pb{}class uM extends pb{async _call(t){return new Be(await super._call(t))}}class fb extends J{}class dM extends fb{}class cM extends fb{async _call(t){return new Be(await super._call(t))}}class hb extends J{}class pM extends hb{}class fM extends hb{async _call(t){return new hM(await super._call(t))}}class hM extends vn{constructor({logits:t,pred_boxes:n}){super(),this.logits=t,this.pred_boxes=n}}class mM extends J{}class gM extends mM{constructor(t,n,r){super(t,n),this.prompt_encoder_mask_decoder=r}async get_image_embeddings({pixel_values:t}){return await Fs(this,{pixel_values:t})}async forward(t){if((!t.image_embeddings||!t.image_positional_embeddings)&&(t={...t,...await this.get_image_embeddings(t)}),!t.input_labels){const n=t.input_points.dims.slice(0,-1),r=n.reduce((i,a)=>i*a,1);t.input_labels=new _e("int64",new BigInt64Array(r).fill(1n),n)}return await Hr(this.prompt_encoder_mask_decoder,{input_points:t.input_points,input_labels:t.input_labels,image_embeddings:t.image_embeddings,image_positional_embeddings:t.image_positional_embeddings})}async _call(t){return new yM(await super._call(t))}}class yM extends vn{constructor({iou_scores:t,pred_masks:n}){super(),this.iou_scores=t,this.pred_masks=n}}class mb extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class wM extends mb{}class _M extends mb{}class gb extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class vM extends gb{}class $M extends gb{}class Si extends J{}class bM extends Si{}class xM extends Si{async _call(t){return new _a(await super._call(t))}}class SM extends Si{async _call(t){return new Be(await super._call(t))}}class EM extends Si{async _call(t){return new Pt(await super._call(t))}}class Mf extends J{}class kM extends Mf{}class CM extends Mf{async _call(t){return new _a(await super._call(t))}}class TM extends Mf{async _call(t){return new Be(await super._call(t))}}class zu extends J{}class IM extends zu{}class AM extends zu{async _call(t){return new _a(await super._call(t))}}class OM extends zu{async _call(t){return new Be(await super._call(t))}}class MM extends zu{async _call(t){return new Pt(await super._call(t))}}class zf extends J{}class zM extends zf{}class RM extends zf{async _call(t){return new _a(await super._call(t))}}class PM extends zf{async _call(t){return new Be(await super._call(t))}}class BM extends Si{}class DM extends Si{async _call(t){return new _a(await super._call(t))}}class NM extends Si{async _call(t){return new Be(await super._call(t))}}class lo extends J{}class LM extends lo{}class FM extends lo{async _call(t){return new _a(await super._call(t))}}class UM extends lo{async _call(t){return new Be(await super._call(t))}}class WM extends lo{async _call(t){return new Rz(await super._call(t))}}class VM extends lo{async _call(t){return new Pt(await super._call(t))}}class yb extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.hidden_size/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.hidden_size/this.num_encoder_heads}}class jM extends yb{}class GM extends yb{async generate_speech(t,n,{threshold:r=.5,minlenratio:i=0,maxlenratio:a=20,vocoder:s=null}={}){const o={input_ids:t},{encoder_outputs:l,encoder_attention_mask:d}=await Fs(this,o),c=l.dims[1]/this.config.reduction_factor,u=Math.floor(c*a),f=Math.floor(c*i),h=this.config.num_mel_bins;let g=[],$=null,E=null,y=0;for(;;){++y;const b=A$(!!E);let C;E?C=E.output_sequence_out:C=new _e("float32",new Float32Array(h),[1,1,h]);let I={use_cache_branch:b,output_sequence:C,encoder_attention_mask:d,speaker_embeddings:n,encoder_hidden_states:l};this.addPastKeyValues(I,$),E=await Hr(this.sessions.decoder_model_merged,I),$=this.getPastKeyValues(E,$);const{prob:O,spectrum:M}=E;if(g.push(M),y>=f&&(Array.from(O.data).filter(W=>W>=r).length>0||y>=u))break}const _=pr(g),{waveform:v}=await Hr(s.sessions.model,{spectrogram:_});return{spectrogram:_,waveform:v}}}class HM extends J{constructor(){super(...arguments);j(this,"main_input_name","spectrogram")}}class qM extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_encoder_layers=this.num_decoder_layers=this.config.decoder_layers,this.num_encoder_heads=this.num_decoder_heads=this.config.decoder_attention_heads,this.encoder_dim_kv=this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads}}class KM extends qM{}class wb extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class YM extends wb{}class QM extends wb{}class _b extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class XM extends _b{}class ZM extends _b{}class vb extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class JM extends vb{}class ez extends vb{}class Rf extends J{}class tz extends Rf{}class nz extends Rf{static async from_pretrained(t,n={}){return n.model_file_name??(n.model_file_name="text_model"),super.from_pretrained(t,n)}}class rz extends Rf{static async from_pretrained(t,n={}){return n.model_file_name??(n.model_file_name="audio_model"),super.from_pretrained(t,n)}}class iz extends J{}class $b extends iz{async _call(t){return new Bz(await super._call(t))}}class bb extends J{}class az extends bb{}class sz extends bb{}class oz extends J{constructor(t,n,r){super(t,n),this.generation_config=r,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class lz extends oz{}class xb extends J{}class uz extends xb{}class dz extends xb{async _call(t){return new Be(await super._call(t))}}class Pf extends J{constructor(n,r,i){super(n,r);j(this,"forward_params",["input_ids","attention_mask","encoder_outputs","decoder_input_ids","decoder_attention_mask","past_key_values"]);this.generation_config=i;const a=n.decoder;this.num_encoder_layers=this.num_decoder_layers=a.num_hidden_layers,this.num_encoder_heads=this.num_decoder_heads=a.num_attention_heads,this.encoder_dim_kv=this.decoder_dim_kv=a.hidden_size/this.num_decoder_heads}_apply_and_filter_by_delay_pattern_mask(n){const[r,i]=n.dims,a=this.config.decoder.num_codebooks,s=i-a;let o=0;for(let c=0;c<n.size;++c){if(n.data[c]===this.config.decoder.pad_token_id)continue;const u=c%i,f=Math.floor(c/i)%a,h=u-f;h>0&&h<=s&&(n.data[o++]=n.data[c])}const l=Math.floor(r/a),d=o/(l*a);return new _e(n.type,n.data.slice(0,o),[l,a,d])}prepare_inputs_for_generation(n,r,i){let a=structuredClone(n);for(let o=0;o<a.length;++o)for(let l=0;l<a[o].length;++l)o%this.config.decoder.num_codebooks>=l&&(a[o][l]=BigInt(this.config.decoder.pad_token_id));return i.guidance_scale!==null&&i.guidance_scale>1&&(a=a.concat(a)),super.prepare_inputs_for_generation(a,r,i)}async generate(n){const r=await super.generate(n),i=this._apply_and_filter_by_delay_pattern_mask(r).unsqueeze_(0),{audio_values:a}=await Hr(this.sessions.encodec_decode,{audio_codes:i});return a}}class np{static async from_pretrained(t,{progress_callback:n=null,config:r=null,cache_dir:i=null,local_files_only:a=!1,revision:s="main",model_file_name:o=null,subfolder:l="onnx",device:d=null,dtype:c=null,session_options:u={}}={}){let f={progress_callback:n,config:r,cache_dir:i,local_files_only:a,revision:s,model_file_name:o,subfolder:l,device:d,dtype:c,session_options:u};if(r=await Cr.from_pretrained(t,f),f.config||(f.config=r),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let h of this.MODEL_CLASS_MAPPINGS){const g=h.get(r.model_type);if(g)return await g[1].from_pretrained(t,f)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${r.model_type}", attempting to construct from base class.`),await J.from_pretrained(t,f);throw Error(`Unsupported model type: ${r.model_type}`)}}j(np,"MODEL_CLASS_MAPPINGS",null),j(np,"BASE_IF_FAIL",!1);const Sb=new Map([["bert",["BertModel",XT]],["nomic_bert",["NomicBertModel",rI]],["roformer",["RoFormerModel",iI]],["electra",["ElectraModel",hI]],["esm",["EsmModel",LI]],["convbert",["ConvBertModel",uI]],["camembert",["CamembertModel",_I]],["deberta",["DebertaModel",SI]],["deberta-v2",["DebertaV2Model",II]],["mpnet",["MPNetModel",qI]],["albert",["AlbertModel",nA]],["distilbert",["DistilBertModel",RI]],["roberta",["RobertaModel",xA]],["xlm",["XLMModel",TA]],["xlm-roberta",["XLMRobertaModel",zA]],["clap",["ClapModel",tz]],["clip",["CLIPModel",GA]],["clipseg",["CLIPSegModel",JA]],["chinese_clip",["ChineseCLIPModel",ZA]],["siglip",["SiglipModel",KA]],["mobilebert",["MobileBertModel",VI]],["squeezebert",["SqueezeBertModel",ZI]],["wav2vec2",["Wav2Vec2Model",bM]],["wav2vec2-bert",["Wav2Vec2BertModel",zM]],["unispeech",["UniSpeechModel",kM]],["unispeech-sat",["UniSpeechSatModel",IM]],["hubert",["HubertModel",BM]],["wavlm",["WavLMModel",LM]],["audio-spectrogram-transformer",["ASTModel",NA]],["vits",["VitsModel",$b]],["detr",["DetrModel",DO]],["table-transformer",["TableTransformerModel",UO]],["vit",["ViTModel",EO]],["mobilevit",["MobileViTModel",IO]],["owlvit",["OwlViTModel",OO]],["owlv2",["Owlv2Model",zO]],["beit",["BeitModel",PO]],["deit",["DeiTModel",jO]],["convnext",["ConvNextModel",sM]],["convnextv2",["ConvNextV2Model",lM]],["dinov2",["Dinov2Model",dM]],["resnet",["ResNetModel",HO]],["swin",["SwinModel",KO]],["swin2sr",["Swin2SRModel",QO]],["donut-swin",["DonutSwinModel",aM]],["yolos",["YolosModel",pM]],["dpt",["DPTModel",ZO]],["glpn",["GLPNModel",nM]],["hifigan",["SpeechT5HifiGan",HM]],["efficientnet",["EfficientNetModel",uz]]]),Eb=new Map([["t5",["T5Model",sA]],["longt5",["LongT5Model",lA]],["mt5",["MT5Model",dA]],["bart",["BartModel",pA]],["mbart",["MBartModel",mA]],["marian",["MarianModel",wM]],["whisper",["WhisperModel",FA]],["m2m_100",["M2M100Model",vM]],["blenderbot",["BlenderbotModel",_A]],["blenderbot-small",["BlenderbotSmallModel",$A]]]),cz=new Map([["bloom",["BloomModel",_O]],["gpt2",["GPT2Model",tO]],["gptj",["GPTJModel",oO]],["gpt_bigcode",["GPTBigCodeModel",uO]],["gpt_neo",["GPTNeoModel",rO]],["gpt_neox",["GPTNeoXModel",aO]],["codegen",["CodeGenModel",cO]],["llama",["LlamaModel",fO]],["qwen2",["Qwen2Model",mO]],["phi",["PhiModel",yO]],["mpt",["MptModel",$O]],["opt",["OPTModel",xO]],["mistral",["MistralModel",YM]],["starcoder2",["Starcoder2Model",XM]],["falcon",["FalconModel",JM]]]),kb=new Map([["speecht5",["SpeechT5ForSpeechToText",jM]],["whisper",["WhisperForConditionalGeneration",UA]]]),pz=new Map([["speecht5",["SpeechT5ForTextToSpeech",GM]]]),fz=new Map([["vits",["VitsModel",$b]],["musicgen",["MusicgenForConditionalGeneration",Pf]]]),hz=new Map([["bert",["BertForSequenceClassification",JT]],["roformer",["RoFormerForSequenceClassification",sI]],["electra",["ElectraForSequenceClassification",gI]],["esm",["EsmForSequenceClassification",UI]],["convbert",["ConvBertForSequenceClassification",cI]],["camembert",["CamembertForSequenceClassification",$I]],["deberta",["DebertaForSequenceClassification",kI]],["deberta-v2",["DebertaV2ForSequenceClassification",OI]],["mpnet",["MPNetForSequenceClassification",YI]],["albert",["AlbertForSequenceClassification",rA]],["distilbert",["DistilBertForSequenceClassification",PI]],["roberta",["RobertaForSequenceClassification",EA]],["xlm",["XLMForSequenceClassification",AA]],["xlm-roberta",["XLMRobertaForSequenceClassification",PA]],["bart",["BartForSequenceClassification",hA]],["mbart",["MBartForSequenceClassification",yA]],["mobilebert",["MobileBertForSequenceClassification",GI]],["squeezebert",["SqueezeBertForSequenceClassification",eA]]]),mz=new Map([["bert",["BertForTokenClassification",eI]],["roformer",["RoFormerForTokenClassification",oI]],["electra",["ElectraForTokenClassification",yI]],["esm",["EsmForTokenClassification",WI]],["convbert",["ConvBertForTokenClassification",pI]],["camembert",["CamembertForTokenClassification",bI]],["deberta",["DebertaForTokenClassification",CI]],["deberta-v2",["DebertaV2ForTokenClassification",MI]],["mpnet",["MPNetForTokenClassification",QI]],["distilbert",["DistilBertForTokenClassification",BI]],["roberta",["RobertaForTokenClassification",kA]],["xlm",["XLMForTokenClassification",OA]],["xlm-roberta",["XLMRobertaForTokenClassification",BA]]]),Cb=new Map([["t5",["T5ForConditionalGeneration",oA]],["longt5",["LongT5ForConditionalGeneration",uA]],["mt5",["MT5ForConditionalGeneration",cA]],["bart",["BartForConditionalGeneration",fA]],["mbart",["MBartForConditionalGeneration",gA]],["marian",["MarianMTModel",_M]],["m2m_100",["M2M100ForConditionalGeneration",$M]],["blenderbot",["BlenderbotForConditionalGeneration",vA]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",bA]]]),Bf=new Map([["bloom",["BloomForCausalLM",vO]],["gpt2",["GPT2LMHeadModel",nO]],["gptj",["GPTJForCausalLM",lO]],["gpt_bigcode",["GPTBigCodeForCausalLM",dO]],["gpt_neo",["GPTNeoForCausalLM",iO]],["gpt_neox",["GPTNeoXForCausalLM",sO]],["codegen",["CodeGenForCausalLM",pO]],["llama",["LlamaForCausalLM",hO]],["qwen2",["Qwen2ForCausalLM",gO]],["phi",["PhiForCausalLM",wO]],["mpt",["MptForCausalLM",bO]],["opt",["OPTForCausalLM",SO]],["mbart",["MBartForCausalLM",wA]],["mistral",["MistralForCausalLM",QM]],["starcoder2",["Starcoder2ForCausalLM",ZM]],["falcon",["FalconForCausalLM",ez]],["trocr",["TrOCRForCausalLM",KM]],["stablelm",["StableLmForCausalLM",lz]]]),gz=new Map([["bert",["BertForMaskedLM",ZT]],["roformer",["RoFormerForMaskedLM",aI]],["electra",["ElectraForMaskedLM",mI]],["esm",["EsmForMaskedLM",FI]],["convbert",["ConvBertForMaskedLM",dI]],["camembert",["CamembertForMaskedLM",vI]],["deberta",["DebertaForMaskedLM",EI]],["deberta-v2",["DebertaV2ForMaskedLM",AI]],["mpnet",["MPNetForMaskedLM",KI]],["albert",["AlbertForMaskedLM",aA]],["distilbert",["DistilBertForMaskedLM",NI]],["roberta",["RobertaForMaskedLM",SA]],["xlm",["XLMWithLMHeadModel",IA]],["xlm-roberta",["XLMRobertaForMaskedLM",RA]],["mobilebert",["MobileBertForMaskedLM",jI]],["squeezebert",["SqueezeBertForMaskedLM",JI]]]),yz=new Map([["bert",["BertForQuestionAnswering",tI]],["roformer",["RoFormerForQuestionAnswering",lI]],["electra",["ElectraForQuestionAnswering",wI]],["convbert",["ConvBertForQuestionAnswering",fI]],["camembert",["CamembertForQuestionAnswering",xI]],["deberta",["DebertaForQuestionAnswering",TI]],["deberta-v2",["DebertaV2ForQuestionAnswering",zI]],["mpnet",["MPNetForQuestionAnswering",XI]],["albert",["AlbertForQuestionAnswering",iA]],["distilbert",["DistilBertForQuestionAnswering",DI]],["roberta",["RobertaForQuestionAnswering",CA]],["xlm",["XLMForQuestionAnswering",MA]],["xlm-roberta",["XLMRobertaForQuestionAnswering",DA]],["mobilebert",["MobileBertForQuestionAnswering",HI]],["squeezebert",["SqueezeBertForQuestionAnswering",tA]]]),Tb=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",WA]]]),wz=new Map([["llava",["LlavaForConditionalGeneration",jA]]]),_z=new Map([["vit",["ViTForImageClassification",kO]],["mobilevit",["MobileViTForImageClassification",AO]],["beit",["BeitForImageClassification",BO]],["deit",["DeiTForImageClassification",GO]],["convnext",["ConvNextForImageClassification",oM]],["convnextv2",["ConvNextV2ForImageClassification",uM]],["dinov2",["Dinov2ForImageClassification",cM]],["resnet",["ResNetForImageClassification",qO]],["swin",["SwinForImageClassification",YO]],["segformer",["SegformerForImageClassification",az]],["efficientnet",["EfficientNetForImageClassification",dz]]]),vz=new Map([["detr",["DetrForObjectDetection",NO]],["table-transformer",["TableTransformerForObjectDetection",WO]],["yolos",["YolosForObjectDetection",fM]]]),$z=new Map([["owlvit",["OwlViTForObjectDetection",MO]],["owlv2",["Owlv2ForObjectDetection",RO]]]),bz=new Map([["detr",["DetrForSegmentation",LO]],["clipseg",["CLIPSegForImageSegmentation",eO]]]),xz=new Map([["segformer",["SegformerForSemanticSegmentation",sz]]]),Sz=new Map([["sam",["SamModel",gM]]]),Ez=new Map([["wav2vec2",["Wav2Vec2ForCTC",xM]],["wav2vec2-bert",["Wav2Vec2BertForCTC",RM]],["unispeech",["UniSpeechForCTC",CM]],["unispeech-sat",["UniSpeechSatForCTC",AM]],["wavlm",["WavLMForCTC",FM]],["hubert",["HubertForCTC",DM]]]),kz=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",SM]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",PM]],["unispeech",["UniSpeechForSequenceClassification",TM]],["unispeech-sat",["UniSpeechSatForSequenceClassification",OM]],["wavlm",["WavLMForSequenceClassification",UM]],["hubert",["HubertForSequenceClassification",NM]],["audio-spectrogram-transformer",["ASTForAudioClassification",LA]]]),Cz=new Map([["wavlm",["WavLMForXVector",WM]]]),Tz=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",MM]],["wavlm",["WavLMForAudioFrameClassification",VM]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",EM]]]),Iz=new Map([["vitmatte",["VitMatteForImageMatting",TO]]]),Az=new Map([["swin2sr",["Swin2SRForImageSuperResolution",XO]]]),Oz=new Map([["dpt",["DPTForDepthEstimation",JO]],["depth_anything",["DepthAnythingForDepthEstimation",tM]],["glpn",["GLPNForDepthEstimation",rM]]]),Mz=new Map([["clip",["CLIPVisionModelWithProjection",qA]],["siglip",["SiglipVisionModel",QA]]]),Ib=[[Sb,Ee.EncoderOnly],[Eb,Ee.EncoderDecoder],[cz,Ee.DecoderOnly],[hz,Ee.EncoderOnly],[mz,Ee.EncoderOnly],[Cb,Ee.Seq2Seq],[kb,Ee.Seq2Seq],[Bf,Ee.DecoderOnly],[gz,Ee.EncoderOnly],[yz,Ee.EncoderOnly],[Tb,Ee.Vision2Seq],[wz,Ee.ImageTextToText],[_z,Ee.EncoderOnly],[bz,Ee.EncoderOnly],[xz,Ee.EncoderOnly],[Iz,Ee.EncoderOnly],[Az,Ee.EncoderOnly],[Oz,Ee.EncoderOnly],[vz,Ee.EncoderOnly],[$z,Ee.EncoderOnly],[Sz,Ee.MaskGeneration],[Ez,Ee.EncoderOnly],[kz,Ee.EncoderOnly],[pz,Ee.Seq2Seq],[fz,Ee.EncoderOnly],[Cz,Ee.EncoderOnly],[Tz,Ee.EncoderOnly],[Mz,Ee.EncoderOnly]];for(const[e,t]of Ib)for(const[n,r]of e.values())iu.set(n,t),_s.set(r,n),T$.set(n,r);const zz=[["MusicgenForConditionalGeneration",Pf,Ee.Musicgen],["CLIPTextModelWithProjection",HA,Ee.EncoderOnly],["SiglipTextModel",YA,Ee.EncoderOnly],["ClapTextModelWithProjection",nz,Ee.EncoderOnly],["ClapAudioModelWithProjection",rz,Ee.EncoderOnly]];for(const[e,t,n]of zz)iu.set(e,n),_s.set(t,e),T$.set(e,t);class Cy extends np{}j(Cy,"MODEL_CLASS_MAPPINGS",Ib.map(t=>t[0])),j(Cy,"BASE_IF_FAIL",!0);class Be extends vn{constructor({logits:t}){super(),this.logits=t}}class Rz extends vn{constructor({logits:t,embeddings:n}){super(),this.logits=t,this.embeddings=n}}class Pt extends vn{constructor({logits:t}){super(),this.logits=t}}class Bt extends vn{constructor({logits:t}){super(),this.logits=t}}class Vt extends vn{constructor({start_logits:t,end_logits:n}){super(),this.start_logits=t,this.end_logits=n}}class _a extends vn{constructor({logits:t}){super(),this.logits=t}}class Pz extends vn{constructor({alphas:t}){super(),this.alphas=t}}class Bz extends vn{constructor({waveform:t,spectrogram:n}){super(),this.waveform=t,this.spectrogram=n}}const Dz=typeof self<"u";if(!Dz){if(!ft)throw new Error("Unable to load image processing library.")}class Nz{put(t){throw Error("Not implemented")}end(){throw Error("Not implemented")}}function Lz(e,t=16e3){let n=44;const r=new ArrayBuffer(n+e.length*4),i=new DataView(r);nl(i,0,"RIFF"),i.setUint32(4,36+e.length*4,!0),nl(i,8,"WAVE"),nl(i,12,"fmt "),i.setUint32(16,16,!0),i.setUint16(20,3,!0),i.setUint16(22,1,!0),i.setUint32(24,t,!0),i.setUint32(28,t*4,!0),i.setUint16(32,4,!0),i.setUint16(34,32,!0),nl(i,36,"data"),i.setUint32(40,e.length*4,!0);for(let a=0;a<e.length;++a,n+=4)i.setFloat32(n,e[a],!0);return r}function nl(e,t,n){for(let r=0;r<n.length;++r)e.setUint8(t+r,n.charCodeAt(r))}async function Fz(e,t){const n=await fetch("https://huggingface.co/uploads",{method:"POST",body:e});if(!n.ok)throw new Error(`Failed to upload audio: ${n.statusText}`);const r=await n.text(),a=`https://huggingface.co/spaces/Xenova/musicgen-web/discussions/new?${new URLSearchParams({title:`🎵 ${t.prompt}`,description:`<audio controls src="${r}"></audio>
${JSON.stringify(t,null,2)}`}).toString()}`;window.open(a,"_blank")}const Ty="Xenova/musicgen-small",Iy=["80s pop track with bassy drums and synth","90s rock song with loud guitars and heavy drums","a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130","A cheerful country song with acoustic guitars","lofi slow bpm electro chill with organic samples"],Uz=window.location.host.endsWith(".hf.space");class Wz extends Nz{constructor(t){super(),this.callback_fn=t}put(t){return this.callback_fn(t)}end(){return this.callback_fn()}}const Vz=()=>{const[e,t]=Ot.useState(Iy[0]),[n,r]=Ot.useState(0),[i,a]=Ot.useState({}),[s,o]=Ot.useState("Loading model (656MB)..."),[l,d]=Ot.useState(null),c=Ot.useRef(null),u=Ot.useRef(null),f=Ot.useRef(null),[h,g]=Ot.useState(3),[$,E]=Ot.useState(1),[y,_]=Ot.useState(10);Ot.useEffect(()=>{u.current??(u.current=Pf.from_pretrained(Ty,{progress_callback:b=>{b.status==="progress"&&a(C=>({...C,[b.file]:b}))},dtype:{text_encoder:"q8",decoder_model_merged:"q8",encodec_decode:"fp32"},device:"wasm"})),f.current??(f.current=k$.from_pretrained(Ty))},[]),Ot.useEffect(()=>{if(Object.values(i).length!==5)return;let C=0,I=0;for(const M of Object.values(i))C+=M.loaded,I+=M.total;const O=C/I;r(O),o(O===1?"Ready!":`Loading model (${(O*100).toFixed()}% of 656MB)...`)},[i]);const v=async()=>{c.current.src="",d(null);const b=await f.current,C=await u.current,I=Math.min(Math.max(Math.floor(y*50),1)+4,C.generation_config.max_length??1500),O=new Wz(ae=>{const ue=ae===void 0?1:ae[0].length/I;o(`Generating (${(ue*100).toFixed()}%)...`),r(ue)}),M=b(e),W=await C.generate({...M,max_length:I,guidance_scale:h,temperature:$,streamer:O});o("Encoding audio...");const G=C.config.audio_encoder.sampling_rate,z=Lz(W.data,G),K=new Blob([z],{type:"audio/wav"});d(K),c.current.src=URL.createObjectURL(K),o("Done!")};return We.jsxs("div",{className:"container mx-auto p-8",children:[We.jsx("h1",{className:"text-5xl font-bold mb-2",children:"MusicGen Web"}),We.jsxs("h2",{className:"text-2xl font-semibold mb-4",children:["In-browser text-to-music w/ ",We.jsx("a",{className:"underline",href:"http://github.com/xenova/transformers.js",children:"🤗 Transformers.js!"})]}),We.jsx("input",{type:"text",placeholder:"Describe the music to generate...",value:e,onChange:b=>t(b.target.value),className:"border border-gray-300 p-2 mb-4 w-full rounded"}),We.jsx("div",{className:"mb-4 flex gap-2 justify-center text-sm",children:Iy.map((b,C)=>We.jsx("button",{className:"bg-blue-500 hover:bg-blue-400 transition-colors duration-100 text-white px-2 py-2 rounded",onClick:I=>t(I.target.innerText),children:b},C))}),We.jsxs("div",{className:"flex mb-4 justify-center gap-2",children:[We.jsxs("div",{children:[We.jsx("label",{className:"block text-sm font-semibold mb-1",children:"Duration"}),We.jsx("input",{type:"range",min:1,max:30,value:y,onChange:b=>_(b.target.value)}),We.jsx("p",{className:"text-sm text-center",children:`${y} second${y>1?"s":""}`})]}),We.jsxs("div",{className:"mr-4",children:[We.jsx("label",{className:"block text-sm font-semibold mb-1",children:"Guidance Scale"}),We.jsx("input",{type:"range",min:1,max:10,value:h,onChange:b=>g(b.target.value)}),We.jsx("p",{className:"text-sm text-center",children:h})]}),We.jsxs("div",{children:[We.jsx("label",{className:"block text-sm font-semibold mb-1",children:"Temperature"}),We.jsx("input",{type:"range",min:.1,max:2,step:.1,value:$,onChange:b=>E(b.target.value)}),We.jsx("p",{className:"text-sm text-center",children:$})]})]}),We.jsx("button",{className:"mb-4 bg-green-500 hover:bg-green-400 transition-colors duration-100 text-white px-4 py-3 rounded-lg font-semibold",onClick:v,children:"Generate Music"}),We.jsxs("div",{className:"mb-4",children:[We.jsx("div",{className:"bg-gray-200 h-4 w-full rounded-full",children:We.jsx("div",{className:"bg-blue-500 h-4 rounded-full",style:{width:`${100*n}%`}})}),We.jsx("p",{className:"text-sm text-center mt-1",children:s})]}),We.jsxs("div",{className:"flex justify-center flex-col items-center",children:[We.jsx("audio",{ref:c,controls:!0,type:"audio/wav"}),Uz&&l&&We.jsx("button",{className:"bg-red-500 hover:bg-red-400 transition-colors duration-100 text-white px-2 py-1 my-2 rounded-lg text-sm",onClick:async b=>{b.target.disabled=!0,b.target.innerText="Uploading...",await Fz(l,{prompt:e,duration:y,guidance_scale:h,temperature:$}),b.target.disabled=!1,b.target.innerText="Share"},children:"Share"})]})]})};Gd.createRoot(document.getElementById("root")).render(We.jsx(ox.StrictMode,{children:We.jsx(Vz,{})}));